From 03ee67bfd34b9e872b33eb05fef5db83410b16f3 Mon Sep 17 00:00:00 2001 From: WDevelopsWebApps <97454358+WDevelopsWebApps@users.noreply.github.com> Date: Wed, 28 Sep 2022 10:53:40 +0200 Subject: add advanced saving for save button --- modules/images.py | 5 ++++- modules/ui.py | 37 +++++++++++++++++++++++++++++-------- 2 files changed, 33 insertions(+), 9 deletions(-) (limited to 'modules') diff --git a/modules/images.py b/modules/images.py index 9458bf8d..923f81df 100644 --- a/modules/images.py +++ b/modules/images.py @@ -290,7 +290,10 @@ def apply_filename_pattern(x, p, seed, prompt): x = x.replace("[cfg]", str(p.cfg_scale)) x = x.replace("[width]", str(p.width)) x = x.replace("[height]", str(p.height)) - x = x.replace("[styles]", sanitize_filename_part(", ".join(p.styles), replace_spaces=False)) + #currently disabled if using the save button, will work otherwise + # if enabled it will cause a bug because styles is not included in the save_files data dictionary + if hasattr(p, "styles"): + x = x.replace("[styles]", sanitize_filename_part(", ".join(p.styles), replace_spaces=False)) x = x.replace("[sampler]", sanitize_filename_part(sd_samplers.samplers[p.sampler_index].name, replace_spaces=False)) x = x.replace("[model_hash]", shared.sd_model.sd_model_hash) diff --git a/modules/ui.py b/modules/ui.py index 7db8edbd..87a86a45 100644 --- a/modules/ui.py +++ b/modules/ui.py @@ -28,6 +28,7 @@ import modules.gfpgan_model import modules.codeformer_model import modules.styles import modules.generation_parameters_copypaste +from modules.images import apply_filename_pattern, get_next_sequence_number # this is a fix for Windows users. Without it, javascript files will be served with text/html content-type and the bowser will not show any UI mimetypes.init() @@ -90,13 +91,26 @@ def send_gradio_gallery_to_image(x): def save_files(js_data, images, index): - import csv - - os.makedirs(opts.outdir_save, exist_ok=True) - + import csv filenames = [] + #quick dictionary to class object conversion. Its neccesary due apply_filename_pattern requiring it + class MyObject: + def __init__(self, d=None): + if d is not None: + for key, value in d.items(): + setattr(self, key, value) + data = json.loads(js_data) + p = MyObject(data) + path = opts.outdir_save + save_to_dirs = opts.save_to_dirs + + if save_to_dirs: + dirname = apply_filename_pattern(opts.directories_filename_pattern or "[prompt_words]", p, p.seed, p.prompt) + path = os.path.join(opts.outdir_save, dirname) + + os.makedirs(path, exist_ok=True) if index > -1 and opts.save_selected_only and (index > 0 or not opts.return_grid): # ensures we are looking at a specific non-grid picture, and we have save_selected_only images = [images[index]] @@ -107,11 +121,18 @@ def save_files(js_data, images, index): writer = csv.writer(file) if at_start: writer.writerow(["prompt", "seed", "width", "height", "sampler", "cfgs", "steps", "filename", "negative_prompt"]) - - filename_base = str(int(time.time() * 1000)) + file_decoration = opts.samples_filename_pattern or "[seed]-[prompt_spaces]" + if file_decoration != "": + file_decoration = "-" + file_decoration.lower() + file_decoration = apply_filename_pattern(file_decoration, p, p.seed, p.prompt) + truncated = (file_decoration[:240] + '..') if len(file_decoration) > 240 else file_decoration + filename_base = truncated + + basecount = get_next_sequence_number(path, "") for i, filedata in enumerate(images): - filename = filename_base + ("" if len(images) == 1 else "-" + str(i + 1)) + ".png" - filepath = os.path.join(opts.outdir_save, filename) + file_number = f"{basecount+i:05}" + filename = file_number + filename_base + ".png" + filepath = os.path.join(path, filename) if filedata.startswith("data:image/png;base64,"): filedata = filedata[len("data:image/png;base64,"):] -- cgit v1.2.1 From c938679de7b87b4f14894d9f57fe0f40dd6e3c06 Mon Sep 17 00:00:00 2001 From: Jairo Correa Date: Wed, 28 Sep 2022 22:14:13 -0300 Subject: Fix memory leak and reduce memory usage --- modules/codeformer_model.py | 6 ++++-- modules/devices.py | 3 ++- modules/extras.py | 2 ++ modules/gfpgan_model.py | 11 +++++------ modules/processing.py | 33 ++++++++++++++++++++++++++------- 5 files changed, 39 insertions(+), 16 deletions(-) (limited to 'modules') diff --git a/modules/codeformer_model.py b/modules/codeformer_model.py index 8fbdea24..2177291a 100644 --- a/modules/codeformer_model.py +++ b/modules/codeformer_model.py @@ -89,7 +89,7 @@ def setup_codeformer(): output = self.net(cropped_face_t, w=w if w is not None else shared.opts.code_former_weight, adain=True)[0] restored_face = tensor2img(output, rgb2bgr=True, min_max=(-1, 1)) del output - torch.cuda.empty_cache() + devices.torch_gc() except Exception as error: print(f'\tFailed inference for CodeFormer: {error}', file=sys.stderr) restored_face = tensor2img(cropped_face_t, rgb2bgr=True, min_max=(-1, 1)) @@ -106,7 +106,9 @@ def setup_codeformer(): restored_img = cv2.resize(restored_img, (0, 0), fx=original_resolution[1]/restored_img.shape[1], fy=original_resolution[0]/restored_img.shape[0], interpolation=cv2.INTER_LINEAR) if shared.opts.face_restoration_unload: - self.net.to(devices.cpu) + self.net = None + self.face_helper = None + devices.torch_gc() return restored_img diff --git a/modules/devices.py b/modules/devices.py index 07bb2339..df63dd88 100644 --- a/modules/devices.py +++ b/modules/devices.py @@ -1,4 +1,5 @@ import torch +import gc # has_mps is only available in nightly pytorch (for now), `getattr` for compatibility from modules import errors @@ -17,8 +18,8 @@ def get_optimal_device(): return cpu - def torch_gc(): + gc.collect() if torch.cuda.is_available(): torch.cuda.empty_cache() torch.cuda.ipc_collect() diff --git a/modules/extras.py b/modules/extras.py index 9a825530..38b86167 100644 --- a/modules/extras.py +++ b/modules/extras.py @@ -98,6 +98,8 @@ def run_extras(extras_mode, image, image_folder, gfpgan_visibility, codeformer_v outputs.append(image) + devices.torch_gc() + return outputs, plaintext_to_html(info), '' diff --git a/modules/gfpgan_model.py b/modules/gfpgan_model.py index 44c5dc6c..b1288f0c 100644 --- a/modules/gfpgan_model.py +++ b/modules/gfpgan_model.py @@ -49,6 +49,7 @@ def gfpgan(): def gfpgan_fix_faces(np_image): + global loaded_gfpgan_model model = gfpgan() np_image_bgr = np_image[:, :, ::-1] @@ -56,7 +57,9 @@ def gfpgan_fix_faces(np_image): np_image = gfpgan_output_bgr[:, :, ::-1] if shared.opts.face_restoration_unload: - model.gfpgan.to(devices.cpu) + del model + loaded_gfpgan_model = None + devices.torch_gc() return np_image @@ -83,11 +86,7 @@ def setup_gfpgan(): return "GFPGAN" def restore(self, np_image): - np_image_bgr = np_image[:, :, ::-1] - cropped_faces, restored_faces, gfpgan_output_bgr = gfpgan().enhance(np_image_bgr, has_aligned=False, only_center_face=False, paste_back=True) - np_image = gfpgan_output_bgr[:, :, ::-1] - - return np_image + return gfpgan_fix_faces(np_image) shared.face_restorers.append(FaceRestorerGFPGAN()) except Exception: diff --git a/modules/processing.py b/modules/processing.py index 4ecdfcd2..de5cda79 100644 --- a/modules/processing.py +++ b/modules/processing.py @@ -12,7 +12,7 @@ import cv2 from skimage import exposure import modules.sd_hijack -from modules import devices, prompt_parser, masking +from modules import devices, prompt_parser, masking, lowvram from modules.sd_hijack import model_hijack from modules.sd_samplers import samplers, samplers_for_img2img from modules.shared import opts, cmd_opts, state @@ -335,7 +335,8 @@ def process_images(p: StableDiffusionProcessing) -> Processed: if state.job_count == -1: state.job_count = p.n_iter - for n in range(p.n_iter): + for n in range(p.n_iter): + with torch.no_grad(), precision_scope("cuda"), ema_scope(): if state.interrupted: break @@ -368,22 +369,32 @@ def process_images(p: StableDiffusionProcessing) -> Processed: x_samples_ddim = p.sd_model.decode_first_stage(samples_ddim) x_samples_ddim = torch.clamp((x_samples_ddim + 1.0) / 2.0, min=0.0, max=1.0) + del samples_ddim + + if shared.cmd_opts.lowvram or shared.cmd_opts.medvram: + lowvram.send_everything_to_cpu() + + devices.torch_gc() + if opts.filter_nsfw: import modules.safety as safety x_samples_ddim = modules.safety.censor_batch(x_samples_ddim) - for i, x_sample in enumerate(x_samples_ddim): + for i, x_sample in enumerate(x_samples_ddim): + with torch.no_grad(), precision_scope("cuda"), ema_scope(): x_sample = 255. * np.moveaxis(x_sample.cpu().numpy(), 0, 2) x_sample = x_sample.astype(np.uint8) - if p.restore_faces: + if p.restore_faces: + with torch.no_grad(), precision_scope("cuda"), ema_scope(): if opts.save and not p.do_not_save_samples and opts.save_images_before_face_restoration: images.save_image(Image.fromarray(x_sample), p.outpath_samples, "", seeds[i], prompts[i], opts.samples_format, info=infotext(n, i), p=p, suffix="-before-face-restoration") - devices.torch_gc() - x_sample = modules.face_restoration.restore_faces(x_sample) + devices.torch_gc() + + with torch.no_grad(), precision_scope("cuda"), ema_scope(): image = Image.fromarray(x_sample) if p.color_corrections is not None and i < len(p.color_corrections): @@ -411,8 +422,13 @@ def process_images(p: StableDiffusionProcessing) -> Processed: infotexts.append(infotext(n, i)) output_images.append(image) - state.nextjob() + del x_samples_ddim + devices.torch_gc() + + state.nextjob() + + with torch.no_grad(), precision_scope("cuda"), ema_scope(): p.color_corrections = None index_of_first_image = 0 @@ -648,4 +664,7 @@ class StableDiffusionProcessingImg2Img(StableDiffusionProcessing): if self.mask is not None: samples = samples * self.nmask + self.init_latent * self.mask + del x + devices.torch_gc() + return samples -- cgit v1.2.1 From c2d5b29040132c171bc4d77f1f63da972306f22c Mon Sep 17 00:00:00 2001 From: Jairo Correa Date: Thu, 29 Sep 2022 01:14:54 -0300 Subject: Move silu to sd_hijack --- modules/sd_hijack.py | 12 +++--------- 1 file changed, 3 insertions(+), 9 deletions(-) (limited to 'modules') diff --git a/modules/sd_hijack.py b/modules/sd_hijack.py index bfbd07f9..4bc58fa2 100644 --- a/modules/sd_hijack.py +++ b/modules/sd_hijack.py @@ -12,6 +12,7 @@ from ldm.util import default from einops import rearrange import ldm.modules.attention import ldm.modules.diffusionmodules.model +from torch.nn.functional import silu # see https://github.com/basujindal/stable-diffusion/pull/117 for discussion @@ -100,14 +101,6 @@ def split_cross_attention_forward(self, x, context=None, mask=None): return self.to_out(r2) -def nonlinearity_hijack(x): - # swish - t = torch.sigmoid(x) - x *= t - del t - - return x - def cross_attention_attnblock_forward(self, x): h_ = x h_ = self.norm(h_) @@ -245,11 +238,12 @@ class StableDiffusionModelHijack: m.cond_stage_model = FrozenCLIPEmbedderWithCustomWords(m.cond_stage_model, self) self.clip = m.cond_stage_model + ldm.modules.diffusionmodules.model.nonlinearity = silu + if cmd_opts.opt_split_attention_v1: ldm.modules.attention.CrossAttention.forward = split_cross_attention_forward_v1 elif not cmd_opts.disable_opt_split_attention and (cmd_opts.opt_split_attention or torch.cuda.is_available()): ldm.modules.attention.CrossAttention.forward = split_cross_attention_forward - ldm.modules.diffusionmodules.model.nonlinearity = nonlinearity_hijack ldm.modules.diffusionmodules.model.AttnBlock.forward = cross_attention_attnblock_forward def flatten(el): -- cgit v1.2.1 From e82ea202997cbcd2ab72891cd075d9ba270eb67d Mon Sep 17 00:00:00 2001 From: d8ahazard Date: Fri, 30 Sep 2022 15:26:18 -0500 Subject: Optimize model loader Child classes only get populated to __subclassess__ when they are imported. We don't actually need to import any of them to webui any more, so clean up webUI imports and make sure loader imports children. Also, fix command line paths not actually being passed to the scalers. --- modules/modelloader.py | 19 ++++++++++++++++--- 1 file changed, 16 insertions(+), 3 deletions(-) (limited to 'modules') diff --git a/modules/modelloader.py b/modules/modelloader.py index 1106aeb7..b1721671 100644 --- a/modules/modelloader.py +++ b/modules/modelloader.py @@ -4,7 +4,6 @@ import importlib from urllib.parse import urlparse from basicsr.utils.download_util import load_file_from_url - from modules import shared from modules.upscaler import Upscaler from modules.paths import script_path, models_path @@ -120,16 +119,30 @@ def move_files(src_path: str, dest_path: str, ext_filter: str = None): def load_upscalers(): + sd = shared.script_path + # We can only do this 'magic' method to dynamically load upscalers if they are referenced, + # so we'll try to import any _model.py files before looking in __subclasses__ + modules_dir = os.path.join(sd, "modules") + for file in os.listdir(modules_dir): + if "_model.py" in file: + model_name = file.replace("_model.py", "") + full_model = f"modules.{model_name}_model" + try: + importlib.import_module(full_model) + except: + pass datas = [] + c_o = vars(shared.cmd_opts) for cls in Upscaler.__subclasses__(): name = cls.__name__ module_name = cls.__module__ module = importlib.import_module(module_name) class_ = getattr(module, name) - cmd_name = f"{name.lower().replace('upscaler', '')}-models-path" + cmd_name = f"{name.lower().replace('upscaler', '')}_models_path" opt_string = None try: - opt_string = shared.opts.__getattr__(cmd_name) + if cmd_name in c_o: + opt_string = c_o[cmd_name] except: pass scaler = class_(opt_string) -- cgit v1.2.1 From 8deae077004f0332ca607fc3a5d568b1a4705bec Mon Sep 17 00:00:00 2001 From: d8ahazard Date: Fri, 30 Sep 2022 15:28:37 -0500 Subject: Add ScuNET DeNoiser/Upscaler Q&D Implementation of ScuNET, thanks to our handy model loader. :P https://github.com/cszn/SCUNet --- modules/scunet_model.py | 90 +++++++++++++++ modules/scunet_model_arch.py | 265 +++++++++++++++++++++++++++++++++++++++++++ modules/shared.py | 1 + 3 files changed, 356 insertions(+) create mode 100644 modules/scunet_model.py create mode 100644 modules/scunet_model_arch.py (limited to 'modules') diff --git a/modules/scunet_model.py b/modules/scunet_model.py new file mode 100644 index 00000000..7987ac14 --- /dev/null +++ b/modules/scunet_model.py @@ -0,0 +1,90 @@ +import os.path +import sys +import traceback + +import PIL.Image +import numpy as np +import torch +from basicsr.utils.download_util import load_file_from_url + +import modules.upscaler +from modules import shared, modelloader +from modules.paths import models_path +from modules.scunet_model_arch import SCUNet as net + + +class UpscalerScuNET(modules.upscaler.Upscaler): + def __init__(self, dirname): + self.name = "ScuNET" + self.model_path = os.path.join(models_path, self.name) + self.model_name = "ScuNET GAN" + self.model_name2 = "ScuNET PSNR" + self.model_url = "https://github.com/cszn/KAIR/releases/download/v1.0/scunet_color_real_gan.pth" + self.model_url2 = "https://github.com/cszn/KAIR/releases/download/v1.0/scunet_color_real_psnr.pth" + self.user_path = dirname + super().__init__() + model_paths = self.find_models(ext_filter=[".pth"]) + scalers = [] + add_model2 = True + for file in model_paths: + if "http" in file: + name = self.model_name + else: + name = modelloader.friendly_name(file) + if name == self.model_name2 or file == self.model_url2: + add_model2 = False + try: + scaler_data = modules.upscaler.UpscalerData(name, file, self, 4) + scalers.append(scaler_data) + except Exception: + print(f"Error loading ScuNET model: {file}", file=sys.stderr) + print(traceback.format_exc(), file=sys.stderr) + if add_model2: + scaler_data2 = modules.upscaler.UpscalerData(self.model_name2, self.model_url2, self) + scalers.append(scaler_data2) + self.scalers = scalers + + def do_upscale(self, img: PIL.Image, selected_file): + torch.cuda.empty_cache() + + model = self.load_model(selected_file) + if model is None: + return img + + device = shared.device + img = np.array(img) + img = img[:, :, ::-1] + img = np.moveaxis(img, 2, 0) / 255 + img = torch.from_numpy(img).float() + img = img.unsqueeze(0).to(shared.device) + + img = img.to(device) + with torch.no_grad(): + output = model(img) + output = output.squeeze().float().cpu().clamp_(0, 1).numpy() + output = 255. * np.moveaxis(output, 0, 2) + output = output.astype(np.uint8) + output = output[:, :, ::-1] + torch.cuda.empty_cache() + return PIL.Image.fromarray(output, 'RGB') + + def load_model(self, path: str): + device = shared.device + if "http" in path: + filename = load_file_from_url(url=self.model_url, model_dir=self.model_path, file_name="%s.pth" % self.name, + progress=True) + else: + filename = path + if not os.path.exists(os.path.join(self.model_path, filename)) or filename is None: + print(f"ScuNET: Unable to load model from {filename}", file=sys.stderr) + return None + + model = net(in_nc=3, config=[4, 4, 4, 4, 4, 4, 4], dim=64) + model.load_state_dict(torch.load(filename), strict=True) + model.eval() + for k, v in model.named_parameters(): + v.requires_grad = False + model = model.to(device) + + return model + diff --git a/modules/scunet_model_arch.py b/modules/scunet_model_arch.py new file mode 100644 index 00000000..972a2639 --- /dev/null +++ b/modules/scunet_model_arch.py @@ -0,0 +1,265 @@ +# -*- coding: utf-8 -*- +import numpy as np +import torch +import torch.nn as nn +from einops import rearrange +from einops.layers.torch import Rearrange +from timm.models.layers import trunc_normal_, DropPath + + +class WMSA(nn.Module): + """ Self-attention module in Swin Transformer + """ + + def __init__(self, input_dim, output_dim, head_dim, window_size, type): + super(WMSA, self).__init__() + self.input_dim = input_dim + self.output_dim = output_dim + self.head_dim = head_dim + self.scale = self.head_dim ** -0.5 + self.n_heads = input_dim // head_dim + self.window_size = window_size + self.type = type + self.embedding_layer = nn.Linear(self.input_dim, 3 * self.input_dim, bias=True) + + self.relative_position_params = nn.Parameter( + torch.zeros((2 * window_size - 1) * (2 * window_size - 1), self.n_heads)) + + self.linear = nn.Linear(self.input_dim, self.output_dim) + + trunc_normal_(self.relative_position_params, std=.02) + self.relative_position_params = torch.nn.Parameter( + self.relative_position_params.view(2 * window_size - 1, 2 * window_size - 1, self.n_heads).transpose(1, + 2).transpose( + 0, 1)) + + def generate_mask(self, h, w, p, shift): + """ generating the mask of SW-MSA + Args: + shift: shift parameters in CyclicShift. + Returns: + attn_mask: should be (1 1 w p p), + """ + # supporting sqaure. + attn_mask = torch.zeros(h, w, p, p, p, p, dtype=torch.bool, device=self.relative_position_params.device) + if self.type == 'W': + return attn_mask + + s = p - shift + attn_mask[-1, :, :s, :, s:, :] = True + attn_mask[-1, :, s:, :, :s, :] = True + attn_mask[:, -1, :, :s, :, s:] = True + attn_mask[:, -1, :, s:, :, :s] = True + attn_mask = rearrange(attn_mask, 'w1 w2 p1 p2 p3 p4 -> 1 1 (w1 w2) (p1 p2) (p3 p4)') + return attn_mask + + def forward(self, x): + """ Forward pass of Window Multi-head Self-attention module. + Args: + x: input tensor with shape of [b h w c]; + attn_mask: attention mask, fill -inf where the value is True; + Returns: + output: tensor shape [b h w c] + """ + if self.type != 'W': x = torch.roll(x, shifts=(-(self.window_size // 2), -(self.window_size // 2)), dims=(1, 2)) + x = rearrange(x, 'b (w1 p1) (w2 p2) c -> b w1 w2 p1 p2 c', p1=self.window_size, p2=self.window_size) + h_windows = x.size(1) + w_windows = x.size(2) + # sqaure validation + # assert h_windows == w_windows + + x = rearrange(x, 'b w1 w2 p1 p2 c -> b (w1 w2) (p1 p2) c', p1=self.window_size, p2=self.window_size) + qkv = self.embedding_layer(x) + q, k, v = rearrange(qkv, 'b nw np (threeh c) -> threeh b nw np c', c=self.head_dim).chunk(3, dim=0) + sim = torch.einsum('hbwpc,hbwqc->hbwpq', q, k) * self.scale + # Adding learnable relative embedding + sim = sim + rearrange(self.relative_embedding(), 'h p q -> h 1 1 p q') + # Using Attn Mask to distinguish different subwindows. + if self.type != 'W': + attn_mask = self.generate_mask(h_windows, w_windows, self.window_size, shift=self.window_size // 2) + sim = sim.masked_fill_(attn_mask, float("-inf")) + + probs = nn.functional.softmax(sim, dim=-1) + output = torch.einsum('hbwij,hbwjc->hbwic', probs, v) + output = rearrange(output, 'h b w p c -> b w p (h c)') + output = self.linear(output) + output = rearrange(output, 'b (w1 w2) (p1 p2) c -> b (w1 p1) (w2 p2) c', w1=h_windows, p1=self.window_size) + + if self.type != 'W': output = torch.roll(output, shifts=(self.window_size // 2, self.window_size // 2), + dims=(1, 2)) + return output + + def relative_embedding(self): + cord = torch.tensor(np.array([[i, j] for i in range(self.window_size) for j in range(self.window_size)])) + relation = cord[:, None, :] - cord[None, :, :] + self.window_size - 1 + # negative is allowed + return self.relative_position_params[:, relation[:, :, 0].long(), relation[:, :, 1].long()] + + +class Block(nn.Module): + def __init__(self, input_dim, output_dim, head_dim, window_size, drop_path, type='W', input_resolution=None): + """ SwinTransformer Block + """ + super(Block, self).__init__() + self.input_dim = input_dim + self.output_dim = output_dim + assert type in ['W', 'SW'] + self.type = type + if input_resolution <= window_size: + self.type = 'W' + + self.ln1 = nn.LayerNorm(input_dim) + self.msa = WMSA(input_dim, input_dim, head_dim, window_size, self.type) + self.drop_path = DropPath(drop_path) if drop_path > 0. else nn.Identity() + self.ln2 = nn.LayerNorm(input_dim) + self.mlp = nn.Sequential( + nn.Linear(input_dim, 4 * input_dim), + nn.GELU(), + nn.Linear(4 * input_dim, output_dim), + ) + + def forward(self, x): + x = x + self.drop_path(self.msa(self.ln1(x))) + x = x + self.drop_path(self.mlp(self.ln2(x))) + return x + + +class ConvTransBlock(nn.Module): + def __init__(self, conv_dim, trans_dim, head_dim, window_size, drop_path, type='W', input_resolution=None): + """ SwinTransformer and Conv Block + """ + super(ConvTransBlock, self).__init__() + self.conv_dim = conv_dim + self.trans_dim = trans_dim + self.head_dim = head_dim + self.window_size = window_size + self.drop_path = drop_path + self.type = type + self.input_resolution = input_resolution + + assert self.type in ['W', 'SW'] + if self.input_resolution <= self.window_size: + self.type = 'W' + + self.trans_block = Block(self.trans_dim, self.trans_dim, self.head_dim, self.window_size, self.drop_path, + self.type, self.input_resolution) + self.conv1_1 = nn.Conv2d(self.conv_dim + self.trans_dim, self.conv_dim + self.trans_dim, 1, 1, 0, bias=True) + self.conv1_2 = nn.Conv2d(self.conv_dim + self.trans_dim, self.conv_dim + self.trans_dim, 1, 1, 0, bias=True) + + self.conv_block = nn.Sequential( + nn.Conv2d(self.conv_dim, self.conv_dim, 3, 1, 1, bias=False), + nn.ReLU(True), + nn.Conv2d(self.conv_dim, self.conv_dim, 3, 1, 1, bias=False) + ) + + def forward(self, x): + conv_x, trans_x = torch.split(self.conv1_1(x), (self.conv_dim, self.trans_dim), dim=1) + conv_x = self.conv_block(conv_x) + conv_x + trans_x = Rearrange('b c h w -> b h w c')(trans_x) + trans_x = self.trans_block(trans_x) + trans_x = Rearrange('b h w c -> b c h w')(trans_x) + res = self.conv1_2(torch.cat((conv_x, trans_x), dim=1)) + x = x + res + + return x + + +class SCUNet(nn.Module): + # def __init__(self, in_nc=3, config=[2, 2, 2, 2, 2, 2, 2], dim=64, drop_path_rate=0.0, input_resolution=256): + def __init__(self, in_nc=3, config=None, dim=64, drop_path_rate=0.0, input_resolution=256): + super(SCUNet, self).__init__() + if config is None: + config = [2, 2, 2, 2, 2, 2, 2] + self.config = config + self.dim = dim + self.head_dim = 32 + self.window_size = 8 + + # drop path rate for each layer + dpr = [x.item() for x in torch.linspace(0, drop_path_rate, sum(config))] + + self.m_head = [nn.Conv2d(in_nc, dim, 3, 1, 1, bias=False)] + + begin = 0 + self.m_down1 = [ConvTransBlock(dim // 2, dim // 2, self.head_dim, self.window_size, dpr[i + begin], + 'W' if not i % 2 else 'SW', input_resolution) + for i in range(config[0])] + \ + [nn.Conv2d(dim, 2 * dim, 2, 2, 0, bias=False)] + + begin += config[0] + self.m_down2 = [ConvTransBlock(dim, dim, self.head_dim, self.window_size, dpr[i + begin], + 'W' if not i % 2 else 'SW', input_resolution // 2) + for i in range(config[1])] + \ + [nn.Conv2d(2 * dim, 4 * dim, 2, 2, 0, bias=False)] + + begin += config[1] + self.m_down3 = [ConvTransBlock(2 * dim, 2 * dim, self.head_dim, self.window_size, dpr[i + begin], + 'W' if not i % 2 else 'SW', input_resolution // 4) + for i in range(config[2])] + \ + [nn.Conv2d(4 * dim, 8 * dim, 2, 2, 0, bias=False)] + + begin += config[2] + self.m_body = [ConvTransBlock(4 * dim, 4 * dim, self.head_dim, self.window_size, dpr[i + begin], + 'W' if not i % 2 else 'SW', input_resolution // 8) + for i in range(config[3])] + + begin += config[3] + self.m_up3 = [nn.ConvTranspose2d(8 * dim, 4 * dim, 2, 2, 0, bias=False), ] + \ + [ConvTransBlock(2 * dim, 2 * dim, self.head_dim, self.window_size, dpr[i + begin], + 'W' if not i % 2 else 'SW', input_resolution // 4) + for i in range(config[4])] + + begin += config[4] + self.m_up2 = [nn.ConvTranspose2d(4 * dim, 2 * dim, 2, 2, 0, bias=False), ] + \ + [ConvTransBlock(dim, dim, self.head_dim, self.window_size, dpr[i + begin], + 'W' if not i % 2 else 'SW', input_resolution // 2) + for i in range(config[5])] + + begin += config[5] + self.m_up1 = [nn.ConvTranspose2d(2 * dim, dim, 2, 2, 0, bias=False), ] + \ + [ConvTransBlock(dim // 2, dim // 2, self.head_dim, self.window_size, dpr[i + begin], + 'W' if not i % 2 else 'SW', input_resolution) + for i in range(config[6])] + + self.m_tail = [nn.Conv2d(dim, in_nc, 3, 1, 1, bias=False)] + + self.m_head = nn.Sequential(*self.m_head) + self.m_down1 = nn.Sequential(*self.m_down1) + self.m_down2 = nn.Sequential(*self.m_down2) + self.m_down3 = nn.Sequential(*self.m_down3) + self.m_body = nn.Sequential(*self.m_body) + self.m_up3 = nn.Sequential(*self.m_up3) + self.m_up2 = nn.Sequential(*self.m_up2) + self.m_up1 = nn.Sequential(*self.m_up1) + self.m_tail = nn.Sequential(*self.m_tail) + # self.apply(self._init_weights) + + def forward(self, x0): + + h, w = x0.size()[-2:] + paddingBottom = int(np.ceil(h / 64) * 64 - h) + paddingRight = int(np.ceil(w / 64) * 64 - w) + x0 = nn.ReplicationPad2d((0, paddingRight, 0, paddingBottom))(x0) + + x1 = self.m_head(x0) + x2 = self.m_down1(x1) + x3 = self.m_down2(x2) + x4 = self.m_down3(x3) + x = self.m_body(x4) + x = self.m_up3(x + x4) + x = self.m_up2(x + x3) + x = self.m_up1(x + x2) + x = self.m_tail(x + x1) + + x = x[..., :h, :w] + + return x + + def _init_weights(self, m): + if isinstance(m, nn.Linear): + trunc_normal_(m.weight, std=.02) + if m.bias is not None: + nn.init.constant_(m.bias, 0) + elif isinstance(m, nn.LayerNorm): + nn.init.constant_(m.bias, 0) + nn.init.constant_(m.weight, 1.0) \ No newline at end of file diff --git a/modules/shared.py b/modules/shared.py index 8428c7a3..a48b995a 100644 --- a/modules/shared.py +++ b/modules/shared.py @@ -40,6 +40,7 @@ parser.add_argument("--gfpgan-models-path", type=str, help="Path to directory wi parser.add_argument("--esrgan-models-path", type=str, help="Path to directory with ESRGAN model file(s).", default=os.path.join(model_path, 'ESRGAN')) parser.add_argument("--bsrgan-models-path", type=str, help="Path to directory with BSRGAN model file(s).", default=os.path.join(model_path, 'BSRGAN')) parser.add_argument("--realesrgan-models-path", type=str, help="Path to directory with RealESRGAN model file(s).", default=os.path.join(model_path, 'RealESRGAN')) +parser.add_argument("--scunet-models-path", type=str, help="Path to directory with ScuNET model file(s).", default=os.path.join(model_path, 'ScuNET')) parser.add_argument("--swinir-models-path", type=str, help="Path to directory with SwinIR model file(s).", default=os.path.join(model_path, 'SwinIR')) parser.add_argument("--ldsr-models-path", type=str, help="Path to directory with LDSR model file(s).", default=os.path.join(model_path, 'LDSR')) parser.add_argument("--opt-split-attention", action='store_true', help="force-enables cross-attention layer optimization. By default, it's on for torch.cuda and off for other torch devices.") -- cgit v1.2.1 From abdbf1de646f007b6d76cfb3f416fdfaadb57903 Mon Sep 17 00:00:00 2001 From: Liam Date: Thu, 29 Sep 2022 14:40:47 -0400 Subject: token counters now update when roll artist and style buttons are pressed https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/1194#issuecomment-1261203893 --- modules/ui.py | 6 +++++- 1 file changed, 5 insertions(+), 1 deletion(-) (limited to 'modules') diff --git a/modules/ui.py b/modules/ui.py index 15572bb0..5eea1860 100644 --- a/modules/ui.py +++ b/modules/ui.py @@ -539,6 +539,7 @@ def create_ui(txt2img, img2img, run_extras, run_pnginfo, run_modelmerger): roll.click( fn=roll_artist, + _js="roll_artist_txt2img", inputs=[ txt2img_prompt, ], @@ -743,6 +744,7 @@ def create_ui(txt2img, img2img, run_extras, run_pnginfo, run_modelmerger): roll.click( fn=roll_artist, + _js="roll_artist_img2img", inputs=[ img2img_prompt, ], @@ -753,6 +755,7 @@ def create_ui(txt2img, img2img, run_extras, run_pnginfo, run_modelmerger): prompts = [(txt2img_prompt, txt2img_negative_prompt), (img2img_prompt, img2img_negative_prompt)] style_dropdowns = [(txt2img_prompt_style, txt2img_prompt_style2), (img2img_prompt_style, img2img_prompt_style2)] + style_js_funcs = ["update_style_txt2img", "update_style_img2img"] for button, (prompt, negative_prompt) in zip([txt2img_save_style, img2img_save_style], prompts): button.click( @@ -764,9 +767,10 @@ def create_ui(txt2img, img2img, run_extras, run_pnginfo, run_modelmerger): outputs=[txt2img_prompt_style, img2img_prompt_style, txt2img_prompt_style2, img2img_prompt_style2], ) - for button, (prompt, negative_prompt), (style1, style2) in zip([txt2img_prompt_style_apply, img2img_prompt_style_apply], prompts, style_dropdowns): + for button, (prompt, negative_prompt), (style1, style2), js_func in zip([txt2img_prompt_style_apply, img2img_prompt_style_apply], prompts, style_dropdowns, style_js_funcs): button.click( fn=apply_styles, + _js=js_func, inputs=[prompt, negative_prompt, style1, style2], outputs=[prompt, negative_prompt, style1, style2], ) -- cgit v1.2.1 From ff8dc1908af088d0ed43fb85baad662733c5ca9c Mon Sep 17 00:00:00 2001 From: Liam Date: Thu, 29 Sep 2022 15:47:06 -0400 Subject: fixed token counter for prompt editing --- modules/ui.py | 20 +++++++++++++------- 1 file changed, 13 insertions(+), 7 deletions(-) (limited to 'modules') diff --git a/modules/ui.py b/modules/ui.py index 5eea1860..6bf28562 100644 --- a/modules/ui.py +++ b/modules/ui.py @@ -11,6 +11,7 @@ import time import traceback import platform import subprocess as sp +from functools import reduce import numpy as np import torch @@ -32,6 +33,7 @@ import modules.gfpgan_model import modules.codeformer_model import modules.styles import modules.generation_parameters_copypaste +from modules.prompt_parser import get_learned_conditioning_prompt_schedules # this is a fix for Windows users. Without it, javascript files will be served with text/html content-type and the bowser will not show any UI mimetypes.init() @@ -345,8 +347,11 @@ def connect_reuse_seed(seed: gr.Number, reuse_seed: gr.Button, generation_info: outputs=[seed, dummy_component] ) -def update_token_counter(text): - tokens, token_count, max_length = model_hijack.tokenize(text) +def update_token_counter(text, steps): + prompt_schedules = get_learned_conditioning_prompt_schedules([text], steps) + flat_prompts = reduce(lambda list1, list2: list1+list2, prompt_schedules) + prompts = [prompt_text for step,prompt_text in flat_prompts] + tokens, token_count, max_length = max([model_hijack.tokenize(prompt) for prompt in prompts], key=lambda args: args[1]) style_class = ' class="red"' if (token_count > max_length) else "" return f"{token_count}/{max_length}" @@ -364,8 +369,7 @@ def create_toprow(is_img2img): roll = gr.Button(value=art_symbol, elem_id="roll", visible=len(shared.artist_db.artists) > 0) paste = gr.Button(value=paste_symbol, elem_id="paste") token_counter = gr.HTML(value="", elem_id=f"{id_part}_token_counter") - hidden_button = gr.Button(visible=False, elem_id=f"{id_part}_token_button") - hidden_button.click(fn=update_token_counter, inputs=[prompt], outputs=[token_counter]) + token_button = gr.Button(visible=False, elem_id=f"{id_part}_token_button") with gr.Column(scale=10, elem_id="style_pos_col"): prompt_style = gr.Dropdown(label="Style 1", elem_id=f"{id_part}_style_index", choices=[k for k, v in shared.prompt_styles.styles.items()], value=next(iter(shared.prompt_styles.styles.keys())), visible=len(shared.prompt_styles.styles) > 1) @@ -396,7 +400,7 @@ def create_toprow(is_img2img): prompt_style_apply = gr.Button('Apply style', elem_id="style_apply") save_style = gr.Button('Create style', elem_id="style_create") - return prompt, roll, prompt_style, negative_prompt, prompt_style2, submit, interrogate, prompt_style_apply, save_style, paste + return prompt, roll, prompt_style, negative_prompt, prompt_style2, submit, interrogate, prompt_style_apply, save_style, paste, token_counter, token_button def setup_progressbar(progressbar, preview, id_part): @@ -419,7 +423,7 @@ def setup_progressbar(progressbar, preview, id_part): def create_ui(txt2img, img2img, run_extras, run_pnginfo, run_modelmerger): with gr.Blocks(analytics_enabled=False) as txt2img_interface: - txt2img_prompt, roll, txt2img_prompt_style, txt2img_negative_prompt, txt2img_prompt_style2, submit, _, txt2img_prompt_style_apply, txt2img_save_style, paste = create_toprow(is_img2img=False) + txt2img_prompt, roll, txt2img_prompt_style, txt2img_negative_prompt, txt2img_prompt_style2, submit, _, txt2img_prompt_style_apply, txt2img_save_style, paste, token_counter, token_button = create_toprow(is_img2img=False) dummy_component = gr.Label(visible=False) with gr.Row(elem_id='txt2img_progress_row'): @@ -568,9 +572,10 @@ def create_ui(txt2img, img2img, run_extras, run_pnginfo, run_modelmerger): (hr_options, lambda d: gr.Row.update(visible="Denoising strength" in d)), ] modules.generation_parameters_copypaste.connect_paste(paste, txt2img_paste_fields, txt2img_prompt) + token_button.click(fn=update_token_counter, inputs=[txt2img_prompt, steps], outputs=[token_counter]) with gr.Blocks(analytics_enabled=False) as img2img_interface: - img2img_prompt, roll, img2img_prompt_style, img2img_negative_prompt, img2img_prompt_style2, submit, img2img_interrogate, img2img_prompt_style_apply, img2img_save_style, paste = create_toprow(is_img2img=True) + img2img_prompt, roll, img2img_prompt_style, img2img_negative_prompt, img2img_prompt_style2, submit, img2img_interrogate, img2img_prompt_style_apply, img2img_save_style, paste, token_counter, token_button = create_toprow(is_img2img=True) with gr.Row(elem_id='img2img_progress_row'): with gr.Column(scale=1): @@ -793,6 +798,7 @@ def create_ui(txt2img, img2img, run_extras, run_pnginfo, run_modelmerger): (denoising_strength, "Denoising strength"), ] modules.generation_parameters_copypaste.connect_paste(paste, img2img_paste_fields, img2img_prompt) + token_button.click(fn=update_token_counter, inputs=[img2img_prompt, steps], outputs=[token_counter]) with gr.Blocks(analytics_enabled=False) as extras_interface: with gr.Row().style(equal_height=False): -- cgit v1.2.1 From 3c6a049fc3c6b54ada3736710a7e86663ea7f3d9 Mon Sep 17 00:00:00 2001 From: Liam Date: Fri, 30 Sep 2022 12:12:44 -0400 Subject: consolidated token counter functions --- modules/ui.py | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) (limited to 'modules') diff --git a/modules/ui.py b/modules/ui.py index 6bf28562..40c08984 100644 --- a/modules/ui.py +++ b/modules/ui.py @@ -543,7 +543,7 @@ def create_ui(txt2img, img2img, run_extras, run_pnginfo, run_modelmerger): roll.click( fn=roll_artist, - _js="roll_artist_txt2img", + _js="update_txt2img_tokens", inputs=[ txt2img_prompt, ], @@ -749,7 +749,7 @@ def create_ui(txt2img, img2img, run_extras, run_pnginfo, run_modelmerger): roll.click( fn=roll_artist, - _js="roll_artist_img2img", + _js="update_img2img_tokens", inputs=[ img2img_prompt, ], @@ -760,7 +760,7 @@ def create_ui(txt2img, img2img, run_extras, run_pnginfo, run_modelmerger): prompts = [(txt2img_prompt, txt2img_negative_prompt), (img2img_prompt, img2img_negative_prompt)] style_dropdowns = [(txt2img_prompt_style, txt2img_prompt_style2), (img2img_prompt_style, img2img_prompt_style2)] - style_js_funcs = ["update_style_txt2img", "update_style_img2img"] + style_js_funcs = ["update_txt2img_tokens", "update_img2img_tokens"] for button, (prompt, negative_prompt) in zip([txt2img_save_style, img2img_save_style], prompts): button.click( -- cgit v1.2.1 From bdaa36c84470adbdce3e98c01a69af5e95adfb02 Mon Sep 17 00:00:00 2001 From: brkirch Date: Fri, 30 Sep 2022 23:53:25 -0400 Subject: When device is MPS, use CPU for GFPGAN instead GFPGAN will not work if the device is MPS, so default to CPU instead. --- modules/devices.py | 2 +- modules/gfpgan_model.py | 6 +++--- 2 files changed, 4 insertions(+), 4 deletions(-) (limited to 'modules') diff --git a/modules/devices.py b/modules/devices.py index 07bb2339..08bb26d6 100644 --- a/modules/devices.py +++ b/modules/devices.py @@ -34,7 +34,7 @@ errors.run(enable_tf32, "Enabling TF32") device = get_optimal_device() -device_codeformer = cpu if has_mps else device +device_gfpgan = device_codeformer = cpu if device.type == 'mps' else device def randn(seed, shape): diff --git a/modules/gfpgan_model.py b/modules/gfpgan_model.py index bb30d733..fcd8544a 100644 --- a/modules/gfpgan_model.py +++ b/modules/gfpgan_model.py @@ -21,7 +21,7 @@ def gfpgann(): global loaded_gfpgan_model global model_path if loaded_gfpgan_model is not None: - loaded_gfpgan_model.gfpgan.to(shared.device) + loaded_gfpgan_model.gfpgan.to(devices.device_gfpgan) return loaded_gfpgan_model if gfpgan_constructor is None: @@ -36,8 +36,8 @@ def gfpgann(): else: print("Unable to load gfpgan model!") return None - model = gfpgan_constructor(model_path=model_file, upscale=1, arch='clean', channel_multiplier=2, bg_upsampler=None) - model.gfpgan.to(shared.device) + model = gfpgan_constructor(model_path=model_file, upscale=1, arch='clean', channel_multiplier=2, bg_upsampler=None, device=devices.device_gfpgan) + model.gfpgan.to(devices.device_gfpgan) loaded_gfpgan_model = model return model -- cgit v1.2.1 From 4c2478a68a4f11959fe4887d38e0436eac19f97e Mon Sep 17 00:00:00 2001 From: DepFA <35278260+dfaker@users.noreply.github.com> Date: Sat, 1 Oct 2022 18:30:53 +0100 Subject: add script reload method --- modules/scripts.py | 9 +++++++++ 1 file changed, 9 insertions(+) (limited to 'modules') diff --git a/modules/scripts.py b/modules/scripts.py index 7c3bd5e7..3c14b9e3 100644 --- a/modules/scripts.py +++ b/modules/scripts.py @@ -165,3 +165,12 @@ class ScriptRunner: scripts_txt2img = ScriptRunner() scripts_img2img = ScriptRunner() + +def reload_scripts(basedir): + global scripts_txt2img,scripts_img2img + + scripts_data.clear() + load_scripts(basedir) + + scripts_txt2img = ScriptRunner() + scripts_img2img = ScriptRunner() -- cgit v1.2.1 From 4f8490cd5630823ac44de8b5c5e4325bdbbea7fa Mon Sep 17 00:00:00 2001 From: DepFA <35278260+dfaker@users.noreply.github.com> Date: Sat, 1 Oct 2022 18:33:31 +0100 Subject: add restart button --- modules/ui.py | 15 ++++++++++++++- 1 file changed, 14 insertions(+), 1 deletion(-) (limited to 'modules') diff --git a/modules/ui.py b/modules/ui.py index 15572bb0..ec6aaa28 100644 --- a/modules/ui.py +++ b/modules/ui.py @@ -1002,6 +1002,17 @@ def create_ui(txt2img, img2img, run_extras, run_pnginfo, run_modelmerger): _js='function(){}' ) + def request_restart(): + settings_interface.gradio_ref.do_restart = True + + restart_gradio = gr.Button(value='Restart Gradio and Refresh Scripts') + restart_gradio.click( + fn=request_restart, + inputs=[], + outputs=[], + _js='function(){document.body.innerHTML=\'

Reloading

\';setTimeout(function(){location.reload()},2000)}' + ) + if column is not None: column.__exit__() @@ -1026,7 +1037,9 @@ def create_ui(txt2img, img2img, run_extras, run_pnginfo, run_modelmerger): css += css_hide_progressbar with gr.Blocks(css=css, analytics_enabled=False, title="Stable Diffusion") as demo: - + + settings_interface.gradio_ref = demo + with gr.Tabs() as tabs: for interface, label, ifid in interfaces: with gr.TabItem(label, id=ifid): -- cgit v1.2.1 From 121ed7d36febe94995774973b5edc1ba2ba84aad Mon Sep 17 00:00:00 2001 From: Alexandre Simard Date: Sat, 1 Oct 2022 14:04:20 -0400 Subject: Add progress bar for SwinIR in cmd I do not know how to add them to the UI... --- modules/swinir_model.py | 27 +++++++++++++++------------ 1 file changed, 15 insertions(+), 12 deletions(-) (limited to 'modules') diff --git a/modules/swinir_model.py b/modules/swinir_model.py index 41fda5a7..9bd454c6 100644 --- a/modules/swinir_model.py +++ b/modules/swinir_model.py @@ -5,6 +5,7 @@ import numpy as np import torch from PIL import Image from basicsr.utils.download_util import load_file_from_url +from tqdm import tqdm from modules import modelloader from modules.paths import models_path @@ -122,18 +123,20 @@ def inference(img, model, tile, tile_overlap, window_size, scale): E = torch.zeros(b, c, h * sf, w * sf, dtype=torch.half, device=device).type_as(img) W = torch.zeros_like(E, dtype=torch.half, device=device) - for h_idx in h_idx_list: - for w_idx in w_idx_list: - in_patch = img[..., h_idx: h_idx + tile, w_idx: w_idx + tile] - out_patch = model(in_patch) - out_patch_mask = torch.ones_like(out_patch) - - E[ - ..., h_idx * sf: (h_idx + tile) * sf, w_idx * sf: (w_idx + tile) * sf - ].add_(out_patch) - W[ - ..., h_idx * sf: (h_idx + tile) * sf, w_idx * sf: (w_idx + tile) * sf - ].add_(out_patch_mask) + with tqdm(total=len(h_idx_list) * len(w_idx_list), desc="SwinIR tiles") as pbar: + for h_idx in h_idx_list: + for w_idx in w_idx_list: + in_patch = img[..., h_idx: h_idx + tile, w_idx: w_idx + tile] + out_patch = model(in_patch) + out_patch_mask = torch.ones_like(out_patch) + + E[ + ..., h_idx * sf: (h_idx + tile) * sf, w_idx * sf: (w_idx + tile) * sf + ].add_(out_patch) + W[ + ..., h_idx * sf: (h_idx + tile) * sf, w_idx * sf: (w_idx + tile) * sf + ].add_(out_patch_mask) + pbar.update(1) output = E.div_(W) return output -- cgit v1.2.1 From afaa03c5fd05f48ed9c9f15558ea6f0bc4f61628 Mon Sep 17 00:00:00 2001 From: DepFA <35278260+dfaker@users.noreply.github.com> Date: Sat, 1 Oct 2022 22:43:45 +0100 Subject: add redefinition guard to gradio_routes_templates_response --- modules/ui.py | 15 ++++++++------- 1 file changed, 8 insertions(+), 7 deletions(-) (limited to 'modules') diff --git a/modules/ui.py b/modules/ui.py index ec6aaa28..fd057916 100644 --- a/modules/ui.py +++ b/modules/ui.py @@ -1219,12 +1219,13 @@ for filename in sorted(os.listdir(jsdir)): javascript += f"\n" -def template_response(*args, **kwargs): - res = gradio_routes_templates_response(*args, **kwargs) - res.body = res.body.replace(b'', f'{javascript}'.encode("utf8")) - res.init_headers() - return res +if 'gradio_routes_templates_response' not in globals(): + def template_response(*args, **kwargs): + res = gradio_routes_templates_response(*args, **kwargs) + res.body = res.body.replace(b'', f'{javascript}'.encode("utf8")) + res.init_headers() + return res + gradio_routes_templates_response = gradio.routes.templates.TemplateResponse + gradio.routes.templates.TemplateResponse = template_response -gradio_routes_templates_response = gradio.routes.templates.TemplateResponse -gradio.routes.templates.TemplateResponse = template_response -- cgit v1.2.1 From 6048002dade91b82b1ce9fea3c6ff5b5c1f8c990 Mon Sep 17 00:00:00 2001 From: DepFA <35278260+dfaker@users.noreply.github.com> Date: Sat, 1 Oct 2022 23:10:07 +0100 Subject: Add scope warning to refresh button --- modules/ui.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) (limited to 'modules') diff --git a/modules/ui.py b/modules/ui.py index fd057916..72846a12 100644 --- a/modules/ui.py +++ b/modules/ui.py @@ -1005,7 +1005,7 @@ def create_ui(txt2img, img2img, run_extras, run_pnginfo, run_modelmerger): def request_restart(): settings_interface.gradio_ref.do_restart = True - restart_gradio = gr.Button(value='Restart Gradio and Refresh Scripts') + restart_gradio = gr.Button(value='Restart Gradio and Refresh components (Custom Scripts, ui.py, js and css only)', variant='primary') restart_gradio.click( fn=request_restart, inputs=[], -- cgit v1.2.1 From 027c5aae5546ff3650347cb3c2b87df4415ab900 Mon Sep 17 00:00:00 2001 From: DepFA <35278260+dfaker@users.noreply.github.com> Date: Sat, 1 Oct 2022 23:29:26 +0100 Subject: update reloading message style --- modules/ui.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) (limited to 'modules') diff --git a/modules/ui.py b/modules/ui.py index 72846a12..7b2359c2 100644 --- a/modules/ui.py +++ b/modules/ui.py @@ -1010,7 +1010,7 @@ def create_ui(txt2img, img2img, run_extras, run_pnginfo, run_modelmerger): fn=request_restart, inputs=[], outputs=[], - _js='function(){document.body.innerHTML=\'

Reloading

\';setTimeout(function(){location.reload()},2000)}' + _js='function(){document.body.innerHTML=\'

Reloading...

\';setTimeout(function(){location.reload()},2000)}' ) if column is not None: -- cgit v1.2.1 From 0aa354bd5e811e2b41b17a3052cf5d4c8190d533 Mon Sep 17 00:00:00 2001 From: DepFA <35278260+dfaker@users.noreply.github.com> Date: Sun, 2 Oct 2022 00:13:47 +0100 Subject: remove styling from python side --- modules/ui.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) (limited to 'modules') diff --git a/modules/ui.py b/modules/ui.py index 7b2359c2..cb859ac4 100644 --- a/modules/ui.py +++ b/modules/ui.py @@ -1010,7 +1010,7 @@ def create_ui(txt2img, img2img, run_extras, run_pnginfo, run_modelmerger): fn=request_restart, inputs=[], outputs=[], - _js='function(){document.body.innerHTML=\'

Reloading...

\';setTimeout(function(){location.reload()},2000)}' + _js='function(){restart_reload()}' ) if column is not None: -- cgit v1.2.1 From cf33268d686986a24f2e04eb615f01ed53bfe308 Mon Sep 17 00:00:00 2001 From: DepFA <35278260+dfaker@users.noreply.github.com> Date: Sun, 2 Oct 2022 01:18:42 +0100 Subject: add script body only refresh --- modules/scripts.py | 23 +++++++++++++++++++++++ 1 file changed, 23 insertions(+) (limited to 'modules') diff --git a/modules/scripts.py b/modules/scripts.py index 3c14b9e3..788397f5 100644 --- a/modules/scripts.py +++ b/modules/scripts.py @@ -162,10 +162,33 @@ class ScriptRunner: return processed + def reload_sources(self): + for si,script in list(enumerate(self.scripts)): + with open(script.filename, "r", encoding="utf8") as file: + args_from = script.args_from + args_to = script.args_to + filename = script.filename + text = file.read() + + from types import ModuleType + compiled = compile(text, filename, 'exec') + module = ModuleType(script.filename) + exec(compiled, module.__dict__) + + for key, script_class in module.__dict__.items(): + if type(script_class) == type and issubclass(script_class, Script): + self.scripts[si] = script_class() + self.scripts[si].filename = filename + self.scripts[si].args_from = args_from + self.scripts[si].args_to = args_to scripts_txt2img = ScriptRunner() scripts_img2img = ScriptRunner() +def reload_script_body_only(): + scripts_txt2img.reload_sources() + scripts_img2img.reload_sources() + def reload_scripts(basedir): global scripts_txt2img,scripts_img2img -- cgit v1.2.1 From 07e40ad7f23472fc1c781fe1cc6c1ee403413918 Mon Sep 17 00:00:00 2001 From: DepFA <35278260+dfaker@users.noreply.github.com> Date: Sun, 2 Oct 2022 01:19:55 +0100 Subject: add custom script body only refresh option --- modules/ui.py | 11 +++++++++++ 1 file changed, 11 insertions(+) (limited to 'modules') diff --git a/modules/ui.py b/modules/ui.py index cb859ac4..eb7c0585 100644 --- a/modules/ui.py +++ b/modules/ui.py @@ -1012,6 +1012,17 @@ def create_ui(txt2img, img2img, run_extras, run_pnginfo, run_modelmerger): outputs=[], _js='function(){restart_reload()}' ) + + def reload_scripts(): + modules.scripts.reload_script_body_only() + + reload_script_bodies = gr.Button(value='Reload custom script bodies (No ui updates, No restart)', variant='primary') + reload_script_bodies.click( + fn=reload_scripts, + inputs=[], + outputs=[], + _js='function(){}' + ) if column is not None: column.__exit__() -- cgit v1.2.1 From 2deea867814272f1f089b60e9ba8d587c16b2fb1 Mon Sep 17 00:00:00 2001 From: DepFA <35278260+dfaker@users.noreply.github.com> Date: Sun, 2 Oct 2022 01:36:30 +0100 Subject: Put reload buttons in row and add secondary style --- modules/ui.py | 23 +++++++++++++---------- 1 file changed, 13 insertions(+), 10 deletions(-) (limited to 'modules') diff --git a/modules/ui.py b/modules/ui.py index eb7c0585..963a2c61 100644 --- a/modules/ui.py +++ b/modules/ui.py @@ -1002,27 +1002,30 @@ def create_ui(txt2img, img2img, run_extras, run_pnginfo, run_modelmerger): _js='function(){}' ) - def request_restart(): - settings_interface.gradio_ref.do_restart = True + with gr.Row(): + reload_script_bodies = gr.Button(value='Reload custom script bodies (No ui updates, No restart)', variant='secondary') + restart_gradio = gr.Button(value='Restart Gradio and Refresh components (Custom Scripts, ui.py, js and css only)', variant='primary') - restart_gradio = gr.Button(value='Restart Gradio and Refresh components (Custom Scripts, ui.py, js and css only)', variant='primary') - restart_gradio.click( - fn=request_restart, - inputs=[], - outputs=[], - _js='function(){restart_reload()}' - ) def reload_scripts(): modules.scripts.reload_script_body_only() - reload_script_bodies = gr.Button(value='Reload custom script bodies (No ui updates, No restart)', variant='primary') reload_script_bodies.click( fn=reload_scripts, inputs=[], outputs=[], _js='function(){}' ) + + def request_restart(): + settings_interface.gradio_ref.do_restart = True + + restart_gradio.click( + fn=request_restart, + inputs=[], + outputs=[], + _js='function(){restart_reload()}' + ) if column is not None: column.__exit__() -- cgit v1.2.1 From 3cf1a96006daffedb8ecd0ae142eca4c4da06105 Mon Sep 17 00:00:00 2001 From: RnDMonkey Date: Sat, 1 Oct 2022 21:11:03 -0700 Subject: added safety for blank directory naming patterns --- modules/images.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) (limited to 'modules') diff --git a/modules/images.py b/modules/images.py index f1aed5d6..e7894b4c 100644 --- a/modules/images.py +++ b/modules/images.py @@ -311,7 +311,7 @@ def apply_filename_pattern(x, p, seed, prompt): x = x.replace("[cfg]", str(p.cfg_scale)) x = x.replace("[width]", str(p.width)) x = x.replace("[height]", str(p.height)) - x = x.replace("[styles]", sanitize_filename_part(", ".join([x for x in p.styles if not x == "None"]), replace_spaces=False)) + x = x.replace("[styles]", sanitize_filename_part(", ".join([x for x in p.styles if not x == "None"]) or "No styles", replace_spaces=False)) x = x.replace("[sampler]", sanitize_filename_part(sd_samplers.samplers[p.sampler_index].name, replace_spaces=False)) x = x.replace("[model_hash]", shared.sd_model.sd_model_hash) @@ -374,7 +374,7 @@ def save_image(image, path, basename, seed=None, prompt=None, extension='png', i save_to_dirs = (grid and opts.grid_save_to_dirs) or (not grid and opts.save_to_dirs and not no_prompt) if save_to_dirs: - dirname = apply_filename_pattern(opts.directories_filename_pattern or "[prompt_words]", p, seed, prompt) + dirname = apply_filename_pattern(opts.directories_filename_pattern or "[prompt_words]", p, seed, prompt).strip('\\ ') path = os.path.join(path, dirname) os.makedirs(path, exist_ok=True) -- cgit v1.2.1 From 70f526704721a303ae045f6406439dcceee4302e Mon Sep 17 00:00:00 2001 From: RnDMonkey Date: Sat, 1 Oct 2022 21:18:15 -0700 Subject: use os.path.normpath for better safety checking --- modules/images.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) (limited to 'modules') diff --git a/modules/images.py b/modules/images.py index e7894b4c..5ef7eb92 100644 --- a/modules/images.py +++ b/modules/images.py @@ -374,8 +374,8 @@ def save_image(image, path, basename, seed=None, prompt=None, extension='png', i save_to_dirs = (grid and opts.grid_save_to_dirs) or (not grid and opts.save_to_dirs and not no_prompt) if save_to_dirs: - dirname = apply_filename_pattern(opts.directories_filename_pattern or "[prompt_words]", p, seed, prompt).strip('\\ ') - path = os.path.join(path, dirname) + dirname = apply_filename_pattern(opts.directories_filename_pattern or "[prompt_words]", p, seed, prompt) + path = os.path.normpath(os.path.join(path, dirname)) os.makedirs(path, exist_ok=True) -- cgit v1.2.1 From 32edf1732f27a1fad5133667c22b948adda1b070 Mon Sep 17 00:00:00 2001 From: RnDMonkey Date: Sat, 1 Oct 2022 21:37:14 -0700 Subject: os.path.normpath wasn't working, reverting to manual strip --- modules/images.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) (limited to 'modules') diff --git a/modules/images.py b/modules/images.py index 5ef7eb92..4998e92c 100644 --- a/modules/images.py +++ b/modules/images.py @@ -374,8 +374,8 @@ def save_image(image, path, basename, seed=None, prompt=None, extension='png', i save_to_dirs = (grid and opts.grid_save_to_dirs) or (not grid and opts.save_to_dirs and not no_prompt) if save_to_dirs: - dirname = apply_filename_pattern(opts.directories_filename_pattern or "[prompt_words]", p, seed, prompt) - path = os.path.normpath(os.path.join(path, dirname)) + dirname = apply_filename_pattern(opts.directories_filename_pattern or "[prompt_words]", p, seed, prompt).strip('\\ /') + path = os.path.join(path, dirname) os.makedirs(path, exist_ok=True) -- cgit v1.2.1 From 820f1dc96b1979d7e92170c161db281ee8bd988b Mon Sep 17 00:00:00 2001 From: AUTOMATIC <16777216c@gmail.com> Date: Sun, 2 Oct 2022 15:03:39 +0300 Subject: initial support for training textual inversion --- modules/devices.py | 3 +- modules/processing.py | 13 +- modules/sd_hijack.py | 324 ++++--------------------- modules/sd_hijack_optimizations.py | 164 +++++++++++++ modules/sd_models.py | 4 +- modules/shared.py | 3 +- modules/textual_inversion/dataset.py | 76 ++++++ modules/textual_inversion/textual_inversion.py | 258 ++++++++++++++++++++ modules/textual_inversion/ui.py | 32 +++ modules/ui.py | 139 +++++++++-- 10 files changed, 717 insertions(+), 299 deletions(-) create mode 100644 modules/sd_hijack_optimizations.py create mode 100644 modules/textual_inversion/dataset.py create mode 100644 modules/textual_inversion/textual_inversion.py create mode 100644 modules/textual_inversion/ui.py (limited to 'modules') diff --git a/modules/devices.py b/modules/devices.py index 07bb2339..ff82f2f6 100644 --- a/modules/devices.py +++ b/modules/devices.py @@ -32,10 +32,9 @@ def enable_tf32(): errors.run(enable_tf32, "Enabling TF32") - device = get_optimal_device() device_codeformer = cpu if has_mps else device - +dtype = torch.float16 def randn(seed, shape): # Pytorch currently doesn't handle setting randomness correctly when the metal backend is used. diff --git a/modules/processing.py b/modules/processing.py index 7eeb5191..8223423a 100644 --- a/modules/processing.py +++ b/modules/processing.py @@ -56,7 +56,7 @@ class StableDiffusionProcessing: self.prompt: str = prompt self.prompt_for_display: str = None self.negative_prompt: str = (negative_prompt or "") - self.styles: str = styles + self.styles: list = styles or [] self.seed: int = seed self.subseed: int = subseed self.subseed_strength: float = subseed_strength @@ -271,7 +271,7 @@ def create_infotext(p, all_prompts, all_seeds, all_subseeds, comments, iteration "Variation seed strength": (None if p.subseed_strength == 0 else p.subseed_strength), "Seed resize from": (None if p.seed_resize_from_w == 0 or p.seed_resize_from_h == 0 else f"{p.seed_resize_from_w}x{p.seed_resize_from_h}"), "Denoising strength": getattr(p, 'denoising_strength', None), - "Eta": (None if p.sampler.eta == p.sampler.default_eta else p.sampler.eta), + "Eta": (None if p.sampler is None or p.sampler.eta == p.sampler.default_eta else p.sampler.eta), } generation_params.update(p.extra_generation_params) @@ -295,8 +295,11 @@ def process_images(p: StableDiffusionProcessing) -> Processed: fix_seed(p) - os.makedirs(p.outpath_samples, exist_ok=True) - os.makedirs(p.outpath_grids, exist_ok=True) + if p.outpath_samples is not None: + os.makedirs(p.outpath_samples, exist_ok=True) + + if p.outpath_grids is not None: + os.makedirs(p.outpath_grids, exist_ok=True) modules.sd_hijack.model_hijack.apply_circular(p.tiling) @@ -323,7 +326,7 @@ def process_images(p: StableDiffusionProcessing) -> Processed: return create_infotext(p, all_prompts, all_seeds, all_subseeds, comments, iteration, position_in_batch) if os.path.exists(cmd_opts.embeddings_dir): - model_hijack.load_textual_inversion_embeddings(cmd_opts.embeddings_dir, p.sd_model) + model_hijack.embedding_db.load_textual_inversion_embeddings() infotexts = [] output_images = [] diff --git a/modules/sd_hijack.py b/modules/sd_hijack.py index fa7eaeb8..fd57e5c5 100644 --- a/modules/sd_hijack.py +++ b/modules/sd_hijack.py @@ -6,244 +6,41 @@ import torch import numpy as np from torch import einsum -from modules import prompt_parser +import modules.textual_inversion.textual_inversion +from modules import prompt_parser, devices, sd_hijack_optimizations, shared from modules.shared import opts, device, cmd_opts -from ldm.util import default -from einops import rearrange import ldm.modules.attention import ldm.modules.diffusionmodules.model +attention_CrossAttention_forward = ldm.modules.attention.CrossAttention.forward +diffusionmodules_model_nonlinearity = ldm.modules.diffusionmodules.model.nonlinearity +diffusionmodules_model_AttnBlock_forward = ldm.modules.diffusionmodules.model.AttnBlock.forward -# see https://github.com/basujindal/stable-diffusion/pull/117 for discussion -def split_cross_attention_forward_v1(self, x, context=None, mask=None): - h = self.heads - q = self.to_q(x) - context = default(context, x) - k = self.to_k(context) - v = self.to_v(context) - del context, x +def apply_optimizations(): + if cmd_opts.opt_split_attention_v1: + ldm.modules.attention.CrossAttention.forward = sd_hijack_optimizations.split_cross_attention_forward_v1 + elif not cmd_opts.disable_opt_split_attention and (cmd_opts.opt_split_attention or torch.cuda.is_available()): + ldm.modules.attention.CrossAttention.forward = sd_hijack_optimizations.split_cross_attention_forward + ldm.modules.diffusionmodules.model.nonlinearity = sd_hijack_optimizations.nonlinearity_hijack + ldm.modules.diffusionmodules.model.AttnBlock.forward = sd_hijack_optimizations.cross_attention_attnblock_forward - q, k, v = map(lambda t: rearrange(t, 'b n (h d) -> (b h) n d', h=h), (q, k, v)) - r1 = torch.zeros(q.shape[0], q.shape[1], v.shape[2], device=q.device) - for i in range(0, q.shape[0], 2): - end = i + 2 - s1 = einsum('b i d, b j d -> b i j', q[i:end], k[i:end]) - s1 *= self.scale +def undo_optimizations(): + ldm.modules.attention.CrossAttention.forward = attention_CrossAttention_forward + ldm.modules.diffusionmodules.model.nonlinearity = diffusionmodules_model_nonlinearity + ldm.modules.diffusionmodules.model.AttnBlock.forward = diffusionmodules_model_AttnBlock_forward - s2 = s1.softmax(dim=-1) - del s1 - - r1[i:end] = einsum('b i j, b j d -> b i d', s2, v[i:end]) - del s2 - - r2 = rearrange(r1, '(b h) n d -> b n (h d)', h=h) - del r1 - - return self.to_out(r2) - - -# taken from https://github.com/Doggettx/stable-diffusion -def split_cross_attention_forward(self, x, context=None, mask=None): - h = self.heads - - q_in = self.to_q(x) - context = default(context, x) - k_in = self.to_k(context) * self.scale - v_in = self.to_v(context) - del context, x - - q, k, v = map(lambda t: rearrange(t, 'b n (h d) -> (b h) n d', h=h), (q_in, k_in, v_in)) - del q_in, k_in, v_in - - r1 = torch.zeros(q.shape[0], q.shape[1], v.shape[2], device=q.device, dtype=q.dtype) - - stats = torch.cuda.memory_stats(q.device) - mem_active = stats['active_bytes.all.current'] - mem_reserved = stats['reserved_bytes.all.current'] - mem_free_cuda, _ = torch.cuda.mem_get_info(torch.cuda.current_device()) - mem_free_torch = mem_reserved - mem_active - mem_free_total = mem_free_cuda + mem_free_torch - - gb = 1024 ** 3 - tensor_size = q.shape[0] * q.shape[1] * k.shape[1] * q.element_size() - modifier = 3 if q.element_size() == 2 else 2.5 - mem_required = tensor_size * modifier - steps = 1 - - if mem_required > mem_free_total: - steps = 2 ** (math.ceil(math.log(mem_required / mem_free_total, 2))) - # print(f"Expected tensor size:{tensor_size/gb:0.1f}GB, cuda free:{mem_free_cuda/gb:0.1f}GB " - # f"torch free:{mem_free_torch/gb:0.1f} total:{mem_free_total/gb:0.1f} steps:{steps}") - - if steps > 64: - max_res = math.floor(math.sqrt(math.sqrt(mem_free_total / 2.5)) / 8) * 64 - raise RuntimeError(f'Not enough memory, use lower resolution (max approx. {max_res}x{max_res}). ' - f'Need: {mem_required / 64 / gb:0.1f}GB free, Have:{mem_free_total / gb:0.1f}GB free') - - slice_size = q.shape[1] // steps if (q.shape[1] % steps) == 0 else q.shape[1] - for i in range(0, q.shape[1], slice_size): - end = i + slice_size - s1 = einsum('b i d, b j d -> b i j', q[:, i:end], k) - - s2 = s1.softmax(dim=-1, dtype=q.dtype) - del s1 - - r1[:, i:end] = einsum('b i j, b j d -> b i d', s2, v) - del s2 - - del q, k, v - - r2 = rearrange(r1, '(b h) n d -> b n (h d)', h=h) - del r1 - - return self.to_out(r2) - -def nonlinearity_hijack(x): - # swish - t = torch.sigmoid(x) - x *= t - del t - - return x - -def cross_attention_attnblock_forward(self, x): - h_ = x - h_ = self.norm(h_) - q1 = self.q(h_) - k1 = self.k(h_) - v = self.v(h_) - - # compute attention - b, c, h, w = q1.shape - - q2 = q1.reshape(b, c, h*w) - del q1 - - q = q2.permute(0, 2, 1) # b,hw,c - del q2 - - k = k1.reshape(b, c, h*w) # b,c,hw - del k1 - - h_ = torch.zeros_like(k, device=q.device) - - stats = torch.cuda.memory_stats(q.device) - mem_active = stats['active_bytes.all.current'] - mem_reserved = stats['reserved_bytes.all.current'] - mem_free_cuda, _ = torch.cuda.mem_get_info(torch.cuda.current_device()) - mem_free_torch = mem_reserved - mem_active - mem_free_total = mem_free_cuda + mem_free_torch - - tensor_size = q.shape[0] * q.shape[1] * k.shape[2] * q.element_size() - mem_required = tensor_size * 2.5 - steps = 1 - - if mem_required > mem_free_total: - steps = 2**(math.ceil(math.log(mem_required / mem_free_total, 2))) - - slice_size = q.shape[1] // steps if (q.shape[1] % steps) == 0 else q.shape[1] - for i in range(0, q.shape[1], slice_size): - end = i + slice_size - - w1 = torch.bmm(q[:, i:end], k) # b,hw,hw w[b,i,j]=sum_c q[b,i,c]k[b,c,j] - w2 = w1 * (int(c)**(-0.5)) - del w1 - w3 = torch.nn.functional.softmax(w2, dim=2, dtype=q.dtype) - del w2 - - # attend to values - v1 = v.reshape(b, c, h*w) - w4 = w3.permute(0, 2, 1) # b,hw,hw (first hw of k, second of q) - del w3 - - h_[:, :, i:end] = torch.bmm(v1, w4) # b, c,hw (hw of q) h_[b,c,j] = sum_i v[b,c,i] w_[b,i,j] - del v1, w4 - - h2 = h_.reshape(b, c, h, w) - del h_ - - h3 = self.proj_out(h2) - del h2 - - h3 += x - - return h3 class StableDiffusionModelHijack: - ids_lookup = {} - word_embeddings = {} - word_embeddings_checksums = {} fixes = None comments = [] - dir_mtime = None layers = None circular_enabled = False clip = None - def load_textual_inversion_embeddings(self, dirname, model): - mt = os.path.getmtime(dirname) - if self.dir_mtime is not None and mt <= self.dir_mtime: - return - - self.dir_mtime = mt - self.ids_lookup.clear() - self.word_embeddings.clear() - - tokenizer = model.cond_stage_model.tokenizer - - def const_hash(a): - r = 0 - for v in a: - r = (r * 281 ^ int(v) * 997) & 0xFFFFFFFF - return r - - def process_file(path, filename): - name = os.path.splitext(filename)[0] - - data = torch.load(path, map_location="cpu") - - # textual inversion embeddings - if 'string_to_param' in data: - param_dict = data['string_to_param'] - if hasattr(param_dict, '_parameters'): - param_dict = getattr(param_dict, '_parameters') # fix for torch 1.12.1 loading saved file from torch 1.11 - assert len(param_dict) == 1, 'embedding file has multiple terms in it' - emb = next(iter(param_dict.items()))[1] - # diffuser concepts - elif type(data) == dict and type(next(iter(data.values()))) == torch.Tensor: - assert len(data.keys()) == 1, 'embedding file has multiple terms in it' - - emb = next(iter(data.values())) - if len(emb.shape) == 1: - emb = emb.unsqueeze(0) - - self.word_embeddings[name] = emb.detach().to(device) - self.word_embeddings_checksums[name] = f'{const_hash(emb.reshape(-1)*100)&0xffff:04x}' - - ids = tokenizer([name], add_special_tokens=False)['input_ids'][0] - - first_id = ids[0] - if first_id not in self.ids_lookup: - self.ids_lookup[first_id] = [] - self.ids_lookup[first_id].append((ids, name)) - - for fn in os.listdir(dirname): - try: - fullfn = os.path.join(dirname, fn) - - if os.stat(fullfn).st_size == 0: - continue - - process_file(fullfn, fn) - except Exception: - print(f"Error loading emedding {fn}:", file=sys.stderr) - print(traceback.format_exc(), file=sys.stderr) - continue - - print(f"Loaded a total of {len(self.word_embeddings)} textual inversion embeddings.") + embedding_db = modules.textual_inversion.textual_inversion.EmbeddingDatabase(cmd_opts.embeddings_dir) def hijack(self, m): model_embeddings = m.cond_stage_model.transformer.text_model.embeddings @@ -253,12 +50,7 @@ class StableDiffusionModelHijack: self.clip = m.cond_stage_model - if cmd_opts.opt_split_attention_v1: - ldm.modules.attention.CrossAttention.forward = split_cross_attention_forward_v1 - elif not cmd_opts.disable_opt_split_attention and (cmd_opts.opt_split_attention or torch.cuda.is_available()): - ldm.modules.attention.CrossAttention.forward = split_cross_attention_forward - ldm.modules.diffusionmodules.model.nonlinearity = nonlinearity_hijack - ldm.modules.diffusionmodules.model.AttnBlock.forward = cross_attention_attnblock_forward + apply_optimizations() def flatten(el): flattened = [flatten(children) for children in el.children()] @@ -296,7 +88,7 @@ class FrozenCLIPEmbedderWithCustomWords(torch.nn.Module): def __init__(self, wrapped, hijack): super().__init__() self.wrapped = wrapped - self.hijack = hijack + self.hijack: StableDiffusionModelHijack = hijack self.tokenizer = wrapped.tokenizer self.max_length = wrapped.max_length self.token_mults = {} @@ -317,7 +109,6 @@ class FrozenCLIPEmbedderWithCustomWords(torch.nn.Module): if mult != 1.0: self.token_mults[ident] = mult - def tokenize_line(self, line, used_custom_terms, hijack_comments): id_start = self.wrapped.tokenizer.bos_token_id id_end = self.wrapped.tokenizer.eos_token_id @@ -339,28 +130,19 @@ class FrozenCLIPEmbedderWithCustomWords(torch.nn.Module): while i < len(tokens): token = tokens[i] - possible_matches = self.hijack.ids_lookup.get(token, None) + embedding = self.hijack.embedding_db.find_embedding_at_position(tokens, i) - if possible_matches is None: + if embedding is None: remade_tokens.append(token) multipliers.append(weight) + i += 1 else: - found = False - for ids, word in possible_matches: - if tokens[i:i + len(ids)] == ids: - emb_len = int(self.hijack.word_embeddings[word].shape[0]) - fixes.append((len(remade_tokens), word)) - remade_tokens += [0] * emb_len - multipliers += [weight] * emb_len - i += len(ids) - 1 - found = True - used_custom_terms.append((word, self.hijack.word_embeddings_checksums[word])) - break - - if not found: - remade_tokens.append(token) - multipliers.append(weight) - i += 1 + emb_len = int(embedding.vec.shape[0]) + fixes.append((len(remade_tokens), embedding)) + remade_tokens += [0] * emb_len + multipliers += [weight] * emb_len + used_custom_terms.append((embedding.name, embedding.checksum())) + i += emb_len if len(remade_tokens) > maxlen - 2: vocab = {v: k for k, v in self.wrapped.tokenizer.get_vocab().items()} @@ -431,32 +213,23 @@ class FrozenCLIPEmbedderWithCustomWords(torch.nn.Module): while i < len(tokens): token = tokens[i] - possible_matches = self.hijack.ids_lookup.get(token, None) + embedding = self.hijack.embedding_db.find_embedding_at_position(tokens, i) mult_change = self.token_mults.get(token) if opts.enable_emphasis else None if mult_change is not None: mult *= mult_change - elif possible_matches is None: + i += 1 + elif embedding is None: remade_tokens.append(token) multipliers.append(mult) + i += 1 else: - found = False - for ids, word in possible_matches: - if tokens[i:i+len(ids)] == ids: - emb_len = int(self.hijack.word_embeddings[word].shape[0]) - fixes.append((len(remade_tokens), word)) - remade_tokens += [0] * emb_len - multipliers += [mult] * emb_len - i += len(ids) - 1 - found = True - used_custom_terms.append((word, self.hijack.word_embeddings_checksums[word])) - break - - if not found: - remade_tokens.append(token) - multipliers.append(mult) - - i += 1 + emb_len = int(embedding.vec.shape[0]) + fixes.append((len(remade_tokens), embedding)) + remade_tokens += [0] * emb_len + multipliers += [mult] * emb_len + used_custom_terms.append((embedding.name, embedding.checksum())) + i += emb_len if len(remade_tokens) > maxlen - 2: vocab = {v: k for k, v in self.wrapped.tokenizer.get_vocab().items()} @@ -464,6 +237,7 @@ class FrozenCLIPEmbedderWithCustomWords(torch.nn.Module): overflowing_words = [vocab.get(int(x), "") for x in ovf] overflowing_text = self.wrapped.tokenizer.convert_tokens_to_string(''.join(overflowing_words)) hijack_comments.append(f"Warning: too many input tokens; some ({len(overflowing_words)}) have been truncated:\n{overflowing_text}\n") + token_count = len(remade_tokens) remade_tokens = remade_tokens + [id_end] * (maxlen - 2 - len(remade_tokens)) remade_tokens = [id_start] + remade_tokens[0:maxlen-2] + [id_end] @@ -484,7 +258,6 @@ class FrozenCLIPEmbedderWithCustomWords(torch.nn.Module): else: batch_multipliers, remade_batch_tokens, used_custom_terms, hijack_comments, hijack_fixes, token_count = self.process_text(text) - self.hijack.fixes = hijack_fixes self.hijack.comments = hijack_comments @@ -517,14 +290,19 @@ class EmbeddingsWithFixes(torch.nn.Module): inputs_embeds = self.wrapped(input_ids) - if batch_fixes is not None: - for fixes, tensor in zip(batch_fixes, inputs_embeds): - for offset, word in fixes: - emb = self.embeddings.word_embeddings[word] - emb_len = min(tensor.shape[0]-offset-1, emb.shape[0]) - tensor[offset+1:offset+1+emb_len] = self.embeddings.word_embeddings[word][0:emb_len] + if batch_fixes is None or len(batch_fixes) == 0 or max([len(x) for x in batch_fixes]) == 0: + return inputs_embeds + + vecs = [] + for fixes, tensor in zip(batch_fixes, inputs_embeds): + for offset, embedding in fixes: + emb = embedding.vec + emb_len = min(tensor.shape[0]-offset-1, emb.shape[0]) + tensor = torch.cat([tensor[0:offset+1], emb[0:emb_len], tensor[offset+1+emb_len:]]) + + vecs.append(tensor) - return inputs_embeds + return torch.stack(vecs) def add_circular_option_to_conv_2d(): diff --git a/modules/sd_hijack_optimizations.py b/modules/sd_hijack_optimizations.py new file mode 100644 index 00000000..9c079e57 --- /dev/null +++ b/modules/sd_hijack_optimizations.py @@ -0,0 +1,164 @@ +import math +import torch +from torch import einsum + +from ldm.util import default +from einops import rearrange + + +# see https://github.com/basujindal/stable-diffusion/pull/117 for discussion +def split_cross_attention_forward_v1(self, x, context=None, mask=None): + h = self.heads + + q = self.to_q(x) + context = default(context, x) + k = self.to_k(context) + v = self.to_v(context) + del context, x + + q, k, v = map(lambda t: rearrange(t, 'b n (h d) -> (b h) n d', h=h), (q, k, v)) + + r1 = torch.zeros(q.shape[0], q.shape[1], v.shape[2], device=q.device) + for i in range(0, q.shape[0], 2): + end = i + 2 + s1 = einsum('b i d, b j d -> b i j', q[i:end], k[i:end]) + s1 *= self.scale + + s2 = s1.softmax(dim=-1) + del s1 + + r1[i:end] = einsum('b i j, b j d -> b i d', s2, v[i:end]) + del s2 + + r2 = rearrange(r1, '(b h) n d -> b n (h d)', h=h) + del r1 + + return self.to_out(r2) + + +# taken from https://github.com/Doggettx/stable-diffusion +def split_cross_attention_forward(self, x, context=None, mask=None): + h = self.heads + + q_in = self.to_q(x) + context = default(context, x) + k_in = self.to_k(context) * self.scale + v_in = self.to_v(context) + del context, x + + q, k, v = map(lambda t: rearrange(t, 'b n (h d) -> (b h) n d', h=h), (q_in, k_in, v_in)) + del q_in, k_in, v_in + + r1 = torch.zeros(q.shape[0], q.shape[1], v.shape[2], device=q.device, dtype=q.dtype) + + stats = torch.cuda.memory_stats(q.device) + mem_active = stats['active_bytes.all.current'] + mem_reserved = stats['reserved_bytes.all.current'] + mem_free_cuda, _ = torch.cuda.mem_get_info(torch.cuda.current_device()) + mem_free_torch = mem_reserved - mem_active + mem_free_total = mem_free_cuda + mem_free_torch + + gb = 1024 ** 3 + tensor_size = q.shape[0] * q.shape[1] * k.shape[1] * q.element_size() + modifier = 3 if q.element_size() == 2 else 2.5 + mem_required = tensor_size * modifier + steps = 1 + + if mem_required > mem_free_total: + steps = 2 ** (math.ceil(math.log(mem_required / mem_free_total, 2))) + # print(f"Expected tensor size:{tensor_size/gb:0.1f}GB, cuda free:{mem_free_cuda/gb:0.1f}GB " + # f"torch free:{mem_free_torch/gb:0.1f} total:{mem_free_total/gb:0.1f} steps:{steps}") + + if steps > 64: + max_res = math.floor(math.sqrt(math.sqrt(mem_free_total / 2.5)) / 8) * 64 + raise RuntimeError(f'Not enough memory, use lower resolution (max approx. {max_res}x{max_res}). ' + f'Need: {mem_required / 64 / gb:0.1f}GB free, Have:{mem_free_total / gb:0.1f}GB free') + + slice_size = q.shape[1] // steps if (q.shape[1] % steps) == 0 else q.shape[1] + for i in range(0, q.shape[1], slice_size): + end = i + slice_size + s1 = einsum('b i d, b j d -> b i j', q[:, i:end], k) + + s2 = s1.softmax(dim=-1, dtype=q.dtype) + del s1 + + r1[:, i:end] = einsum('b i j, b j d -> b i d', s2, v) + del s2 + + del q, k, v + + r2 = rearrange(r1, '(b h) n d -> b n (h d)', h=h) + del r1 + + return self.to_out(r2) + +def nonlinearity_hijack(x): + # swish + t = torch.sigmoid(x) + x *= t + del t + + return x + +def cross_attention_attnblock_forward(self, x): + h_ = x + h_ = self.norm(h_) + q1 = self.q(h_) + k1 = self.k(h_) + v = self.v(h_) + + # compute attention + b, c, h, w = q1.shape + + q2 = q1.reshape(b, c, h*w) + del q1 + + q = q2.permute(0, 2, 1) # b,hw,c + del q2 + + k = k1.reshape(b, c, h*w) # b,c,hw + del k1 + + h_ = torch.zeros_like(k, device=q.device) + + stats = torch.cuda.memory_stats(q.device) + mem_active = stats['active_bytes.all.current'] + mem_reserved = stats['reserved_bytes.all.current'] + mem_free_cuda, _ = torch.cuda.mem_get_info(torch.cuda.current_device()) + mem_free_torch = mem_reserved - mem_active + mem_free_total = mem_free_cuda + mem_free_torch + + tensor_size = q.shape[0] * q.shape[1] * k.shape[2] * q.element_size() + mem_required = tensor_size * 2.5 + steps = 1 + + if mem_required > mem_free_total: + steps = 2**(math.ceil(math.log(mem_required / mem_free_total, 2))) + + slice_size = q.shape[1] // steps if (q.shape[1] % steps) == 0 else q.shape[1] + for i in range(0, q.shape[1], slice_size): + end = i + slice_size + + w1 = torch.bmm(q[:, i:end], k) # b,hw,hw w[b,i,j]=sum_c q[b,i,c]k[b,c,j] + w2 = w1 * (int(c)**(-0.5)) + del w1 + w3 = torch.nn.functional.softmax(w2, dim=2, dtype=q.dtype) + del w2 + + # attend to values + v1 = v.reshape(b, c, h*w) + w4 = w3.permute(0, 2, 1) # b,hw,hw (first hw of k, second of q) + del w3 + + h_[:, :, i:end] = torch.bmm(v1, w4) # b, c,hw (hw of q) h_[b,c,j] = sum_i v[b,c,i] w_[b,i,j] + del v1, w4 + + h2 = h_.reshape(b, c, h, w) + del h_ + + h3 = self.proj_out(h2) + del h2 + + h3 += x + + return h3 diff --git a/modules/sd_models.py b/modules/sd_models.py index 2539f14c..5b3dbdc7 100644 --- a/modules/sd_models.py +++ b/modules/sd_models.py @@ -8,7 +8,7 @@ from omegaconf import OmegaConf from ldm.util import instantiate_from_config -from modules import shared, modelloader +from modules import shared, modelloader, devices from modules.paths import models_path model_dir = "Stable-diffusion" @@ -134,6 +134,8 @@ def load_model_weights(model, checkpoint_file, sd_model_hash): if not shared.cmd_opts.no_half: model.half() + devices.dtype = torch.float32 if shared.cmd_opts.no_half else torch.float16 + model.sd_model_hash = sd_model_hash model.sd_model_checkpint = checkpoint_file diff --git a/modules/shared.py b/modules/shared.py index ac968b2d..ac0bc480 100644 --- a/modules/shared.py +++ b/modules/shared.py @@ -78,6 +78,7 @@ class State: current_latent = None current_image = None current_image_sampling_step = 0 + textinfo = None def interrupt(self): self.interrupted = True @@ -88,7 +89,7 @@ class State: self.current_image_sampling_step = 0 def get_job_timestamp(self): - return datetime.datetime.now().strftime("%Y%m%d%H%M%S") + return datetime.datetime.now().strftime("%Y%m%d%H%M%S") # shouldn't this return job_timestamp? state = State() diff --git a/modules/textual_inversion/dataset.py b/modules/textual_inversion/dataset.py new file mode 100644 index 00000000..7e134a08 --- /dev/null +++ b/modules/textual_inversion/dataset.py @@ -0,0 +1,76 @@ +import os +import numpy as np +import PIL +import torch +from PIL import Image +from torch.utils.data import Dataset +from torchvision import transforms + +import random +import tqdm + + +class PersonalizedBase(Dataset): + def __init__(self, data_root, size=None, repeats=100, flip_p=0.5, placeholder_token="*", width=512, height=512, model=None, device=None, template_file=None): + + self.placeholder_token = placeholder_token + + self.size = size + self.width = width + self.height = height + self.flip = transforms.RandomHorizontalFlip(p=flip_p) + + self.dataset = [] + + with open(template_file, "r") as file: + lines = [x.strip() for x in file.readlines()] + + self.lines = lines + + assert data_root, 'dataset directory not specified' + + self.image_paths = [os.path.join(data_root, file_path) for file_path in os.listdir(data_root)] + print("Preparing dataset...") + for path in tqdm.tqdm(self.image_paths): + image = Image.open(path) + image = image.convert('RGB') + image = image.resize((self.width, self.height), PIL.Image.BICUBIC) + + filename = os.path.basename(path) + filename_tokens = os.path.splitext(filename)[0].replace('_', '-').replace(' ', '-').split('-') + filename_tokens = [token for token in filename_tokens if token.isalpha()] + + npimage = np.array(image).astype(np.uint8) + npimage = (npimage / 127.5 - 1.0).astype(np.float32) + + torchdata = torch.from_numpy(npimage).to(device=device, dtype=torch.float32) + torchdata = torch.moveaxis(torchdata, 2, 0) + + init_latent = model.get_first_stage_encoding(model.encode_first_stage(torchdata.unsqueeze(dim=0))).squeeze() + + self.dataset.append((init_latent, filename_tokens)) + + self.length = len(self.dataset) * repeats + + self.initial_indexes = np.arange(self.length) % len(self.dataset) + self.indexes = None + self.shuffle() + + def shuffle(self): + self.indexes = self.initial_indexes[torch.randperm(self.initial_indexes.shape[0])] + + def __len__(self): + return self.length + + def __getitem__(self, i): + if i % len(self.dataset) == 0: + self.shuffle() + + index = self.indexes[i % len(self.indexes)] + x, filename_tokens = self.dataset[index] + + text = random.choice(self.lines) + text = text.replace("[name]", self.placeholder_token) + text = text.replace("[filewords]", ' '.join(filename_tokens)) + + return x, text diff --git a/modules/textual_inversion/textual_inversion.py b/modules/textual_inversion/textual_inversion.py new file mode 100644 index 00000000..c0baaace --- /dev/null +++ b/modules/textual_inversion/textual_inversion.py @@ -0,0 +1,258 @@ +import os +import sys +import traceback + +import torch +import tqdm +import html +import datetime + +from modules import shared, devices, sd_hijack, processing +import modules.textual_inversion.dataset + + +class Embedding: + def __init__(self, vec, name, step=None): + self.vec = vec + self.name = name + self.step = step + self.cached_checksum = None + + def save(self, filename): + embedding_data = { + "string_to_token": {"*": 265}, + "string_to_param": {"*": self.vec}, + "name": self.name, + "step": self.step, + } + + torch.save(embedding_data, filename) + + def checksum(self): + if self.cached_checksum is not None: + return self.cached_checksum + + def const_hash(a): + r = 0 + for v in a: + r = (r * 281 ^ int(v) * 997) & 0xFFFFFFFF + return r + + self.cached_checksum = f'{const_hash(self.vec.reshape(-1) * 100) & 0xffff:04x}' + return self.cached_checksum + +class EmbeddingDatabase: + def __init__(self, embeddings_dir): + self.ids_lookup = {} + self.word_embeddings = {} + self.dir_mtime = None + self.embeddings_dir = embeddings_dir + + def register_embedding(self, embedding, model): + + self.word_embeddings[embedding.name] = embedding + + ids = model.cond_stage_model.tokenizer([embedding.name], add_special_tokens=False)['input_ids'][0] + + first_id = ids[0] + if first_id not in self.ids_lookup: + self.ids_lookup[first_id] = [] + self.ids_lookup[first_id].append((ids, embedding)) + + return embedding + + def load_textual_inversion_embeddings(self): + mt = os.path.getmtime(self.embeddings_dir) + if self.dir_mtime is not None and mt <= self.dir_mtime: + return + + self.dir_mtime = mt + self.ids_lookup.clear() + self.word_embeddings.clear() + + def process_file(path, filename): + name = os.path.splitext(filename)[0] + + data = torch.load(path, map_location="cpu") + + # textual inversion embeddings + if 'string_to_param' in data: + param_dict = data['string_to_param'] + if hasattr(param_dict, '_parameters'): + param_dict = getattr(param_dict, '_parameters') # fix for torch 1.12.1 loading saved file from torch 1.11 + assert len(param_dict) == 1, 'embedding file has multiple terms in it' + emb = next(iter(param_dict.items()))[1] + # diffuser concepts + elif type(data) == dict and type(next(iter(data.values()))) == torch.Tensor: + assert len(data.keys()) == 1, 'embedding file has multiple terms in it' + + emb = next(iter(data.values())) + if len(emb.shape) == 1: + emb = emb.unsqueeze(0) + else: + raise Exception(f"Couldn't identify {filename} as neither textual inversion embedding nor diffuser concept.") + + vec = emb.detach().to(devices.device, dtype=torch.float32) + embedding = Embedding(vec, name) + embedding.step = data.get('step', None) + self.register_embedding(embedding, shared.sd_model) + + for fn in os.listdir(self.embeddings_dir): + try: + fullfn = os.path.join(self.embeddings_dir, fn) + + if os.stat(fullfn).st_size == 0: + continue + + process_file(fullfn, fn) + except Exception: + print(f"Error loading emedding {fn}:", file=sys.stderr) + print(traceback.format_exc(), file=sys.stderr) + continue + + print(f"Loaded a total of {len(self.word_embeddings)} textual inversion embeddings.") + + def find_embedding_at_position(self, tokens, offset): + token = tokens[offset] + possible_matches = self.ids_lookup.get(token, None) + + if possible_matches is None: + return None + + for ids, embedding in possible_matches: + if tokens[offset:offset + len(ids)] == ids: + return embedding + + return None + + + +def create_embedding(name, num_vectors_per_token): + init_text = '*' + + cond_model = shared.sd_model.cond_stage_model + embedding_layer = cond_model.wrapped.transformer.text_model.embeddings + + ids = cond_model.tokenizer(init_text, max_length=num_vectors_per_token, return_tensors="pt", add_special_tokens=False)["input_ids"] + embedded = embedding_layer(ids.to(devices.device)).squeeze(0) + vec = torch.zeros((num_vectors_per_token, embedded.shape[1]), device=devices.device) + + for i in range(num_vectors_per_token): + vec[i] = embedded[i * int(embedded.shape[0]) // num_vectors_per_token] + + fn = os.path.join(shared.cmd_opts.embeddings_dir, f"{name}.pt") + assert not os.path.exists(fn), f"file {fn} already exists" + + embedding = Embedding(vec, name) + embedding.step = 0 + embedding.save(fn) + + return fn + + +def train_embedding(embedding_name, learn_rate, data_root, log_directory, steps, create_image_every, save_embedding_every, template_file): + assert embedding_name, 'embedding not selected' + + shared.state.textinfo = "Initializing textual inversion training..." + shared.state.job_count = steps + + filename = os.path.join(shared.cmd_opts.embeddings_dir, f'{embedding_name}.pt') + + log_directory = os.path.join(log_directory, datetime.datetime.now().strftime("%Y-%d-%m"), embedding_name) + + if save_embedding_every > 0: + embedding_dir = os.path.join(log_directory, "embeddings") + os.makedirs(embedding_dir, exist_ok=True) + else: + embedding_dir = None + + if create_image_every > 0: + images_dir = os.path.join(log_directory, "images") + os.makedirs(images_dir, exist_ok=True) + else: + images_dir = None + + cond_model = shared.sd_model.cond_stage_model + + shared.state.textinfo = f"Preparing dataset from {html.escape(data_root)}..." + with torch.autocast("cuda"): + ds = modules.textual_inversion.dataset.PersonalizedBase(data_root=data_root, size=512, placeholder_token=embedding_name, model=shared.sd_model, device=devices.device, template_file=template_file) + + hijack = sd_hijack.model_hijack + + embedding = hijack.embedding_db.word_embeddings[embedding_name] + embedding.vec.requires_grad = True + + optimizer = torch.optim.AdamW([embedding.vec], lr=learn_rate) + + losses = torch.zeros((32,)) + + last_saved_file = "" + last_saved_image = "" + + ititial_step = embedding.step or 0 + if ititial_step > steps: + return embedding, filename + + pbar = tqdm.tqdm(enumerate(ds), total=steps-ititial_step) + for i, (x, text) in pbar: + embedding.step = i + ititial_step + + if embedding.step > steps: + break + + if shared.state.interrupted: + break + + with torch.autocast("cuda"): + c = cond_model([text]) + loss = shared.sd_model(x.unsqueeze(0), c)[0] + + losses[embedding.step % losses.shape[0]] = loss.item() + + optimizer.zero_grad() + loss.backward() + optimizer.step() + + pbar.set_description(f"loss: {losses.mean():.7f}") + + if embedding.step > 0 and embedding_dir is not None and embedding.step % save_embedding_every == 0: + last_saved_file = os.path.join(embedding_dir, f'{embedding_name}-{embedding.step}.pt') + embedding.save(last_saved_file) + + if embedding.step > 0 and images_dir is not None and embedding.step % create_image_every == 0: + last_saved_image = os.path.join(images_dir, f'{embedding_name}-{embedding.step}.png') + + p = processing.StableDiffusionProcessingTxt2Img( + sd_model=shared.sd_model, + prompt=text, + steps=20, + do_not_save_grid=True, + do_not_save_samples=True, + ) + + processed = processing.process_images(p) + image = processed.images[0] + + shared.state.current_image = image + image.save(last_saved_image) + + last_saved_image += f", prompt: {text}" + + shared.state.job_no = embedding.step + + shared.state.textinfo = f""" +

+Loss: {losses.mean():.7f}
+Step: {embedding.step}
+Last prompt: {html.escape(text)}
+Last saved embedding: {html.escape(last_saved_file)}
+Last saved image: {html.escape(last_saved_image)}
+

+""" + + embedding.cached_checksum = None + embedding.save(filename) + + return embedding, filename + diff --git a/modules/textual_inversion/ui.py b/modules/textual_inversion/ui.py new file mode 100644 index 00000000..ce3677a9 --- /dev/null +++ b/modules/textual_inversion/ui.py @@ -0,0 +1,32 @@ +import html + +import gradio as gr + +import modules.textual_inversion.textual_inversion as ti +from modules import sd_hijack, shared + + +def create_embedding(name, nvpt): + filename = ti.create_embedding(name, nvpt) + + sd_hijack.model_hijack.embedding_db.load_textual_inversion_embeddings() + + return gr.Dropdown.update(choices=sorted(sd_hijack.model_hijack.embedding_db.word_embeddings.keys())), f"Created: {filename}", "" + + +def train_embedding(*args): + + try: + sd_hijack.undo_optimizations() + + embedding, filename = ti.train_embedding(*args) + + res = f""" +Training {'interrupted' if shared.state.interrupted else 'finished'} after {embedding.step} steps. +Embedding saved to {html.escape(filename)} +""" + return res, "" + except Exception: + raise + finally: + sd_hijack.apply_optimizations() diff --git a/modules/ui.py b/modules/ui.py index 15572bb0..57aef6ff 100644 --- a/modules/ui.py +++ b/modules/ui.py @@ -21,6 +21,7 @@ import gradio as gr import gradio.utils import gradio.routes +from modules import sd_hijack from modules.paths import script_path from modules.shared import opts, cmd_opts import modules.shared as shared @@ -32,6 +33,7 @@ import modules.gfpgan_model import modules.codeformer_model import modules.styles import modules.generation_parameters_copypaste +import modules.textual_inversion.ui # this is a fix for Windows users. Without it, javascript files will be served with text/html content-type and the bowser will not show any UI mimetypes.init() @@ -142,8 +144,8 @@ def save_files(js_data, images, index): return '', '', plaintext_to_html(f"Saved: {filenames[0]}") -def wrap_gradio_call(func): - def f(*args, **kwargs): +def wrap_gradio_call(func, extra_outputs=None): + def f(*args, extra_outputs_array=extra_outputs, **kwargs): run_memmon = opts.memmon_poll_rate > 0 and not shared.mem_mon.disabled if run_memmon: shared.mem_mon.monitor() @@ -159,7 +161,10 @@ def wrap_gradio_call(func): shared.state.job = "" shared.state.job_count = 0 - res = [None, '', f"
{plaintext_to_html(type(e).__name__+': '+str(e))}
"] + if extra_outputs_array is None: + extra_outputs_array = [None, ''] + + res = extra_outputs_array + [f"
{plaintext_to_html(type(e).__name__+': '+str(e))}
"] elapsed = time.perf_counter() - t @@ -179,6 +184,7 @@ def wrap_gradio_call(func): res[-1] += f"

Time taken: {elapsed:.2f}s

{vram_html}
" shared.state.interrupted = False + shared.state.job_count = 0 return tuple(res) @@ -187,7 +193,7 @@ def wrap_gradio_call(func): def check_progress_call(id_part): if shared.state.job_count == 0: - return "", gr_show(False), gr_show(False) + return "", gr_show(False), gr_show(False), gr_show(False) progress = 0 @@ -219,13 +225,19 @@ def check_progress_call(id_part): else: preview_visibility = gr_show(True) - return f"

{progressbar}

", preview_visibility, image + if shared.state.textinfo is not None: + textinfo_result = gr.HTML.update(value=shared.state.textinfo, visible=True) + else: + textinfo_result = gr_show(False) + + return f"

{progressbar}

", preview_visibility, image, textinfo_result def check_progress_call_initial(id_part): shared.state.job_count = -1 shared.state.current_latent = None shared.state.current_image = None + shared.state.textinfo = None return check_progress_call(id_part) @@ -399,13 +411,16 @@ def create_toprow(is_img2img): return prompt, roll, prompt_style, negative_prompt, prompt_style2, submit, interrogate, prompt_style_apply, save_style, paste -def setup_progressbar(progressbar, preview, id_part): +def setup_progressbar(progressbar, preview, id_part, textinfo=None): + if textinfo is None: + textinfo = gr.HTML(visible=False) + check_progress = gr.Button('Check progress', elem_id=f"{id_part}_check_progress", visible=False) check_progress.click( fn=lambda: check_progress_call(id_part), show_progress=False, inputs=[], - outputs=[progressbar, preview, preview], + outputs=[progressbar, preview, preview, textinfo], ) check_progress_initial = gr.Button('Check progress (first)', elem_id=f"{id_part}_check_progress_initial", visible=False) @@ -413,11 +428,14 @@ def setup_progressbar(progressbar, preview, id_part): fn=lambda: check_progress_call_initial(id_part), show_progress=False, inputs=[], - outputs=[progressbar, preview, preview], + outputs=[progressbar, preview, preview, textinfo], ) -def create_ui(txt2img, img2img, run_extras, run_pnginfo, run_modelmerger): +def create_ui(wrap_gradio_gpu_call): + import modules.img2img + import modules.txt2img + with gr.Blocks(analytics_enabled=False) as txt2img_interface: txt2img_prompt, roll, txt2img_prompt_style, txt2img_negative_prompt, txt2img_prompt_style2, submit, _, txt2img_prompt_style_apply, txt2img_save_style, paste = create_toprow(is_img2img=False) dummy_component = gr.Label(visible=False) @@ -483,7 +501,7 @@ def create_ui(txt2img, img2img, run_extras, run_pnginfo, run_modelmerger): connect_reuse_seed(subseed, reuse_subseed, generation_info, dummy_component, is_subseed=True) txt2img_args = dict( - fn=txt2img, + fn=wrap_gradio_gpu_call(modules.txt2img.txt2img), _js="submit", inputs=[ txt2img_prompt, @@ -675,7 +693,7 @@ def create_ui(txt2img, img2img, run_extras, run_pnginfo, run_modelmerger): ) img2img_args = dict( - fn=img2img, + fn=wrap_gradio_gpu_call(modules.img2img.img2img), _js="submit_img2img", inputs=[ dummy_component, @@ -828,7 +846,7 @@ def create_ui(txt2img, img2img, run_extras, run_pnginfo, run_modelmerger): open_extras_folder = gr.Button('Open output directory', elem_id=button_id) submit.click( - fn=run_extras, + fn=wrap_gradio_gpu_call(modules.extras.run_extras), _js="get_extras_tab_index", inputs=[ dummy_component, @@ -878,7 +896,7 @@ def create_ui(txt2img, img2img, run_extras, run_pnginfo, run_modelmerger): pnginfo_send_to_img2img = gr.Button('Send to img2img') image.change( - fn=wrap_gradio_call(run_pnginfo), + fn=wrap_gradio_call(modules.extras.run_pnginfo), inputs=[image], outputs=[html, generation_info, html2], ) @@ -887,7 +905,7 @@ def create_ui(txt2img, img2img, run_extras, run_pnginfo, run_modelmerger): with gr.Row().style(equal_height=False): with gr.Column(variant='panel'): gr.HTML(value="

A merger of the two checkpoints will be generated in your checkpoint directory.

") - + with gr.Row(): primary_model_name = gr.Dropdown(modules.sd_models.checkpoint_tiles(), elem_id="modelmerger_primary_model_name", label="Primary Model Name") secondary_model_name = gr.Dropdown(modules.sd_models.checkpoint_tiles(), elem_id="modelmerger_secondary_model_name", label="Secondary Model Name") @@ -896,10 +914,96 @@ def create_ui(txt2img, img2img, run_extras, run_pnginfo, run_modelmerger): interp_method = gr.Radio(choices=["Weighted Sum", "Sigmoid", "Inverse Sigmoid"], value="Weighted Sum", label="Interpolation Method") save_as_half = gr.Checkbox(value=False, label="Safe as float16") modelmerger_merge = gr.Button(elem_id="modelmerger_merge", label="Merge", variant='primary') - + with gr.Column(variant='panel'): submit_result = gr.Textbox(elem_id="modelmerger_result", show_label=False) + sd_hijack.model_hijack.embedding_db.load_textual_inversion_embeddings() + + with gr.Blocks() as textual_inversion_interface: + with gr.Row().style(equal_height=False): + with gr.Column(): + with gr.Group(): + gr.HTML(value="

Create a new embedding

") + + new_embedding_name = gr.Textbox(label="Name") + nvpt = gr.Slider(label="Number of vectors per token", minimum=1, maximum=75, step=1, value=1) + + with gr.Row(): + with gr.Column(scale=3): + gr.HTML(value="") + + with gr.Column(): + create_embedding = gr.Button(value="Create", variant='primary') + + with gr.Group(): + gr.HTML(value="

Train an embedding; must specify a directory with a set of 512x512 images

") + train_embedding_name = gr.Dropdown(label='Embedding', choices=sorted(sd_hijack.model_hijack.embedding_db.word_embeddings.keys())) + learn_rate = gr.Number(label='Learning rate', value=5.0e-03) + dataset_directory = gr.Textbox(label='Dataset directory', placeholder="Path to directory with input images") + log_directory = gr.Textbox(label='Log directory', placeholder="Path to directory where to write outputs", value="textual_inversion") + template_file = gr.Textbox(label='Prompt template file', value=os.path.join(script_path, "textual_inversion_templates", "style_filewords.txt")) + steps = gr.Number(label='Max steps', value=100000, precision=0) + create_image_every = gr.Number(label='Save an image to log directory every N steps, 0 to disable', value=1000, precision=0) + save_embedding_every = gr.Number(label='Save a copy of embedding to log directory every N steps, 0 to disable', value=1000, precision=0) + + with gr.Row(): + with gr.Column(scale=2): + gr.HTML(value="") + + with gr.Column(): + with gr.Row(): + interrupt_training = gr.Button(value="Interrupt") + train_embedding = gr.Button(value="Train", variant='primary') + + with gr.Column(): + progressbar = gr.HTML(elem_id="ti_progressbar") + ti_output = gr.Text(elem_id="ti_output", value="", show_label=False) + + ti_gallery = gr.Gallery(label='Output', show_label=False, elem_id='ti_gallery').style(grid=4) + ti_preview = gr.Image(elem_id='ti_preview', visible=False) + ti_progress = gr.HTML(elem_id="ti_progress", value="") + ti_outcome = gr.HTML(elem_id="ti_error", value="") + setup_progressbar(progressbar, ti_preview, 'ti', textinfo=ti_progress) + + create_embedding.click( + fn=modules.textual_inversion.ui.create_embedding, + inputs=[ + new_embedding_name, + nvpt, + ], + outputs=[ + train_embedding_name, + ti_output, + ti_outcome, + ] + ) + + train_embedding.click( + fn=wrap_gradio_gpu_call(modules.textual_inversion.ui.train_embedding, extra_outputs=[gr.update()]), + _js="start_training_textual_inversion", + inputs=[ + train_embedding_name, + learn_rate, + dataset_directory, + log_directory, + steps, + create_image_every, + save_embedding_every, + template_file, + ], + outputs=[ + ti_output, + ti_outcome, + ] + ) + + interrupt_training.click( + fn=lambda: shared.state.interrupt(), + inputs=[], + outputs=[], + ) + def create_setting_component(key): def fun(): return opts.data[key] if key in opts.data else opts.data_labels[key].default @@ -1011,6 +1115,7 @@ def create_ui(txt2img, img2img, run_extras, run_pnginfo, run_modelmerger): (extras_interface, "Extras", "extras"), (pnginfo_interface, "PNG Info", "pnginfo"), (modelmerger_interface, "Checkpoint Merger", "modelmerger"), + (textual_inversion_interface, "Textual inversion", "ti"), (settings_interface, "Settings", "settings"), ] @@ -1044,11 +1149,11 @@ def create_ui(txt2img, img2img, run_extras, run_pnginfo, run_modelmerger): def modelmerger(*args): try: - results = run_modelmerger(*args) + results = modules.extras.run_modelmerger(*args) except Exception as e: print("Error loading/saving model file:", file=sys.stderr) print(traceback.format_exc(), file=sys.stderr) - modules.sd_models.list_models() #To remove the potentially missing models from the list + modules.sd_models.list_models() # to remove the potentially missing models from the list return ["Error loading/saving model file. It doesn't exist or the name contains illegal characters"] + [gr.Dropdown.update(choices=modules.sd_models.checkpoint_tiles()) for _ in range(3)] return results -- cgit v1.2.1 From 0114057ad672a581bd0b598870b58b674b1a3624 Mon Sep 17 00:00:00 2001 From: AUTOMATIC <16777216c@gmail.com> Date: Sun, 2 Oct 2022 15:49:42 +0300 Subject: fix incorrect use of glob in modelloader for #1410 --- modules/modelloader.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) (limited to 'modules') diff --git a/modules/modelloader.py b/modules/modelloader.py index 8c862b42..015aeafa 100644 --- a/modules/modelloader.py +++ b/modules/modelloader.py @@ -43,7 +43,7 @@ def load_models(model_path: str, model_url: str = None, command_path: str = None for place in places: if os.path.exists(place): for file in glob.iglob(place + '**/**', recursive=True): - full_path = os.path.join(place, file) + full_path = file if os.path.isdir(full_path): continue if len(ext_filter) != 0: -- cgit v1.2.1 From 0758f6e641b5790ce566a998d43e0ea74a627766 Mon Sep 17 00:00:00 2001 From: AUTOMATIC <16777216c@gmail.com> Date: Sun, 2 Oct 2022 17:24:50 +0300 Subject: fix --ckpt option breaking model selection --- modules/sd_models.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) (limited to 'modules') diff --git a/modules/sd_models.py b/modules/sd_models.py index 5b3dbdc7..9259d69e 100644 --- a/modules/sd_models.py +++ b/modules/sd_models.py @@ -69,7 +69,7 @@ def list_models(): h = model_hash(cmd_ckpt) title, short_model_name = modeltitle(cmd_ckpt, h) checkpoints_list[title] = CheckpointInfo(cmd_ckpt, title, h, short_model_name) - shared.opts.sd_model_checkpoint = title + shared.opts.data['sd_model_checkpoint'] = title elif cmd_ckpt is not None and cmd_ckpt != shared.default_sd_model_file: print(f"Checkpoint in --ckpt argument not found (Possible it was moved to {model_path}: {cmd_ckpt}", file=sys.stderr) for filename in model_list: -- cgit v1.2.1 From 88ec0cf5571883d84abd09196652b3679e359f2e Mon Sep 17 00:00:00 2001 From: AUTOMATIC <16777216c@gmail.com> Date: Sun, 2 Oct 2022 19:40:51 +0300 Subject: fix for incorrect embedding token length calculation (will break seeds that use embeddings, you're welcome!) add option to input initialization text for embeddings --- modules/sd_hijack.py | 8 ++++---- modules/textual_inversion/textual_inversion.py | 13 +++++-------- modules/textual_inversion/ui.py | 4 ++-- modules/ui.py | 2 ++ 4 files changed, 13 insertions(+), 14 deletions(-) (limited to 'modules') diff --git a/modules/sd_hijack.py b/modules/sd_hijack.py index fd57e5c5..3fa06242 100644 --- a/modules/sd_hijack.py +++ b/modules/sd_hijack.py @@ -130,7 +130,7 @@ class FrozenCLIPEmbedderWithCustomWords(torch.nn.Module): while i < len(tokens): token = tokens[i] - embedding = self.hijack.embedding_db.find_embedding_at_position(tokens, i) + embedding, embedding_length_in_tokens = self.hijack.embedding_db.find_embedding_at_position(tokens, i) if embedding is None: remade_tokens.append(token) @@ -142,7 +142,7 @@ class FrozenCLIPEmbedderWithCustomWords(torch.nn.Module): remade_tokens += [0] * emb_len multipliers += [weight] * emb_len used_custom_terms.append((embedding.name, embedding.checksum())) - i += emb_len + i += embedding_length_in_tokens if len(remade_tokens) > maxlen - 2: vocab = {v: k for k, v in self.wrapped.tokenizer.get_vocab().items()} @@ -213,7 +213,7 @@ class FrozenCLIPEmbedderWithCustomWords(torch.nn.Module): while i < len(tokens): token = tokens[i] - embedding = self.hijack.embedding_db.find_embedding_at_position(tokens, i) + embedding, embedding_length_in_tokens = self.hijack.embedding_db.find_embedding_at_position(tokens, i) mult_change = self.token_mults.get(token) if opts.enable_emphasis else None if mult_change is not None: @@ -229,7 +229,7 @@ class FrozenCLIPEmbedderWithCustomWords(torch.nn.Module): remade_tokens += [0] * emb_len multipliers += [mult] * emb_len used_custom_terms.append((embedding.name, embedding.checksum())) - i += emb_len + i += embedding_length_in_tokens if len(remade_tokens) > maxlen - 2: vocab = {v: k for k, v in self.wrapped.tokenizer.get_vocab().items()} diff --git a/modules/textual_inversion/textual_inversion.py b/modules/textual_inversion/textual_inversion.py index c0baaace..0c50161d 100644 --- a/modules/textual_inversion/textual_inversion.py +++ b/modules/textual_inversion/textual_inversion.py @@ -117,24 +117,21 @@ class EmbeddingDatabase: possible_matches = self.ids_lookup.get(token, None) if possible_matches is None: - return None + return None, None for ids, embedding in possible_matches: if tokens[offset:offset + len(ids)] == ids: - return embedding + return embedding, len(ids) - return None + return None, None - -def create_embedding(name, num_vectors_per_token): - init_text = '*' - +def create_embedding(name, num_vectors_per_token, init_text='*'): cond_model = shared.sd_model.cond_stage_model embedding_layer = cond_model.wrapped.transformer.text_model.embeddings ids = cond_model.tokenizer(init_text, max_length=num_vectors_per_token, return_tensors="pt", add_special_tokens=False)["input_ids"] - embedded = embedding_layer(ids.to(devices.device)).squeeze(0) + embedded = embedding_layer.token_embedding.wrapped(ids.to(devices.device)).squeeze(0) vec = torch.zeros((num_vectors_per_token, embedded.shape[1]), device=devices.device) for i in range(num_vectors_per_token): diff --git a/modules/textual_inversion/ui.py b/modules/textual_inversion/ui.py index ce3677a9..66c43ffb 100644 --- a/modules/textual_inversion/ui.py +++ b/modules/textual_inversion/ui.py @@ -6,8 +6,8 @@ import modules.textual_inversion.textual_inversion as ti from modules import sd_hijack, shared -def create_embedding(name, nvpt): - filename = ti.create_embedding(name, nvpt) +def create_embedding(name, initialization_text, nvpt): + filename = ti.create_embedding(name, nvpt, init_text=initialization_text) sd_hijack.model_hijack.embedding_db.load_textual_inversion_embeddings() diff --git a/modules/ui.py b/modules/ui.py index 3b81a4f7..eca50df0 100644 --- a/modules/ui.py +++ b/modules/ui.py @@ -954,6 +954,7 @@ def create_ui(wrap_gradio_gpu_call): gr.HTML(value="

Create a new embedding

") new_embedding_name = gr.Textbox(label="Name") + initialization_text = gr.Textbox(label="Initialization text", value="*") nvpt = gr.Slider(label="Number of vectors per token", minimum=1, maximum=75, step=1, value=1) with gr.Row(): @@ -997,6 +998,7 @@ def create_ui(wrap_gradio_gpu_call): fn=modules.textual_inversion.ui.create_embedding, inputs=[ new_embedding_name, + initialization_text, nvpt, ], outputs=[ -- cgit v1.2.1 From 71fe7fa49f5eb1a2c89932a9d217ed153c12fc8b Mon Sep 17 00:00:00 2001 From: AUTOMATIC <16777216c@gmail.com> Date: Sun, 2 Oct 2022 19:56:37 +0300 Subject: fix using aaaa-100 embedding when the prompt has aaaa-10000 and you have both aaaa-100 and aaaa-10000 in the directory with embeddings. --- modules/textual_inversion/textual_inversion.py | 3 ++- 1 file changed, 2 insertions(+), 1 deletion(-) (limited to 'modules') diff --git a/modules/textual_inversion/textual_inversion.py b/modules/textual_inversion/textual_inversion.py index 0c50161d..9d2241ce 100644 --- a/modules/textual_inversion/textual_inversion.py +++ b/modules/textual_inversion/textual_inversion.py @@ -57,7 +57,8 @@ class EmbeddingDatabase: first_id = ids[0] if first_id not in self.ids_lookup: self.ids_lookup[first_id] = [] - self.ids_lookup[first_id].append((ids, embedding)) + + self.ids_lookup[first_id] = sorted(self.ids_lookup[first_id] + [(ids, embedding)], key=lambda x: len(x[0]), reverse=True) return embedding -- cgit v1.2.1 From 4ec4af6e0b7addeee5221a03f32d117ccdc875d9 Mon Sep 17 00:00:00 2001 From: AUTOMATIC <16777216c@gmail.com> Date: Sun, 2 Oct 2022 20:15:25 +0300 Subject: add checkpoint info to saved embeddings --- modules/textual_inversion/textual_inversion.py | 13 ++++++++++++- 1 file changed, 12 insertions(+), 1 deletion(-) (limited to 'modules') diff --git a/modules/textual_inversion/textual_inversion.py b/modules/textual_inversion/textual_inversion.py index 9d2241ce..1183aab7 100644 --- a/modules/textual_inversion/textual_inversion.py +++ b/modules/textual_inversion/textual_inversion.py @@ -7,7 +7,7 @@ import tqdm import html import datetime -from modules import shared, devices, sd_hijack, processing +from modules import shared, devices, sd_hijack, processing, sd_models import modules.textual_inversion.dataset @@ -17,6 +17,8 @@ class Embedding: self.name = name self.step = step self.cached_checksum = None + self.sd_checkpoint = None + self.sd_checkpoint_name = None def save(self, filename): embedding_data = { @@ -24,6 +26,8 @@ class Embedding: "string_to_param": {"*": self.vec}, "name": self.name, "step": self.step, + "sd_checkpoint": self.sd_checkpoint, + "sd_checkpoint_name": self.sd_checkpoint_name, } torch.save(embedding_data, filename) @@ -41,6 +45,7 @@ class Embedding: self.cached_checksum = f'{const_hash(self.vec.reshape(-1) * 100) & 0xffff:04x}' return self.cached_checksum + class EmbeddingDatabase: def __init__(self, embeddings_dir): self.ids_lookup = {} @@ -96,6 +101,8 @@ class EmbeddingDatabase: vec = emb.detach().to(devices.device, dtype=torch.float32) embedding = Embedding(vec, name) embedding.step = data.get('step', None) + embedding.sd_checkpoint = data.get('hash', None) + embedding.sd_checkpoint_name = data.get('sd_checkpoint_name', None) self.register_embedding(embedding, shared.sd_model) for fn in os.listdir(self.embeddings_dir): @@ -249,6 +256,10 @@ Last saved image: {html.escape(last_saved_image)}

""" + checkpoint = sd_models.select_checkpoint() + + embedding.sd_checkpoint = checkpoint.hash + embedding.sd_checkpoint_name = checkpoint.model_name embedding.cached_checksum = None embedding.save(filename) -- cgit v1.2.1 From 3ff0de2c594b786ef948a89efb1814c59bb42117 Mon Sep 17 00:00:00 2001 From: AUTOMATIC <16777216c@gmail.com> Date: Sun, 2 Oct 2022 20:23:40 +0300 Subject: added --disable-console-progressbars to disable progressbars in console disabled printing prompts to console by default, enabled by --enable-console-prompts --- modules/img2img.py | 4 +++- modules/sd_samplers.py | 8 ++++++-- modules/shared.py | 7 +++++-- modules/txt2img.py | 4 +++- 4 files changed, 17 insertions(+), 6 deletions(-) (limited to 'modules') diff --git a/modules/img2img.py b/modules/img2img.py index 03e934e9..f4455c90 100644 --- a/modules/img2img.py +++ b/modules/img2img.py @@ -103,7 +103,9 @@ def img2img(mode: int, prompt: str, negative_prompt: str, prompt_style: str, pro inpaint_full_res_padding=inpaint_full_res_padding, inpainting_mask_invert=inpainting_mask_invert, ) - print(f"\nimg2img: {prompt}", file=shared.progress_print_out) + + if shared.cmd_opts.enable_console_prompts: + print(f"\nimg2img: {prompt}", file=shared.progress_print_out) p.extra_generation_params["Mask blur"] = mask_blur diff --git a/modules/sd_samplers.py b/modules/sd_samplers.py index 92522214..9316875a 100644 --- a/modules/sd_samplers.py +++ b/modules/sd_samplers.py @@ -77,7 +77,9 @@ def extended_tdqm(sequence, *args, desc=None, **kwargs): state.sampling_steps = len(sequence) state.sampling_step = 0 - for x in tqdm.tqdm(sequence, *args, desc=state.job, file=shared.progress_print_out, **kwargs): + seq = sequence if cmd_opts.disable_console_progressbars else tqdm.tqdm(sequence, *args, desc=state.job, file=shared.progress_print_out, **kwargs) + + for x in seq: if state.interrupted: break @@ -207,7 +209,9 @@ def extended_trange(sampler, count, *args, **kwargs): state.sampling_steps = count state.sampling_step = 0 - for x in tqdm.trange(count, *args, desc=state.job, file=shared.progress_print_out, **kwargs): + seq = range(count) if cmd_opts.disable_console_progressbars else tqdm.trange(count, *args, desc=state.job, file=shared.progress_print_out, **kwargs) + + for x in seq: if state.interrupted: break diff --git a/modules/shared.py b/modules/shared.py index 5a591dc9..1bf7a6c1 100644 --- a/modules/shared.py +++ b/modules/shared.py @@ -58,6 +58,9 @@ parser.add_argument("--opt-channelslast", action='store_true', help="change memo parser.add_argument("--styles-file", type=str, help="filename to use for styles", default=os.path.join(script_path, 'styles.csv')) parser.add_argument("--autolaunch", action='store_true', help="open the webui URL in the system's default browser upon launch", default=False) parser.add_argument("--use-textbox-seed", action='store_true', help="use textbox for seeds in UI (no up/down, but possible to input long seeds)", default=False) +parser.add_argument("--disable-console-progressbars", action='store_true', help="do not output progressbars to console", default=False) +parser.add_argument("--enable-console-prompts", action='store_true', help="print prompts to console when generating with txt2img and img2img", default=False) + cmd_opts = parser.parse_args() device = get_optimal_device() @@ -320,14 +323,14 @@ class TotalTQDM: ) def update(self): - if not opts.multiple_tqdm: + if not opts.multiple_tqdm or cmd_opts.disable_console_progressbars: return if self._tqdm is None: self.reset() self._tqdm.update() def updateTotal(self, new_total): - if not opts.multiple_tqdm: + if not opts.multiple_tqdm or cmd_opts.disable_console_progressbars: return if self._tqdm is None: self.reset() diff --git a/modules/txt2img.py b/modules/txt2img.py index 5368e4d0..d4406c3c 100644 --- a/modules/txt2img.py +++ b/modules/txt2img.py @@ -34,7 +34,9 @@ def txt2img(prompt: str, negative_prompt: str, prompt_style: str, prompt_style2: denoising_strength=denoising_strength if enable_hr else None, ) - print(f"\ntxt2img: {prompt}", file=shared.progress_print_out) + if cmd_opts.enable_console_prompts: + print(f"\ntxt2img: {prompt}", file=shared.progress_print_out) + processed = modules.scripts.scripts_txt2img.run(p, *args) if processed is None: -- cgit v1.2.1 From 6365a41f5981efa506dfe4e8fa878b43ca2d8d0c Mon Sep 17 00:00:00 2001 From: d8ahazard Date: Sun, 2 Oct 2022 12:58:17 -0500 Subject: Update esrgan_model.py Use alternate ESRGAN Model download path. --- modules/esrgan_model.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) (limited to 'modules') diff --git a/modules/esrgan_model.py b/modules/esrgan_model.py index ea91abfe..4aed9283 100644 --- a/modules/esrgan_model.py +++ b/modules/esrgan_model.py @@ -73,8 +73,8 @@ def fix_model_layers(crt_model, pretrained_net): class UpscalerESRGAN(Upscaler): def __init__(self, dirname): self.name = "ESRGAN" - self.model_url = "https://drive.google.com/u/0/uc?id=1TPrz5QKd8DHHt1k8SRtm6tMiPjz_Qene&export=download" - self.model_name = "ESRGAN 4x" + self.model_url = "https://github.com/cszn/KAIR/releases/download/v1.0/ESRGAN.pth" + self.model_name = "ESRGAN_4x" self.scalers = [] self.user_path = dirname self.model_path = os.path.join(models_path, self.name) -- cgit v1.2.1 From a1cde7e6468f80584030525a1b07cbf0f4ee42eb Mon Sep 17 00:00:00 2001 From: AUTOMATIC <16777216c@gmail.com> Date: Sun, 2 Oct 2022 21:09:10 +0300 Subject: disabled SD model download after multiple complaints --- modules/sd_models.py | 18 ++++++++---------- modules/textual_inversion/ui.py | 2 +- 2 files changed, 9 insertions(+), 11 deletions(-) (limited to 'modules') diff --git a/modules/sd_models.py b/modules/sd_models.py index 9259d69e..9a6b568f 100644 --- a/modules/sd_models.py +++ b/modules/sd_models.py @@ -13,9 +13,6 @@ from modules.paths import models_path model_dir = "Stable-diffusion" model_path = os.path.abspath(os.path.join(models_path, model_dir)) -model_name = "sd-v1-4.ckpt" -model_url = "https://drive.yerf.org/wl/?id=EBfTrmcCCUAGaQBXVIj5lJmEhjoP1tgl&mode=grid&download=1" -user_dir = None CheckpointInfo = namedtuple("CheckpointInfo", ['filename', 'title', 'hash', 'model_name']) checkpoints_list = {} @@ -30,12 +27,10 @@ except Exception: pass -def setup_model(dirname): - global user_dir - user_dir = dirname +def setup_model(): if not os.path.exists(model_path): os.makedirs(model_path) - checkpoints_list.clear() + list_models() @@ -45,7 +40,7 @@ def checkpoint_tiles(): def list_models(): checkpoints_list.clear() - model_list = modelloader.load_models(model_path=model_path, model_url=model_url, command_path=user_dir, ext_filter=[".ckpt"], download_name=model_name) + model_list = modelloader.load_models(model_path=model_path, command_path=shared.cmd_opts.ckpt_dir, ext_filter=[".ckpt"]) def modeltitle(path, shorthash): abspath = os.path.abspath(path) @@ -106,8 +101,11 @@ def select_checkpoint(): if len(checkpoints_list) == 0: print(f"No checkpoints found. When searching for checkpoints, looked at:", file=sys.stderr) - print(f" - file {os.path.abspath(shared.cmd_opts.ckpt)}", file=sys.stderr) - print(f" - directory {os.path.abspath(shared.cmd_opts.ckpt_dir)}", file=sys.stderr) + if shared.cmd_opts.ckpt is not None: + print(f" - file {os.path.abspath(shared.cmd_opts.ckpt)}", file=sys.stderr) + print(f" - directory {model_path}", file=sys.stderr) + if shared.cmd_opts.ckpt_dir is not None: + print(f" - directory {os.path.abspath(shared.cmd_opts.ckpt_dir)}", file=sys.stderr) print(f"Can't run without a checkpoint. Find and place a .ckpt file into any of those locations. The program will exit.", file=sys.stderr) exit(1) diff --git a/modules/textual_inversion/ui.py b/modules/textual_inversion/ui.py index 66c43ffb..633037d8 100644 --- a/modules/textual_inversion/ui.py +++ b/modules/textual_inversion/ui.py @@ -22,7 +22,7 @@ def train_embedding(*args): embedding, filename = ti.train_embedding(*args) res = f""" -Training {'interrupted' if shared.state.interrupted else 'finished'} after {embedding.step} steps. +Training {'interrupted' if shared.state.interrupted else 'finished'} at {embedding.step} steps. Embedding saved to {html.escape(filename)} """ return res, "" -- cgit v1.2.1 From 852fd90c0dcda9cb5fbbfdf0c7308ce58034935c Mon Sep 17 00:00:00 2001 From: AUTOMATIC <16777216c@gmail.com> Date: Sun, 2 Oct 2022 21:22:20 +0300 Subject: emergency fix for disabling SD model download after multiple complaints --- modules/sd_models.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) (limited to 'modules') diff --git a/modules/sd_models.py b/modules/sd_models.py index 9a6b568f..5f992064 100644 --- a/modules/sd_models.py +++ b/modules/sd_models.py @@ -45,8 +45,8 @@ def list_models(): def modeltitle(path, shorthash): abspath = os.path.abspath(path) - if user_dir is not None and abspath.startswith(user_dir): - name = abspath.replace(user_dir, '') + if shared.cmd_opts.ckpt_dir is not None and abspath.startswith(shared.cmd_opts.ckpt_dir): + name = abspath.replace(shared.cmd_opts.ckpt_dir, '') elif abspath.startswith(model_path): name = abspath.replace(model_path, '') else: -- cgit v1.2.1 From e808096cf641d868f88465515d70d40fc46125d4 Mon Sep 17 00:00:00 2001 From: DepFA <35278260+dfaker@users.noreply.github.com> Date: Sun, 2 Oct 2022 19:26:06 +0100 Subject: correct indent --- modules/scripts.py | 48 +++++++++++++++++++++++++----------------------- modules/ui.py | 25 ++++++++++++------------- 2 files changed, 37 insertions(+), 36 deletions(-) (limited to 'modules') diff --git a/modules/scripts.py b/modules/scripts.py index 788397f5..45230f9a 100644 --- a/modules/scripts.py +++ b/modules/scripts.py @@ -163,37 +163,39 @@ class ScriptRunner: return processed def reload_sources(self): - for si,script in list(enumerate(self.scripts)): - with open(script.filename, "r", encoding="utf8") as file: - args_from = script.args_from - args_to = script.args_to - filename = script.filename - text = file.read() + for si, script in list(enumerate(self.scripts)): + with open(script.filename, "r", encoding="utf8") as file: + args_from = script.args_from + args_to = script.args_to + filename = script.filename + text = file.read() - from types import ModuleType - compiled = compile(text, filename, 'exec') - module = ModuleType(script.filename) - exec(compiled, module.__dict__) + from types import ModuleType - for key, script_class in module.__dict__.items(): - if type(script_class) == type and issubclass(script_class, Script): - self.scripts[si] = script_class() - self.scripts[si].filename = filename - self.scripts[si].args_from = args_from - self.scripts[si].args_to = args_to + compiled = compile(text, filename, 'exec') + module = ModuleType(script.filename) + exec(compiled, module.__dict__) + + for key, script_class in module.__dict__.items(): + if type(script_class) == type and issubclass(script_class, Script): + self.scripts[si] = script_class() + self.scripts[si].filename = filename + self.scripts[si].args_from = args_from + self.scripts[si].args_to = args_to scripts_txt2img = ScriptRunner() scripts_img2img = ScriptRunner() def reload_script_body_only(): - scripts_txt2img.reload_sources() - scripts_img2img.reload_sources() + scripts_txt2img.reload_sources() + scripts_img2img.reload_sources() + def reload_scripts(basedir): - global scripts_txt2img,scripts_img2img + global scripts_txt2img, scripts_img2img - scripts_data.clear() - load_scripts(basedir) + scripts_data.clear() + load_scripts(basedir) - scripts_txt2img = ScriptRunner() - scripts_img2img = ScriptRunner() + scripts_txt2img = ScriptRunner() + scripts_img2img = ScriptRunner() diff --git a/modules/ui.py b/modules/ui.py index 963a2c61..6b30f84b 100644 --- a/modules/ui.py +++ b/modules/ui.py @@ -1003,12 +1003,12 @@ def create_ui(txt2img, img2img, run_extras, run_pnginfo, run_modelmerger): ) with gr.Row(): - reload_script_bodies = gr.Button(value='Reload custom script bodies (No ui updates, No restart)', variant='secondary') - restart_gradio = gr.Button(value='Restart Gradio and Refresh components (Custom Scripts, ui.py, js and css only)', variant='primary') + reload_script_bodies = gr.Button(value='Reload custom script bodies (No ui updates, No restart)', variant='secondary') + restart_gradio = gr.Button(value='Restart Gradio and Refresh components (Custom Scripts, ui.py, js and css only)', variant='primary') def reload_scripts(): - modules.scripts.reload_script_body_only() + modules.scripts.reload_script_body_only() reload_script_bodies.click( fn=reload_scripts, @@ -1018,7 +1018,7 @@ def create_ui(txt2img, img2img, run_extras, run_pnginfo, run_modelmerger): ) def request_restart(): - settings_interface.gradio_ref.do_restart = True + settings_interface.gradio_ref.do_restart = True restart_gradio.click( fn=request_restart, @@ -1234,12 +1234,11 @@ for filename in sorted(os.listdir(jsdir)): if 'gradio_routes_templates_response' not in globals(): - def template_response(*args, **kwargs): - res = gradio_routes_templates_response(*args, **kwargs) - res.body = res.body.replace(b'', f'{javascript}'.encode("utf8")) - res.init_headers() - return res - - gradio_routes_templates_response = gradio.routes.templates.TemplateResponse - gradio.routes.templates.TemplateResponse = template_response - + def template_response(*args, **kwargs): + res = gradio_routes_templates_response(*args, **kwargs) + res.body = res.body.replace(b'', f'{javascript}'.encode("utf8")) + res.init_headers() + return res + + gradio_routes_templates_response = gradio.routes.templates.TemplateResponse + gradio.routes.templates.TemplateResponse = template_response -- cgit v1.2.1 From 91f327f22bb2feb780c424c74723cc0629dc34a1 Mon Sep 17 00:00:00 2001 From: Lopyter Date: Sun, 2 Oct 2022 18:15:31 +0200 Subject: make save to dirs optional for imgs saved from ui --- modules/shared.py | 1 + modules/ui.py | 2 +- 2 files changed, 2 insertions(+), 1 deletion(-) (limited to 'modules') diff --git a/modules/shared.py b/modules/shared.py index 1bf7a6c1..785e7af6 100644 --- a/modules/shared.py +++ b/modules/shared.py @@ -173,6 +173,7 @@ options_templates.update(options_section(('saving-to-dirs', "Saving to a directo "grid_save_to_dirs": OptionInfo(False, "Save grids to subdirectory"), "directories_filename_pattern": OptionInfo("", "Directory name pattern"), "directories_max_prompt_words": OptionInfo(8, "Max prompt words", gr.Slider, {"minimum": 1, "maximum": 20, "step": 1}), + "use_save_to_dirs_for_ui": OptionInfo(False, "Use \"Save images to a subdirectory\" option for images saved from UI"), })) options_templates.update(options_section(('upscaling', "Upscaling"), { diff --git a/modules/ui.py b/modules/ui.py index 78a15d83..8912deff 100644 --- a/modules/ui.py +++ b/modules/ui.py @@ -113,7 +113,7 @@ def save_files(js_data, images, index): p = MyObject(data) path = opts.outdir_save - save_to_dirs = opts.save_to_dirs + save_to_dirs = opts.use_save_to_dirs_for_ui if save_to_dirs: dirname = apply_filename_pattern(opts.directories_filename_pattern or "[prompt_words]", p, p.seed, p.prompt) -- cgit v1.2.1 From c4445225f79f1c57afe52358ff4b205864eb7aac Mon Sep 17 00:00:00 2001 From: AUTOMATIC <16777216c@gmail.com> Date: Sun, 2 Oct 2022 21:50:14 +0300 Subject: change wording for options --- modules/shared.py | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) (limited to 'modules') diff --git a/modules/shared.py b/modules/shared.py index 785e7af6..7246eadc 100644 --- a/modules/shared.py +++ b/modules/shared.py @@ -170,10 +170,10 @@ options_templates.update(options_section(('saving-paths', "Paths for saving"), { options_templates.update(options_section(('saving-to-dirs', "Saving to a directory"), { "save_to_dirs": OptionInfo(False, "Save images to a subdirectory"), - "grid_save_to_dirs": OptionInfo(False, "Save grids to subdirectory"), + "grid_save_to_dirs": OptionInfo(False, "Save grids to a subdirectory"), + "use_save_to_dirs_for_ui": OptionInfo(False, "When using \"Save\" button, save images to a subdirectory"), "directories_filename_pattern": OptionInfo("", "Directory name pattern"), - "directories_max_prompt_words": OptionInfo(8, "Max prompt words", gr.Slider, {"minimum": 1, "maximum": 20, "step": 1}), - "use_save_to_dirs_for_ui": OptionInfo(False, "Use \"Save images to a subdirectory\" option for images saved from UI"), + "directories_max_prompt_words": OptionInfo(8, "Max prompt words for [prompt_words] pattern", gr.Slider, {"minimum": 1, "maximum": 20, "step": 1}), })) options_templates.update(options_section(('upscaling', "Upscaling"), { -- cgit v1.2.1 From c7543d4940da672d970124ae8f2fec9de7bdc1da Mon Sep 17 00:00:00 2001 From: AUTOMATIC <16777216c@gmail.com> Date: Sun, 2 Oct 2022 22:41:21 +0300 Subject: preprocessing for textual inversion added --- modules/interrogate.py | 1 + modules/textual_inversion/preprocess.py | 75 ++++++++++++++++++++++++++ modules/textual_inversion/textual_inversion.py | 1 + modules/textual_inversion/ui.py | 14 +++-- modules/ui.py | 36 +++++++++++++ 5 files changed, 124 insertions(+), 3 deletions(-) create mode 100644 modules/textual_inversion/preprocess.py (limited to 'modules') diff --git a/modules/interrogate.py b/modules/interrogate.py index f62a4745..eed87144 100644 --- a/modules/interrogate.py +++ b/modules/interrogate.py @@ -21,6 +21,7 @@ Category = namedtuple("Category", ["name", "topn", "items"]) re_topn = re.compile(r"\.top(\d+)\.") + class InterrogateModels: blip_model = None clip_model = None diff --git a/modules/textual_inversion/preprocess.py b/modules/textual_inversion/preprocess.py new file mode 100644 index 00000000..209e928f --- /dev/null +++ b/modules/textual_inversion/preprocess.py @@ -0,0 +1,75 @@ +import os +from PIL import Image, ImageOps +import tqdm + +from modules import shared, images + + +def preprocess(process_src, process_dst, process_flip, process_split, process_caption): + size = 512 + src = os.path.abspath(process_src) + dst = os.path.abspath(process_dst) + + assert src != dst, 'same directory specified as source and desitnation' + + os.makedirs(dst, exist_ok=True) + + files = os.listdir(src) + + shared.state.textinfo = "Preprocessing..." + shared.state.job_count = len(files) + + if process_caption: + shared.interrogator.load() + + def save_pic_with_caption(image, index): + if process_caption: + caption = "-" + shared.interrogator.generate_caption(image) + else: + caption = "" + + image.save(os.path.join(dst, f"{index:05}-{subindex[0]}{caption}.png")) + subindex[0] += 1 + + def save_pic(image, index): + save_pic_with_caption(image, index) + + if process_flip: + save_pic_with_caption(ImageOps.mirror(image), index) + + for index, imagefile in enumerate(tqdm.tqdm(files)): + subindex = [0] + filename = os.path.join(src, imagefile) + img = Image.open(filename).convert("RGB") + + if shared.state.interrupted: + break + + ratio = img.height / img.width + is_tall = ratio > 1.35 + is_wide = ratio < 1 / 1.35 + + if process_split and is_tall: + img = img.resize((size, size * img.height // img.width)) + + top = img.crop((0, 0, size, size)) + save_pic(top, index) + + bot = img.crop((0, img.height - size, size, img.height)) + save_pic(bot, index) + elif process_split and is_wide: + img = img.resize((size * img.width // img.height, size)) + + left = img.crop((0, 0, size, size)) + save_pic(left, index) + + right = img.crop((img.width - size, 0, img.width, size)) + save_pic(right, index) + else: + img = images.resize_image(1, img, size, size) + save_pic(img, index) + + shared.state.nextjob() + + if process_caption: + shared.interrogator.send_blip_to_ram() diff --git a/modules/textual_inversion/textual_inversion.py b/modules/textual_inversion/textual_inversion.py index 1183aab7..d4e250d8 100644 --- a/modules/textual_inversion/textual_inversion.py +++ b/modules/textual_inversion/textual_inversion.py @@ -7,6 +7,7 @@ import tqdm import html import datetime + from modules import shared, devices, sd_hijack, processing, sd_models import modules.textual_inversion.dataset diff --git a/modules/textual_inversion/ui.py b/modules/textual_inversion/ui.py index 633037d8..f19ac5e0 100644 --- a/modules/textual_inversion/ui.py +++ b/modules/textual_inversion/ui.py @@ -2,24 +2,31 @@ import html import gradio as gr -import modules.textual_inversion.textual_inversion as ti +import modules.textual_inversion.textual_inversion +import modules.textual_inversion.preprocess from modules import sd_hijack, shared def create_embedding(name, initialization_text, nvpt): - filename = ti.create_embedding(name, nvpt, init_text=initialization_text) + filename = modules.textual_inversion.textual_inversion.create_embedding(name, nvpt, init_text=initialization_text) sd_hijack.model_hijack.embedding_db.load_textual_inversion_embeddings() return gr.Dropdown.update(choices=sorted(sd_hijack.model_hijack.embedding_db.word_embeddings.keys())), f"Created: {filename}", "" +def preprocess(*args): + modules.textual_inversion.preprocess.preprocess(*args) + + return "Preprocessing finished.", "" + + def train_embedding(*args): try: sd_hijack.undo_optimizations() - embedding, filename = ti.train_embedding(*args) + embedding, filename = modules.textual_inversion.textual_inversion.train_embedding(*args) res = f""" Training {'interrupted' if shared.state.interrupted else 'finished'} at {embedding.step} steps. @@ -30,3 +37,4 @@ Embedding saved to {html.escape(filename)} raise finally: sd_hijack.apply_optimizations() + diff --git a/modules/ui.py b/modules/ui.py index 8912deff..e7bde53b 100644 --- a/modules/ui.py +++ b/modules/ui.py @@ -961,6 +961,8 @@ def create_ui(wrap_gradio_gpu_call): with gr.Row().style(equal_height=False): with gr.Column(): with gr.Group(): + gr.HTML(value="

See wiki for detailed explanation.

") + gr.HTML(value="

Create a new embedding

") new_embedding_name = gr.Textbox(label="Name") @@ -974,6 +976,24 @@ def create_ui(wrap_gradio_gpu_call): with gr.Column(): create_embedding = gr.Button(value="Create", variant='primary') + with gr.Group(): + gr.HTML(value="

Preprocess images

") + + process_src = gr.Textbox(label='Source directory') + process_dst = gr.Textbox(label='Destination directory') + + with gr.Row(): + process_flip = gr.Checkbox(label='Flip') + process_split = gr.Checkbox(label='Split into two') + process_caption = gr.Checkbox(label='Add caption') + + with gr.Row(): + with gr.Column(scale=3): + gr.HTML(value="") + + with gr.Column(): + run_preprocess = gr.Button(value="Preprocess", variant='primary') + with gr.Group(): gr.HTML(value="

Train an embedding; must specify a directory with a set of 512x512 images

") train_embedding_name = gr.Dropdown(label='Embedding', choices=sorted(sd_hijack.model_hijack.embedding_db.word_embeddings.keys())) @@ -1018,6 +1038,22 @@ def create_ui(wrap_gradio_gpu_call): ] ) + run_preprocess.click( + fn=wrap_gradio_gpu_call(modules.textual_inversion.ui.preprocess, extra_outputs=[gr.update()]), + _js="start_training_textual_inversion", + inputs=[ + process_src, + process_dst, + process_flip, + process_split, + process_caption, + ], + outputs=[ + ti_output, + ti_outcome, + ], + ) + train_embedding.click( fn=wrap_gradio_gpu_call(modules.textual_inversion.ui.train_embedding, extra_outputs=[gr.update()]), _js="start_training_textual_inversion", -- cgit v1.2.1 From 6785331e22d6a488fbf5905fab56d7fec867e038 Mon Sep 17 00:00:00 2001 From: AUTOMATIC <16777216c@gmail.com> Date: Sun, 2 Oct 2022 22:59:01 +0300 Subject: keep textual inversion dataset latents in CPU memory to save a bit of VRAM --- modules/textual_inversion/dataset.py | 2 ++ modules/textual_inversion/textual_inversion.py | 3 +++ modules/ui.py | 4 ++-- 3 files changed, 7 insertions(+), 2 deletions(-) (limited to 'modules') diff --git a/modules/textual_inversion/dataset.py b/modules/textual_inversion/dataset.py index 7e134a08..e8394ff6 100644 --- a/modules/textual_inversion/dataset.py +++ b/modules/textual_inversion/dataset.py @@ -8,6 +8,7 @@ from torchvision import transforms import random import tqdm +from modules import devices class PersonalizedBase(Dataset): @@ -47,6 +48,7 @@ class PersonalizedBase(Dataset): torchdata = torch.moveaxis(torchdata, 2, 0) init_latent = model.get_first_stage_encoding(model.encode_first_stage(torchdata.unsqueeze(dim=0))).squeeze() + init_latent = init_latent.to(devices.cpu) self.dataset.append((init_latent, filename_tokens)) diff --git a/modules/textual_inversion/textual_inversion.py b/modules/textual_inversion/textual_inversion.py index d4e250d8..8686f534 100644 --- a/modules/textual_inversion/textual_inversion.py +++ b/modules/textual_inversion/textual_inversion.py @@ -212,7 +212,10 @@ def train_embedding(embedding_name, learn_rate, data_root, log_directory, steps, with torch.autocast("cuda"): c = cond_model([text]) + + x = x.to(devices.device) loss = shared.sd_model(x.unsqueeze(0), c)[0] + del x losses[embedding.step % losses.shape[0]] = loss.item() diff --git a/modules/ui.py b/modules/ui.py index e7bde53b..d9d02ece 100644 --- a/modules/ui.py +++ b/modules/ui.py @@ -1002,8 +1002,8 @@ def create_ui(wrap_gradio_gpu_call): log_directory = gr.Textbox(label='Log directory', placeholder="Path to directory where to write outputs", value="textual_inversion") template_file = gr.Textbox(label='Prompt template file', value=os.path.join(script_path, "textual_inversion_templates", "style_filewords.txt")) steps = gr.Number(label='Max steps', value=100000, precision=0) - create_image_every = gr.Number(label='Save an image to log directory every N steps, 0 to disable', value=1000, precision=0) - save_embedding_every = gr.Number(label='Save a copy of embedding to log directory every N steps, 0 to disable', value=1000, precision=0) + create_image_every = gr.Number(label='Save an image to log directory every N steps, 0 to disable', value=500, precision=0) + save_embedding_every = gr.Number(label='Save a copy of embedding to log directory every N steps, 0 to disable', value=500, precision=0) with gr.Row(): with gr.Column(scale=2): -- cgit v1.2.1 From 166283653cfe7521a422c91e8fb801f3ecb4adc8 Mon Sep 17 00:00:00 2001 From: AUTOMATIC <16777216c@gmail.com> Date: Sun, 2 Oct 2022 23:18:13 +0300 Subject: remove LDSR warning --- modules/paths.py | 1 - 1 file changed, 1 deletion(-) (limited to 'modules') diff --git a/modules/paths.py b/modules/paths.py index ceb80417..606f7d66 100644 --- a/modules/paths.py +++ b/modules/paths.py @@ -20,7 +20,6 @@ path_dirs = [ (os.path.join(sd_path, '../taming-transformers'), 'taming', 'Taming Transformers', []), (os.path.join(sd_path, '../CodeFormer'), 'inference_codeformer.py', 'CodeFormer', []), (os.path.join(sd_path, '../BLIP'), 'models/blip.py', 'BLIP', []), - (os.path.join(sd_path, '../latent-diffusion'), 'LDSR.py', 'LDSR', []), (os.path.join(sd_path, '../k-diffusion'), 'k_diffusion/sampling.py', 'k_diffusion', ["atstart"]), ] -- cgit v1.2.1 From 138662734c25dab4e73e632b7eaff9ad9c0ce2b4 Mon Sep 17 00:00:00 2001 From: AUTOMATIC <16777216c@gmail.com> Date: Mon, 3 Oct 2022 07:57:59 +0300 Subject: use dropdown instead of radio for img2img upscaler selection --- modules/shared.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) (limited to 'modules') diff --git a/modules/shared.py b/modules/shared.py index 7246eadc..2a599e9c 100644 --- a/modules/shared.py +++ b/modules/shared.py @@ -183,7 +183,7 @@ options_templates.update(options_section(('upscaling', "Upscaling"), { "SWIN_tile": OptionInfo(192, "Tile size for all SwinIR.", gr.Slider, {"minimum": 16, "maximum": 512, "step": 16}), "SWIN_tile_overlap": OptionInfo(8, "Tile overlap, in pixels for SwinIR. Low values = visible seam.", gr.Slider, {"minimum": 0, "maximum": 48, "step": 1}), "ldsr_steps": OptionInfo(100, "LDSR processing steps. Lower = faster", gr.Slider, {"minimum": 1, "maximum": 200, "step": 1}), - "upscaler_for_img2img": OptionInfo(None, "Upscaler for img2img", gr.Radio, lambda: {"choices": [x.name for x in sd_upscalers]}), + "upscaler_for_img2img": OptionInfo(None, "Upscaler for img2img", gr.Dropdown, lambda: {"choices": [x.name for x in sd_upscalers]}), })) options_templates.update(options_section(('face-restoration', "Face restoration"), { -- cgit v1.2.1 From e615d4f9d101e2712c7c2d0e3e8feb19cb430c74 Mon Sep 17 00:00:00 2001 From: Hanusz Leszek Date: Sun, 2 Oct 2022 21:08:23 +0200 Subject: Convert folder icon surrogate pair to valid utf8 --- modules/ui.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) (limited to 'modules') diff --git a/modules/ui.py b/modules/ui.py index d9d02ece..16432151 100644 --- a/modules/ui.py +++ b/modules/ui.py @@ -69,7 +69,7 @@ random_symbol = '\U0001f3b2\ufe0f' # 🎲️ reuse_symbol = '\u267b\ufe0f' # ♻️ art_symbol = '\U0001f3a8' # 🎨 paste_symbol = '\u2199\ufe0f' # ↙ -folder_symbol = '\uD83D\uDCC2' +folder_symbol = '\U0001f4c2' # 📂 def plaintext_to_html(text): text = "

" + "
\n".join([f"{html.escape(x)}" for x in text.split('\n')]) + "

" -- cgit v1.2.1 From 34c638142eaa57f89b86545ba3c72085036398bb Mon Sep 17 00:00:00 2001 From: hentailord85ez <112723046+hentailord85ez@users.noreply.github.com> Date: Fri, 30 Sep 2022 22:38:14 +0100 Subject: Fixed when eta = 0 Unexpected behavior when using eta = 0 in something like XY, but your default eta was set to something not 0. --- modules/sd_samplers.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) (limited to 'modules') diff --git a/modules/sd_samplers.py b/modules/sd_samplers.py index 9316875a..dbf570d2 100644 --- a/modules/sd_samplers.py +++ b/modules/sd_samplers.py @@ -127,7 +127,7 @@ class VanillaStableDiffusionSampler: return res def initialize(self, p): - self.eta = p.eta or opts.eta_ddim + self.eta = p.eta if p.eta is not None else opts.eta_ddim for fieldname in ['p_sample_ddim', 'p_sample_plms']: if hasattr(self.sampler, fieldname): -- cgit v1.2.1 From 36ea4ac0f5844e5c8dec124edbdb714ccdd6013c Mon Sep 17 00:00:00 2001 From: RnDMonkey Date: Sun, 2 Oct 2022 22:21:16 -0700 Subject: moved no-style return outside join function --- modules/images.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) (limited to 'modules') diff --git a/modules/images.py b/modules/images.py index bba55158..1a046aca 100644 --- a/modules/images.py +++ b/modules/images.py @@ -315,7 +315,7 @@ def apply_filename_pattern(x, p, seed, prompt): #currently disabled if using the save button, will work otherwise # if enabled it will cause a bug because styles is not included in the save_files data dictionary if hasattr(p, "styles"): - x = x.replace("[styles]", sanitize_filename_part(", ".join([x for x in p.styles if not x == "None"] or "None"), replace_spaces=False)) + x = x.replace("[styles]", sanitize_filename_part(", ".join([x for x in p.styles if not x == "None"]) or "None", replace_spaces=False)) x = x.replace("[sampler]", sanitize_filename_part(sd_samplers.samplers[p.sampler_index].name, replace_spaces=False)) -- cgit v1.2.1 From 6491b09c24ea77f1f69990ea80a216f9ce319589 Mon Sep 17 00:00:00 2001 From: AUTOMATIC <16777216c@gmail.com> Date: Mon, 3 Oct 2022 08:53:52 +0300 Subject: use existing function for gfpgan --- modules/gfpgan_model.py | 6 +----- 1 file changed, 1 insertion(+), 5 deletions(-) (limited to 'modules') diff --git a/modules/gfpgan_model.py b/modules/gfpgan_model.py index bb30d733..dd3fbcab 100644 --- a/modules/gfpgan_model.py +++ b/modules/gfpgan_model.py @@ -97,11 +97,7 @@ def setup_model(dirname): return "GFPGAN" def restore(self, np_image): - np_image_bgr = np_image[:, :, ::-1] - cropped_faces, restored_faces, gfpgan_output_bgr = gfpgann().enhance(np_image_bgr, has_aligned=False, only_center_face=False, paste_back=True) - np_image = gfpgan_output_bgr[:, :, ::-1] - - return np_image + return gfpgan_fix_faces(np_image) shared.face_restorers.append(FaceRestorerGFPGAN()) except Exception: -- cgit v1.2.1 From 43a74fa595003321200a40bd2431e56c245e75ed Mon Sep 17 00:00:00 2001 From: AUTOMATIC <16777216c@gmail.com> Date: Mon, 3 Oct 2022 11:48:19 +0300 Subject: batch processing for img2img with an empty output directory, by request --- modules/img2img.py | 7 +++++-- modules/ui.py | 2 +- 2 files changed, 6 insertions(+), 3 deletions(-) (limited to 'modules') diff --git a/modules/img2img.py b/modules/img2img.py index f4455c90..2ff8e261 100644 --- a/modules/img2img.py +++ b/modules/img2img.py @@ -23,8 +23,10 @@ def process_batch(p, input_dir, output_dir, args): print(f"Will process {len(images)} images, creating {p.n_iter * p.batch_size} new images for each.") + save_normally = output_dir == '' + p.do_not_save_grid = True - p.do_not_save_samples = True + p.do_not_save_samples = not save_normally state.job_count = len(images) * p.n_iter @@ -48,7 +50,8 @@ def process_batch(p, input_dir, output_dir, args): left, right = os.path.splitext(filename) filename = f"{left}-{n}{right}" - processed_image.save(os.path.join(output_dir, filename)) + if not save_normally: + processed_image.save(os.path.join(output_dir, filename)) def img2img(mode: int, prompt: str, negative_prompt: str, prompt_style: str, prompt_style2: str, init_img, init_img_with_mask, init_img_inpaint, init_mask_inpaint, mask_mode, steps: int, sampler_index: int, mask_blur: int, inpainting_fill: int, restore_faces: bool, tiling: bool, n_iter: int, batch_size: int, cfg_scale: float, denoising_strength: float, seed: int, subseed: int, subseed_strength: float, seed_resize_from_h: int, seed_resize_from_w: int, seed_enable_extras: bool, height: int, width: int, resize_mode: int, inpaint_full_res: bool, inpaint_full_res_padding: int, inpainting_mask_invert: int, img2img_batch_input_dir: str, img2img_batch_output_dir: str, *args): diff --git a/modules/ui.py b/modules/ui.py index 16432151..55f7aa95 100644 --- a/modules/ui.py +++ b/modules/ui.py @@ -658,7 +658,7 @@ def create_ui(wrap_gradio_gpu_call): with gr.TabItem('Batch img2img', id='batch'): hidden = '
Disabled when launched with --hide-ui-dir-config.' if shared.cmd_opts.hide_ui_dir_config else '' - gr.HTML(f"

Process images in a directory on the same machine where the server is running.{hidden}

") + gr.HTML(f"

Process images in a directory on the same machine where the server is running.
Use an empty output directory to save pictures normally instead of writing to the output directory.{hidden}

") img2img_batch_input_dir = gr.Textbox(label="Input directory", **shared.hide_dirs) img2img_batch_output_dir = gr.Textbox(label="Output directory", **shared.hide_dirs) -- cgit v1.2.1 From 2865ef4b9ab16d56326cc805541bebcf01d099bc Mon Sep 17 00:00:00 2001 From: AUTOMATIC <16777216c@gmail.com> Date: Mon, 3 Oct 2022 13:10:03 +0300 Subject: fix broken date in TI --- modules/textual_inversion/textual_inversion.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) (limited to 'modules') diff --git a/modules/textual_inversion/textual_inversion.py b/modules/textual_inversion/textual_inversion.py index 8686f534..cd9f3498 100644 --- a/modules/textual_inversion/textual_inversion.py +++ b/modules/textual_inversion/textual_inversion.py @@ -164,7 +164,7 @@ def train_embedding(embedding_name, learn_rate, data_root, log_directory, steps, filename = os.path.join(shared.cmd_opts.embeddings_dir, f'{embedding_name}.pt') - log_directory = os.path.join(log_directory, datetime.datetime.now().strftime("%Y-%d-%m"), embedding_name) + log_directory = os.path.join(log_directory, datetime.datetime.now().strftime("%Y-%m-%d"), embedding_name) if save_embedding_every > 0: embedding_dir = os.path.join(log_directory, "embeddings") -- cgit v1.2.1 From 5ef0baf5eaec7f21a1666af424405cbee19f3764 Mon Sep 17 00:00:00 2001 From: AUTOMATIC <16777216c@gmail.com> Date: Tue, 4 Oct 2022 08:52:11 +0300 Subject: add support for gelbooru tags in filenames for textual inversion --- modules/textual_inversion/dataset.py | 7 +++++-- modules/textual_inversion/preprocess.py | 4 +++- 2 files changed, 8 insertions(+), 3 deletions(-) (limited to 'modules') diff --git a/modules/textual_inversion/dataset.py b/modules/textual_inversion/dataset.py index e8394ff6..7c44ea5b 100644 --- a/modules/textual_inversion/dataset.py +++ b/modules/textual_inversion/dataset.py @@ -9,6 +9,9 @@ from torchvision import transforms import random import tqdm from modules import devices +import re + +re_tag = re.compile(r"[a-zA-Z][_\w\d()]+") class PersonalizedBase(Dataset): @@ -38,8 +41,8 @@ class PersonalizedBase(Dataset): image = image.resize((self.width, self.height), PIL.Image.BICUBIC) filename = os.path.basename(path) - filename_tokens = os.path.splitext(filename)[0].replace('_', '-').replace(' ', '-').split('-') - filename_tokens = [token for token in filename_tokens if token.isalpha()] + filename_tokens = os.path.splitext(filename)[0] + filename_tokens = re_tag.findall(filename_tokens) npimage = np.array(image).astype(np.uint8) npimage = (npimage / 127.5 - 1.0).astype(np.float32) diff --git a/modules/textual_inversion/preprocess.py b/modules/textual_inversion/preprocess.py index 209e928f..f545a993 100644 --- a/modules/textual_inversion/preprocess.py +++ b/modules/textual_inversion/preprocess.py @@ -26,7 +26,9 @@ def preprocess(process_src, process_dst, process_flip, process_split, process_ca if process_caption: caption = "-" + shared.interrogator.generate_caption(image) else: - caption = "" + caption = filename + caption = os.path.splitext(caption)[0] + caption = os.path.basename(caption) image.save(os.path.join(dst, f"{index:05}-{subindex[0]}{caption}.png")) subindex[0] += 1 -- cgit v1.2.1 From eeab7aedf532680a6ae9058ee272450bb07e41eb Mon Sep 17 00:00:00 2001 From: brkirch Date: Tue, 4 Oct 2022 04:24:35 -0400 Subject: Add --use-cpu command line option Remove MPS detection to use CPU for GFPGAN / CodeFormer and add a --use-cpu command line option. --- modules/devices.py | 5 ++--- modules/esrgan_model.py | 9 ++++----- modules/scunet_model.py | 8 ++++---- modules/shared.py | 9 +++++++-- 4 files changed, 17 insertions(+), 14 deletions(-) (limited to 'modules') diff --git a/modules/devices.py b/modules/devices.py index 5d9c7a07..b5a0cd29 100644 --- a/modules/devices.py +++ b/modules/devices.py @@ -1,8 +1,8 @@ import torch -# has_mps is only available in nightly pytorch (for now), `getattr` for compatibility from modules import errors +# has_mps is only available in nightly pytorch (for now), `getattr` for compatibility has_mps = getattr(torch, 'has_mps', False) cpu = torch.device("cpu") @@ -32,8 +32,7 @@ def enable_tf32(): errors.run(enable_tf32, "Enabling TF32") -device = get_optimal_device() -device_gfpgan = device_codeformer = cpu if device.type == 'mps' else device +device = device_gfpgan = device_esrgan = device_scunet = device_codeformer = get_optimal_device() dtype = torch.float16 def randn(seed, shape): diff --git a/modules/esrgan_model.py b/modules/esrgan_model.py index 4aed9283..d17e730f 100644 --- a/modules/esrgan_model.py +++ b/modules/esrgan_model.py @@ -6,8 +6,7 @@ from PIL import Image from basicsr.utils.download_util import load_file_from_url import modules.esrgam_model_arch as arch -from modules import shared, modelloader, images -from modules.devices import has_mps +from modules import shared, modelloader, images, devices from modules.paths import models_path from modules.upscaler import Upscaler, UpscalerData from modules.shared import opts @@ -97,7 +96,7 @@ class UpscalerESRGAN(Upscaler): model = self.load_model(selected_model) if model is None: return img - model.to(shared.device) + model.to(devices.device_esrgan) img = esrgan_upscale(model, img) return img @@ -112,7 +111,7 @@ class UpscalerESRGAN(Upscaler): print("Unable to load %s from %s" % (self.model_path, filename)) return None - pretrained_net = torch.load(filename, map_location='cpu' if has_mps else None) + pretrained_net = torch.load(filename, map_location='cpu' if shared.device.type == 'mps' else None) crt_model = arch.RRDBNet(3, 3, 64, 23, gc=32) pretrained_net = fix_model_layers(crt_model, pretrained_net) @@ -127,7 +126,7 @@ def upscale_without_tiling(model, img): img = img[:, :, ::-1] img = np.moveaxis(img, 2, 0) / 255 img = torch.from_numpy(img).float() - img = img.unsqueeze(0).to(shared.device) + img = img.unsqueeze(0).to(devices.device_esrgan) with torch.no_grad(): output = model(img) output = output.squeeze().float().cpu().clamp_(0, 1).numpy() diff --git a/modules/scunet_model.py b/modules/scunet_model.py index 7987ac14..fb64b740 100644 --- a/modules/scunet_model.py +++ b/modules/scunet_model.py @@ -8,7 +8,7 @@ import torch from basicsr.utils.download_util import load_file_from_url import modules.upscaler -from modules import shared, modelloader +from modules import devices, modelloader from modules.paths import models_path from modules.scunet_model_arch import SCUNet as net @@ -51,12 +51,12 @@ class UpscalerScuNET(modules.upscaler.Upscaler): if model is None: return img - device = shared.device + device = devices.device_scunet img = np.array(img) img = img[:, :, ::-1] img = np.moveaxis(img, 2, 0) / 255 img = torch.from_numpy(img).float() - img = img.unsqueeze(0).to(shared.device) + img = img.unsqueeze(0).to(device) img = img.to(device) with torch.no_grad(): @@ -69,7 +69,7 @@ class UpscalerScuNET(modules.upscaler.Upscaler): return PIL.Image.fromarray(output, 'RGB') def load_model(self, path: str): - device = shared.device + device = devices.device_scunet if "http" in path: filename = load_file_from_url(url=self.model_url, model_dir=self.model_path, file_name="%s.pth" % self.name, progress=True) diff --git a/modules/shared.py b/modules/shared.py index 2a599e9c..7899ab8d 100644 --- a/modules/shared.py +++ b/modules/shared.py @@ -12,7 +12,7 @@ import modules.interrogate import modules.memmon import modules.sd_models import modules.styles -from modules.devices import get_optimal_device +import modules.devices as devices from modules.paths import script_path, sd_path sd_model_file = os.path.join(script_path, 'model.ckpt') @@ -46,6 +46,7 @@ parser.add_argument("--ldsr-models-path", type=str, help="Path to directory with parser.add_argument("--opt-split-attention", action='store_true', help="force-enables cross-attention layer optimization. By default, it's on for torch.cuda and off for other torch devices.") parser.add_argument("--disable-opt-split-attention", action='store_true', help="force-disables cross-attention layer optimization") parser.add_argument("--opt-split-attention-v1", action='store_true', help="enable older version of split attention optimization that does not consume all the VRAM it can find") +parser.add_argument("--use-cpu", nargs='+',choices=['SD', 'GFPGAN', 'ESRGAN', 'SCUNet', 'CodeFormer'], help="use CPU for specified modules", default=[]) parser.add_argument("--listen", action='store_true', help="launch gradio with 0.0.0.0 as server name, allowing to respond to network requests") parser.add_argument("--port", type=int, help="launch gradio with given server port, you need root/admin rights for ports < 1024, defaults to 7860 if available", default=None) parser.add_argument("--show-negative-prompt", action='store_true', help="does not do anything", default=False) @@ -63,7 +64,11 @@ parser.add_argument("--enable-console-prompts", action='store_true', help="print cmd_opts = parser.parse_args() -device = get_optimal_device() + +devices.device, devices.device_gfpgan, devices.device_esrgan, devices.device_scunet, devices.device_codeformer = \ +(devices.cpu if x in cmd_opts.use_cpu else devices.get_optimal_device() for x in ['SD', 'GFPGAN', 'ESRGAN', 'SCUNet', 'CodeFormer']) + +device = devices.device batch_cond_uncond = cmd_opts.always_batch_cond_uncond or not (cmd_opts.lowvram or cmd_opts.medvram) parallel_processing_allowed = not cmd_opts.lowvram and not cmd_opts.medvram -- cgit v1.2.1 From 27ddc24fdee1fbe709054a43235ab7f9c51b3e9f Mon Sep 17 00:00:00 2001 From: brkirch Date: Tue, 4 Oct 2022 05:18:17 -0400 Subject: Add BSRGAN to --add-cpu --- modules/bsrgan_model.py | 6 +++--- modules/devices.py | 2 +- modules/shared.py | 6 +++--- 3 files changed, 7 insertions(+), 7 deletions(-) (limited to 'modules') diff --git a/modules/bsrgan_model.py b/modules/bsrgan_model.py index e62c6657..3bd80791 100644 --- a/modules/bsrgan_model.py +++ b/modules/bsrgan_model.py @@ -8,7 +8,7 @@ import torch from basicsr.utils.download_util import load_file_from_url import modules.upscaler -from modules import shared, modelloader +from modules import devices, modelloader from modules.bsrgan_model_arch import RRDBNet from modules.paths import models_path @@ -44,13 +44,13 @@ class UpscalerBSRGAN(modules.upscaler.Upscaler): model = self.load_model(selected_file) if model is None: return img - model.to(shared.device) + model.to(devices.device_bsrgan) torch.cuda.empty_cache() img = np.array(img) img = img[:, :, ::-1] img = np.moveaxis(img, 2, 0) / 255 img = torch.from_numpy(img).float() - img = img.unsqueeze(0).to(shared.device) + img = img.unsqueeze(0).to(devices.device_bsrgan) with torch.no_grad(): output = model(img) output = output.squeeze().float().cpu().clamp_(0, 1).numpy() diff --git a/modules/devices.py b/modules/devices.py index b5a0cd29..b7899632 100644 --- a/modules/devices.py +++ b/modules/devices.py @@ -32,7 +32,7 @@ def enable_tf32(): errors.run(enable_tf32, "Enabling TF32") -device = device_gfpgan = device_esrgan = device_scunet = device_codeformer = get_optimal_device() +device = device_gfpgan = device_bsrgan = device_esrgan = device_scunet = device_codeformer = get_optimal_device() dtype = torch.float16 def randn(seed, shape): diff --git a/modules/shared.py b/modules/shared.py index 7899ab8d..95b98a06 100644 --- a/modules/shared.py +++ b/modules/shared.py @@ -46,7 +46,7 @@ parser.add_argument("--ldsr-models-path", type=str, help="Path to directory with parser.add_argument("--opt-split-attention", action='store_true', help="force-enables cross-attention layer optimization. By default, it's on for torch.cuda and off for other torch devices.") parser.add_argument("--disable-opt-split-attention", action='store_true', help="force-disables cross-attention layer optimization") parser.add_argument("--opt-split-attention-v1", action='store_true', help="enable older version of split attention optimization that does not consume all the VRAM it can find") -parser.add_argument("--use-cpu", nargs='+',choices=['SD', 'GFPGAN', 'ESRGAN', 'SCUNet', 'CodeFormer'], help="use CPU for specified modules", default=[]) +parser.add_argument("--use-cpu", nargs='+',choices=['SD', 'GFPGAN', 'BSRGAN', 'ESRGAN', 'SCUNet', 'CodeFormer'], help="use CPU for specified modules", default=[]) parser.add_argument("--listen", action='store_true', help="launch gradio with 0.0.0.0 as server name, allowing to respond to network requests") parser.add_argument("--port", type=int, help="launch gradio with given server port, you need root/admin rights for ports < 1024, defaults to 7860 if available", default=None) parser.add_argument("--show-negative-prompt", action='store_true', help="does not do anything", default=False) @@ -65,8 +65,8 @@ parser.add_argument("--enable-console-prompts", action='store_true', help="print cmd_opts = parser.parse_args() -devices.device, devices.device_gfpgan, devices.device_esrgan, devices.device_scunet, devices.device_codeformer = \ -(devices.cpu if x in cmd_opts.use_cpu else devices.get_optimal_device() for x in ['SD', 'GFPGAN', 'ESRGAN', 'SCUNet', 'CodeFormer']) +devices.device, devices.device_gfpgan, devices.device_bsrgan, devices.device_esrgan, devices.device_scunet, devices.device_codeformer = \ +(devices.cpu if x in cmd_opts.use_cpu else devices.get_optimal_device() for x in ['SD', 'GFPGAN', 'BSRGAN', 'ESRGAN', 'SCUNet', 'CodeFormer']) device = devices.device -- cgit v1.2.1 From dc9c5a97742e3a34d37da7108642d8adc0dc5858 Mon Sep 17 00:00:00 2001 From: brkirch Date: Tue, 4 Oct 2022 05:22:50 -0400 Subject: Modify --add-cpu description --- modules/shared.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) (limited to 'modules') diff --git a/modules/shared.py b/modules/shared.py index 95b98a06..25aff5b0 100644 --- a/modules/shared.py +++ b/modules/shared.py @@ -46,7 +46,7 @@ parser.add_argument("--ldsr-models-path", type=str, help="Path to directory with parser.add_argument("--opt-split-attention", action='store_true', help="force-enables cross-attention layer optimization. By default, it's on for torch.cuda and off for other torch devices.") parser.add_argument("--disable-opt-split-attention", action='store_true', help="force-disables cross-attention layer optimization") parser.add_argument("--opt-split-attention-v1", action='store_true', help="enable older version of split attention optimization that does not consume all the VRAM it can find") -parser.add_argument("--use-cpu", nargs='+',choices=['SD', 'GFPGAN', 'BSRGAN', 'ESRGAN', 'SCUNet', 'CodeFormer'], help="use CPU for specified modules", default=[]) +parser.add_argument("--use-cpu", nargs='+',choices=['SD', 'GFPGAN', 'BSRGAN', 'ESRGAN', 'SCUNet', 'CodeFormer'], help="use CPU as torch device for specified modules", default=[]) parser.add_argument("--listen", action='store_true', help="launch gradio with 0.0.0.0 as server name, allowing to respond to network requests") parser.add_argument("--port", type=int, help="launch gradio with given server port, you need root/admin rights for ports < 1024, defaults to 7860 if available", default=None) parser.add_argument("--show-negative-prompt", action='store_true', help="does not do anything", default=False) -- cgit v1.2.1 From 6c6ae28bf5fd1e8bc3e8f64a3430b6f29f338f77 Mon Sep 17 00:00:00 2001 From: AUTOMATIC <16777216c@gmail.com> Date: Tue, 4 Oct 2022 12:32:22 +0300 Subject: send all three of GFPGAN's and codeformer's models to CPU memory instead of just one for #1283 --- modules/codeformer_model.py | 12 ++++++++++-- modules/devices.py | 10 ++++++++++ modules/gfpgan_model.py | 14 ++++++++++++-- modules/processing.py | 16 +++++++++------- 4 files changed, 41 insertions(+), 11 deletions(-) (limited to 'modules') diff --git a/modules/codeformer_model.py b/modules/codeformer_model.py index a29f3855..e6d9fa4f 100644 --- a/modules/codeformer_model.py +++ b/modules/codeformer_model.py @@ -69,10 +69,14 @@ def setup_model(dirname): self.net = net self.face_helper = face_helper - self.net.to(devices.device_codeformer) return net, face_helper + def send_model_to(self, device): + self.net.to(device) + self.face_helper.face_det.to(device) + self.face_helper.face_parse.to(device) + def restore(self, np_image, w=None): np_image = np_image[:, :, ::-1] @@ -82,6 +86,8 @@ def setup_model(dirname): if self.net is None or self.face_helper is None: return np_image + self.send_model_to(devices.device_codeformer) + self.face_helper.clean_all() self.face_helper.read_image(np_image) self.face_helper.get_face_landmarks_5(only_center_face=False, resize=640, eye_dist_threshold=5) @@ -113,8 +119,10 @@ def setup_model(dirname): if original_resolution != restored_img.shape[0:2]: restored_img = cv2.resize(restored_img, (0, 0), fx=original_resolution[1]/restored_img.shape[1], fy=original_resolution[0]/restored_img.shape[0], interpolation=cv2.INTER_LINEAR) + self.face_helper.clean_all() + if shared.opts.face_restoration_unload: - self.net.to(devices.cpu) + self.send_model_to(devices.cpu) return restored_img diff --git a/modules/devices.py b/modules/devices.py index ff82f2f6..12aab665 100644 --- a/modules/devices.py +++ b/modules/devices.py @@ -1,3 +1,5 @@ +import contextlib + import torch # has_mps is only available in nightly pytorch (for now), `getattr` for compatibility @@ -57,3 +59,11 @@ def randn_without_seed(shape): return torch.randn(shape, device=device) + +def autocast(): + from modules import shared + + if dtype == torch.float32 or shared.cmd_opts.precision == "full": + return contextlib.nullcontext() + + return torch.autocast("cuda") diff --git a/modules/gfpgan_model.py b/modules/gfpgan_model.py index dd3fbcab..5586b554 100644 --- a/modules/gfpgan_model.py +++ b/modules/gfpgan_model.py @@ -37,22 +37,32 @@ def gfpgann(): print("Unable to load gfpgan model!") return None model = gfpgan_constructor(model_path=model_file, upscale=1, arch='clean', channel_multiplier=2, bg_upsampler=None) - model.gfpgan.to(shared.device) loaded_gfpgan_model = model return model +def send_model_to(model, device): + model.gfpgan.to(device) + model.face_helper.face_det.to(device) + model.face_helper.face_parse.to(device) + + def gfpgan_fix_faces(np_image): model = gfpgann() if model is None: return np_image + + send_model_to(model, devices.device) + np_image_bgr = np_image[:, :, ::-1] cropped_faces, restored_faces, gfpgan_output_bgr = model.enhance(np_image_bgr, has_aligned=False, only_center_face=False, paste_back=True) np_image = gfpgan_output_bgr[:, :, ::-1] + model.face_helper.clean_all() + if shared.opts.face_restoration_unload: - model.gfpgan.to(devices.cpu) + send_model_to(model, devices.cpu) return np_image diff --git a/modules/processing.py b/modules/processing.py index 0a4b6198..9cbecdd8 100644 --- a/modules/processing.py +++ b/modules/processing.py @@ -1,4 +1,3 @@ -import contextlib import json import math import os @@ -330,9 +329,8 @@ def process_images(p: StableDiffusionProcessing) -> Processed: infotexts = [] output_images = [] - precision_scope = torch.autocast if cmd_opts.precision == "autocast" else contextlib.nullcontext - ema_scope = (contextlib.nullcontext if cmd_opts.lowvram else p.sd_model.ema_scope) - with torch.no_grad(), precision_scope("cuda"), ema_scope(): + + with torch.no_grad(): p.init(all_prompts, all_seeds, all_subseeds) if state.job_count == -1: @@ -351,8 +349,9 @@ def process_images(p: StableDiffusionProcessing) -> Processed: #uc = p.sd_model.get_learned_conditioning(len(prompts) * [p.negative_prompt]) #c = p.sd_model.get_learned_conditioning(prompts) - uc = prompt_parser.get_learned_conditioning(len(prompts) * [p.negative_prompt], p.steps) - c = prompt_parser.get_learned_conditioning(prompts, p.steps) + with devices.autocast(): + uc = prompt_parser.get_learned_conditioning(len(prompts) * [p.negative_prompt], p.steps) + c = prompt_parser.get_learned_conditioning(prompts, p.steps) if len(model_hijack.comments) > 0: for comment in model_hijack.comments: @@ -361,7 +360,9 @@ def process_images(p: StableDiffusionProcessing) -> Processed: if p.n_iter > 1: shared.state.job = f"Batch {n+1} out of {p.n_iter}" - samples_ddim = p.sample(conditioning=c, unconditional_conditioning=uc, seeds=seeds, subseeds=subseeds, subseed_strength=p.subseed_strength) + with devices.autocast(): + samples_ddim = p.sample(conditioning=c, unconditional_conditioning=uc, seeds=seeds, subseeds=subseeds, subseed_strength=p.subseed_strength).to(devices.dtype) + if state.interrupted: # if we are interruped, sample returns just noise @@ -386,6 +387,7 @@ def process_images(p: StableDiffusionProcessing) -> Processed: devices.torch_gc() x_sample = modules.face_restoration.restore_faces(x_sample) + devices.torch_gc() image = Image.fromarray(x_sample) -- cgit v1.2.1 From 2f1b61d97987ae0a52a7dfc6bc99c68928bdb594 Mon Sep 17 00:00:00 2001 From: dan Date: Mon, 3 Oct 2022 19:25:36 +0800 Subject: Allow nested structures inside schedules --- modules/prompt_parser.py | 119 +++++++++++++++++++++-------------------------- 1 file changed, 53 insertions(+), 66 deletions(-) (limited to 'modules') diff --git a/modules/prompt_parser.py b/modules/prompt_parser.py index e811eb9e..99c8ed99 100644 --- a/modules/prompt_parser.py +++ b/modules/prompt_parser.py @@ -1,20 +1,11 @@ import re from collections import namedtuple import torch +from lark import Lark, Transformer, Visitor +import functools import modules.shared as shared -re_prompt = re.compile(r''' -(.*?) -\[ - ([^]:]+): - (?:([^]:]*):)? - ([0-9]*\.?[0-9]+) -] -| -(.+) -''', re.X) - # a prompt like this: "fantasy landscape with a [mountain:lake:0.25] and [an oak:a christmas tree:0.75][ in foreground::0.6][ in background:0.25] [shoddy:masterful:0.5]" # will be represented with prompt_schedule like this (assuming steps=100): # [25, 'fantasy landscape with a mountain and an oak in foreground shoddy'] @@ -25,61 +16,57 @@ re_prompt = re.compile(r''' def get_learned_conditioning_prompt_schedules(prompts, steps): - res = [] - cache = {} - - for prompt in prompts: - prompt_schedule: list[list[str | int]] = [[steps, ""]] - - cached = cache.get(prompt, None) - if cached is not None: - res.append(cached) - continue - - for m in re_prompt.finditer(prompt): - plaintext = m.group(1) if m.group(5) is None else m.group(5) - concept_from = m.group(2) - concept_to = m.group(3) - if concept_to is None: - concept_to = concept_from - concept_from = "" - swap_position = float(m.group(4)) if m.group(4) is not None else None - - if swap_position is not None: - if swap_position < 1: - swap_position = swap_position * steps - swap_position = int(min(swap_position, steps)) - - swap_index = None - found_exact_index = False - for i in range(len(prompt_schedule)): - end_step = prompt_schedule[i][0] - prompt_schedule[i][1] += plaintext - - if swap_position is not None and swap_index is None: - if swap_position == end_step: - swap_index = i - found_exact_index = True - - if swap_position < end_step: - swap_index = i - - if swap_index is not None: - if not found_exact_index: - prompt_schedule.insert(swap_index, [swap_position, prompt_schedule[swap_index][1]]) - - for i in range(len(prompt_schedule)): - end_step = prompt_schedule[i][0] - must_replace = swap_position < end_step - - prompt_schedule[i][1] += concept_to if must_replace else concept_from - - res.append(prompt_schedule) - cache[prompt] = prompt_schedule - #for t in prompt_schedule: - # print(t) - - return res + grammar = r""" + start: prompt + prompt: (emphasized | scheduled | weighted | plain)* + !emphasized: "(" prompt ")" + | "(" prompt ":" prompt ")" + | "[" prompt "]" + scheduled: "[" (prompt ":")? prompt ":" NUMBER "]" + !weighted: "{" weighted_item ("|" weighted_item)* "}" + !weighted_item: prompt (":" prompt)? + plain: /([^\\\[\](){}:|]|\\.)+/ + %import common.SIGNED_NUMBER -> NUMBER + """ + parser = Lark(grammar, parser='lalr') + def collect_steps(steps, tree): + l = [steps] + class CollectSteps(Visitor): + def scheduled(self, tree): + tree.children[-1] = float(tree.children[-1]) + if tree.children[-1] < 1: + tree.children[-1] *= steps + tree.children[-1] = min(steps, int(tree.children[-1])) + l.append(tree.children[-1]) + CollectSteps().visit(tree) + return sorted(set(l)) + def at_step(step, tree): + class AtStep(Transformer): + def scheduled(self, args): + if len(args) == 2: + before, after, when = (), *args + else: + before, after, when = args + yield before if step <= when else after + def start(self, args): + def flatten(x): + if type(x) == str: + yield x + else: + for gen in x: + yield from flatten(gen) + return ''.join(flatten(args[0])) + def plain(self, args): + yield args[0].value + def __default__(self, data, children, meta): + for child in children: + yield from child + return AtStep().transform(tree) + @functools.cache + def get_schedule(prompt): + tree = parser.parse(prompt) + return [[t, at_step(t, tree)] for t in collect_steps(steps, tree)] + return [get_schedule(prompt) for prompt in prompts] ScheduledPromptConditioning = namedtuple("ScheduledPromptConditioning", ["end_at_step", "cond"]) -- cgit v1.2.1 From 61652461242951966e5b4cee83ce359cefa91c17 Mon Sep 17 00:00:00 2001 From: AUTOMATIC <16777216c@gmail.com> Date: Tue, 4 Oct 2022 14:23:22 +0300 Subject: support interrupting after the previous change --- modules/processing.py | 4 +++- 1 file changed, 3 insertions(+), 1 deletion(-) (limited to 'modules') diff --git a/modules/processing.py b/modules/processing.py index 9cbecdd8..6f5599c7 100644 --- a/modules/processing.py +++ b/modules/processing.py @@ -361,7 +361,7 @@ def process_images(p: StableDiffusionProcessing) -> Processed: shared.state.job = f"Batch {n+1} out of {p.n_iter}" with devices.autocast(): - samples_ddim = p.sample(conditioning=c, unconditional_conditioning=uc, seeds=seeds, subseeds=subseeds, subseed_strength=p.subseed_strength).to(devices.dtype) + samples_ddim = p.sample(conditioning=c, unconditional_conditioning=uc, seeds=seeds, subseeds=subseeds, subseed_strength=p.subseed_strength) if state.interrupted: @@ -369,6 +369,8 @@ def process_images(p: StableDiffusionProcessing) -> Processed: # use the image collected previously in sampler loop samples_ddim = shared.state.current_latent + samples_ddim = samples_ddim.to(devices.dtype) + x_samples_ddim = p.sd_model.decode_first_stage(samples_ddim) x_samples_ddim = torch.clamp((x_samples_ddim + 1.0) / 2.0, min=0.0, max=1.0) -- cgit v1.2.1 From d5bba20a58f43a9f984bb67b4e17f48661f6b818 Mon Sep 17 00:00:00 2001 From: AUTOMATIC <16777216c@gmail.com> Date: Tue, 4 Oct 2022 14:35:12 +0300 Subject: ignore errors in parse for purposes of token counting for #1564 --- modules/ui.py | 12 ++++++++++-- 1 file changed, 10 insertions(+), 2 deletions(-) (limited to 'modules') diff --git a/modules/ui.py b/modules/ui.py index 55f7aa95..20dc8c37 100644 --- a/modules/ui.py +++ b/modules/ui.py @@ -386,14 +386,22 @@ def connect_reuse_seed(seed: gr.Number, reuse_seed: gr.Button, generation_info: outputs=[seed, dummy_component] ) + def update_token_counter(text, steps): - prompt_schedules = get_learned_conditioning_prompt_schedules([text], steps) + try: + prompt_schedules = get_learned_conditioning_prompt_schedules([text], steps) + except Exception: + # a parsing error can happen here during typing, and we don't want to bother the user with + # messages related to it in console + prompt_schedules = [[[steps, text]]] + flat_prompts = reduce(lambda list1, list2: list1+list2, prompt_schedules) - prompts = [prompt_text for step,prompt_text in flat_prompts] + prompts = [prompt_text for step, prompt_text in flat_prompts] tokens, token_count, max_length = max([model_hijack.tokenize(prompt) for prompt in prompts], key=lambda args: args[1]) style_class = ' class="red"' if (token_count > max_length) else "" return f"{token_count}/{max_length}" + def create_toprow(is_img2img): id_part = "img2img" if is_img2img else "txt2img" -- cgit v1.2.1 From accd00d6b8258c12b5168918a4c546b02357924a Mon Sep 17 00:00:00 2001 From: Justin Riddiough Date: Tue, 4 Oct 2022 01:14:28 -0500 Subject: Explain how to use second progress bar in pycharm --- modules/shared.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) (limited to 'modules') diff --git a/modules/shared.py b/modules/shared.py index 25aff5b0..11bdf01a 100644 --- a/modules/shared.py +++ b/modules/shared.py @@ -200,7 +200,7 @@ options_templates.update(options_section(('face-restoration', "Face restoration" options_templates.update(options_section(('system', "System"), { "memmon_poll_rate": OptionInfo(8, "VRAM usage polls per second during generation. Set to 0 to disable.", gr.Slider, {"minimum": 0, "maximum": 40, "step": 1}), "samples_log_stdout": OptionInfo(False, "Always print all generation info to standard output"), - "multiple_tqdm": OptionInfo(True, "Add a second progress bar to the console that shows progress for an entire job. Broken in PyCharm console."), + "multiple_tqdm": OptionInfo(True, "Add a second progress bar to the console that shows progress for an entire job. In PyCharm select 'emulate terminal in console output'."), })) options_templates.update(options_section(('sd', "Stable Diffusion"), { -- cgit v1.2.1 From ea6b0d98a64290a0305e27126ea59ce1da7959a2 Mon Sep 17 00:00:00 2001 From: Justin Riddiough Date: Tue, 4 Oct 2022 06:38:45 -0500 Subject: Remove pycharm note, fix typo --- modules/shared.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) (limited to 'modules') diff --git a/modules/shared.py b/modules/shared.py index 11bdf01a..a7d13b2d 100644 --- a/modules/shared.py +++ b/modules/shared.py @@ -200,7 +200,7 @@ options_templates.update(options_section(('face-restoration', "Face restoration" options_templates.update(options_section(('system', "System"), { "memmon_poll_rate": OptionInfo(8, "VRAM usage polls per second during generation. Set to 0 to disable.", gr.Slider, {"minimum": 0, "maximum": 40, "step": 1}), "samples_log_stdout": OptionInfo(False, "Always print all generation info to standard output"), - "multiple_tqdm": OptionInfo(True, "Add a second progress bar to the console that shows progress for an entire job. In PyCharm select 'emulate terminal in console output'."), + "multiple_tqdm": OptionInfo(True, "Add a second progress bar to the console that shows progress for an entire job."), })) options_templates.update(options_section(('sd', "Stable Diffusion"), { @@ -209,7 +209,7 @@ options_templates.update(options_section(('sd', "Stable Diffusion"), { "save_images_before_color_correction": OptionInfo(False, "Save a copy of image before applying color correction to img2img results"), "img2img_fix_steps": OptionInfo(False, "With img2img, do exactly the amount of steps the slider specifies (normally you'd do less with less denoising)."), "enable_quantization": OptionInfo(False, "Enable quantization in K samplers for sharper and cleaner results. This may change existing seeds. Requires restart to apply."), - "enable_emphasis": OptionInfo(True, "Eemphasis: use (text) to make model pay more attention to text and [text] to make it pay less attention"), + "enable_emphasis": OptionInfo(True, "Emphasis: use (text) to make model pay more attention to text and [text] to make it pay less attention"), "use_old_emphasis_implementation": OptionInfo(False, "Use old emphasis implementation. Can be useful to reproduce old seeds."), "enable_batch_seeds": OptionInfo(True, "Make K-diffusion samplers produce same images in a batch as when making a single image"), "filter_nsfw": OptionInfo(False, "Filter NSFW content"), -- cgit v1.2.1 From eec1b39bd54711ca31e43022d2d6ac8c6d7281da Mon Sep 17 00:00:00 2001 From: Milly Date: Tue, 4 Oct 2022 20:16:52 +0900 Subject: Apply prompt pattern last --- modules/images.py | 39 ++++++++++++++++++++------------------- 1 file changed, 20 insertions(+), 19 deletions(-) (limited to 'modules') diff --git a/modules/images.py b/modules/images.py index bba55158..5b56c7e3 100644 --- a/modules/images.py +++ b/modules/images.py @@ -287,6 +287,25 @@ def apply_filename_pattern(x, p, seed, prompt): if seed is not None: x = x.replace("[seed]", str(seed)) + if p is not None: + x = x.replace("[steps]", str(p.steps)) + x = x.replace("[cfg]", str(p.cfg_scale)) + x = x.replace("[width]", str(p.width)) + x = x.replace("[height]", str(p.height)) + + #currently disabled if using the save button, will work otherwise + # if enabled it will cause a bug because styles is not included in the save_files data dictionary + if hasattr(p, "styles"): + x = x.replace("[styles]", sanitize_filename_part(", ".join([x for x in p.styles if not x == "None"] or "None"), replace_spaces=False)) + + x = x.replace("[sampler]", sanitize_filename_part(sd_samplers.samplers[p.sampler_index].name, replace_spaces=False)) + + x = x.replace("[model_hash]", shared.sd_model.sd_model_hash) + x = x.replace("[date]", datetime.date.today().isoformat()) + x = x.replace("[datetime]", datetime.datetime.now().strftime("%Y%m%d%H%M%S")) + x = x.replace("[job_timestamp]", shared.state.job_timestamp) + + # Apply [prompt] at last. Because it may contain any replacement word.^M if prompt is not None: x = x.replace("[prompt]", sanitize_filename_part(prompt)) if "[prompt_no_styles]" in x: @@ -295,7 +314,7 @@ def apply_filename_pattern(x, p, seed, prompt): if len(style) > 0: style_parts = [y for y in style.split("{prompt}")] for part in style_parts: - prompt_no_style = prompt_no_style.replace(part, "").replace(", ,", ",").strip().strip(',') + prompt_no_style = prompt_no_style.replace(part, "").replace(", ,", ",").strip().strip(',') prompt_no_style = prompt_no_style.replace(style, "").strip().strip(',').strip() x = x.replace("[prompt_no_styles]", sanitize_filename_part(prompt_no_style, replace_spaces=False)) @@ -306,24 +325,6 @@ def apply_filename_pattern(x, p, seed, prompt): words = ["empty"] x = x.replace("[prompt_words]", sanitize_filename_part(" ".join(words[0:max_prompt_words]), replace_spaces=False)) - if p is not None: - x = x.replace("[steps]", str(p.steps)) - x = x.replace("[cfg]", str(p.cfg_scale)) - x = x.replace("[width]", str(p.width)) - x = x.replace("[height]", str(p.height)) - - #currently disabled if using the save button, will work otherwise - # if enabled it will cause a bug because styles is not included in the save_files data dictionary - if hasattr(p, "styles"): - x = x.replace("[styles]", sanitize_filename_part(", ".join([x for x in p.styles if not x == "None"] or "None"), replace_spaces=False)) - - x = x.replace("[sampler]", sanitize_filename_part(sd_samplers.samplers[p.sampler_index].name, replace_spaces=False)) - - x = x.replace("[model_hash]", shared.sd_model.sd_model_hash) - x = x.replace("[date]", datetime.date.today().isoformat()) - x = x.replace("[datetime]", datetime.datetime.now().strftime("%Y%m%d%H%M%S")) - x = x.replace("[job_timestamp]", shared.state.job_timestamp) - if cmd_opts.hide_ui_dir_config: x = re.sub(r'^[\\/]+|\.{2,}[\\/]+|[\\/]+\.{2,}', '', x) -- cgit v1.2.1 From 52cef36f6ba169a8e606ecdcaed73d47378f0e8e Mon Sep 17 00:00:00 2001 From: AUTOMATIC <16777216c@gmail.com> Date: Tue, 4 Oct 2022 16:54:31 +0300 Subject: emergency fix for img2img --- modules/processing.py | 3 ++- 1 file changed, 2 insertions(+), 1 deletion(-) (limited to 'modules') diff --git a/modules/processing.py b/modules/processing.py index 6f5599c7..e9c45394 100644 --- a/modules/processing.py +++ b/modules/processing.py @@ -331,7 +331,8 @@ def process_images(p: StableDiffusionProcessing) -> Processed: output_images = [] with torch.no_grad(): - p.init(all_prompts, all_seeds, all_subseeds) + with devices.autocast(): + p.init(all_prompts, all_seeds, all_subseeds) if state.job_count == -1: state.job_count = p.n_iter -- cgit v1.2.1 From 957e29a8e9cb8ca069799ec69263e188c89ed6a6 Mon Sep 17 00:00:00 2001 From: AUTOMATIC <16777216c@gmail.com> Date: Tue, 4 Oct 2022 17:23:48 +0300 Subject: option to not show images in web ui --- modules/img2img.py | 3 +++ modules/shared.py | 1 + modules/txt2img.py | 3 +++ 3 files changed, 7 insertions(+) (limited to 'modules') diff --git a/modules/img2img.py b/modules/img2img.py index 2ff8e261..da212d72 100644 --- a/modules/img2img.py +++ b/modules/img2img.py @@ -129,4 +129,7 @@ def img2img(mode: int, prompt: str, negative_prompt: str, prompt_style: str, pro if opts.samples_log_stdout: print(generation_info_js) + if opts.do_not_show_images: + processed.images = [] + return processed.images, generation_info_js, plaintext_to_html(processed.info) diff --git a/modules/shared.py b/modules/shared.py index a7d13b2d..ff4e5fa3 100644 --- a/modules/shared.py +++ b/modules/shared.py @@ -229,6 +229,7 @@ options_templates.update(options_section(('ui', "User interface"), { "show_progressbar": OptionInfo(True, "Show progressbar"), "show_progress_every_n_steps": OptionInfo(0, "Show show image creation progress every N sampling steps. Set 0 to disable.", gr.Slider, {"minimum": 0, "maximum": 32, "step": 1}), "return_grid": OptionInfo(True, "Show grid in results for web"), + "do_not_show_images": OptionInfo(False, "Do not show any images in results for web"), "add_model_hash_to_info": OptionInfo(True, "Add model hash to generation information"), "font": OptionInfo("", "Font for image grids that have text"), "js_modal_lightbox": OptionInfo(True, "Enable full page image viewer"), diff --git a/modules/txt2img.py b/modules/txt2img.py index d4406c3c..e985242b 100644 --- a/modules/txt2img.py +++ b/modules/txt2img.py @@ -48,5 +48,8 @@ def txt2img(prompt: str, negative_prompt: str, prompt_style: str, prompt_style2: if opts.samples_log_stdout: print(generation_info_js) + if opts.do_not_show_images: + processed.images = [] + return processed.images, generation_info_js, plaintext_to_html(processed.info) -- cgit v1.2.1 From e1b128d8e46bddb9c0b2fd3ee0eefd57e0527ee0 Mon Sep 17 00:00:00 2001 From: AUTOMATIC <16777216c@gmail.com> Date: Tue, 4 Oct 2022 17:36:39 +0300 Subject: do not touch p.seed/p.subseed during processing #1181 --- modules/processing.py | 26 +++++++++++++++++--------- 1 file changed, 17 insertions(+), 9 deletions(-) (limited to 'modules') diff --git a/modules/processing.py b/modules/processing.py index e9c45394..8180c63d 100644 --- a/modules/processing.py +++ b/modules/processing.py @@ -248,9 +248,16 @@ def create_random_tensors(shape, seeds, subseeds=None, subseed_strength=0.0, see return x +def get_fixed_seed(seed): + if seed is None or seed == '' or seed == -1: + return int(random.randrange(4294967294)) + + return seed + + def fix_seed(p): - p.seed = int(random.randrange(4294967294)) if p.seed is None or p.seed == '' or p.seed == -1 else p.seed - p.subseed = int(random.randrange(4294967294)) if p.subseed is None or p.subseed == '' or p.subseed == -1 else p.subseed + p.seed = get_fixed_seed(p.seed) + p.subseed = get_fixed_seed(p.subseed) def create_infotext(p, all_prompts, all_seeds, all_subseeds, comments, iteration=0, position_in_batch=0): @@ -292,7 +299,8 @@ def process_images(p: StableDiffusionProcessing) -> Processed: devices.torch_gc() - fix_seed(p) + seed = get_fixed_seed(p.seed) + subseed = get_fixed_seed(p.subseed) if p.outpath_samples is not None: os.makedirs(p.outpath_samples, exist_ok=True) @@ -311,15 +319,15 @@ def process_images(p: StableDiffusionProcessing) -> Processed: else: all_prompts = p.batch_size * p.n_iter * [p.prompt] - if type(p.seed) == list: - all_seeds = p.seed + if type(seed) == list: + all_seeds = seed else: - all_seeds = [int(p.seed) + (x if p.subseed_strength == 0 else 0) for x in range(len(all_prompts))] + all_seeds = [int(seed) + (x if p.subseed_strength == 0 else 0) for x in range(len(all_prompts))] - if type(p.subseed) == list: - all_subseeds = p.subseed + if type(subseed) == list: + all_subseeds = subseed else: - all_subseeds = [int(p.subseed) + x for x in range(len(all_prompts))] + all_subseeds = [int(subseed) + x for x in range(len(all_prompts))] def infotext(iteration=0, position_in_batch=0): return create_infotext(p, all_prompts, all_seeds, all_subseeds, comments, iteration, position_in_batch) -- cgit v1.2.1 From 1eb588cbf19924333b88beaa1ac0041904966640 Mon Sep 17 00:00:00 2001 From: AUTOMATIC <16777216c@gmail.com> Date: Tue, 4 Oct 2022 18:02:01 +0300 Subject: remove functools.cache as some people are having issues with it --- modules/prompt_parser.py | 8 ++++++-- 1 file changed, 6 insertions(+), 2 deletions(-) (limited to 'modules') diff --git a/modules/prompt_parser.py b/modules/prompt_parser.py index 99c8ed99..5d58c4ed 100644 --- a/modules/prompt_parser.py +++ b/modules/prompt_parser.py @@ -29,6 +29,7 @@ def get_learned_conditioning_prompt_schedules(prompts, steps): %import common.SIGNED_NUMBER -> NUMBER """ parser = Lark(grammar, parser='lalr') + def collect_steps(steps, tree): l = [steps] class CollectSteps(Visitor): @@ -40,6 +41,7 @@ def get_learned_conditioning_prompt_schedules(prompts, steps): l.append(tree.children[-1]) CollectSteps().visit(tree) return sorted(set(l)) + def at_step(step, tree): class AtStep(Transformer): def scheduled(self, args): @@ -62,11 +64,13 @@ def get_learned_conditioning_prompt_schedules(prompts, steps): for child in children: yield from child return AtStep().transform(tree) - @functools.cache + def get_schedule(prompt): tree = parser.parse(prompt) return [[t, at_step(t, tree)] for t in collect_steps(steps, tree)] - return [get_schedule(prompt) for prompt in prompts] + + promptdict = {prompt: get_schedule(prompt) for prompt in set(prompts)} + return [promptdict[prompt] for prompt in prompts] ScheduledPromptConditioning = namedtuple("ScheduledPromptConditioning", ["end_at_step", "cond"]) -- cgit v1.2.1 From 90e911fd546e76f879b38a764473569911a0f845 Mon Sep 17 00:00:00 2001 From: Rae Fu Date: Tue, 4 Oct 2022 09:49:51 -0600 Subject: prompt_parser: allow spaces in schedules, add test, log/ignore errors Only build the parser once (at import time) instead of for each step. doctest is run by simply executing modules/prompt_parser.py --- modules/processing.py | 10 ++-- modules/prompt_parser.py | 139 ++++++++++++++++++++++++++++++----------------- 2 files changed, 95 insertions(+), 54 deletions(-) (limited to 'modules') diff --git a/modules/processing.py b/modules/processing.py index 8180c63d..bb94033b 100644 --- a/modules/processing.py +++ b/modules/processing.py @@ -84,7 +84,7 @@ class StableDiffusionProcessing: self.s_tmin = opts.s_tmin self.s_tmax = float('inf') # not representable as a standard ui option self.s_noise = opts.s_noise - + if not seed_enable_extras: self.subseed = -1 self.subseed_strength = 0 @@ -296,7 +296,7 @@ def process_images(p: StableDiffusionProcessing) -> Processed: assert(len(p.prompt) > 0) else: assert p.prompt is not None - + devices.torch_gc() seed = get_fixed_seed(p.seed) @@ -359,8 +359,8 @@ def process_images(p: StableDiffusionProcessing) -> Processed: #uc = p.sd_model.get_learned_conditioning(len(prompts) * [p.negative_prompt]) #c = p.sd_model.get_learned_conditioning(prompts) with devices.autocast(): - uc = prompt_parser.get_learned_conditioning(len(prompts) * [p.negative_prompt], p.steps) - c = prompt_parser.get_learned_conditioning(prompts, p.steps) + uc = prompt_parser.get_learned_conditioning(shared.sd_model, len(prompts) * [p.negative_prompt], p.steps) + c = prompt_parser.get_learned_conditioning(shared.sd_model, prompts, p.steps) if len(model_hijack.comments) > 0: for comment in model_hijack.comments: @@ -527,7 +527,7 @@ class StableDiffusionProcessingTxt2Img(StableDiffusionProcessing): # GC now before running the next img2img to prevent running out of memory x = None devices.torch_gc() - + samples = self.sampler.sample_img2img(self, samples, noise, conditioning, unconditional_conditioning, steps=self.steps) return samples diff --git a/modules/prompt_parser.py b/modules/prompt_parser.py index 5d58c4ed..a3b12421 100644 --- a/modules/prompt_parser.py +++ b/modules/prompt_parser.py @@ -1,10 +1,7 @@ import re from collections import namedtuple -import torch -from lark import Lark, Transformer, Visitor -import functools -import modules.shared as shared +import lark # a prompt like this: "fantasy landscape with a [mountain:lake:0.25] and [an oak:a christmas tree:0.75][ in foreground::0.6][ in background:0.25] [shoddy:masterful:0.5]" # will be represented with prompt_schedule like this (assuming steps=100): @@ -14,25 +11,48 @@ import modules.shared as shared # [75, 'fantasy landscape with a lake and an oak in background masterful'] # [100, 'fantasy landscape with a lake and a christmas tree in background masterful'] +schedule_parser = lark.Lark(r""" +!start: (prompt | /[][():]/+)* +prompt: (emphasized | scheduled | plain | WHITESPACE)* +!emphasized: "(" prompt ")" + | "(" prompt ":" prompt ")" + | "[" prompt "]" +scheduled: "[" [prompt ":"] prompt ":" [WHITESPACE] NUMBER "]" +WHITESPACE: /\s+/ +plain: /([^\\\[\]():]|\\.)+/ +%import common.SIGNED_NUMBER -> NUMBER +""") def get_learned_conditioning_prompt_schedules(prompts, steps): - grammar = r""" - start: prompt - prompt: (emphasized | scheduled | weighted | plain)* - !emphasized: "(" prompt ")" - | "(" prompt ":" prompt ")" - | "[" prompt "]" - scheduled: "[" (prompt ":")? prompt ":" NUMBER "]" - !weighted: "{" weighted_item ("|" weighted_item)* "}" - !weighted_item: prompt (":" prompt)? - plain: /([^\\\[\](){}:|]|\\.)+/ - %import common.SIGNED_NUMBER -> NUMBER """ - parser = Lark(grammar, parser='lalr') + >>> g = lambda p: get_learned_conditioning_prompt_schedules([p], 10)[0] + >>> g("test") + [[10, 'test']] + >>> g("a [b:3]") + [[3, 'a '], [10, 'a b']] + >>> g("a [b: 3]") + [[3, 'a '], [10, 'a b']] + >>> g("a [[[b]]:2]") + [[2, 'a '], [10, 'a [[b]]']] + >>> g("[(a:2):3]") + [[3, ''], [10, '(a:2)']] + >>> g("a [b : c : 1] d") + [[1, 'a b d'], [10, 'a c d']] + >>> g("a[b:[c:d:2]:1]e") + [[1, 'abe'], [2, 'ace'], [10, 'ade']] + >>> g("a [unbalanced") + [[10, 'a [unbalanced']] + >>> g("a [b:.5] c") + [[5, 'a c'], [10, 'a b c']] + >>> g("a [{b|d{:.5] c") # not handling this right now + [[5, 'a c'], [10, 'a {b|d{ c']] + >>> g("((a][:b:c [d:3]") + [[3, '((a][:b:c '], [10, '((a][:b:c d']] + """ def collect_steps(steps, tree): l = [steps] - class CollectSteps(Visitor): + class CollectSteps(lark.Visitor): def scheduled(self, tree): tree.children[-1] = float(tree.children[-1]) if tree.children[-1] < 1: @@ -43,13 +63,10 @@ def get_learned_conditioning_prompt_schedules(prompts, steps): return sorted(set(l)) def at_step(step, tree): - class AtStep(Transformer): + class AtStep(lark.Transformer): def scheduled(self, args): - if len(args) == 2: - before, after, when = (), *args - else: - before, after, when = args - yield before if step <= when else after + before, after, _, when = args + yield before or () if step <= when else after def start(self, args): def flatten(x): if type(x) == str: @@ -57,16 +74,22 @@ def get_learned_conditioning_prompt_schedules(prompts, steps): else: for gen in x: yield from flatten(gen) - return ''.join(flatten(args[0])) + return ''.join(flatten(args)) def plain(self, args): yield args[0].value def __default__(self, data, children, meta): for child in children: yield from child return AtStep().transform(tree) - + def get_schedule(prompt): - tree = parser.parse(prompt) + try: + tree = schedule_parser.parse(prompt) + except lark.exceptions.LarkError as e: + if 0: + import traceback + traceback.print_exc() + return [[steps, prompt]] return [[t, at_step(t, tree)] for t in collect_steps(steps, tree)] promptdict = {prompt: get_schedule(prompt) for prompt in set(prompts)} @@ -77,8 +100,7 @@ ScheduledPromptConditioning = namedtuple("ScheduledPromptConditioning", ["end_at ScheduledPromptBatch = namedtuple("ScheduledPromptBatch", ["shape", "schedules"]) -def get_learned_conditioning(prompts, steps): - +def get_learned_conditioning(model, prompts, steps): res = [] prompt_schedules = get_learned_conditioning_prompt_schedules(prompts, steps) @@ -92,7 +114,7 @@ def get_learned_conditioning(prompts, steps): continue texts = [x[1] for x in prompt_schedule] - conds = shared.sd_model.get_learned_conditioning(texts) + conds = model.get_learned_conditioning(texts) cond_schedule = [] for i, (end_at_step, text) in enumerate(prompt_schedule): @@ -105,12 +127,13 @@ def get_learned_conditioning(prompts, steps): def reconstruct_cond_batch(c: ScheduledPromptBatch, current_step): - res = torch.zeros(c.shape, device=shared.device, dtype=next(shared.sd_model.parameters()).dtype) + param = c.schedules[0][0].cond + res = torch.zeros(c.shape, device=param.device, dtype=param.dtype) for i, cond_schedule in enumerate(c.schedules): target_index = 0 - for curret_index, (end_at, cond) in enumerate(cond_schedule): + for current, (end_at, cond) in enumerate(cond_schedule): if current_step <= end_at: - target_index = curret_index + target_index = current break res[i] = cond_schedule[target_index].cond @@ -148,23 +171,26 @@ def parse_prompt_attention(text): \\ - literal character '\' anything else - just text - Example: - - 'a (((house:1.3)) [on] a (hill:0.5), sun, (((sky))).' - - produces: - - [ - ['a ', 1.0], - ['house', 1.5730000000000004], - [' ', 1.1], - ['on', 1.0], - [' a ', 1.1], - ['hill', 0.55], - [', sun, ', 1.1], - ['sky', 1.4641000000000006], - ['.', 1.1] - ] + >>> parse_prompt_attention('normal text') + [['normal text', 1.0]] + >>> parse_prompt_attention('an (important) word') + [['an ', 1.0], ['important', 1.1], [' word', 1.0]] + >>> parse_prompt_attention('(unbalanced') + [['unbalanced', 1.1]] + >>> parse_prompt_attention('\(literal\]') + [['(literal]', 1.0]] + >>> parse_prompt_attention('(unnecessary)(parens)') + [['unnecessaryparens', 1.1]] + >>> parse_prompt_attention('a (((house:1.3)) [on] a (hill:0.5), sun, (((sky))).') + [['a ', 1.0], + ['house', 1.5730000000000004], + [' ', 1.1], + ['on', 1.0], + [' a ', 1.1], + ['hill', 0.55], + [', sun, ', 1.1], + ['sky', 1.4641000000000006], + ['.', 1.1]] """ res = [] @@ -206,4 +232,19 @@ def parse_prompt_attention(text): if len(res) == 0: res = [["", 1.0]] + # merge runs of identical weights + i = 0 + while i + 1 < len(res): + if res[i][1] == res[i + 1][1]: + res[i][0] += res[i + 1][0] + res.pop(i + 1) + else: + i += 1 + return res + +if __name__ == "__main__": + import doctest + doctest.testmod(optionflags=doctest.NORMALIZE_WHITESPACE) +else: + import torch # doctest faster -- cgit v1.2.1 From b32852ef037251eb3d846af76e2965594e1ac7a5 Mon Sep 17 00:00:00 2001 From: AUTOMATIC <16777216c@gmail.com> Date: Tue, 4 Oct 2022 20:49:54 +0300 Subject: add editor to img2img --- modules/shared.py | 1 + modules/ui.py | 2 +- 2 files changed, 2 insertions(+), 1 deletion(-) (limited to 'modules') diff --git a/modules/shared.py b/modules/shared.py index ff4e5fa3..e52c9b1d 100644 --- a/modules/shared.py +++ b/modules/shared.py @@ -55,6 +55,7 @@ parser.add_argument("--hide-ui-dir-config", action='store_true', help="hide dire parser.add_argument("--ui-settings-file", type=str, help="filename to use for ui settings", default=os.path.join(script_path, 'config.json')) parser.add_argument("--gradio-debug", action='store_true', help="launch gradio with --debug option") parser.add_argument("--gradio-auth", type=str, help='set gradio authentication like "username:password"; or comma-delimit multiple like "u1:p1,u2:p2,u3:p3"', default=None) +parser.add_argument("--gradio-img2img-tool", type=str, help='gradio image uploader tool: can be either editor for ctopping, or color-sketch for drawing', choices=["color-sketch", "editor"], default="color-sketch") parser.add_argument("--opt-channelslast", action='store_true', help="change memory type for stable diffusion to channels last") parser.add_argument("--styles-file", type=str, help="filename to use for styles", default=os.path.join(script_path, 'styles.csv')) parser.add_argument("--autolaunch", action='store_true', help="open the webui URL in the system's default browser upon launch", default=False) diff --git a/modules/ui.py b/modules/ui.py index 20dc8c37..6cd6761b 100644 --- a/modules/ui.py +++ b/modules/ui.py @@ -644,7 +644,7 @@ def create_ui(wrap_gradio_gpu_call): with gr.Tabs(elem_id="mode_img2img") as tabs_img2img_mode: with gr.TabItem('img2img', id='img2img'): - init_img = gr.Image(label="Image for img2img", show_label=False, source="upload", interactive=True, type="pil") + init_img = gr.Image(label="Image for img2img", elem_id="img2img_image", show_label=False, source="upload", interactive=True, type="pil", tool=cmd_opts.gradio_img2img_tool) with gr.TabItem('Inpaint', id='inpaint'): init_img_with_mask = gr.Image(label="Image for inpainting with mask", show_label=False, elem_id="img2maskimg", source="upload", interactive=True, type="pil", tool="sketch", image_mode="RGBA") -- cgit v1.2.1 From ef40e4cd4d383a3405e03f1da3f5b5a1820a8f53 Mon Sep 17 00:00:00 2001 From: xpscyho Date: Tue, 4 Oct 2022 15:12:38 -0400 Subject: Display time taken in mins, secs when relevant Fixes #1656 --- modules/ui.py | 7 ++++++- 1 file changed, 6 insertions(+), 1 deletion(-) (limited to 'modules') diff --git a/modules/ui.py b/modules/ui.py index 6cd6761b..de6342a4 100644 --- a/modules/ui.py +++ b/modules/ui.py @@ -196,6 +196,11 @@ def wrap_gradio_call(func, extra_outputs=None): res = extra_outputs_array + [f"
{plaintext_to_html(type(e).__name__+': '+str(e))}
"] elapsed = time.perf_counter() - t + elapsed_m = int(elapsed // 60) + elapsed_s = elapsed % 60 + elapsed_text = f"{elapsed_s:.2f}s" + if (elapsed_m > 0): + elapsed_text = f"{elapsed_m}m "+elapsed_text if run_memmon: mem_stats = {k: -(v//-(1024*1024)) for k, v in shared.mem_mon.stop().items()} @@ -210,7 +215,7 @@ def wrap_gradio_call(func, extra_outputs=None): vram_html = '' # last item is always HTML - res[-1] += f"

Time taken: {elapsed:.2f}s

{vram_html}
" + res[-1] += f"

Time taken: {elapsed_text}

{vram_html}
" shared.state.interrupted = False shared.state.job_count = 0 -- cgit v1.2.1 From 82380d9ac18614c87bebba1b4cfd4b147cc76a18 Mon Sep 17 00:00:00 2001 From: Jairo Correa Date: Tue, 4 Oct 2022 22:28:50 -0300 Subject: Removing parts no longer needed to fix vram --- modules/devices.py | 3 +-- modules/processing.py | 21 ++++++++------------- 2 files changed, 9 insertions(+), 15 deletions(-) (limited to 'modules') diff --git a/modules/devices.py b/modules/devices.py index 6db4e57c..0158b11f 100644 --- a/modules/devices.py +++ b/modules/devices.py @@ -1,7 +1,6 @@ import contextlib import torch -import gc from modules import errors @@ -20,8 +19,8 @@ def get_optimal_device(): return cpu + def torch_gc(): - gc.collect() if torch.cuda.is_available(): torch.cuda.empty_cache() torch.cuda.ipc_collect() diff --git a/modules/processing.py b/modules/processing.py index e7f9c85e..f666ba81 100644 --- a/modules/processing.py +++ b/modules/processing.py @@ -345,8 +345,7 @@ def process_images(p: StableDiffusionProcessing) -> Processed: if state.job_count == -1: state.job_count = p.n_iter - for n in range(p.n_iter): - with torch.no_grad(), precision_scope("cuda"), ema_scope(): + for n in range(p.n_iter): if state.interrupted: break @@ -395,22 +394,19 @@ def process_images(p: StableDiffusionProcessing) -> Processed: import modules.safety as safety x_samples_ddim = modules.safety.censor_batch(x_samples_ddim) - for i, x_sample in enumerate(x_samples_ddim): - with torch.no_grad(), precision_scope("cuda"), ema_scope(): + for i, x_sample in enumerate(x_samples_ddim): x_sample = 255. * np.moveaxis(x_sample.cpu().numpy(), 0, 2) x_sample = x_sample.astype(np.uint8) - if p.restore_faces: - with torch.no_grad(), precision_scope("cuda"), ema_scope(): + if p.restore_faces: if opts.save and not p.do_not_save_samples and opts.save_images_before_face_restoration: images.save_image(Image.fromarray(x_sample), p.outpath_samples, "", seeds[i], prompts[i], opts.samples_format, info=infotext(n, i), p=p, suffix="-before-face-restoration") - x_sample = modules.face_restoration.restore_faces(x_sample) devices.torch_gc() - devices.torch_gc() + x_sample = modules.face_restoration.restore_faces(x_sample) + devices.torch_gc() - with torch.no_grad(), precision_scope("cuda"), ema_scope(): image = Image.fromarray(x_sample) if p.color_corrections is not None and i < len(p.color_corrections): @@ -438,13 +434,12 @@ def process_images(p: StableDiffusionProcessing) -> Processed: infotexts.append(infotext(n, i)) output_images.append(image) - del x_samples_ddim + del x_samples_ddim - devices.torch_gc() + devices.torch_gc() - state.nextjob() + state.nextjob() - with torch.no_grad(), precision_scope("cuda"), ema_scope(): p.color_corrections = None index_of_first_image = 0 -- cgit v1.2.1 From bbdbbd36eda870cf0bd49fdf28476c78919a123e Mon Sep 17 00:00:00 2001 From: DepFA <35278260+dfaker@users.noreply.github.com> Date: Wed, 5 Oct 2022 04:43:05 +0100 Subject: shared.state.interrupt when restart is requested --- modules/ui.py | 1 + 1 file changed, 1 insertion(+) (limited to 'modules') diff --git a/modules/ui.py b/modules/ui.py index de6342a4..523ab25b 100644 --- a/modules/ui.py +++ b/modules/ui.py @@ -1210,6 +1210,7 @@ def create_ui(wrap_gradio_gpu_call): ) def request_restart(): + shared.state.interrupt() settings_interface.gradio_ref.do_restart = True restart_gradio.click( -- cgit v1.2.1 From 59a2b9e5afc27d2fda72069ca0635070535d18fe Mon Sep 17 00:00:00 2001 From: Greendayle Date: Wed, 5 Oct 2022 20:50:10 +0200 Subject: deepdanbooru interrogator --- modules/deepbooru.py | 60 ++++++++++++++++++++++++++++++++++++++++++++++++++++ modules/ui.py | 24 ++++++++++++++++----- 2 files changed, 79 insertions(+), 5 deletions(-) create mode 100644 modules/deepbooru.py (limited to 'modules') diff --git a/modules/deepbooru.py b/modules/deepbooru.py new file mode 100644 index 00000000..958b1c3d --- /dev/null +++ b/modules/deepbooru.py @@ -0,0 +1,60 @@ +import os.path +from concurrent.futures import ProcessPoolExecutor + +import numpy as np +import deepdanbooru as dd +import tensorflow as tf + + +def _load_tf_and_return_tags(pil_image, threshold): + this_folder = os.path.dirname(__file__) + model_path = os.path.join(this_folder, '..', 'models', 'deepbooru', 'deepdanbooru-v3-20211112-sgd-e28') + if not os.path.exists(model_path): + return "Download https://github.com/KichangKim/DeepDanbooru/releases/download/v3-20211112-sgd-e28/deepdanbooru-v3-20211112-sgd-e28.zip unpack and put into models/deepbooru" + + tags = dd.project.load_tags_from_project(model_path) + model = dd.project.load_model_from_project( + model_path, compile_model=True + ) + + width = model.input_shape[2] + height = model.input_shape[1] + image = np.array(pil_image) + image = tf.image.resize( + image, + size=(height, width), + method=tf.image.ResizeMethod.AREA, + preserve_aspect_ratio=True, + ) + image = image.numpy() # EagerTensor to np.array + image = dd.image.transform_and_pad_image(image, width, height) + image = image / 255.0 + image_shape = image.shape + image = image.reshape((1, image_shape[0], image_shape[1], image_shape[2])) + + y = model.predict(image)[0] + + result_dict = {} + + for i, tag in enumerate(tags): + result_dict[tag] = y[i] + + + + result_tags_out = [] + result_tags_print = [] + for tag in tags: + if result_dict[tag] >= threshold: + result_tags_out.append(tag) + result_tags_print.append(f'{result_dict[tag]} {tag}') + + print('\n'.join(sorted(result_tags_print, reverse=True))) + + return ', '.join(result_tags_out) + + +def get_deepbooru_tags(pil_image, threshold=0.5): + with ProcessPoolExecutor() as executor: + f = executor.submit(_load_tf_and_return_tags, pil_image, threshold) + ret = f.result() # will rethrow any exceptions + return ret \ No newline at end of file diff --git a/modules/ui.py b/modules/ui.py index 20dc8c37..ae98219a 100644 --- a/modules/ui.py +++ b/modules/ui.py @@ -23,6 +23,7 @@ import gradio.utils import gradio.routes from modules import sd_hijack +from modules.deepbooru import get_deepbooru_tags from modules.paths import script_path from modules.shared import opts, cmd_opts import modules.shared as shared @@ -312,6 +313,11 @@ def interrogate(image): return gr_show(True) if prompt is None else prompt +def interrogate_deepbooru(image): + prompt = get_deepbooru_tags(image) + return gr_show(True) if prompt is None else prompt + + def create_seed_inputs(): with gr.Row(): with gr.Box(): @@ -439,15 +445,17 @@ def create_toprow(is_img2img): outputs=[], ) - with gr.Row(): + with gr.Row(scale=1): if is_img2img: - interrogate = gr.Button('Interrogate', elem_id="interrogate") + interrogate = gr.Button('Interrogate\nCLIP', elem_id="interrogate") + deepbooru = gr.Button('Interrogate\nDeepBooru', elem_id="deepbooru") else: interrogate = None + deepbooru = None prompt_style_apply = gr.Button('Apply style', elem_id="style_apply") save_style = gr.Button('Create style', elem_id="style_create") - return prompt, roll, prompt_style, negative_prompt, prompt_style2, submit, interrogate, prompt_style_apply, save_style, paste, token_counter, token_button + return prompt, roll, prompt_style, negative_prompt, prompt_style2, submit, interrogate, deepbooru, prompt_style_apply, save_style, paste, token_counter, token_button def setup_progressbar(progressbar, preview, id_part, textinfo=None): @@ -476,7 +484,7 @@ def create_ui(wrap_gradio_gpu_call): import modules.txt2img with gr.Blocks(analytics_enabled=False) as txt2img_interface: - txt2img_prompt, roll, txt2img_prompt_style, txt2img_negative_prompt, txt2img_prompt_style2, submit, _, txt2img_prompt_style_apply, txt2img_save_style, paste, token_counter, token_button = create_toprow(is_img2img=False) + txt2img_prompt, roll, txt2img_prompt_style, txt2img_negative_prompt, txt2img_prompt_style2, submit, _, _, txt2img_prompt_style_apply, txt2img_save_style, paste, token_counter, token_button = create_toprow(is_img2img=False) dummy_component = gr.Label(visible=False) with gr.Row(elem_id='txt2img_progress_row'): @@ -628,7 +636,7 @@ def create_ui(wrap_gradio_gpu_call): token_button.click(fn=update_token_counter, inputs=[txt2img_prompt, steps], outputs=[token_counter]) with gr.Blocks(analytics_enabled=False) as img2img_interface: - img2img_prompt, roll, img2img_prompt_style, img2img_negative_prompt, img2img_prompt_style2, submit, img2img_interrogate, img2img_prompt_style_apply, img2img_save_style, paste, token_counter, token_button = create_toprow(is_img2img=True) + img2img_prompt, roll, img2img_prompt_style, img2img_negative_prompt, img2img_prompt_style2, submit, img2img_interrogate, img2img_deepbooru, img2img_prompt_style_apply, img2img_save_style, paste, token_counter, token_button = create_toprow(is_img2img=True) with gr.Row(elem_id='img2img_progress_row'): with gr.Column(scale=1): @@ -785,6 +793,12 @@ def create_ui(wrap_gradio_gpu_call): outputs=[img2img_prompt], ) + img2img_deepbooru.click( + fn=interrogate_deepbooru, + inputs=[init_img], + outputs=[img2img_prompt], + ) + save.click( fn=wrap_gradio_call(save_files), _js="(x, y, z) => [x, y, selected_gallery_index()]", -- cgit v1.2.1 From 1506fab29ad54beb9f52236912abc432209c8089 Mon Sep 17 00:00:00 2001 From: Greendayle Date: Wed, 5 Oct 2022 21:15:08 +0200 Subject: removing problematic tag --- modules/deepbooru.py | 5 ++--- 1 file changed, 2 insertions(+), 3 deletions(-) (limited to 'modules') diff --git a/modules/deepbooru.py b/modules/deepbooru.py index 958b1c3d..841cb9c5 100644 --- a/modules/deepbooru.py +++ b/modules/deepbooru.py @@ -38,13 +38,12 @@ def _load_tf_and_return_tags(pil_image, threshold): for i, tag in enumerate(tags): result_dict[tag] = y[i] - - - result_tags_out = [] result_tags_print = [] for tag in tags: if result_dict[tag] >= threshold: + if tag.startswith("rating:"): + continue result_tags_out.append(tag) result_tags_print.append(f'{result_dict[tag]} {tag}') -- cgit v1.2.1 From 17a99baf0c929e5df4dfc4b2a96aa3890a141112 Mon Sep 17 00:00:00 2001 From: Greendayle Date: Wed, 5 Oct 2022 22:05:24 +0200 Subject: better model search --- modules/deepbooru.py | 11 +++++++++-- 1 file changed, 9 insertions(+), 2 deletions(-) (limited to 'modules') diff --git a/modules/deepbooru.py b/modules/deepbooru.py index 841cb9c5..a64fd9cd 100644 --- a/modules/deepbooru.py +++ b/modules/deepbooru.py @@ -9,8 +9,15 @@ import tensorflow as tf def _load_tf_and_return_tags(pil_image, threshold): this_folder = os.path.dirname(__file__) model_path = os.path.join(this_folder, '..', 'models', 'deepbooru', 'deepdanbooru-v3-20211112-sgd-e28') - if not os.path.exists(model_path): - return "Download https://github.com/KichangKim/DeepDanbooru/releases/download/v3-20211112-sgd-e28/deepdanbooru-v3-20211112-sgd-e28.zip unpack and put into models/deepbooru" + + model_good = False + for path_candidate in [model_path, os.path.dirname(model_path)]: + if os.path.exists(os.path.join(path_candidate, 'project.json')): + model_path = path_candidate + model_good = True + if not model_good: + return ("Download https://github.com/KichangKim/DeepDanbooru/releases/download/v3-20211112-sgd-e28/" + "deepdanbooru-v3-20211112-sgd-e28.zip unpack and put into models/deepbooru") tags = dd.project.load_tags_from_project(model_path) model = dd.project.load_model_from_project( -- cgit v1.2.1 From c26732fbee2a57e621ac22bf70decf7496daa4cd Mon Sep 17 00:00:00 2001 From: AUTOMATIC <16777216c@gmail.com> Date: Wed, 5 Oct 2022 23:16:27 +0300 Subject: added support for AND from https://energy-based-model.github.io/Compositional-Visual-Generation-with-Composable-Diffusion-Models/ --- modules/processing.py | 2 +- modules/prompt_parser.py | 114 ++++++++++++++++++++++++++++++++++++++++++++--- modules/sd_samplers.py | 35 ++++++++++----- modules/ui.py | 6 ++- 4 files changed, 138 insertions(+), 19 deletions(-) (limited to 'modules') diff --git a/modules/processing.py b/modules/processing.py index bb94033b..d8c6b8d5 100644 --- a/modules/processing.py +++ b/modules/processing.py @@ -360,7 +360,7 @@ def process_images(p: StableDiffusionProcessing) -> Processed: #c = p.sd_model.get_learned_conditioning(prompts) with devices.autocast(): uc = prompt_parser.get_learned_conditioning(shared.sd_model, len(prompts) * [p.negative_prompt], p.steps) - c = prompt_parser.get_learned_conditioning(shared.sd_model, prompts, p.steps) + c = prompt_parser.get_multicond_learned_conditioning(shared.sd_model, prompts, p.steps) if len(model_hijack.comments) > 0: for comment in model_hijack.comments: diff --git a/modules/prompt_parser.py b/modules/prompt_parser.py index a3b12421..f7420daf 100644 --- a/modules/prompt_parser.py +++ b/modules/prompt_parser.py @@ -97,10 +97,26 @@ def get_learned_conditioning_prompt_schedules(prompts, steps): ScheduledPromptConditioning = namedtuple("ScheduledPromptConditioning", ["end_at_step", "cond"]) -ScheduledPromptBatch = namedtuple("ScheduledPromptBatch", ["shape", "schedules"]) def get_learned_conditioning(model, prompts, steps): + """converts a list of prompts into a list of prompt schedules - each schedule is a list of ScheduledPromptConditioning, specifying the comdition (cond), + and the sampling step at which this condition is to be replaced by the next one. + + Input: + (model, ['a red crown', 'a [blue:green:5] jeweled crown'], 20) + + Output: + [ + [ + ScheduledPromptConditioning(end_at_step=20, cond=tensor([[-0.3886, 0.0229, -0.0523, ..., -0.4901, -0.3066, 0.0674], ..., [ 0.3317, -0.5102, -0.4066, ..., 0.4119, -0.7647, -1.0160]], device='cuda:0')) + ], + [ + ScheduledPromptConditioning(end_at_step=5, cond=tensor([[-0.3886, 0.0229, -0.0522, ..., -0.4901, -0.3067, 0.0673], ..., [-0.0192, 0.3867, -0.4644, ..., 0.1135, -0.3696, -0.4625]], device='cuda:0')), + ScheduledPromptConditioning(end_at_step=20, cond=tensor([[-0.3886, 0.0229, -0.0522, ..., -0.4901, -0.3067, 0.0673], ..., [-0.7352, -0.4356, -0.7888, ..., 0.6994, -0.4312, -1.2593]], device='cuda:0')) + ] + ] + """ res = [] prompt_schedules = get_learned_conditioning_prompt_schedules(prompts, steps) @@ -123,13 +139,75 @@ def get_learned_conditioning(model, prompts, steps): cache[prompt] = cond_schedule res.append(cond_schedule) - return ScheduledPromptBatch((len(prompts),) + res[0][0].cond.shape, res) + return res + + +re_AND = re.compile(r"\bAND\b") +re_weight = re.compile(r"^(.*?)(?:\s*:\s*([-+]?\s*(?:\d+|\d*\.\d+)?))?\s*$") + + +def get_multicond_prompt_list(prompts): + res_indexes = [] + + prompt_flat_list = [] + prompt_indexes = {} + + for prompt in prompts: + subprompts = re_AND.split(prompt) + + indexes = [] + for subprompt in subprompts: + text, weight = re_weight.search(subprompt).groups() + + weight = float(weight) if weight is not None else 1.0 + + index = prompt_indexes.get(text, None) + if index is None: + index = len(prompt_flat_list) + prompt_flat_list.append(text) + prompt_indexes[text] = index + + indexes.append((index, weight)) + + res_indexes.append(indexes) + + return res_indexes, prompt_flat_list, prompt_indexes + + +class ComposableScheduledPromptConditioning: + def __init__(self, schedules, weight=1.0): + self.schedules: list[ScheduledPromptConditioning] = schedules + self.weight: float = weight + + +class MulticondLearnedConditioning: + def __init__(self, shape, batch): + self.shape: tuple = shape # the shape field is needed to send this object to DDIM/PLMS + self.batch: list[list[ComposableScheduledPromptConditioning]] = batch -def reconstruct_cond_batch(c: ScheduledPromptBatch, current_step): - param = c.schedules[0][0].cond - res = torch.zeros(c.shape, device=param.device, dtype=param.dtype) - for i, cond_schedule in enumerate(c.schedules): +def get_multicond_learned_conditioning(model, prompts, steps) -> MulticondLearnedConditioning: + """same as get_learned_conditioning, but returns a list of ScheduledPromptConditioning along with the weight objects for each prompt. + For each prompt, the list is obtained by splitting the prompt using the AND separator. + + https://energy-based-model.github.io/Compositional-Visual-Generation-with-Composable-Diffusion-Models/ + """ + + res_indexes, prompt_flat_list, prompt_indexes = get_multicond_prompt_list(prompts) + + learned_conditioning = get_learned_conditioning(model, prompt_flat_list, steps) + + res = [] + for indexes in res_indexes: + res.append([ComposableScheduledPromptConditioning(learned_conditioning[i], weight) for i, weight in indexes]) + + return MulticondLearnedConditioning(shape=(len(prompts),), batch=res) + + +def reconstruct_cond_batch(c: list[list[ScheduledPromptConditioning]], current_step): + param = c[0][0].cond + res = torch.zeros((len(c),) + param.shape, device=param.device, dtype=param.dtype) + for i, cond_schedule in enumerate(c): target_index = 0 for current, (end_at, cond) in enumerate(cond_schedule): if current_step <= end_at: @@ -140,6 +218,30 @@ def reconstruct_cond_batch(c: ScheduledPromptBatch, current_step): return res +def reconstruct_multicond_batch(c: MulticondLearnedConditioning, current_step): + param = c.batch[0][0].schedules[0].cond + + tensors = [] + conds_list = [] + + for batch_no, composable_prompts in enumerate(c.batch): + conds_for_batch = [] + + for cond_index, composable_prompt in enumerate(composable_prompts): + target_index = 0 + for current, (end_at, cond) in enumerate(composable_prompt.schedules): + if current_step <= end_at: + target_index = current + break + + conds_for_batch.append((len(tensors), composable_prompt.weight)) + tensors.append(composable_prompt.schedules[target_index].cond) + + conds_list.append(conds_for_batch) + + return conds_list, torch.stack(tensors).to(device=param.device, dtype=param.dtype) + + re_attention = re.compile(r""" \\\(| \\\)| diff --git a/modules/sd_samplers.py b/modules/sd_samplers.py index dbf570d2..d27c547b 100644 --- a/modules/sd_samplers.py +++ b/modules/sd_samplers.py @@ -109,9 +109,12 @@ class VanillaStableDiffusionSampler: return 0 def p_sample_ddim_hook(self, x_dec, cond, ts, unconditional_conditioning, *args, **kwargs): - cond = prompt_parser.reconstruct_cond_batch(cond, self.step) + conds_list, tensor = prompt_parser.reconstruct_multicond_batch(cond, self.step) unconditional_conditioning = prompt_parser.reconstruct_cond_batch(unconditional_conditioning, self.step) + assert all([len(conds) == 1 for conds in conds_list]), 'composition via AND is not supported for DDIM/PLMS samplers' + cond = tensor + if self.mask is not None: img_orig = self.sampler.model.q_sample(self.init_latent, ts) x_dec = img_orig * self.mask + self.nmask * x_dec @@ -183,19 +186,31 @@ class CFGDenoiser(torch.nn.Module): self.step = 0 def forward(self, x, sigma, uncond, cond, cond_scale): - cond = prompt_parser.reconstruct_cond_batch(cond, self.step) + conds_list, tensor = prompt_parser.reconstruct_multicond_batch(cond, self.step) uncond = prompt_parser.reconstruct_cond_batch(uncond, self.step) + batch_size = len(conds_list) + repeats = [len(conds_list[i]) for i in range(batch_size)] + + x_in = torch.cat([torch.stack([x[i] for _ in range(n)]) for i, n in enumerate(repeats)] + [x]) + sigma_in = torch.cat([torch.stack([sigma[i] for _ in range(n)]) for i, n in enumerate(repeats)] + [sigma]) + cond_in = torch.cat([tensor, uncond]) + if shared.batch_cond_uncond: - x_in = torch.cat([x] * 2) - sigma_in = torch.cat([sigma] * 2) - cond_in = torch.cat([uncond, cond]) - uncond, cond = self.inner_model(x_in, sigma_in, cond=cond_in).chunk(2) - denoised = uncond + (cond - uncond) * cond_scale + x_out = self.inner_model(x_in, sigma_in, cond=cond_in) else: - uncond = self.inner_model(x, sigma, cond=uncond) - cond = self.inner_model(x, sigma, cond=cond) - denoised = uncond + (cond - uncond) * cond_scale + x_out = torch.zeros_like(x_in) + for batch_offset in range(0, x_out.shape[0], batch_size): + a = batch_offset + b = a + batch_size + x_out[a:b] = self.inner_model(x_in[a:b], sigma_in[a:b], cond=cond_in[a:b]) + + denoised_uncond = x_out[-batch_size:] + denoised = torch.clone(denoised_uncond) + + for i, conds in enumerate(conds_list): + for cond_index, weight in conds: + denoised[i] += (x_out[cond_index] - denoised_uncond[i]) * (weight * cond_scale) if self.mask is not None: denoised = self.init_latent * self.mask + self.nmask * denoised diff --git a/modules/ui.py b/modules/ui.py index 523ab25b..9620350f 100644 --- a/modules/ui.py +++ b/modules/ui.py @@ -34,7 +34,7 @@ import modules.gfpgan_model import modules.codeformer_model import modules.styles import modules.generation_parameters_copypaste -from modules.prompt_parser import get_learned_conditioning_prompt_schedules +from modules import prompt_parser from modules.images import apply_filename_pattern, get_next_sequence_number import modules.textual_inversion.ui @@ -394,7 +394,9 @@ def connect_reuse_seed(seed: gr.Number, reuse_seed: gr.Button, generation_info: def update_token_counter(text, steps): try: - prompt_schedules = get_learned_conditioning_prompt_schedules([text], steps) + _, prompt_flat_list, _ = prompt_parser.get_multicond_prompt_list([text]) + prompt_schedules = prompt_parser.get_learned_conditioning_prompt_schedules(prompt_flat_list, steps) + except Exception: # a parsing error can happen here during typing, and we don't want to bother the user with # messages related to it in console -- cgit v1.2.1 From 4320f386d9641c7c234589c4cb0c0c6cbeb156ad Mon Sep 17 00:00:00 2001 From: Greendayle Date: Wed, 5 Oct 2022 22:39:32 +0200 Subject: removing underscores and colons --- modules/deepbooru.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) (limited to 'modules') diff --git a/modules/deepbooru.py b/modules/deepbooru.py index a64fd9cd..fb5018a6 100644 --- a/modules/deepbooru.py +++ b/modules/deepbooru.py @@ -56,7 +56,7 @@ def _load_tf_and_return_tags(pil_image, threshold): print('\n'.join(sorted(result_tags_print, reverse=True))) - return ', '.join(result_tags_out) + return ', '.join(result_tags_out).replace('_', ' ').replace(':', ' ') def get_deepbooru_tags(pil_image, threshold=0.5): -- cgit v1.2.1 From f8e41a96bb30a04dd5e294c7e1178c1c3b09d481 Mon Sep 17 00:00:00 2001 From: AUTOMATIC <16777216c@gmail.com> Date: Wed, 5 Oct 2022 23:52:05 +0300 Subject: fix various float parsing errors --- modules/prompt_parser.py | 3 +-- 1 file changed, 1 insertion(+), 2 deletions(-) (limited to 'modules') diff --git a/modules/prompt_parser.py b/modules/prompt_parser.py index f7420daf..800b12c7 100644 --- a/modules/prompt_parser.py +++ b/modules/prompt_parser.py @@ -143,8 +143,7 @@ def get_learned_conditioning(model, prompts, steps): re_AND = re.compile(r"\bAND\b") -re_weight = re.compile(r"^(.*?)(?:\s*:\s*([-+]?\s*(?:\d+|\d*\.\d+)?))?\s*$") - +re_weight = re.compile(r"^(.*?)(?:\s*:\s*([-+]?(?:\d+\.?|\d*\.\d+)))?\s*$") def get_multicond_prompt_list(prompts): res_indexes = [] -- cgit v1.2.1 From 20f8ec877a99ce2ebf193cb1e2e773cfc77b7c41 Mon Sep 17 00:00:00 2001 From: AUTOMATIC <16777216c@gmail.com> Date: Thu, 6 Oct 2022 00:09:32 +0300 Subject: remove type annotations in new code because presumably they don't work in 3.7 --- modules/prompt_parser.py | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) (limited to 'modules') diff --git a/modules/prompt_parser.py b/modules/prompt_parser.py index 800b12c7..ee4c5d02 100644 --- a/modules/prompt_parser.py +++ b/modules/prompt_parser.py @@ -175,14 +175,14 @@ def get_multicond_prompt_list(prompts): class ComposableScheduledPromptConditioning: def __init__(self, schedules, weight=1.0): - self.schedules: list[ScheduledPromptConditioning] = schedules + self.schedules = schedules # : list[ScheduledPromptConditioning] self.weight: float = weight class MulticondLearnedConditioning: def __init__(self, shape, batch): self.shape: tuple = shape # the shape field is needed to send this object to DDIM/PLMS - self.batch: list[list[ComposableScheduledPromptConditioning]] = batch + self.batch = batch # : list[list[ComposableScheduledPromptConditioning]] def get_multicond_learned_conditioning(model, prompts, steps) -> MulticondLearnedConditioning: @@ -203,7 +203,7 @@ def get_multicond_learned_conditioning(model, prompts, steps) -> MulticondLearne return MulticondLearnedConditioning(shape=(len(prompts),), batch=res) -def reconstruct_cond_batch(c: list[list[ScheduledPromptConditioning]], current_step): +def reconstruct_cond_batch(c, current_step): # c: list[list[ScheduledPromptConditioning]] param = c[0][0].cond res = torch.zeros((len(c),) + param.shape, device=param.device, dtype=param.dtype) for i, cond_schedule in enumerate(c): -- cgit v1.2.1 From 34c358d10d52817f7a889ae4c52096ee654f3fe6 Mon Sep 17 00:00:00 2001 From: DepFA <35278260+dfaker@users.noreply.github.com> Date: Wed, 5 Oct 2022 22:11:30 +0100 Subject: use typing.list in prompt_parser.py for wider python version support --- modules/prompt_parser.py | 8 ++++---- 1 file changed, 4 insertions(+), 4 deletions(-) (limited to 'modules') diff --git a/modules/prompt_parser.py b/modules/prompt_parser.py index 800b12c7..fdfa21ae 100644 --- a/modules/prompt_parser.py +++ b/modules/prompt_parser.py @@ -1,6 +1,6 @@ import re from collections import namedtuple - +from typing import List import lark # a prompt like this: "fantasy landscape with a [mountain:lake:0.25] and [an oak:a christmas tree:0.75][ in foreground::0.6][ in background:0.25] [shoddy:masterful:0.5]" @@ -175,14 +175,14 @@ def get_multicond_prompt_list(prompts): class ComposableScheduledPromptConditioning: def __init__(self, schedules, weight=1.0): - self.schedules: list[ScheduledPromptConditioning] = schedules + self.schedules: List[ScheduledPromptConditioning] = schedules self.weight: float = weight class MulticondLearnedConditioning: def __init__(self, shape, batch): self.shape: tuple = shape # the shape field is needed to send this object to DDIM/PLMS - self.batch: list[list[ComposableScheduledPromptConditioning]] = batch + self.batch: List[List[ComposableScheduledPromptConditioning]] = batch def get_multicond_learned_conditioning(model, prompts, steps) -> MulticondLearnedConditioning: @@ -203,7 +203,7 @@ def get_multicond_learned_conditioning(model, prompts, steps) -> MulticondLearne return MulticondLearnedConditioning(shape=(len(prompts),), batch=res) -def reconstruct_cond_batch(c: list[list[ScheduledPromptConditioning]], current_step): +def reconstruct_cond_batch(c: List[List[ScheduledPromptConditioning]], current_step): param = c[0][0].cond res = torch.zeros((len(c),) + param.shape, device=param.device, dtype=param.dtype) for i, cond_schedule in enumerate(c): -- cgit v1.2.1 From 55400c981b7c1389482057a35ed6ea11f08da194 Mon Sep 17 00:00:00 2001 From: DepFA <35278260+dfaker@users.noreply.github.com> Date: Thu, 6 Oct 2022 03:11:15 +0100 Subject: Set gradio-img2img-tool default to 'editor' --- modules/shared.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) (limited to 'modules') diff --git a/modules/shared.py b/modules/shared.py index e52c9b1d..bab0fe6e 100644 --- a/modules/shared.py +++ b/modules/shared.py @@ -55,7 +55,7 @@ parser.add_argument("--hide-ui-dir-config", action='store_true', help="hide dire parser.add_argument("--ui-settings-file", type=str, help="filename to use for ui settings", default=os.path.join(script_path, 'config.json')) parser.add_argument("--gradio-debug", action='store_true', help="launch gradio with --debug option") parser.add_argument("--gradio-auth", type=str, help='set gradio authentication like "username:password"; or comma-delimit multiple like "u1:p1,u2:p2,u3:p3"', default=None) -parser.add_argument("--gradio-img2img-tool", type=str, help='gradio image uploader tool: can be either editor for ctopping, or color-sketch for drawing', choices=["color-sketch", "editor"], default="color-sketch") +parser.add_argument("--gradio-img2img-tool", type=str, help='gradio image uploader tool: can be either editor for ctopping, or color-sketch for drawing', choices=["color-sketch", "editor"], default="editor") parser.add_argument("--opt-channelslast", action='store_true', help="change memory type for stable diffusion to channels last") parser.add_argument("--styles-file", type=str, help="filename to use for styles", default=os.path.join(script_path, 'styles.csv')) parser.add_argument("--autolaunch", action='store_true', help="open the webui URL in the system's default browser upon launch", default=False) -- cgit v1.2.1 From 2499fb4e1910d31ff12c24110f161b20641b8835 Mon Sep 17 00:00:00 2001 From: Raphael Stoeckli Date: Wed, 5 Oct 2022 21:57:18 +0200 Subject: Add sanitizer for captions in Textual inversion --- modules/textual_inversion/preprocess.py | 28 ++++++++++++++++++++++++++++ 1 file changed, 28 insertions(+) (limited to 'modules') diff --git a/modules/textual_inversion/preprocess.py b/modules/textual_inversion/preprocess.py index f545a993..4f3df4bd 100644 --- a/modules/textual_inversion/preprocess.py +++ b/modules/textual_inversion/preprocess.py @@ -1,5 +1,8 @@ +from cmath import log import os from PIL import Image, ImageOps +import platform +import sys import tqdm from modules import shared, images @@ -25,6 +28,7 @@ def preprocess(process_src, process_dst, process_flip, process_split, process_ca def save_pic_with_caption(image, index): if process_caption: caption = "-" + shared.interrogator.generate_caption(image) + caption = sanitize_caption(os.path.join(dst, f"{index:05}-{subindex[0]}"), caption, ".png") else: caption = filename caption = os.path.splitext(caption)[0] @@ -75,3 +79,27 @@ def preprocess(process_src, process_dst, process_flip, process_split, process_ca if process_caption: shared.interrogator.send_blip_to_ram() + +def sanitize_caption(base_path, original_caption, suffix): + operating_system = platform.system().lower() + if (operating_system == "windows"): + invalid_path_characters = "\\/:*?\"<>|" + max_path_length = 259 + else: + invalid_path_characters = "/" #linux/macos + max_path_length = 1023 + caption = original_caption + for invalid_character in invalid_path_characters: + caption = caption.replace(invalid_character, "") + fixed_path_length = len(base_path) + len(suffix) + if fixed_path_length + len(caption) <= max_path_length: + return caption + caption_tokens = caption.split() + new_caption = "" + for token in caption_tokens: + last_caption = new_caption + new_caption = new_caption + token + " " + if (len(new_caption) + fixed_path_length - 1 > max_path_length): + break + print(f"\nPath will be too long. Truncated caption: {original_caption}\nto: {last_caption}", file=sys.stderr) + return last_caption.strip() -- cgit v1.2.1 From 4288e53fc2ea25fa49715bf5b7f14603553c9e38 Mon Sep 17 00:00:00 2001 From: Raphael Stoeckli Date: Wed, 5 Oct 2022 23:11:32 +0200 Subject: removed unused import, fixed typo --- modules/textual_inversion/preprocess.py | 3 +-- 1 file changed, 1 insertion(+), 2 deletions(-) (limited to 'modules') diff --git a/modules/textual_inversion/preprocess.py b/modules/textual_inversion/preprocess.py index 4f3df4bd..f1c002a2 100644 --- a/modules/textual_inversion/preprocess.py +++ b/modules/textual_inversion/preprocess.py @@ -1,4 +1,3 @@ -from cmath import log import os from PIL import Image, ImageOps import platform @@ -13,7 +12,7 @@ def preprocess(process_src, process_dst, process_flip, process_split, process_ca src = os.path.abspath(process_src) dst = os.path.abspath(process_dst) - assert src != dst, 'same directory specified as source and desitnation' + assert src != dst, 'same directory specified as source and destination' os.makedirs(dst, exist_ok=True) -- cgit v1.2.1 From 5f24b7bcf4a074fbdec757617fcd1bc82e76551b Mon Sep 17 00:00:00 2001 From: AUTOMATIC <16777216c@gmail.com> Date: Thu, 6 Oct 2022 12:08:48 +0300 Subject: option to let users select which samplers they want to hide --- modules/processing.py | 13 ++++++------- modules/sd_samplers.py | 19 +++++++++++++++++-- modules/shared.py | 15 +++++++++------ 3 files changed, 32 insertions(+), 15 deletions(-) (limited to 'modules') diff --git a/modules/processing.py b/modules/processing.py index d8c6b8d5..e01c8b3f 100644 --- a/modules/processing.py +++ b/modules/processing.py @@ -11,9 +11,8 @@ import cv2 from skimage import exposure import modules.sd_hijack -from modules import devices, prompt_parser, masking +from modules import devices, prompt_parser, masking, sd_samplers from modules.sd_hijack import model_hijack -from modules.sd_samplers import samplers, samplers_for_img2img from modules.shared import opts, cmd_opts, state import modules.shared as shared import modules.face_restoration @@ -110,7 +109,7 @@ class Processed: self.width = p.width self.height = p.height self.sampler_index = p.sampler_index - self.sampler = samplers[p.sampler_index].name + self.sampler = sd_samplers.samplers[p.sampler_index].name self.cfg_scale = p.cfg_scale self.steps = p.steps self.batch_size = p.batch_size @@ -265,7 +264,7 @@ def create_infotext(p, all_prompts, all_seeds, all_subseeds, comments, iteration generation_params = { "Steps": p.steps, - "Sampler": samplers[p.sampler_index].name, + "Sampler": sd_samplers.samplers[p.sampler_index].name, "CFG scale": p.cfg_scale, "Seed": all_seeds[index], "Face restoration": (opts.face_restoration_model if p.restore_faces else None), @@ -478,7 +477,7 @@ class StableDiffusionProcessingTxt2Img(StableDiffusionProcessing): self.firstphase_height_truncated = int(scale * self.height) def sample(self, conditioning, unconditional_conditioning, seeds, subseeds, subseed_strength): - self.sampler = samplers[self.sampler_index].constructor(self.sd_model) + self.sampler = sd_samplers.samplers[self.sampler_index].constructor(self.sd_model) if not self.enable_hr: x = create_random_tensors([opt_C, self.height // opt_f, self.width // opt_f], seeds=seeds, subseeds=subseeds, subseed_strength=self.subseed_strength, seed_resize_from_h=self.seed_resize_from_h, seed_resize_from_w=self.seed_resize_from_w, p=self) @@ -521,7 +520,7 @@ class StableDiffusionProcessingTxt2Img(StableDiffusionProcessing): shared.state.nextjob() - self.sampler = samplers[self.sampler_index].constructor(self.sd_model) + self.sampler = sd_samplers.samplers[self.sampler_index].constructor(self.sd_model) noise = create_random_tensors(samples.shape[1:], seeds=seeds, subseeds=subseeds, subseed_strength=subseed_strength, seed_resize_from_h=self.seed_resize_from_h, seed_resize_from_w=self.seed_resize_from_w, p=self) # GC now before running the next img2img to prevent running out of memory @@ -556,7 +555,7 @@ class StableDiffusionProcessingImg2Img(StableDiffusionProcessing): self.nmask = None def init(self, all_prompts, all_seeds, all_subseeds): - self.sampler = samplers_for_img2img[self.sampler_index].constructor(self.sd_model) + self.sampler = sd_samplers.samplers_for_img2img[self.sampler_index].constructor(self.sd_model) crop_region = None if self.image_mask is not None: diff --git a/modules/sd_samplers.py b/modules/sd_samplers.py index d27c547b..2e1f7715 100644 --- a/modules/sd_samplers.py +++ b/modules/sd_samplers.py @@ -32,12 +32,27 @@ samplers_data_k_diffusion = [ if hasattr(k_diffusion.sampling, funcname) ] -samplers = [ +all_samplers = [ *samplers_data_k_diffusion, SamplerData('DDIM', lambda model: VanillaStableDiffusionSampler(ldm.models.diffusion.ddim.DDIMSampler, model), []), SamplerData('PLMS', lambda model: VanillaStableDiffusionSampler(ldm.models.diffusion.plms.PLMSSampler, model), []), ] -samplers_for_img2img = [x for x in samplers if x.name not in ['PLMS', 'DPM fast', 'DPM adaptive']] + +samplers = [] +samplers_for_img2img = [] + + +def set_samplers(): + global samplers, samplers_for_img2img + + hidden = set(opts.hide_samplers) + hidden_img2img = set(opts.hide_samplers + ['PLMS', 'DPM fast', 'DPM adaptive']) + + samplers = [x for x in all_samplers if x.name not in hidden] + samplers_for_img2img = [x for x in all_samplers if x.name not in hidden_img2img] + + +set_samplers() sampler_extra_params = { 'sample_euler': ['s_churn', 's_tmin', 's_tmax', 's_noise'], diff --git a/modules/shared.py b/modules/shared.py index bab0fe6e..ca2e4c74 100644 --- a/modules/shared.py +++ b/modules/shared.py @@ -13,6 +13,7 @@ import modules.memmon import modules.sd_models import modules.styles import modules.devices as devices +from modules import sd_samplers from modules.paths import script_path, sd_path sd_model_file = os.path.join(script_path, 'model.ckpt') @@ -238,14 +239,16 @@ options_templates.update(options_section(('ui', "User interface"), { })) options_templates.update(options_section(('sampler-params', "Sampler parameters"), { - "eta_ddim": OptionInfo(0.0, "eta (noise multiplier) for DDIM", gr.Slider, {"minimum": 0.0, "maximum": 1.0, "step": 0.01}), - "eta_ancestral": OptionInfo(1.0, "eta (noise multiplier) for ancestral samplers", gr.Slider, {"minimum": 0.0, "maximum": 1.0, "step": 0.01}), - "ddim_discretize": OptionInfo('uniform', "img2img DDIM discretize", gr.Radio, {"choices": ['uniform', 'quad']}), - 's_churn': OptionInfo(0.0, "sigma churn", gr.Slider, {"minimum": 0.0, "maximum": 1.0, "step": 0.01}), - 's_tmin': OptionInfo(0.0, "sigma tmin", gr.Slider, {"minimum": 0.0, "maximum": 1.0, "step": 0.01}), - 's_noise': OptionInfo(1.0, "sigma noise", gr.Slider, {"minimum": 0.0, "maximum": 1.0, "step": 0.01}), + "hide_samplers": OptionInfo([], "Hide samplers in user interface (requires restart)", gr.CheckboxGroup, lambda: {"choices": [x.name for x in sd_samplers.all_samplers]}), + "eta_ddim": OptionInfo(0.0, "eta (noise multiplier) for DDIM", gr.Slider, {"minimum": 0.0, "maximum": 1.0, "step": 0.01}), + "eta_ancestral": OptionInfo(1.0, "eta (noise multiplier) for ancestral samplers", gr.Slider, {"minimum": 0.0, "maximum": 1.0, "step": 0.01}), + "ddim_discretize": OptionInfo('uniform', "img2img DDIM discretize", gr.Radio, {"choices": ['uniform', 'quad']}), + 's_churn': OptionInfo(0.0, "sigma churn", gr.Slider, {"minimum": 0.0, "maximum": 1.0, "step": 0.01}), + 's_tmin': OptionInfo(0.0, "sigma tmin", gr.Slider, {"minimum": 0.0, "maximum": 1.0, "step": 0.01}), + 's_noise': OptionInfo(1.0, "sigma noise", gr.Slider, {"minimum": 0.0, "maximum": 1.0, "step": 0.01}), })) + class Options: data = None data_labels = options_templates -- cgit v1.2.1 From 2d3ea42a2d1e909bbccdb6b49561b187c60a9402 Mon Sep 17 00:00:00 2001 From: AUTOMATIC <16777216c@gmail.com> Date: Thu, 6 Oct 2022 13:21:12 +0300 Subject: workaround for a mysterious bug where prompt weights can't be matched --- modules/prompt_parser.py | 4 +++- 1 file changed, 3 insertions(+), 1 deletion(-) (limited to 'modules') diff --git a/modules/prompt_parser.py b/modules/prompt_parser.py index a7a6aa31..f00256f2 100644 --- a/modules/prompt_parser.py +++ b/modules/prompt_parser.py @@ -156,7 +156,9 @@ def get_multicond_prompt_list(prompts): indexes = [] for subprompt in subprompts: - text, weight = re_weight.search(subprompt).groups() + match = re_weight.search(subprompt) + + text, weight = match.groups() if match is not None else (subprompt, 1.0) weight = float(weight) if weight is not None else 1.0 -- cgit v1.2.1 From 71901b3d3bea1d035bf4a7229d19356b4b062151 Mon Sep 17 00:00:00 2001 From: C43H66N12O12S2 <36072735+C43H66N12O12S2@users.noreply.github.com> Date: Wed, 5 Oct 2022 14:30:57 +0300 Subject: add karras scheduling variants --- modules/sd_samplers.py | 13 +++++++++++++ 1 file changed, 13 insertions(+) (limited to 'modules') diff --git a/modules/sd_samplers.py b/modules/sd_samplers.py index 2e1f7715..8d6eb762 100644 --- a/modules/sd_samplers.py +++ b/modules/sd_samplers.py @@ -26,6 +26,17 @@ samplers_k_diffusion = [ ('DPM adaptive', 'sample_dpm_adaptive', ['k_dpm_ad']), ] +if opts.show_karras_scheduler_variants: + k_diffusion.sampling.sample_dpm_2_ka = k_diffusion.sampling.sample_dpm_2 + k_diffusion.sampling.sample_dpm_2_ancestral_ka = k_diffusion.sampling.sample_dpm_2_ancestral + k_diffusion.sampling.sample_lms_ka = k_diffusion.sampling.sample_lms + samplers_k_diffusion_ka = [ + ('LMS K Scheduling', 'sample_lms_ka', ['k_lms_ka']), + ('DPM2 K Scheduling', 'sample_dpm_2_ka', ['k_dpm_2_ka']), + ('DPM2 a K Scheduling', 'sample_dpm_2_ancestral_ka', ['k_dpm_2_a_ka']), + ] + samplers_k_diffusion.extend(samplers_k_diffusion_ka) + samplers_data_k_diffusion = [ SamplerData(label, lambda model, funcname=funcname: KDiffusionSampler(funcname, model), aliases) for label, funcname, aliases in samplers_k_diffusion @@ -345,6 +356,8 @@ class KDiffusionSampler: if p.sampler_noise_scheduler_override: sigmas = p.sampler_noise_scheduler_override(steps) + elif self.funcname.endswith('ka'): + sigmas = k_diffusion.sampling.get_sigmas_karras(n=steps, sigma_min=0.1, sigma_max=10, device=shared.device) else: sigmas = self.model_wrap.get_sigmas(steps) x = x * sigmas[0] -- cgit v1.2.1 From 3ddf80a9db8793188e2fe9488233d2b272cceb33 Mon Sep 17 00:00:00 2001 From: C43H66N12O12S2 <36072735+C43H66N12O12S2@users.noreply.github.com> Date: Wed, 5 Oct 2022 14:31:51 +0300 Subject: add variant setting --- modules/shared.py | 1 + 1 file changed, 1 insertion(+) (limited to 'modules') diff --git a/modules/shared.py b/modules/shared.py index ca2e4c74..9e4860a2 100644 --- a/modules/shared.py +++ b/modules/shared.py @@ -236,6 +236,7 @@ options_templates.update(options_section(('ui', "User interface"), { "font": OptionInfo("", "Font for image grids that have text"), "js_modal_lightbox": OptionInfo(True, "Enable full page image viewer"), "js_modal_lightbox_initialy_zoomed": OptionInfo(True, "Show images zoomed in by default in full page image viewer"), + "show_karras_scheduler_variants": OptionInfo(True, "Show Karras scheduling variants for select samplers. Try these variants if your K sampled images suffer from excessive noise."), })) options_templates.update(options_section(('sampler-params', "Sampler parameters"), { -- cgit v1.2.1 From 5993df24a1026225cb8af89237547c1d9101ce69 Mon Sep 17 00:00:00 2001 From: AUTOMATIC <16777216c@gmail.com> Date: Thu, 6 Oct 2022 14:12:52 +0300 Subject: integrate the new samplers PR --- modules/processing.py | 7 +++--- modules/sd_samplers.py | 59 ++++++++++++++++++++++++++------------------------ modules/shared.py | 1 - 3 files changed, 35 insertions(+), 32 deletions(-) (limited to 'modules') diff --git a/modules/processing.py b/modules/processing.py index e01c8b3f..e567956c 100644 --- a/modules/processing.py +++ b/modules/processing.py @@ -477,7 +477,7 @@ class StableDiffusionProcessingTxt2Img(StableDiffusionProcessing): self.firstphase_height_truncated = int(scale * self.height) def sample(self, conditioning, unconditional_conditioning, seeds, subseeds, subseed_strength): - self.sampler = sd_samplers.samplers[self.sampler_index].constructor(self.sd_model) + self.sampler = sd_samplers.create_sampler_with_index(sd_samplers.samplers, self.sampler_index, self.sd_model) if not self.enable_hr: x = create_random_tensors([opt_C, self.height // opt_f, self.width // opt_f], seeds=seeds, subseeds=subseeds, subseed_strength=self.subseed_strength, seed_resize_from_h=self.seed_resize_from_h, seed_resize_from_w=self.seed_resize_from_w, p=self) @@ -520,7 +520,8 @@ class StableDiffusionProcessingTxt2Img(StableDiffusionProcessing): shared.state.nextjob() - self.sampler = sd_samplers.samplers[self.sampler_index].constructor(self.sd_model) + self.sampler = sd_samplers.create_sampler_with_index(sd_samplers.samplers, self.sampler_index, self.sd_model) + noise = create_random_tensors(samples.shape[1:], seeds=seeds, subseeds=subseeds, subseed_strength=subseed_strength, seed_resize_from_h=self.seed_resize_from_h, seed_resize_from_w=self.seed_resize_from_w, p=self) # GC now before running the next img2img to prevent running out of memory @@ -555,7 +556,7 @@ class StableDiffusionProcessingImg2Img(StableDiffusionProcessing): self.nmask = None def init(self, all_prompts, all_seeds, all_subseeds): - self.sampler = sd_samplers.samplers_for_img2img[self.sampler_index].constructor(self.sd_model) + self.sampler = sd_samplers.create_sampler_with_index(sd_samplers.samplers_for_img2img, self.sampler_index, self.sd_model) crop_region = None if self.image_mask is not None: diff --git a/modules/sd_samplers.py b/modules/sd_samplers.py index 8d6eb762..497df943 100644 --- a/modules/sd_samplers.py +++ b/modules/sd_samplers.py @@ -13,46 +13,46 @@ from modules.shared import opts, cmd_opts, state import modules.shared as shared -SamplerData = namedtuple('SamplerData', ['name', 'constructor', 'aliases']) +SamplerData = namedtuple('SamplerData', ['name', 'constructor', 'aliases', 'options']) samplers_k_diffusion = [ - ('Euler a', 'sample_euler_ancestral', ['k_euler_a']), - ('Euler', 'sample_euler', ['k_euler']), - ('LMS', 'sample_lms', ['k_lms']), - ('Heun', 'sample_heun', ['k_heun']), - ('DPM2', 'sample_dpm_2', ['k_dpm_2']), - ('DPM2 a', 'sample_dpm_2_ancestral', ['k_dpm_2_a']), - ('DPM fast', 'sample_dpm_fast', ['k_dpm_fast']), - ('DPM adaptive', 'sample_dpm_adaptive', ['k_dpm_ad']), + ('Euler a', 'sample_euler_ancestral', ['k_euler_a'], {}), + ('Euler', 'sample_euler', ['k_euler'], {}), + ('LMS', 'sample_lms', ['k_lms'], {}), + ('Heun', 'sample_heun', ['k_heun'], {}), + ('DPM2', 'sample_dpm_2', ['k_dpm_2'], {}), + ('DPM2 a', 'sample_dpm_2_ancestral', ['k_dpm_2_a'], {}), + ('DPM fast', 'sample_dpm_fast', ['k_dpm_fast'], {}), + ('DPM adaptive', 'sample_dpm_adaptive', ['k_dpm_ad'], {}), + ('LMS Karras', 'sample_lms', ['k_lms_ka'], {'scheduler': 'karras'}), + ('DPM2 Karras', 'sample_dpm_2', ['k_dpm_2_ka'], {'scheduler': 'karras'}), + ('DPM2 a Karras', 'sample_dpm_2_ancestral', ['k_dpm_2_a_ka'], {'scheduler': 'karras'}), ] -if opts.show_karras_scheduler_variants: - k_diffusion.sampling.sample_dpm_2_ka = k_diffusion.sampling.sample_dpm_2 - k_diffusion.sampling.sample_dpm_2_ancestral_ka = k_diffusion.sampling.sample_dpm_2_ancestral - k_diffusion.sampling.sample_lms_ka = k_diffusion.sampling.sample_lms - samplers_k_diffusion_ka = [ - ('LMS K Scheduling', 'sample_lms_ka', ['k_lms_ka']), - ('DPM2 K Scheduling', 'sample_dpm_2_ka', ['k_dpm_2_ka']), - ('DPM2 a K Scheduling', 'sample_dpm_2_ancestral_ka', ['k_dpm_2_a_ka']), - ] - samplers_k_diffusion.extend(samplers_k_diffusion_ka) - samplers_data_k_diffusion = [ - SamplerData(label, lambda model, funcname=funcname: KDiffusionSampler(funcname, model), aliases) - for label, funcname, aliases in samplers_k_diffusion + SamplerData(label, lambda model, funcname=funcname: KDiffusionSampler(funcname, model), aliases, options) + for label, funcname, aliases, options in samplers_k_diffusion if hasattr(k_diffusion.sampling, funcname) ] all_samplers = [ *samplers_data_k_diffusion, - SamplerData('DDIM', lambda model: VanillaStableDiffusionSampler(ldm.models.diffusion.ddim.DDIMSampler, model), []), - SamplerData('PLMS', lambda model: VanillaStableDiffusionSampler(ldm.models.diffusion.plms.PLMSSampler, model), []), + SamplerData('DDIM', lambda model: VanillaStableDiffusionSampler(ldm.models.diffusion.ddim.DDIMSampler, model), [], {}), + SamplerData('PLMS', lambda model: VanillaStableDiffusionSampler(ldm.models.diffusion.plms.PLMSSampler, model), [], {}), ] samplers = [] samplers_for_img2img = [] +def create_sampler_with_index(list_of_configs, index, model): + config = list_of_configs[index] + sampler = config.constructor(model) + sampler.config = config + + return sampler + + def set_samplers(): global samplers, samplers_for_img2img @@ -130,6 +130,7 @@ class VanillaStableDiffusionSampler: self.step = 0 self.eta = None self.default_eta = 0.0 + self.config = None def number_of_needed_noises(self, p): return 0 @@ -291,6 +292,7 @@ class KDiffusionSampler: self.stop_at = None self.eta = None self.default_eta = 1.0 + self.config = None def callback_state(self, d): store_latent(d["denoised"]) @@ -355,11 +357,12 @@ class KDiffusionSampler: steps = steps or p.steps if p.sampler_noise_scheduler_override: - sigmas = p.sampler_noise_scheduler_override(steps) - elif self.funcname.endswith('ka'): - sigmas = k_diffusion.sampling.get_sigmas_karras(n=steps, sigma_min=0.1, sigma_max=10, device=shared.device) + sigmas = p.sampler_noise_scheduler_override(steps) + elif self.config is not None and self.config.options.get('scheduler', None) == 'karras': + sigmas = k_diffusion.sampling.get_sigmas_karras(n=steps, sigma_min=0.1, sigma_max=10, device=shared.device) else: - sigmas = self.model_wrap.get_sigmas(steps) + sigmas = self.model_wrap.get_sigmas(steps) + x = x * sigmas[0] extra_params_kwargs = self.initialize(p) diff --git a/modules/shared.py b/modules/shared.py index 9e4860a2..ca2e4c74 100644 --- a/modules/shared.py +++ b/modules/shared.py @@ -236,7 +236,6 @@ options_templates.update(options_section(('ui', "User interface"), { "font": OptionInfo("", "Font for image grids that have text"), "js_modal_lightbox": OptionInfo(True, "Enable full page image viewer"), "js_modal_lightbox_initialy_zoomed": OptionInfo(True, "Show images zoomed in by default in full page image viewer"), - "show_karras_scheduler_variants": OptionInfo(True, "Show Karras scheduling variants for select samplers. Try these variants if your K sampled images suffer from excessive noise."), })) options_templates.update(options_section(('sampler-params', "Sampler parameters"), { -- cgit v1.2.1 From be71115b1a1201d04f0e2a11e718fb31cbd26474 Mon Sep 17 00:00:00 2001 From: DepFA <35278260+dfaker@users.noreply.github.com> Date: Thu, 6 Oct 2022 01:09:44 +0100 Subject: Update shared.py --- modules/shared.py | 1 + 1 file changed, 1 insertion(+) (limited to 'modules') diff --git a/modules/shared.py b/modules/shared.py index ca2e4c74..9f7c6efe 100644 --- a/modules/shared.py +++ b/modules/shared.py @@ -236,6 +236,7 @@ options_templates.update(options_section(('ui', "User interface"), { "font": OptionInfo("", "Font for image grids that have text"), "js_modal_lightbox": OptionInfo(True, "Enable full page image viewer"), "js_modal_lightbox_initialy_zoomed": OptionInfo(True, "Show images zoomed in by default in full page image viewer"), + "show_progress_in_title": OptionInfo(False, "Show generation progress in window title."), })) options_templates.update(options_section(('sampler-params', "Sampler parameters"), { -- cgit v1.2.1 From fec71e4de24b65b0f205a3c071b71651bbcb0dfc Mon Sep 17 00:00:00 2001 From: DepFA <35278260+dfaker@users.noreply.github.com> Date: Thu, 6 Oct 2022 01:35:07 +0100 Subject: Default window title progress updates on --- modules/shared.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) (limited to 'modules') diff --git a/modules/shared.py b/modules/shared.py index 9f7c6efe..5c16f025 100644 --- a/modules/shared.py +++ b/modules/shared.py @@ -236,7 +236,7 @@ options_templates.update(options_section(('ui', "User interface"), { "font": OptionInfo("", "Font for image grids that have text"), "js_modal_lightbox": OptionInfo(True, "Enable full page image viewer"), "js_modal_lightbox_initialy_zoomed": OptionInfo(True, "Show images zoomed in by default in full page image viewer"), - "show_progress_in_title": OptionInfo(False, "Show generation progress in window title."), + "show_progress_in_title": OptionInfo(True, "Show generation progress in window title."), })) options_templates.update(options_section(('sampler-params', "Sampler parameters"), { -- cgit v1.2.1 From 0bb458f0ca06a7be27cf1a1003c536d1f06a5bd3 Mon Sep 17 00:00:00 2001 From: Milly Date: Wed, 5 Oct 2022 01:19:50 +0900 Subject: Removed duplicate image saving codes Use `modules.images.save_image()` instead. --- modules/images.py | 7 ++++--- modules/ui.py | 46 ++++++++++------------------------------------ 2 files changed, 14 insertions(+), 39 deletions(-) (limited to 'modules') diff --git a/modules/images.py b/modules/images.py index c2fadab9..810f1446 100644 --- a/modules/images.py +++ b/modules/images.py @@ -353,7 +353,7 @@ def get_next_sequence_number(path, basename): return result + 1 -def save_image(image, path, basename, seed=None, prompt=None, extension='png', info=None, short_filename=False, no_prompt=False, grid=False, pnginfo_section_name='parameters', p=None, existing_info=None, forced_filename=None, suffix=""): +def save_image(image, path, basename, seed=None, prompt=None, extension='png', info=None, short_filename=False, no_prompt=False, grid=False, pnginfo_section_name='parameters', p=None, existing_info=None, forced_filename=None, suffix="", save_to_dirs=None): if short_filename or prompt is None or seed is None: file_decoration = "" elif opts.save_to_dirs: @@ -377,7 +377,8 @@ def save_image(image, path, basename, seed=None, prompt=None, extension='png', i else: pnginfo = None - save_to_dirs = (grid and opts.grid_save_to_dirs) or (not grid and opts.save_to_dirs and not no_prompt) + if save_to_dirs is None: + save_to_dirs = (grid and opts.grid_save_to_dirs) or (not grid and opts.save_to_dirs and not no_prompt) if save_to_dirs: dirname = apply_filename_pattern(opts.directories_filename_pattern or "[prompt_words]", p, seed, prompt).strip('\\ /') @@ -431,4 +432,4 @@ def save_image(image, path, basename, seed=None, prompt=None, extension='png', i with open(f"{fullfn_without_extension}.txt", "w", encoding="utf8") as file: file.write(info + "\n") - + return fullfn diff --git a/modules/ui.py b/modules/ui.py index 9620350f..4f18126f 100644 --- a/modules/ui.py +++ b/modules/ui.py @@ -35,7 +35,7 @@ import modules.codeformer_model import modules.styles import modules.generation_parameters_copypaste from modules import prompt_parser -from modules.images import apply_filename_pattern, get_next_sequence_number +from modules.images import save_image import modules.textual_inversion.ui # this is a fix for Windows users. Without it, javascript files will be served with text/html content-type and the bowser will not show any UI @@ -114,20 +114,13 @@ def save_files(js_data, images, index): p = MyObject(data) path = opts.outdir_save save_to_dirs = opts.use_save_to_dirs_for_ui - - if save_to_dirs: - dirname = apply_filename_pattern(opts.directories_filename_pattern or "[prompt_words]", p, p.seed, p.prompt) - path = os.path.join(opts.outdir_save, dirname) - - os.makedirs(path, exist_ok=True) - + extension: str = opts.samples_format + start_index = 0 if index > -1 and opts.save_selected_only and (index >= data["index_of_first_image"]): # ensures we are looking at a specific non-grid picture, and we have save_selected_only images = [images[index]] - infotexts = [data["infotexts"][index]] - else: - infotexts = data["infotexts"] + start_index = index with open(os.path.join(opts.outdir_save, "log.csv"), "a", encoding="utf8", newline='') as file: at_start = file.tell() == 0 @@ -135,37 +128,18 @@ def save_files(js_data, images, index): if at_start: writer.writerow(["prompt", "seed", "width", "height", "sampler", "cfgs", "steps", "filename", "negative_prompt"]) - file_decoration = opts.samples_filename_pattern or "[seed]-[prompt_spaces]" - if file_decoration != "": - file_decoration = "-" + file_decoration.lower() - file_decoration = apply_filename_pattern(file_decoration, p, p.seed, p.prompt) - truncated = (file_decoration[:240] + '..') if len(file_decoration) > 240 else file_decoration - filename_base = truncated - extension = opts.samples_format.lower() - - basecount = get_next_sequence_number(path, "") - for i, filedata in enumerate(images): - file_number = f"{basecount+i:05}" - filename = file_number + filename_base + f".{extension}" - filepath = os.path.join(path, filename) - - + for image_index, filedata in enumerate(images, start_index): if filedata.startswith("data:image/png;base64,"): filedata = filedata[len("data:image/png;base64,"):] image = Image.open(io.BytesIO(base64.decodebytes(filedata.encode('utf-8')))) - if opts.enable_pnginfo and extension == 'png': - pnginfo = PngImagePlugin.PngInfo() - pnginfo.add_text('parameters', infotexts[i]) - image.save(filepath, pnginfo=pnginfo) - else: - image.save(filepath, quality=opts.jpeg_quality) - if opts.enable_pnginfo and extension in ("jpg", "jpeg", "webp"): - piexif.insert(piexif.dump({"Exif": { - piexif.ExifIFD.UserComment: piexif.helper.UserComment.dump(infotexts[i], encoding="unicode") - }}), filepath) + is_grid = image_index < p.index_of_first_image + i = 0 if is_grid else (image_index - p.index_of_first_image) + + fullfn = save_image(image, path, "", seed=p.all_seeds[i], prompt=p.all_prompts[i], extension=extension, info=p.infotexts[image_index], grid=is_grid, p=p, save_to_dirs=save_to_dirs) + filename = os.path.relpath(fullfn, path) filenames.append(filename) writer.writerow([data["prompt"], data["seed"], data["width"], data["height"], data["sampler"], data["cfg_scale"], data["steps"], filenames[0], data["negative_prompt"]]) -- cgit v1.2.1 From dbc8a4d35129b08eab30776bbbaf3a2e7ac10a6c Mon Sep 17 00:00:00 2001 From: AUTOMATIC <16777216c@gmail.com> Date: Thu, 6 Oct 2022 20:27:50 +0300 Subject: add generation parameters to images shown in web ui --- modules/processing.py | 8 ++++++-- 1 file changed, 6 insertions(+), 2 deletions(-) (limited to 'modules') diff --git a/modules/processing.py b/modules/processing.py index de818d5b..8faf9095 100644 --- a/modules/processing.py +++ b/modules/processing.py @@ -430,7 +430,9 @@ def process_images(p: StableDiffusionProcessing) -> Processed: if opts.samples_save and not p.do_not_save_samples: images.save_image(image, p.outpath_samples, "", seeds[i], prompts[i], opts.samples_format, info=infotext(n, i), p=p) - infotexts.append(infotext(n, i)) + text = infotext(n, i) + infotexts.append(text) + image.info["parameters"] = text output_images.append(image) del x_samples_ddim @@ -447,7 +449,9 @@ def process_images(p: StableDiffusionProcessing) -> Processed: grid = images.image_grid(output_images, p.batch_size) if opts.return_grid: - infotexts.insert(0, infotext()) + text = infotext() + infotexts.insert(0, text) + grid.info["parameters"] = text output_images.insert(0, grid) index_of_first_image = 1 -- cgit v1.2.1 From cf7c784fcc0c84a8a4edd8d3aca4dda4c7025c43 Mon Sep 17 00:00:00 2001 From: Milly Date: Fri, 7 Oct 2022 00:19:52 +0900 Subject: Removed duplicate defined models_path Use `modules.paths.models_path` instead `modules.shared.model_path`. --- modules/shared.py | 19 +++++++++---------- 1 file changed, 9 insertions(+), 10 deletions(-) (limited to 'modules') diff --git a/modules/shared.py b/modules/shared.py index 5c16f025..25bb6e6c 100644 --- a/modules/shared.py +++ b/modules/shared.py @@ -14,11 +14,10 @@ import modules.sd_models import modules.styles import modules.devices as devices from modules import sd_samplers -from modules.paths import script_path, sd_path +from modules.paths import models_path, script_path, sd_path sd_model_file = os.path.join(script_path, 'model.ckpt') default_sd_model_file = sd_model_file -model_path = os.path.join(script_path, 'models') parser = argparse.ArgumentParser() parser.add_argument("--config", type=str, default=os.path.join(sd_path, "configs/stable-diffusion/v1-inference.yaml"), help="path to config which constructs model",) parser.add_argument("--ckpt", type=str, default=sd_model_file, help="path to checkpoint of stable diffusion model; if specified, this checkpoint will be added to the list of checkpoints and loaded",) @@ -36,14 +35,14 @@ parser.add_argument("--always-batch-cond-uncond", action='store_true', help="dis parser.add_argument("--unload-gfpgan", action='store_true', help="does not do anything.") parser.add_argument("--precision", type=str, help="evaluate at this precision", choices=["full", "autocast"], default="autocast") parser.add_argument("--share", action='store_true', help="use share=True for gradio and make the UI accessible through their site (doesn't work for me but you might have better luck)") -parser.add_argument("--codeformer-models-path", type=str, help="Path to directory with codeformer model file(s).", default=os.path.join(model_path, 'Codeformer')) -parser.add_argument("--gfpgan-models-path", type=str, help="Path to directory with GFPGAN model file(s).", default=os.path.join(model_path, 'GFPGAN')) -parser.add_argument("--esrgan-models-path", type=str, help="Path to directory with ESRGAN model file(s).", default=os.path.join(model_path, 'ESRGAN')) -parser.add_argument("--bsrgan-models-path", type=str, help="Path to directory with BSRGAN model file(s).", default=os.path.join(model_path, 'BSRGAN')) -parser.add_argument("--realesrgan-models-path", type=str, help="Path to directory with RealESRGAN model file(s).", default=os.path.join(model_path, 'RealESRGAN')) -parser.add_argument("--scunet-models-path", type=str, help="Path to directory with ScuNET model file(s).", default=os.path.join(model_path, 'ScuNET')) -parser.add_argument("--swinir-models-path", type=str, help="Path to directory with SwinIR model file(s).", default=os.path.join(model_path, 'SwinIR')) -parser.add_argument("--ldsr-models-path", type=str, help="Path to directory with LDSR model file(s).", default=os.path.join(model_path, 'LDSR')) +parser.add_argument("--codeformer-models-path", type=str, help="Path to directory with codeformer model file(s).", default=os.path.join(models_path, 'Codeformer')) +parser.add_argument("--gfpgan-models-path", type=str, help="Path to directory with GFPGAN model file(s).", default=os.path.join(models_path, 'GFPGAN')) +parser.add_argument("--esrgan-models-path", type=str, help="Path to directory with ESRGAN model file(s).", default=os.path.join(models_path, 'ESRGAN')) +parser.add_argument("--bsrgan-models-path", type=str, help="Path to directory with BSRGAN model file(s).", default=os.path.join(models_path, 'BSRGAN')) +parser.add_argument("--realesrgan-models-path", type=str, help="Path to directory with RealESRGAN model file(s).", default=os.path.join(models_path, 'RealESRGAN')) +parser.add_argument("--scunet-models-path", type=str, help="Path to directory with ScuNET model file(s).", default=os.path.join(models_path, 'ScuNET')) +parser.add_argument("--swinir-models-path", type=str, help="Path to directory with SwinIR model file(s).", default=os.path.join(models_path, 'SwinIR')) +parser.add_argument("--ldsr-models-path", type=str, help="Path to directory with LDSR model file(s).", default=os.path.join(models_path, 'LDSR')) parser.add_argument("--opt-split-attention", action='store_true', help="force-enables cross-attention layer optimization. By default, it's on for torch.cuda and off for other torch devices.") parser.add_argument("--disable-opt-split-attention", action='store_true', help="force-disables cross-attention layer optimization") parser.add_argument("--opt-split-attention-v1", action='store_true', help="enable older version of split attention optimization that does not consume all the VRAM it can find") -- cgit v1.2.1 From 070b7d60cf5dac6387b3bfc8f3b3977b620e4fd5 Mon Sep 17 00:00:00 2001 From: Milly Date: Wed, 5 Oct 2022 02:13:09 +0900 Subject: Added styles to Processed So `[styles]` pattern can use in saving image UI. --- modules/images.py | 7 +------ modules/processing.py | 2 ++ 2 files changed, 3 insertions(+), 6 deletions(-) (limited to 'modules') diff --git a/modules/images.py b/modules/images.py index 810f1446..fa0714fd 100644 --- a/modules/images.py +++ b/modules/images.py @@ -292,12 +292,7 @@ def apply_filename_pattern(x, p, seed, prompt): x = x.replace("[cfg]", str(p.cfg_scale)) x = x.replace("[width]", str(p.width)) x = x.replace("[height]", str(p.height)) - - #currently disabled if using the save button, will work otherwise - # if enabled it will cause a bug because styles is not included in the save_files data dictionary - if hasattr(p, "styles"): - x = x.replace("[styles]", sanitize_filename_part(", ".join([x for x in p.styles if not x == "None"]) or "None", replace_spaces=False)) - + x = x.replace("[styles]", sanitize_filename_part(", ".join([x for x in p.styles if not x == "None"]) or "None", replace_spaces=False)) x = x.replace("[sampler]", sanitize_filename_part(sd_samplers.samplers[p.sampler_index].name, replace_spaces=False)) x = x.replace("[model_hash]", shared.sd_model.sd_model_hash) diff --git a/modules/processing.py b/modules/processing.py index 8faf9095..706dbfa8 100644 --- a/modules/processing.py +++ b/modules/processing.py @@ -121,6 +121,7 @@ class Processed: self.denoising_strength = getattr(p, 'denoising_strength', None) self.extra_generation_params = p.extra_generation_params self.index_of_first_image = index_of_first_image + self.styles = p.styles self.eta = p.eta self.ddim_discretize = p.ddim_discretize @@ -165,6 +166,7 @@ class Processed: "extra_generation_params": self.extra_generation_params, "index_of_first_image": self.index_of_first_image, "infotexts": self.infotexts, + "styles": self.styles, } return json.dumps(obj) -- cgit v1.2.1 From 1cc36d170ac15e7f04208df32db27af1b10c867c Mon Sep 17 00:00:00 2001 From: Milly Date: Wed, 5 Oct 2022 02:17:15 +0900 Subject: Added job_timestamp to Processed So `[job_timestamp]` pattern can use in saving image UI. --- modules/images.py | 2 +- modules/processing.py | 2 ++ 2 files changed, 3 insertions(+), 1 deletion(-) (limited to 'modules') diff --git a/modules/images.py b/modules/images.py index fa0714fd..669d76af 100644 --- a/modules/images.py +++ b/modules/images.py @@ -298,7 +298,7 @@ def apply_filename_pattern(x, p, seed, prompt): x = x.replace("[model_hash]", shared.sd_model.sd_model_hash) x = x.replace("[date]", datetime.date.today().isoformat()) x = x.replace("[datetime]", datetime.datetime.now().strftime("%Y%m%d%H%M%S")) - x = x.replace("[job_timestamp]", shared.state.job_timestamp) + x = x.replace("[job_timestamp]", getattr(p, "job_timestamp", shared.state.job_timestamp)) # Apply [prompt] at last. Because it may contain any replacement word.^M if prompt is not None: diff --git a/modules/processing.py b/modules/processing.py index 706dbfa8..f773a30e 100644 --- a/modules/processing.py +++ b/modules/processing.py @@ -122,6 +122,7 @@ class Processed: self.extra_generation_params = p.extra_generation_params self.index_of_first_image = index_of_first_image self.styles = p.styles + self.job_timestamp = state.job_timestamp self.eta = p.eta self.ddim_discretize = p.ddim_discretize @@ -167,6 +168,7 @@ class Processed: "index_of_first_image": self.index_of_first_image, "infotexts": self.infotexts, "styles": self.styles, + "job_timestamp": self.job_timestamp, } return json.dumps(obj) -- cgit v1.2.1 From 405c8171d1acbb994084d98770bbcb97d01d9406 Mon Sep 17 00:00:00 2001 From: Milly Date: Thu, 6 Oct 2022 00:59:04 +0900 Subject: Prefer using `Processed.sd_model_hash` attribute when filename pattern --- modules/images.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) (limited to 'modules') diff --git a/modules/images.py b/modules/images.py index 669d76af..29c5ee24 100644 --- a/modules/images.py +++ b/modules/images.py @@ -295,7 +295,7 @@ def apply_filename_pattern(x, p, seed, prompt): x = x.replace("[styles]", sanitize_filename_part(", ".join([x for x in p.styles if not x == "None"]) or "None", replace_spaces=False)) x = x.replace("[sampler]", sanitize_filename_part(sd_samplers.samplers[p.sampler_index].name, replace_spaces=False)) - x = x.replace("[model_hash]", shared.sd_model.sd_model_hash) + x = x.replace("[model_hash]", getattr(p, "sd_model_hash", shared.sd_model.sd_model_hash)) x = x.replace("[date]", datetime.date.today().isoformat()) x = x.replace("[datetime]", datetime.datetime.now().strftime("%Y%m%d%H%M%S")) x = x.replace("[job_timestamp]", getattr(p, "job_timestamp", shared.state.job_timestamp)) -- cgit v1.2.1 From b34b25b4c941819d34f29be6c4c1ec01e64585b4 Mon Sep 17 00:00:00 2001 From: AUTOMATIC <16777216c@gmail.com> Date: Thu, 6 Oct 2022 23:27:01 +0300 Subject: karras samplers for img2img? --- modules/sd_samplers.py | 6 ++++-- 1 file changed, 4 insertions(+), 2 deletions(-) (limited to 'modules') diff --git a/modules/sd_samplers.py b/modules/sd_samplers.py index 497df943..df17e93c 100644 --- a/modules/sd_samplers.py +++ b/modules/sd_samplers.py @@ -338,9 +338,11 @@ class KDiffusionSampler: steps, t_enc = setup_img2img_steps(p, steps) if p.sampler_noise_scheduler_override: - sigmas = p.sampler_noise_scheduler_override(steps) + sigmas = p.sampler_noise_scheduler_override(steps) + elif self.config is not None and self.config.options.get('scheduler', None) == 'karras': + sigmas = k_diffusion.sampling.get_sigmas_karras(n=steps, sigma_min=0.1, sigma_max=10, device=shared.device) else: - sigmas = self.model_wrap.get_sigmas(steps) + sigmas = self.model_wrap.get_sigmas(steps) noise = noise * sigmas[steps - t_enc - 1] xi = x + noise -- cgit v1.2.1 From f174fb29228a04955fb951b32b0bab79e33ec2b8 Mon Sep 17 00:00:00 2001 From: C43H66N12O12S2 <36072735+C43H66N12O12S2@users.noreply.github.com> Date: Fri, 7 Oct 2022 05:21:49 +0300 Subject: add xformers attention --- modules/sd_hijack_optimizations.py | 39 +++++++++++++++++++++++++++++++++++++- 1 file changed, 38 insertions(+), 1 deletion(-) (limited to 'modules') diff --git a/modules/sd_hijack_optimizations.py b/modules/sd_hijack_optimizations.py index ea4cfdfc..da1b76e1 100644 --- a/modules/sd_hijack_optimizations.py +++ b/modules/sd_hijack_optimizations.py @@ -1,7 +1,9 @@ import math import torch from torch import einsum - +import xformers.ops +import functorch +xformers._is_functorch_available=True from ldm.util import default from einops import rearrange @@ -92,6 +94,41 @@ def split_cross_attention_forward(self, x, context=None, mask=None): return self.to_out(r2) +def _maybe_init(self, x): + """ + Initialize the attention operator, if required We expect the head dimension to be exposed here, meaning that x + : B, Head, Length + """ + if self.attention_op is not None: + return + _, M, K = x.shape + try: + self.attention_op = xformers.ops.AttentionOpDispatch( + dtype=x.dtype, + device=x.device, + k=K, + attn_bias_type=type(None), + has_dropout=False, + kv_len=M, + q_len=M, + ).op + except NotImplementedError as err: + raise NotImplementedError(f"Please install xformers with the flash attention / cutlass components.\n{err}") + +def xformers_attention_forward(self, x, context=None, mask=None): + h = self.heads + q_in = self.to_q(x) + context = default(context, x) + k_in = self.to_k(context) + v_in = self.to_v(context) + q, k, v = map(lambda t: rearrange(t, 'b n (h d) -> (b h) n d', h=h), (q_in, k_in, v_in)) + del q_in, k_in, v_in + self._maybe_init(q) + out = xformers.ops.memory_efficient_attention(q, k, v, attn_bias=None, op=self.attention_op) + + out = rearrange(out, '(b h) n d -> b n (h d)', h=h) + return self.to_out(out) + def cross_attention_attnblock_forward(self, x): h_ = x h_ = self.norm(h_) -- cgit v1.2.1 From 2eb911b056ce6ff4434f673366782ed34f2b2f12 Mon Sep 17 00:00:00 2001 From: C43H66N12O12S2 <36072735+C43H66N12O12S2@users.noreply.github.com> Date: Fri, 7 Oct 2022 05:22:28 +0300 Subject: Update sd_hijack.py --- modules/sd_hijack.py | 13 +++++++++---- 1 file changed, 9 insertions(+), 4 deletions(-) (limited to 'modules') diff --git a/modules/sd_hijack.py b/modules/sd_hijack.py index a6fa890c..6221ed5a 100644 --- a/modules/sd_hijack.py +++ b/modules/sd_hijack.py @@ -20,12 +20,17 @@ diffusionmodules_model_AttnBlock_forward = ldm.modules.diffusionmodules.model.At def apply_optimizations(): - ldm.modules.diffusionmodules.model.nonlinearity = silu - if cmd_opts.opt_split_attention_v1: ldm.modules.attention.CrossAttention.forward = sd_hijack_optimizations.split_cross_attention_forward_v1 - elif not cmd_opts.disable_opt_split_attention and (cmd_opts.opt_split_attention or torch.cuda.is_available()): - ldm.modules.attention.CrossAttention.forward = sd_hijack_optimizations.split_cross_attention_forward + if cmd_opts.opt_split_attention: + ldm.modules.attention_CrossAttention_forward = sd_hijack_optimizations.split_cross_attention_forward + ldm.modules.diffusionmodules.model.nonlinearity = sd_hijack_optimizations.nonlinearity_hijack + ldm.modules.diffusionmodules.model.AttnBlock.forward = sd_hijack_optimizations.cross_attention_attnblock_forward + elif not cmd_opts.disable_opt_xformers_attention: + ldm.modules.attention.CrossAttention.forward = sd_hijack_optimizations.xformers_attention_forward + ldm.modules.attention.CrossAttention._maybe_init = sd_hijack_optimizations._maybe_init + ldm.modules.attention.CrossAttention.attention_op = None + ldm.modules.diffusionmodules.model.nonlinearity = sd_hijack_optimizations.nonlinearity_hijack ldm.modules.diffusionmodules.model.AttnBlock.forward = sd_hijack_optimizations.cross_attention_attnblock_forward -- cgit v1.2.1 From da4ab2707b4cb0611cf181ba248a271d1937433e Mon Sep 17 00:00:00 2001 From: C43H66N12O12S2 <36072735+C43H66N12O12S2@users.noreply.github.com> Date: Fri, 7 Oct 2022 05:23:06 +0300 Subject: Update shared.py --- modules/shared.py | 1 + 1 file changed, 1 insertion(+) (limited to 'modules') diff --git a/modules/shared.py b/modules/shared.py index 25bb6e6c..8cc3b2fe 100644 --- a/modules/shared.py +++ b/modules/shared.py @@ -43,6 +43,7 @@ parser.add_argument("--realesrgan-models-path", type=str, help="Path to director parser.add_argument("--scunet-models-path", type=str, help="Path to directory with ScuNET model file(s).", default=os.path.join(models_path, 'ScuNET')) parser.add_argument("--swinir-models-path", type=str, help="Path to directory with SwinIR model file(s).", default=os.path.join(models_path, 'SwinIR')) parser.add_argument("--ldsr-models-path", type=str, help="Path to directory with LDSR model file(s).", default=os.path.join(models_path, 'LDSR')) +parser.add_argument("--disable-opt-xformers-attention", action='store_true', help="force-disables xformers attention optimization") parser.add_argument("--opt-split-attention", action='store_true', help="force-enables cross-attention layer optimization. By default, it's on for torch.cuda and off for other torch devices.") parser.add_argument("--disable-opt-split-attention", action='store_true', help="force-disables cross-attention layer optimization") parser.add_argument("--opt-split-attention-v1", action='store_true', help="enable older version of split attention optimization that does not consume all the VRAM it can find") -- cgit v1.2.1 From 35d6b231628d18d53d166c3a92fea1523e88d51e Mon Sep 17 00:00:00 2001 From: C43H66N12O12S2 <36072735+C43H66N12O12S2@users.noreply.github.com> Date: Fri, 7 Oct 2022 05:31:53 +0300 Subject: Update sd_hijack.py --- modules/sd_hijack.py | 3 +-- 1 file changed, 1 insertion(+), 2 deletions(-) (limited to 'modules') diff --git a/modules/sd_hijack.py b/modules/sd_hijack.py index 6221ed5a..a006c0a3 100644 --- a/modules/sd_hijack.py +++ b/modules/sd_hijack.py @@ -20,17 +20,16 @@ diffusionmodules_model_AttnBlock_forward = ldm.modules.diffusionmodules.model.At def apply_optimizations(): + ldm.modules.diffusionmodules.model.nonlinearity = silu if cmd_opts.opt_split_attention_v1: ldm.modules.attention.CrossAttention.forward = sd_hijack_optimizations.split_cross_attention_forward_v1 if cmd_opts.opt_split_attention: ldm.modules.attention_CrossAttention_forward = sd_hijack_optimizations.split_cross_attention_forward - ldm.modules.diffusionmodules.model.nonlinearity = sd_hijack_optimizations.nonlinearity_hijack ldm.modules.diffusionmodules.model.AttnBlock.forward = sd_hijack_optimizations.cross_attention_attnblock_forward elif not cmd_opts.disable_opt_xformers_attention: ldm.modules.attention.CrossAttention.forward = sd_hijack_optimizations.xformers_attention_forward ldm.modules.attention.CrossAttention._maybe_init = sd_hijack_optimizations._maybe_init ldm.modules.attention.CrossAttention.attention_op = None - ldm.modules.diffusionmodules.model.nonlinearity = sd_hijack_optimizations.nonlinearity_hijack ldm.modules.diffusionmodules.model.AttnBlock.forward = sd_hijack_optimizations.cross_attention_attnblock_forward -- cgit v1.2.1 From 5303df24282ba06abb34a423f2967354d37d078e Mon Sep 17 00:00:00 2001 From: C43H66N12O12S2 <36072735+C43H66N12O12S2@users.noreply.github.com> Date: Fri, 7 Oct 2022 06:01:14 +0300 Subject: Update sd_hijack.py --- modules/sd_hijack.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) (limited to 'modules') diff --git a/modules/sd_hijack.py b/modules/sd_hijack.py index a006c0a3..ddacb0ad 100644 --- a/modules/sd_hijack.py +++ b/modules/sd_hijack.py @@ -23,10 +23,10 @@ def apply_optimizations(): ldm.modules.diffusionmodules.model.nonlinearity = silu if cmd_opts.opt_split_attention_v1: ldm.modules.attention.CrossAttention.forward = sd_hijack_optimizations.split_cross_attention_forward_v1 - if cmd_opts.opt_split_attention: + elif cmd_opts.opt_split_attention: ldm.modules.attention_CrossAttention_forward = sd_hijack_optimizations.split_cross_attention_forward ldm.modules.diffusionmodules.model.AttnBlock.forward = sd_hijack_optimizations.cross_attention_attnblock_forward - elif not cmd_opts.disable_opt_xformers_attention: + elif not cmd_opts.disable_opt_xformers_attention and not cmd_opts.opt_split_attention: ldm.modules.attention.CrossAttention.forward = sd_hijack_optimizations.xformers_attention_forward ldm.modules.attention.CrossAttention._maybe_init = sd_hijack_optimizations._maybe_init ldm.modules.attention.CrossAttention.attention_op = None -- cgit v1.2.1 From 5e3ff846c56dc8e1d5c76ea04a8f2f74d7da07fc Mon Sep 17 00:00:00 2001 From: C43H66N12O12S2 <36072735+C43H66N12O12S2@users.noreply.github.com> Date: Fri, 7 Oct 2022 06:38:01 +0300 Subject: Update sd_hijack.py --- modules/sd_hijack.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) (limited to 'modules') diff --git a/modules/sd_hijack.py b/modules/sd_hijack.py index ddacb0ad..cbdb9d3c 100644 --- a/modules/sd_hijack.py +++ b/modules/sd_hijack.py @@ -26,7 +26,7 @@ def apply_optimizations(): elif cmd_opts.opt_split_attention: ldm.modules.attention_CrossAttention_forward = sd_hijack_optimizations.split_cross_attention_forward ldm.modules.diffusionmodules.model.AttnBlock.forward = sd_hijack_optimizations.cross_attention_attnblock_forward - elif not cmd_opts.disable_opt_xformers_attention and not cmd_opts.opt_split_attention: + elif not cmd_opts.disable_opt_xformers_attention and not (cmd_opts.opt_split_attention or torch.version.hip): ldm.modules.attention.CrossAttention.forward = sd_hijack_optimizations.xformers_attention_forward ldm.modules.attention.CrossAttention._maybe_init = sd_hijack_optimizations._maybe_init ldm.modules.attention.CrossAttention.attention_op = None -- cgit v1.2.1 From bad7cb29cecac51c5c0f39afec332b007ed73133 Mon Sep 17 00:00:00 2001 From: AUTOMATIC <16777216c@gmail.com> Date: Fri, 7 Oct 2022 10:17:52 +0300 Subject: added support for hypernetworks (???) --- modules/hypernetwork.py | 55 ++++++++++++++++++++++++++++++++++++++ modules/sd_hijack_optimizations.py | 17 ++++++++++-- modules/shared.py | 9 ++++++- 3 files changed, 78 insertions(+), 3 deletions(-) create mode 100644 modules/hypernetwork.py (limited to 'modules') diff --git a/modules/hypernetwork.py b/modules/hypernetwork.py new file mode 100644 index 00000000..9ed1eed9 --- /dev/null +++ b/modules/hypernetwork.py @@ -0,0 +1,55 @@ +import glob +import os +import torch +from modules import devices + + +class HypernetworkModule(torch.nn.Module): + def __init__(self, dim, state_dict): + super().__init__() + + self.linear1 = torch.nn.Linear(dim, dim * 2) + self.linear2 = torch.nn.Linear(dim * 2, dim) + + self.load_state_dict(state_dict, strict=True) + self.to(devices.device) + + def forward(self, x): + return x + (self.linear2(self.linear1(x))) + + +class Hypernetwork: + filename = None + name = None + + def __init__(self, filename): + self.filename = filename + self.name = os.path.splitext(os.path.basename(filename))[0] + self.layers = {} + + state_dict = torch.load(filename, map_location='cpu') + for size, sd in state_dict.items(): + self.layers[size] = (HypernetworkModule(size, sd[0]), HypernetworkModule(size, sd[1])) + + +def load_hypernetworks(path): + res = {} + + for filename in glob.iglob(path + '**/*.pt', recursive=True): + hn = Hypernetwork(filename) + res[hn.name] = hn + + return res + +def apply(self, x, context=None, mask=None, original=None): + + + if CrossAttention.hypernetwork is not None and context.shape[2] in CrossAttention.hypernetwork: + if context.shape[1] == 77 and CrossAttention.noise_cond: + context = context + (torch.randn_like(context) * 0.1) + h_k, h_v = CrossAttention.hypernetwork[context.shape[2]] + k = self.to_k(h_k(context)) + v = self.to_v(h_v(context)) + else: + k = self.to_k(context) + v = self.to_v(context) diff --git a/modules/sd_hijack_optimizations.py b/modules/sd_hijack_optimizations.py index ea4cfdfc..d9cca485 100644 --- a/modules/sd_hijack_optimizations.py +++ b/modules/sd_hijack_optimizations.py @@ -5,6 +5,8 @@ from torch import einsum from ldm.util import default from einops import rearrange +from modules import shared + # see https://github.com/basujindal/stable-diffusion/pull/117 for discussion def split_cross_attention_forward_v1(self, x, context=None, mask=None): @@ -42,8 +44,19 @@ def split_cross_attention_forward(self, x, context=None, mask=None): q_in = self.to_q(x) context = default(context, x) - k_in = self.to_k(context) * self.scale - v_in = self.to_v(context) + + hypernetwork = shared.selected_hypernetwork() + hypernetwork_layers = (hypernetwork.layers if hypernetwork is not None else {}).get(context.shape[2], None) + + if hypernetwork_layers is not None: + k_in = self.to_k(hypernetwork_layers[0](context)) + v_in = self.to_v(hypernetwork_layers[1](context)) + else: + k_in = self.to_k(context) + v_in = self.to_v(context) + + k_in *= self.scale + del context, x q, k, v = map(lambda t: rearrange(t, 'b n (h d) -> (b h) n d', h=h), (q_in, k_in, v_in)) diff --git a/modules/shared.py b/modules/shared.py index 25bb6e6c..879d8424 100644 --- a/modules/shared.py +++ b/modules/shared.py @@ -13,7 +13,7 @@ import modules.memmon import modules.sd_models import modules.styles import modules.devices as devices -from modules import sd_samplers +from modules import sd_samplers, hypernetwork from modules.paths import models_path, script_path, sd_path sd_model_file = os.path.join(script_path, 'model.ckpt') @@ -76,6 +76,12 @@ parallel_processing_allowed = not cmd_opts.lowvram and not cmd_opts.medvram config_filename = cmd_opts.ui_settings_file +hypernetworks = hypernetwork.load_hypernetworks(os.path.join(models_path, 'hypernetworks')) + + +def selected_hypernetwork(): + return hypernetworks.get(opts.sd_hypernetwork, None) + class State: interrupted = False @@ -206,6 +212,7 @@ options_templates.update(options_section(('system', "System"), { options_templates.update(options_section(('sd', "Stable Diffusion"), { "sd_model_checkpoint": OptionInfo(None, "Stable Diffusion checkpoint", gr.Dropdown, lambda: {"choices": modules.sd_models.checkpoint_tiles()}), + "sd_hypernetwork": OptionInfo("None", "Stable Diffusion finetune hypernetwork", gr.Dropdown, lambda: {"choices": ["None"] + [x for x in hypernetworks.keys()]}), "img2img_color_correction": OptionInfo(False, "Apply color correction to img2img results to match original colors."), "save_images_before_color_correction": OptionInfo(False, "Save a copy of image before applying color correction to img2img results"), "img2img_fix_steps": OptionInfo(False, "With img2img, do exactly the amount of steps the slider specifies (normally you'd do less with less denoising)."), -- cgit v1.2.1 From d15b3ec0013c10f02f0fb80e8448bac8872a151f Mon Sep 17 00:00:00 2001 From: AUTOMATIC <16777216c@gmail.com> Date: Fri, 7 Oct 2022 10:40:22 +0300 Subject: support loading VAE --- modules/sd_models.py | 8 ++++++++ 1 file changed, 8 insertions(+) (limited to 'modules') diff --git a/modules/sd_models.py b/modules/sd_models.py index 5f992064..8f794b47 100644 --- a/modules/sd_models.py +++ b/modules/sd_models.py @@ -134,6 +134,14 @@ def load_model_weights(model, checkpoint_file, sd_model_hash): devices.dtype = torch.float32 if shared.cmd_opts.no_half else torch.float16 + vae_file = os.path.splitext(checkpoint_file)[0] + ".vae.pt" + if os.path.exists(vae_file): + print(f"Loading VAE weights from: {vae_file}") + vae_ckpt = torch.load(vae_file, map_location="cpu") + vae_dict = {k: v for k, v in vae_ckpt["state_dict"].items() if k[0:4] != "loss"} + + model.first_stage_model.load_state_dict(vae_dict) + model.sd_model_hash = sd_model_hash model.sd_model_checkpint = checkpoint_file -- cgit v1.2.1 From 97bc0b9504572d2df80598d0b694703bcd626de6 Mon Sep 17 00:00:00 2001 From: AUTOMATIC <16777216c@gmail.com> Date: Fri, 7 Oct 2022 13:22:50 +0300 Subject: do not stop working on failed hypernetwork load --- modules/hypernetwork.py | 11 +++++++++-- 1 file changed, 9 insertions(+), 2 deletions(-) (limited to 'modules') diff --git a/modules/hypernetwork.py b/modules/hypernetwork.py index 9ed1eed9..c5cf4afa 100644 --- a/modules/hypernetwork.py +++ b/modules/hypernetwork.py @@ -1,5 +1,8 @@ import glob import os +import sys +import traceback + import torch from modules import devices @@ -36,8 +39,12 @@ def load_hypernetworks(path): res = {} for filename in glob.iglob(path + '**/*.pt', recursive=True): - hn = Hypernetwork(filename) - res[hn.name] = hn + try: + hn = Hypernetwork(filename) + res[hn.name] = hn + except Exception: + print(f"Error loading hypernetwork {filename}", file=sys.stderr) + print(traceback.format_exc(), file=sys.stderr) return res -- cgit v1.2.1 From f7c787eb7c295c27439f4fbdf78c26b8389560be Mon Sep 17 00:00:00 2001 From: AUTOMATIC <16777216c@gmail.com> Date: Fri, 7 Oct 2022 16:39:51 +0300 Subject: make it possible to use hypernetworks without opt split attention --- modules/hypernetwork.py | 42 ++++++++++++++++++++++++++++++++++-------- modules/sd_hijack.py | 6 ++++-- 2 files changed, 38 insertions(+), 10 deletions(-) (limited to 'modules') diff --git a/modules/hypernetwork.py b/modules/hypernetwork.py index c5cf4afa..c7b86682 100644 --- a/modules/hypernetwork.py +++ b/modules/hypernetwork.py @@ -4,7 +4,12 @@ import sys import traceback import torch -from modules import devices + +from ldm.util import default +from modules import devices, shared +import torch +from torch import einsum +from einops import rearrange, repeat class HypernetworkModule(torch.nn.Module): @@ -48,15 +53,36 @@ def load_hypernetworks(path): return res -def apply(self, x, context=None, mask=None, original=None): +def attention_CrossAttention_forward(self, x, context=None, mask=None): + h = self.heads + + q = self.to_q(x) + context = default(context, x) - if CrossAttention.hypernetwork is not None and context.shape[2] in CrossAttention.hypernetwork: - if context.shape[1] == 77 and CrossAttention.noise_cond: - context = context + (torch.randn_like(context) * 0.1) - h_k, h_v = CrossAttention.hypernetwork[context.shape[2]] - k = self.to_k(h_k(context)) - v = self.to_v(h_v(context)) + hypernetwork = shared.selected_hypernetwork() + hypernetwork_layers = (hypernetwork.layers if hypernetwork is not None else {}).get(context.shape[2], None) + + if hypernetwork_layers is not None: + k = self.to_k(hypernetwork_layers[0](context)) + v = self.to_v(hypernetwork_layers[1](context)) else: k = self.to_k(context) v = self.to_v(context) + + q, k, v = map(lambda t: rearrange(t, 'b n (h d) -> (b h) n d', h=h), (q, k, v)) + + sim = einsum('b i d, b j d -> b i j', q, k) * self.scale + + if mask is not None: + mask = rearrange(mask, 'b ... -> b (...)') + max_neg_value = -torch.finfo(sim.dtype).max + mask = repeat(mask, 'b j -> (b h) () j', h=h) + sim.masked_fill_(~mask, max_neg_value) + + # attention, what we cannot get enough of + attn = sim.softmax(dim=-1) + + out = einsum('b i j, b j d -> b i d', attn, v) + out = rearrange(out, '(b h) n d -> b n (h d)', h=h) + return self.to_out(out) diff --git a/modules/sd_hijack.py b/modules/sd_hijack.py index a6fa890c..d68f89cc 100644 --- a/modules/sd_hijack.py +++ b/modules/sd_hijack.py @@ -8,7 +8,7 @@ from torch import einsum from torch.nn.functional import silu import modules.textual_inversion.textual_inversion -from modules import prompt_parser, devices, sd_hijack_optimizations, shared +from modules import prompt_parser, devices, sd_hijack_optimizations, shared, hypernetwork from modules.shared import opts, device, cmd_opts import ldm.modules.attention @@ -20,6 +20,8 @@ diffusionmodules_model_AttnBlock_forward = ldm.modules.diffusionmodules.model.At def apply_optimizations(): + undo_optimizations() + ldm.modules.diffusionmodules.model.nonlinearity = silu if cmd_opts.opt_split_attention_v1: @@ -30,7 +32,7 @@ def apply_optimizations(): def undo_optimizations(): - ldm.modules.attention.CrossAttention.forward = attention_CrossAttention_forward + ldm.modules.attention.CrossAttention.forward = hypernetwork.attention_CrossAttention_forward ldm.modules.diffusionmodules.model.nonlinearity = diffusionmodules_model_nonlinearity ldm.modules.diffusionmodules.model.AttnBlock.forward = diffusionmodules_model_AttnBlock_forward -- cgit v1.2.1 From 54fa613c8391e3973cca9d94cdf539061932508b Mon Sep 17 00:00:00 2001 From: Greendayle Date: Fri, 7 Oct 2022 20:37:43 +0200 Subject: loading tf only in interrogation process --- modules/deepbooru.py | 7 ++++--- 1 file changed, 4 insertions(+), 3 deletions(-) (limited to 'modules') diff --git a/modules/deepbooru.py b/modules/deepbooru.py index fb5018a6..79dc59bd 100644 --- a/modules/deepbooru.py +++ b/modules/deepbooru.py @@ -1,12 +1,13 @@ import os.path from concurrent.futures import ProcessPoolExecutor -import numpy as np -import deepdanbooru as dd -import tensorflow as tf def _load_tf_and_return_tags(pil_image, threshold): + import deepdanbooru as dd + import tensorflow as tf + import numpy as np + this_folder = os.path.dirname(__file__) model_path = os.path.join(this_folder, '..', 'models', 'deepbooru', 'deepdanbooru-v3-20211112-sgd-e28') -- cgit v1.2.1 From fa2ea648db81f5723bb5d722f2fe0ebd7dfc319a Mon Sep 17 00:00:00 2001 From: Greendayle Date: Fri, 7 Oct 2022 20:46:38 +0200 Subject: even more powerfull fix --- modules/deepbooru.py | 9 +++++++-- 1 file changed, 7 insertions(+), 2 deletions(-) (limited to 'modules') diff --git a/modules/deepbooru.py b/modules/deepbooru.py index 79dc59bd..60094336 100644 --- a/modules/deepbooru.py +++ b/modules/deepbooru.py @@ -60,8 +60,13 @@ def _load_tf_and_return_tags(pil_image, threshold): return ', '.join(result_tags_out).replace('_', ' ').replace(':', ' ') +def subprocess_init_no_cuda(): + import os + os.environ["CUDA_VISIBLE_DEVICES"] = "-1" + + def get_deepbooru_tags(pil_image, threshold=0.5): - with ProcessPoolExecutor() as executor: - f = executor.submit(_load_tf_and_return_tags, pil_image, threshold) + with ProcessPoolExecutor(initializer=subprocess_init_no_cuda) as executor: + f = executor.submit(_load_tf_and_return_tags, pil_image, threshold, ) ret = f.result() # will rethrow any exceptions return ret \ No newline at end of file -- cgit v1.2.1 From 5f12e7efd92ad802742f96788b4be3249ad02829 Mon Sep 17 00:00:00 2001 From: Greendayle Date: Fri, 7 Oct 2022 20:58:30 +0200 Subject: linux test --- modules/deepbooru.py | 5 +++-- 1 file changed, 3 insertions(+), 2 deletions(-) (limited to 'modules') diff --git a/modules/deepbooru.py b/modules/deepbooru.py index 60094336..781b2249 100644 --- a/modules/deepbooru.py +++ b/modules/deepbooru.py @@ -1,6 +1,6 @@ import os.path from concurrent.futures import ProcessPoolExecutor - +from multiprocessing import get_context def _load_tf_and_return_tags(pil_image, threshold): @@ -66,7 +66,8 @@ def subprocess_init_no_cuda(): def get_deepbooru_tags(pil_image, threshold=0.5): - with ProcessPoolExecutor(initializer=subprocess_init_no_cuda) as executor: + context = get_context('spawn') + with ProcessPoolExecutor(initializer=subprocess_init_no_cuda, mp_context=context) as executor: f = executor.submit(_load_tf_and_return_tags, pil_image, threshold, ) ret = f.result() # will rethrow any exceptions return ret \ No newline at end of file -- cgit v1.2.1 From 12c4d5c6b5bf9dd50d0601c36af4f99b65316d58 Mon Sep 17 00:00:00 2001 From: AUTOMATIC <16777216c@gmail.com> Date: Fri, 7 Oct 2022 23:22:22 +0300 Subject: hypernetwork training mk1 --- modules/hypernetwork.py | 88 ------------ modules/hypernetwork/hypernetwork.py | 267 +++++++++++++++++++++++++++++++++++ modules/hypernetwork/ui.py | 43 ++++++ modules/sd_hijack.py | 4 +- modules/sd_hijack_optimizations.py | 3 +- modules/shared.py | 13 +- modules/textual_inversion/ui.py | 1 - modules/ui.py | 58 +++++++- 8 files changed, 374 insertions(+), 103 deletions(-) delete mode 100644 modules/hypernetwork.py create mode 100644 modules/hypernetwork/hypernetwork.py create mode 100644 modules/hypernetwork/ui.py (limited to 'modules') diff --git a/modules/hypernetwork.py b/modules/hypernetwork.py deleted file mode 100644 index c7b86682..00000000 --- a/modules/hypernetwork.py +++ /dev/null @@ -1,88 +0,0 @@ -import glob -import os -import sys -import traceback - -import torch - -from ldm.util import default -from modules import devices, shared -import torch -from torch import einsum -from einops import rearrange, repeat - - -class HypernetworkModule(torch.nn.Module): - def __init__(self, dim, state_dict): - super().__init__() - - self.linear1 = torch.nn.Linear(dim, dim * 2) - self.linear2 = torch.nn.Linear(dim * 2, dim) - - self.load_state_dict(state_dict, strict=True) - self.to(devices.device) - - def forward(self, x): - return x + (self.linear2(self.linear1(x))) - - -class Hypernetwork: - filename = None - name = None - - def __init__(self, filename): - self.filename = filename - self.name = os.path.splitext(os.path.basename(filename))[0] - self.layers = {} - - state_dict = torch.load(filename, map_location='cpu') - for size, sd in state_dict.items(): - self.layers[size] = (HypernetworkModule(size, sd[0]), HypernetworkModule(size, sd[1])) - - -def load_hypernetworks(path): - res = {} - - for filename in glob.iglob(path + '**/*.pt', recursive=True): - try: - hn = Hypernetwork(filename) - res[hn.name] = hn - except Exception: - print(f"Error loading hypernetwork {filename}", file=sys.stderr) - print(traceback.format_exc(), file=sys.stderr) - - return res - - -def attention_CrossAttention_forward(self, x, context=None, mask=None): - h = self.heads - - q = self.to_q(x) - context = default(context, x) - - hypernetwork = shared.selected_hypernetwork() - hypernetwork_layers = (hypernetwork.layers if hypernetwork is not None else {}).get(context.shape[2], None) - - if hypernetwork_layers is not None: - k = self.to_k(hypernetwork_layers[0](context)) - v = self.to_v(hypernetwork_layers[1](context)) - else: - k = self.to_k(context) - v = self.to_v(context) - - q, k, v = map(lambda t: rearrange(t, 'b n (h d) -> (b h) n d', h=h), (q, k, v)) - - sim = einsum('b i d, b j d -> b i j', q, k) * self.scale - - if mask is not None: - mask = rearrange(mask, 'b ... -> b (...)') - max_neg_value = -torch.finfo(sim.dtype).max - mask = repeat(mask, 'b j -> (b h) () j', h=h) - sim.masked_fill_(~mask, max_neg_value) - - # attention, what we cannot get enough of - attn = sim.softmax(dim=-1) - - out = einsum('b i j, b j d -> b i d', attn, v) - out = rearrange(out, '(b h) n d -> b n (h d)', h=h) - return self.to_out(out) diff --git a/modules/hypernetwork/hypernetwork.py b/modules/hypernetwork/hypernetwork.py new file mode 100644 index 00000000..a3d6a47e --- /dev/null +++ b/modules/hypernetwork/hypernetwork.py @@ -0,0 +1,267 @@ +import datetime +import glob +import html +import os +import sys +import traceback +import tqdm + +import torch + +from ldm.util import default +from modules import devices, shared, processing, sd_models +import torch +from torch import einsum +from einops import rearrange, repeat +import modules.textual_inversion.dataset + + +class HypernetworkModule(torch.nn.Module): + def __init__(self, dim, state_dict=None): + super().__init__() + + self.linear1 = torch.nn.Linear(dim, dim * 2) + self.linear2 = torch.nn.Linear(dim * 2, dim) + + if state_dict is not None: + self.load_state_dict(state_dict, strict=True) + else: + self.linear1.weight.data.fill_(0.0001) + self.linear1.bias.data.fill_(0.0001) + self.linear2.weight.data.fill_(0.0001) + self.linear2.bias.data.fill_(0.0001) + + self.to(devices.device) + + def forward(self, x): + return x + (self.linear2(self.linear1(x))) + + +class Hypernetwork: + filename = None + name = None + + def __init__(self, name=None): + self.filename = None + self.name = name + self.layers = {} + self.step = 0 + self.sd_checkpoint = None + self.sd_checkpoint_name = None + + for size in [320, 640, 768, 1280]: + self.layers[size] = (HypernetworkModule(size), HypernetworkModule(size)) + + def weights(self): + res = [] + + for k, layers in self.layers.items(): + for layer in layers: + layer.train() + res += [layer.linear1.weight, layer.linear1.bias, layer.linear2.weight, layer.linear2.bias] + + return res + + def save(self, filename): + state_dict = {} + + for k, v in self.layers.items(): + state_dict[k] = (v[0].state_dict(), v[1].state_dict()) + + state_dict['step'] = self.step + state_dict['name'] = self.name + state_dict['sd_checkpoint'] = self.sd_checkpoint + state_dict['sd_checkpoint_name'] = self.sd_checkpoint_name + + torch.save(state_dict, filename) + + def load(self, filename): + self.filename = filename + if self.name is None: + self.name = os.path.splitext(os.path.basename(filename))[0] + + state_dict = torch.load(filename, map_location='cpu') + + for size, sd in state_dict.items(): + if type(size) == int: + self.layers[size] = (HypernetworkModule(size, sd[0]), HypernetworkModule(size, sd[1])) + + self.name = state_dict.get('name', self.name) + self.step = state_dict.get('step', 0) + self.sd_checkpoint = state_dict.get('sd_checkpoint', None) + self.sd_checkpoint_name = state_dict.get('sd_checkpoint_name', None) + + +def load_hypernetworks(path): + res = {} + + for filename in glob.iglob(path + '**/*.pt', recursive=True): + try: + hn = Hypernetwork() + hn.load(filename) + res[hn.name] = hn + except Exception: + print(f"Error loading hypernetwork {filename}", file=sys.stderr) + print(traceback.format_exc(), file=sys.stderr) + + return res + + +def attention_CrossAttention_forward(self, x, context=None, mask=None): + h = self.heads + + q = self.to_q(x) + context = default(context, x) + + hypernetwork_layers = (shared.hypernetwork.layers if shared.hypernetwork is not None else {}).get(context.shape[2], None) + + if hypernetwork_layers is not None: + hypernetwork_k, hypernetwork_v = hypernetwork_layers + + self.hypernetwork_k = hypernetwork_k + self.hypernetwork_v = hypernetwork_v + + context_k = hypernetwork_k(context) + context_v = hypernetwork_v(context) + else: + context_k = context + context_v = context + + k = self.to_k(context_k) + v = self.to_v(context_v) + + q, k, v = map(lambda t: rearrange(t, 'b n (h d) -> (b h) n d', h=h), (q, k, v)) + + sim = einsum('b i d, b j d -> b i j', q, k) * self.scale + + if mask is not None: + mask = rearrange(mask, 'b ... -> b (...)') + max_neg_value = -torch.finfo(sim.dtype).max + mask = repeat(mask, 'b j -> (b h) () j', h=h) + sim.masked_fill_(~mask, max_neg_value) + + # attention, what we cannot get enough of + attn = sim.softmax(dim=-1) + + out = einsum('b i j, b j d -> b i d', attn, v) + out = rearrange(out, '(b h) n d -> b n (h d)', h=h) + return self.to_out(out) + + +def train_hypernetwork(hypernetwork_name, learn_rate, data_root, log_directory, steps, create_image_every, save_hypernetwork_every, template_file, preview_image_prompt): + assert hypernetwork_name, 'embedding not selected' + + shared.hypernetwork = shared.hypernetworks[hypernetwork_name] + + shared.state.textinfo = "Initializing hypernetwork training..." + shared.state.job_count = steps + + filename = os.path.join(shared.cmd_opts.hypernetwork_dir, f'{hypernetwork_name}.pt') + + log_directory = os.path.join(log_directory, datetime.datetime.now().strftime("%Y-%m-%d"), hypernetwork_name) + + if save_hypernetwork_every > 0: + hypernetwork_dir = os.path.join(log_directory, "hypernetworks") + os.makedirs(hypernetwork_dir, exist_ok=True) + else: + hypernetwork_dir = None + + if create_image_every > 0: + images_dir = os.path.join(log_directory, "images") + os.makedirs(images_dir, exist_ok=True) + else: + images_dir = None + + cond_model = shared.sd_model.cond_stage_model + + shared.state.textinfo = f"Preparing dataset from {html.escape(data_root)}..." + with torch.autocast("cuda"): + ds = modules.textual_inversion.dataset.PersonalizedBase(data_root=data_root, size=512, placeholder_token=hypernetwork_name, model=shared.sd_model, device=devices.device, template_file=template_file) + + hypernetwork = shared.hypernetworks[hypernetwork_name] + weights = hypernetwork.weights() + for weight in weights: + weight.requires_grad = True + + optimizer = torch.optim.AdamW(weights, lr=learn_rate) + + losses = torch.zeros((32,)) + + last_saved_file = "" + last_saved_image = "" + + ititial_step = hypernetwork.step or 0 + if ititial_step > steps: + return hypernetwork, filename + + pbar = tqdm.tqdm(enumerate(ds), total=steps-ititial_step) + for i, (x, text) in pbar: + hypernetwork.step = i + ititial_step + + if hypernetwork.step > steps: + break + + if shared.state.interrupted: + break + + with torch.autocast("cuda"): + c = cond_model([text]) + + x = x.to(devices.device) + loss = shared.sd_model(x.unsqueeze(0), c)[0] + del x + + losses[hypernetwork.step % losses.shape[0]] = loss.item() + + optimizer.zero_grad() + loss.backward() + optimizer.step() + + pbar.set_description(f"loss: {losses.mean():.7f}") + + if hypernetwork.step > 0 and hypernetwork_dir is not None and hypernetwork.step % save_hypernetwork_every == 0: + last_saved_file = os.path.join(hypernetwork_dir, f'{hypernetwork_name}-{hypernetwork.step}.pt') + hypernetwork.save(last_saved_file) + + if hypernetwork.step > 0 and images_dir is not None and hypernetwork.step % create_image_every == 0: + last_saved_image = os.path.join(images_dir, f'{hypernetwork_name}-{hypernetwork.step}.png') + + preview_text = text if preview_image_prompt == "" else preview_image_prompt + + p = processing.StableDiffusionProcessingTxt2Img( + sd_model=shared.sd_model, + prompt=preview_text, + steps=20, + do_not_save_grid=True, + do_not_save_samples=True, + ) + + processed = processing.process_images(p) + image = processed.images[0] + + shared.state.current_image = image + image.save(last_saved_image) + + last_saved_image += f", prompt: {preview_text}" + + shared.state.job_no = hypernetwork.step + + shared.state.textinfo = f""" +

+Loss: {losses.mean():.7f}
+Step: {hypernetwork.step}
+Last prompt: {html.escape(text)}
+Last saved embedding: {html.escape(last_saved_file)}
+Last saved image: {html.escape(last_saved_image)}
+

+""" + + checkpoint = sd_models.select_checkpoint() + + hypernetwork.sd_checkpoint = checkpoint.hash + hypernetwork.sd_checkpoint_name = checkpoint.model_name + hypernetwork.save(filename) + + return hypernetwork, filename + + diff --git a/modules/hypernetwork/ui.py b/modules/hypernetwork/ui.py new file mode 100644 index 00000000..525f978c --- /dev/null +++ b/modules/hypernetwork/ui.py @@ -0,0 +1,43 @@ +import html +import os + +import gradio as gr + +import modules.textual_inversion.textual_inversion +import modules.textual_inversion.preprocess +from modules import sd_hijack, shared + + +def create_hypernetwork(name): + fn = os.path.join(shared.cmd_opts.hypernetwork_dir, f"{name}.pt") + assert not os.path.exists(fn), f"file {fn} already exists" + + hypernetwork = modules.hypernetwork.hypernetwork.Hypernetwork(name=name) + hypernetwork.save(fn) + + shared.reload_hypernetworks() + shared.hypernetwork = shared.hypernetworks.get(shared.opts.sd_hypernetwork, None) + + return gr.Dropdown.update(choices=sorted([x for x in shared.hypernetworks.keys()])), f"Created: {fn}", "" + + +def train_hypernetwork(*args): + + initial_hypernetwork = shared.hypernetwork + + try: + sd_hijack.undo_optimizations() + + hypernetwork, filename = modules.hypernetwork.hypernetwork.train_hypernetwork(*args) + + res = f""" +Training {'interrupted' if shared.state.interrupted else 'finished'} at {hypernetwork.step} steps. +Hypernetwork saved to {html.escape(filename)} +""" + return res, "" + except Exception: + raise + finally: + shared.hypernetwork = initial_hypernetwork + sd_hijack.apply_optimizations() + diff --git a/modules/sd_hijack.py b/modules/sd_hijack.py index d68f89cc..ec8c9d4b 100644 --- a/modules/sd_hijack.py +++ b/modules/sd_hijack.py @@ -8,7 +8,7 @@ from torch import einsum from torch.nn.functional import silu import modules.textual_inversion.textual_inversion -from modules import prompt_parser, devices, sd_hijack_optimizations, shared, hypernetwork +from modules import prompt_parser, devices, sd_hijack_optimizations, shared from modules.shared import opts, device, cmd_opts import ldm.modules.attention @@ -32,6 +32,8 @@ def apply_optimizations(): def undo_optimizations(): + from modules.hypernetwork import hypernetwork + ldm.modules.attention.CrossAttention.forward = hypernetwork.attention_CrossAttention_forward ldm.modules.diffusionmodules.model.nonlinearity = diffusionmodules_model_nonlinearity ldm.modules.diffusionmodules.model.AttnBlock.forward = diffusionmodules_model_AttnBlock_forward diff --git a/modules/sd_hijack_optimizations.py b/modules/sd_hijack_optimizations.py index d9cca485..3f32e020 100644 --- a/modules/sd_hijack_optimizations.py +++ b/modules/sd_hijack_optimizations.py @@ -45,8 +45,7 @@ def split_cross_attention_forward(self, x, context=None, mask=None): q_in = self.to_q(x) context = default(context, x) - hypernetwork = shared.selected_hypernetwork() - hypernetwork_layers = (hypernetwork.layers if hypernetwork is not None else {}).get(context.shape[2], None) + hypernetwork_layers = (shared.hypernetwork.layers if shared.hypernetwork is not None else {}).get(context.shape[2], None) if hypernetwork_layers is not None: k_in = self.to_k(hypernetwork_layers[0](context)) diff --git a/modules/shared.py b/modules/shared.py index 879d8424..c5a893e8 100644 --- a/modules/shared.py +++ b/modules/shared.py @@ -13,7 +13,7 @@ import modules.memmon import modules.sd_models import modules.styles import modules.devices as devices -from modules import sd_samplers, hypernetwork +from modules import sd_samplers from modules.paths import models_path, script_path, sd_path sd_model_file = os.path.join(script_path, 'model.ckpt') @@ -28,6 +28,7 @@ parser.add_argument("--no-half", action='store_true', help="do not switch the mo parser.add_argument("--no-progressbar-hiding", action='store_true', help="do not hide progressbar in gradio UI (we hide it because it slows down ML if you have hardware acceleration in browser)") parser.add_argument("--max-batch-count", type=int, default=16, help="maximum batch count value for the UI") parser.add_argument("--embeddings-dir", type=str, default=os.path.join(script_path, 'embeddings'), help="embeddings directory for textual inversion (default: embeddings)") +parser.add_argument("--hypernetwork-dir", type=str, default=os.path.join(models_path, 'hypernetworks'), help="hypernetwork directory") parser.add_argument("--allow-code", action='store_true', help="allow custom script execution from webui") parser.add_argument("--medvram", action='store_true', help="enable stable diffusion model optimizations for sacrificing a little speed for low VRM usage") parser.add_argument("--lowvram", action='store_true', help="enable stable diffusion model optimizations for sacrificing a lot of speed for very low VRM usage") @@ -76,11 +77,15 @@ parallel_processing_allowed = not cmd_opts.lowvram and not cmd_opts.medvram config_filename = cmd_opts.ui_settings_file -hypernetworks = hypernetwork.load_hypernetworks(os.path.join(models_path, 'hypernetworks')) +def reload_hypernetworks(): + from modules.hypernetwork import hypernetwork + hypernetworks.clear() + hypernetworks.update(hypernetwork.load_hypernetworks(cmd_opts.hypernetwork_dir)) -def selected_hypernetwork(): - return hypernetworks.get(opts.sd_hypernetwork, None) + +hypernetworks = {} +hypernetwork = None class State: diff --git a/modules/textual_inversion/ui.py b/modules/textual_inversion/ui.py index f19ac5e0..c57de1f9 100644 --- a/modules/textual_inversion/ui.py +++ b/modules/textual_inversion/ui.py @@ -22,7 +22,6 @@ def preprocess(*args): def train_embedding(*args): - try: sd_hijack.undo_optimizations() diff --git a/modules/ui.py b/modules/ui.py index 4f18126f..051908c1 100644 --- a/modules/ui.py +++ b/modules/ui.py @@ -37,6 +37,7 @@ import modules.generation_parameters_copypaste from modules import prompt_parser from modules.images import save_image import modules.textual_inversion.ui +import modules.hypernetwork.ui # this is a fix for Windows users. Without it, javascript files will be served with text/html content-type and the bowser will not show any UI mimetypes.init() @@ -965,6 +966,18 @@ def create_ui(wrap_gradio_gpu_call): with gr.Column(): create_embedding = gr.Button(value="Create", variant='primary') + with gr.Group(): + gr.HTML(value="

Create a new hypernetwork

") + + new_hypernetwork_name = gr.Textbox(label="Name") + + with gr.Row(): + with gr.Column(scale=3): + gr.HTML(value="") + + with gr.Column(): + create_hypernetwork = gr.Button(value="Create", variant='primary') + with gr.Group(): gr.HTML(value="

Preprocess images

") @@ -986,6 +999,7 @@ def create_ui(wrap_gradio_gpu_call): with gr.Group(): gr.HTML(value="

Train an embedding; must specify a directory with a set of 512x512 images

") train_embedding_name = gr.Dropdown(label='Embedding', choices=sorted(sd_hijack.model_hijack.embedding_db.word_embeddings.keys())) + train_hypernetwork_name = gr.Dropdown(label='Hypernetwork', choices=[x for x in shared.hypernetworks.keys()]) learn_rate = gr.Number(label='Learning rate', value=5.0e-03) dataset_directory = gr.Textbox(label='Dataset directory', placeholder="Path to directory with input images") log_directory = gr.Textbox(label='Log directory', placeholder="Path to directory where to write outputs", value="textual_inversion") @@ -993,15 +1007,12 @@ def create_ui(wrap_gradio_gpu_call): steps = gr.Number(label='Max steps', value=100000, precision=0) create_image_every = gr.Number(label='Save an image to log directory every N steps, 0 to disable', value=500, precision=0) save_embedding_every = gr.Number(label='Save a copy of embedding to log directory every N steps, 0 to disable', value=500, precision=0) + preview_image_prompt = gr.Textbox(label='Preview prompt', value="") with gr.Row(): - with gr.Column(scale=2): - gr.HTML(value="") - - with gr.Column(): - with gr.Row(): - interrupt_training = gr.Button(value="Interrupt") - train_embedding = gr.Button(value="Train", variant='primary') + interrupt_training = gr.Button(value="Interrupt") + train_hypernetwork = gr.Button(value="Train Hypernetwork", variant='primary') + train_embedding = gr.Button(value="Train Embedding", variant='primary') with gr.Column(): progressbar = gr.HTML(elem_id="ti_progressbar") @@ -1027,6 +1038,18 @@ def create_ui(wrap_gradio_gpu_call): ] ) + create_hypernetwork.click( + fn=modules.hypernetwork.ui.create_hypernetwork, + inputs=[ + new_hypernetwork_name, + ], + outputs=[ + train_hypernetwork_name, + ti_output, + ti_outcome, + ] + ) + run_preprocess.click( fn=wrap_gradio_gpu_call(modules.textual_inversion.ui.preprocess, extra_outputs=[gr.update()]), _js="start_training_textual_inversion", @@ -1062,12 +1085,33 @@ def create_ui(wrap_gradio_gpu_call): ] ) + train_hypernetwork.click( + fn=wrap_gradio_gpu_call(modules.hypernetwork.ui.train_hypernetwork, extra_outputs=[gr.update()]), + _js="start_training_textual_inversion", + inputs=[ + train_hypernetwork_name, + learn_rate, + dataset_directory, + log_directory, + steps, + create_image_every, + save_embedding_every, + template_file, + preview_image_prompt, + ], + outputs=[ + ti_output, + ti_outcome, + ] + ) + interrupt_training.click( fn=lambda: shared.state.interrupt(), inputs=[], outputs=[], ) + def create_setting_component(key): def fun(): return opts.data[key] if key in opts.data else opts.data_labels[key].default -- cgit v1.2.1 From c9cc65b201679ea43c763b0d85e749d40bbc5433 Mon Sep 17 00:00:00 2001 From: C43H66N12O12S2 <36072735+C43H66N12O12S2@users.noreply.github.com> Date: Sat, 8 Oct 2022 04:09:18 +0300 Subject: switch to the proper way of calling xformers --- modules/sd_hijack_optimizations.py | 28 +++------------------------- 1 file changed, 3 insertions(+), 25 deletions(-) (limited to 'modules') diff --git a/modules/sd_hijack_optimizations.py b/modules/sd_hijack_optimizations.py index da1b76e1..7fb4a45e 100644 --- a/modules/sd_hijack_optimizations.py +++ b/modules/sd_hijack_optimizations.py @@ -94,39 +94,17 @@ def split_cross_attention_forward(self, x, context=None, mask=None): return self.to_out(r2) -def _maybe_init(self, x): - """ - Initialize the attention operator, if required We expect the head dimension to be exposed here, meaning that x - : B, Head, Length - """ - if self.attention_op is not None: - return - _, M, K = x.shape - try: - self.attention_op = xformers.ops.AttentionOpDispatch( - dtype=x.dtype, - device=x.device, - k=K, - attn_bias_type=type(None), - has_dropout=False, - kv_len=M, - q_len=M, - ).op - except NotImplementedError as err: - raise NotImplementedError(f"Please install xformers with the flash attention / cutlass components.\n{err}") - def xformers_attention_forward(self, x, context=None, mask=None): h = self.heads q_in = self.to_q(x) context = default(context, x) k_in = self.to_k(context) v_in = self.to_v(context) - q, k, v = map(lambda t: rearrange(t, 'b n (h d) -> (b h) n d', h=h), (q_in, k_in, v_in)) + q, k, v = map(lambda t: rearrange(t, 'b n (h d) -> b n h d', h=h), (q_in, k_in, v_in)) del q_in, k_in, v_in - self._maybe_init(q) - out = xformers.ops.memory_efficient_attention(q, k, v, attn_bias=None, op=self.attention_op) + out = xformers.ops.memory_efficient_attention(q, k, v, attn_bias=None) - out = rearrange(out, '(b h) n d -> b n (h d)', h=h) + out = rearrange(out, 'b n h d -> b n (h d)', h=h) return self.to_out(out) def cross_attention_attnblock_forward(self, x): -- cgit v1.2.1 From b70eaeb2005a5a9593119e7fd32b8072c2a208d5 Mon Sep 17 00:00:00 2001 From: C43H66N12O12S2 <36072735+C43H66N12O12S2@users.noreply.github.com> Date: Sat, 8 Oct 2022 04:10:35 +0300 Subject: delete broken and unnecessary aliases --- modules/sd_hijack.py | 10 ++++------ 1 file changed, 4 insertions(+), 6 deletions(-) (limited to 'modules') diff --git a/modules/sd_hijack.py b/modules/sd_hijack.py index cbdb9d3c..0e99c319 100644 --- a/modules/sd_hijack.py +++ b/modules/sd_hijack.py @@ -21,16 +21,14 @@ diffusionmodules_model_AttnBlock_forward = ldm.modules.diffusionmodules.model.At def apply_optimizations(): ldm.modules.diffusionmodules.model.nonlinearity = silu - if cmd_opts.opt_split_attention_v1: + if not cmd_opts.disable_opt_xformers_attention and not (cmd_opts.opt_split_attention or torch.version.hip): + ldm.modules.attention.CrossAttention.forward = sd_hijack_optimizations.xformers_attention_forward + ldm.modules.diffusionmodules.model.AttnBlock.forward = sd_hijack_optimizations.cross_attention_attnblock_forward + elif cmd_opts.opt_split_attention_v1: ldm.modules.attention.CrossAttention.forward = sd_hijack_optimizations.split_cross_attention_forward_v1 elif cmd_opts.opt_split_attention: ldm.modules.attention_CrossAttention_forward = sd_hijack_optimizations.split_cross_attention_forward ldm.modules.diffusionmodules.model.AttnBlock.forward = sd_hijack_optimizations.cross_attention_attnblock_forward - elif not cmd_opts.disable_opt_xformers_attention and not (cmd_opts.opt_split_attention or torch.version.hip): - ldm.modules.attention.CrossAttention.forward = sd_hijack_optimizations.xformers_attention_forward - ldm.modules.attention.CrossAttention._maybe_init = sd_hijack_optimizations._maybe_init - ldm.modules.attention.CrossAttention.attention_op = None - ldm.modules.diffusionmodules.model.AttnBlock.forward = sd_hijack_optimizations.cross_attention_attnblock_forward def undo_optimizations(): -- cgit v1.2.1 From f2055cb1d4ce45d7aaacc49d8ab5bec7791a8f47 Mon Sep 17 00:00:00 2001 From: brkirch Date: Sat, 8 Oct 2022 01:47:02 -0400 Subject: Add hypernetwork support to split cross attention v1 * Add hypernetwork support to split_cross_attention_forward_v1 * Fix device check in esrgan_model.py to use devices.device_esrgan instead of shared.device --- modules/esrgan_model.py | 2 +- modules/sd_hijack_optimizations.py | 18 ++++++++++++++---- 2 files changed, 15 insertions(+), 5 deletions(-) (limited to 'modules') diff --git a/modules/esrgan_model.py b/modules/esrgan_model.py index d17e730f..28548124 100644 --- a/modules/esrgan_model.py +++ b/modules/esrgan_model.py @@ -111,7 +111,7 @@ class UpscalerESRGAN(Upscaler): print("Unable to load %s from %s" % (self.model_path, filename)) return None - pretrained_net = torch.load(filename, map_location='cpu' if shared.device.type == 'mps' else None) + pretrained_net = torch.load(filename, map_location='cpu' if devices.device_esrgan.type == 'mps' else None) crt_model = arch.RRDBNet(3, 3, 64, 23, gc=32) pretrained_net = fix_model_layers(crt_model, pretrained_net) diff --git a/modules/sd_hijack_optimizations.py b/modules/sd_hijack_optimizations.py index d9cca485..3351c740 100644 --- a/modules/sd_hijack_optimizations.py +++ b/modules/sd_hijack_optimizations.py @@ -12,13 +12,22 @@ from modules import shared def split_cross_attention_forward_v1(self, x, context=None, mask=None): h = self.heads - q = self.to_q(x) + q_in = self.to_q(x) context = default(context, x) - k = self.to_k(context) - v = self.to_v(context) + + hypernetwork = shared.selected_hypernetwork() + hypernetwork_layers = (hypernetwork.layers if hypernetwork is not None else {}).get(context.shape[2], None) + + if hypernetwork_layers is not None: + k_in = self.to_k(hypernetwork_layers[0](context)) + v_in = self.to_v(hypernetwork_layers[1](context)) + else: + k_in = self.to_k(context) + v_in = self.to_v(context) del context, x - q, k, v = map(lambda t: rearrange(t, 'b n (h d) -> (b h) n d', h=h), (q, k, v)) + q, k, v = map(lambda t: rearrange(t, 'b n (h d) -> (b h) n d', h=h), (q_in, k_in, v_in)) + del q_in, k_in, v_in r1 = torch.zeros(q.shape[0], q.shape[1], v.shape[2], device=q.device) for i in range(0, q.shape[0], 2): @@ -31,6 +40,7 @@ def split_cross_attention_forward_v1(self, x, context=None, mask=None): r1[i:end] = einsum('b i j, b j d -> b i d', s2, v[i:end]) del s2 + del q, k, v r2 = rearrange(r1, '(b h) n d -> b n (h d)', h=h) del r1 -- cgit v1.2.1 From 5d54f35c583bd5a3b0ee271a862827f1ca81ef09 Mon Sep 17 00:00:00 2001 From: C43H66N12O12S2 <36072735+C43H66N12O12S2@users.noreply.github.com> Date: Sat, 8 Oct 2022 11:55:02 +0300 Subject: add xformers attnblock and hypernetwork support --- modules/sd_hijack_optimizations.py | 20 ++++++++++++++++++-- 1 file changed, 18 insertions(+), 2 deletions(-) (limited to 'modules') diff --git a/modules/sd_hijack_optimizations.py b/modules/sd_hijack_optimizations.py index 7fb4a45e..c78d5838 100644 --- a/modules/sd_hijack_optimizations.py +++ b/modules/sd_hijack_optimizations.py @@ -98,8 +98,14 @@ def xformers_attention_forward(self, x, context=None, mask=None): h = self.heads q_in = self.to_q(x) context = default(context, x) - k_in = self.to_k(context) - v_in = self.to_v(context) + hypernetwork = shared.selected_hypernetwork() + hypernetwork_layers = (hypernetwork.layers if hypernetwork is not None else {}).get(context.shape[2], None) + if hypernetwork_layers is not None: + k_in = self.to_k(hypernetwork_layers[0](context)) + v_in = self.to_v(hypernetwork_layers[1](context)) + else: + k_in = self.to_k(context) + v_in = self.to_v(context) q, k, v = map(lambda t: rearrange(t, 'b n (h d) -> b n h d', h=h), (q_in, k_in, v_in)) del q_in, k_in, v_in out = xformers.ops.memory_efficient_attention(q, k, v, attn_bias=None) @@ -169,3 +175,13 @@ def cross_attention_attnblock_forward(self, x): h3 += x return h3 + + def xformers_attnblock_forward(self, x): + h_ = x + h_ = self.norm(h_) + q1 = self.q(h_).contiguous() + k1 = self.k(h_).contiguous() + v = self.v(h_).contiguous() + out = xformers.ops.memory_efficient_attention(q1, k1, v) + out = self.proj_out(out) + return x+out -- cgit v1.2.1 From 76a616fa6b814c681eaf6edc87eb3001b8c2b6be Mon Sep 17 00:00:00 2001 From: C43H66N12O12S2 <36072735+C43H66N12O12S2@users.noreply.github.com> Date: Sat, 8 Oct 2022 11:55:38 +0300 Subject: Update sd_hijack_optimizations.py --- modules/sd_hijack_optimizations.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) (limited to 'modules') diff --git a/modules/sd_hijack_optimizations.py b/modules/sd_hijack_optimizations.py index c78d5838..ee58c7e4 100644 --- a/modules/sd_hijack_optimizations.py +++ b/modules/sd_hijack_optimizations.py @@ -176,7 +176,7 @@ def cross_attention_attnblock_forward(self, x): return h3 - def xformers_attnblock_forward(self, x): +def xformers_attnblock_forward(self, x): h_ = x h_ = self.norm(h_) q1 = self.q(h_).contiguous() -- cgit v1.2.1 From 91d66f5520df416db718103d460550ad495e952d Mon Sep 17 00:00:00 2001 From: C43H66N12O12S2 <36072735+C43H66N12O12S2@users.noreply.github.com> Date: Sat, 8 Oct 2022 11:56:01 +0300 Subject: use new attnblock for xformers path --- modules/sd_hijack.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) (limited to 'modules') diff --git a/modules/sd_hijack.py b/modules/sd_hijack.py index 0e99c319..3da8c8ce 100644 --- a/modules/sd_hijack.py +++ b/modules/sd_hijack.py @@ -23,7 +23,7 @@ def apply_optimizations(): ldm.modules.diffusionmodules.model.nonlinearity = silu if not cmd_opts.disable_opt_xformers_attention and not (cmd_opts.opt_split_attention or torch.version.hip): ldm.modules.attention.CrossAttention.forward = sd_hijack_optimizations.xformers_attention_forward - ldm.modules.diffusionmodules.model.AttnBlock.forward = sd_hijack_optimizations.cross_attention_attnblock_forward + ldm.modules.diffusionmodules.model.AttnBlock.forward = sd_hijack_optimizations.xformers_attnblock_forward elif cmd_opts.opt_split_attention_v1: ldm.modules.attention.CrossAttention.forward = sd_hijack_optimizations.split_cross_attention_forward_v1 elif cmd_opts.opt_split_attention: -- cgit v1.2.1 From 616b7218f7c469d25c138634472017a7e18e742e Mon Sep 17 00:00:00 2001 From: leko Date: Fri, 7 Oct 2022 23:09:21 +0800 Subject: fix: handles when state_dict does not exist --- modules/sd_models.py | 6 +++++- 1 file changed, 5 insertions(+), 1 deletion(-) (limited to 'modules') diff --git a/modules/sd_models.py b/modules/sd_models.py index 8f794b47..9409d070 100644 --- a/modules/sd_models.py +++ b/modules/sd_models.py @@ -122,7 +122,11 @@ def load_model_weights(model, checkpoint_file, sd_model_hash): pl_sd = torch.load(checkpoint_file, map_location="cpu") if "global_step" in pl_sd: print(f"Global Step: {pl_sd['global_step']}") - sd = pl_sd["state_dict"] + + if "state_dict" in pl_sd: + sd = pl_sd["state_dict"] + else: + sd = pl_sd model.load_state_dict(sd, strict=False) -- cgit v1.2.1 From 706d5944a075a6523ea7f00165d630efc085ca22 Mon Sep 17 00:00:00 2001 From: AUTOMATIC <16777216c@gmail.com> Date: Sat, 8 Oct 2022 13:38:57 +0300 Subject: let user choose his own prompt token count limit --- modules/processing.py | 6 ++++++ modules/sd_hijack.py | 13 +++++++------ modules/shared.py | 5 +++-- 3 files changed, 16 insertions(+), 8 deletions(-) (limited to 'modules') diff --git a/modules/processing.py b/modules/processing.py index f773a30e..d814d5ac 100644 --- a/modules/processing.py +++ b/modules/processing.py @@ -123,6 +123,7 @@ class Processed: self.index_of_first_image = index_of_first_image self.styles = p.styles self.job_timestamp = state.job_timestamp + self.max_prompt_tokens = opts.max_prompt_tokens self.eta = p.eta self.ddim_discretize = p.ddim_discretize @@ -141,6 +142,7 @@ class Processed: self.all_subseeds = all_subseeds or [self.subseed] self.infotexts = infotexts or [info] + def js(self): obj = { "prompt": self.prompt, @@ -169,6 +171,7 @@ class Processed: "infotexts": self.infotexts, "styles": self.styles, "job_timestamp": self.job_timestamp, + "max_prompt_tokens": self.max_prompt_tokens, } return json.dumps(obj) @@ -266,6 +269,8 @@ def fix_seed(p): def create_infotext(p, all_prompts, all_seeds, all_subseeds, comments, iteration=0, position_in_batch=0): index = position_in_batch + iteration * p.batch_size + max_tokens = getattr(p, 'max_prompt_tokens', opts.max_prompt_tokens) + generation_params = { "Steps": p.steps, "Sampler": sd_samplers.samplers[p.sampler_index].name, @@ -281,6 +286,7 @@ def create_infotext(p, all_prompts, all_seeds, all_subseeds, comments, iteration "Seed resize from": (None if p.seed_resize_from_w == 0 or p.seed_resize_from_h == 0 else f"{p.seed_resize_from_w}x{p.seed_resize_from_h}"), "Denoising strength": getattr(p, 'denoising_strength', None), "Eta": (None if p.sampler is None or p.sampler.eta == p.sampler.default_eta else p.sampler.eta), + "Max tokens": (None if max_tokens == shared.vanilla_max_prompt_tokens else max_tokens) } generation_params.update(p.extra_generation_params) diff --git a/modules/sd_hijack.py b/modules/sd_hijack.py index d68f89cc..340329c0 100644 --- a/modules/sd_hijack.py +++ b/modules/sd_hijack.py @@ -18,7 +18,6 @@ attention_CrossAttention_forward = ldm.modules.attention.CrossAttention.forward diffusionmodules_model_nonlinearity = ldm.modules.diffusionmodules.model.nonlinearity diffusionmodules_model_AttnBlock_forward = ldm.modules.diffusionmodules.model.AttnBlock.forward - def apply_optimizations(): undo_optimizations() @@ -83,7 +82,7 @@ class StableDiffusionModelHijack: layer.padding_mode = 'circular' if enable else 'zeros' def tokenize(self, text): - max_length = self.clip.max_length - 2 + max_length = opts.max_prompt_tokens - 2 _, remade_batch_tokens, _, _, _, token_count = self.clip.process_text([text]) return remade_batch_tokens[0], token_count, max_length @@ -94,7 +93,6 @@ class FrozenCLIPEmbedderWithCustomWords(torch.nn.Module): self.wrapped = wrapped self.hijack: StableDiffusionModelHijack = hijack self.tokenizer = wrapped.tokenizer - self.max_length = wrapped.max_length self.token_mults = {} tokens_with_parens = [(k, v) for k, v in self.tokenizer.get_vocab().items() if '(' in k or ')' in k or '[' in k or ']' in k] @@ -116,7 +114,7 @@ class FrozenCLIPEmbedderWithCustomWords(torch.nn.Module): def tokenize_line(self, line, used_custom_terms, hijack_comments): id_start = self.wrapped.tokenizer.bos_token_id id_end = self.wrapped.tokenizer.eos_token_id - maxlen = self.wrapped.max_length + maxlen = opts.max_prompt_tokens if opts.enable_emphasis: parsed = prompt_parser.parse_prompt_attention(line) @@ -191,7 +189,7 @@ class FrozenCLIPEmbedderWithCustomWords(torch.nn.Module): def process_text_old(self, text): id_start = self.wrapped.tokenizer.bos_token_id id_end = self.wrapped.tokenizer.eos_token_id - maxlen = self.wrapped.max_length + maxlen = self.wrapped.max_length # you get to stay at 77 used_custom_terms = [] remade_batch_tokens = [] overflowing_words = [] @@ -268,8 +266,11 @@ class FrozenCLIPEmbedderWithCustomWords(torch.nn.Module): if len(used_custom_terms) > 0: self.hijack.comments.append("Used embeddings: " + ", ".join([f'{word} [{checksum}]' for word, checksum in used_custom_terms])) + position_ids_array = [min(x, 75) for x in range(len(remade_batch_tokens[0])-1)] + [76] + position_ids = torch.asarray(position_ids_array, device=devices.device).expand((1, -1)) + tokens = torch.asarray(remade_batch_tokens).to(device) - outputs = self.wrapped.transformer(input_ids=tokens) + outputs = self.wrapped.transformer(input_ids=tokens, position_ids=position_ids) z = outputs.last_hidden_state # restoring original mean is likely not correct, but it seems to work well to prevent artifacts that happen otherwise diff --git a/modules/shared.py b/modules/shared.py index 879d8424..864e772c 100644 --- a/modules/shared.py +++ b/modules/shared.py @@ -118,8 +118,8 @@ prompt_styles = modules.styles.StyleDatabase(styles_filename) interrogator = modules.interrogate.InterrogateModels("interrogate") face_restorers = [] -# This was moved to webui.py with the other model "setup" calls. -# modules.sd_models.list_models() + +vanilla_max_prompt_tokens = 77 def realesrgan_models_names(): @@ -221,6 +221,7 @@ options_templates.update(options_section(('sd', "Stable Diffusion"), { "use_old_emphasis_implementation": OptionInfo(False, "Use old emphasis implementation. Can be useful to reproduce old seeds."), "enable_batch_seeds": OptionInfo(True, "Make K-diffusion samplers produce same images in a batch as when making a single image"), "filter_nsfw": OptionInfo(False, "Filter NSFW content"), + "max_prompt_tokens": OptionInfo(vanilla_max_prompt_tokens, f"Max prompt token count. Two tokens are reserved for for start and end. Default is {vanilla_max_prompt_tokens}. Setting this to a different value will result in different pictures for same seed.", gr.Number, {"precision": 0}), "random_artist_categories": OptionInfo([], "Allowed categories for random artists selection when using the Roll button", gr.CheckboxGroup, {"choices": artist_db.categories()}), })) -- cgit v1.2.1 From 786d9f63aaa4515df82eb2cf357ea92f3dae1e29 Mon Sep 17 00:00:00 2001 From: Trung Ngo Date: Tue, 4 Oct 2022 22:56:30 -0500 Subject: Add button to skip the current iteration --- modules/img2img.py | 4 ++++ modules/processing.py | 4 ++++ modules/shared.py | 5 +++++ modules/ui.py | 8 ++++++++ 4 files changed, 21 insertions(+) (limited to 'modules') diff --git a/modules/img2img.py b/modules/img2img.py index da212d72..e60b7e0f 100644 --- a/modules/img2img.py +++ b/modules/img2img.py @@ -32,6 +32,10 @@ def process_batch(p, input_dir, output_dir, args): for i, image in enumerate(images): state.job = f"{i+1} out of {len(images)}" + if state.skipped: + state.skipped = False + state.interrupted = False + continue if state.interrupted: break diff --git a/modules/processing.py b/modules/processing.py index d814d5ac..6805039c 100644 --- a/modules/processing.py +++ b/modules/processing.py @@ -355,6 +355,10 @@ def process_images(p: StableDiffusionProcessing) -> Processed: state.job_count = p.n_iter for n in range(p.n_iter): + if state.skipped: + state.skipped = False + state.interrupted = False + if state.interrupted: break diff --git a/modules/shared.py b/modules/shared.py index 864e772c..7f802bd9 100644 --- a/modules/shared.py +++ b/modules/shared.py @@ -84,6 +84,7 @@ def selected_hypernetwork(): class State: + skipped = False interrupted = False job = "" job_no = 0 @@ -96,6 +97,10 @@ class State: current_image_sampling_step = 0 textinfo = None + def skip(self): + self.skipped = True + self.interrupted = True + def interrupt(self): self.interrupted = True diff --git a/modules/ui.py b/modules/ui.py index 4f18126f..e3e62fdd 100644 --- a/modules/ui.py +++ b/modules/ui.py @@ -191,6 +191,7 @@ def wrap_gradio_call(func, extra_outputs=None): # last item is always HTML res[-1] += f"

Time taken: {elapsed_text}

{vram_html}
" + shared.state.skipped = False shared.state.interrupted = False shared.state.job_count = 0 @@ -411,9 +412,16 @@ def create_toprow(is_img2img): with gr.Column(scale=1): with gr.Row(): + skip = gr.Button('Skip', elem_id=f"{id_part}_skip") interrupt = gr.Button('Interrupt', elem_id=f"{id_part}_interrupt") submit = gr.Button('Generate', elem_id=f"{id_part}_generate", variant='primary') + skip.click( + fn=lambda: shared.state.skip(), + inputs=[], + outputs=[], + ) + interrupt.click( fn=lambda: shared.state.interrupt(), inputs=[], -- cgit v1.2.1 From 00117a07efbbe8482add12262a179326541467de Mon Sep 17 00:00:00 2001 From: Trung Ngo Date: Sat, 8 Oct 2022 05:33:21 -0500 Subject: check specifically for skipped --- modules/img2img.py | 2 -- modules/processing.py | 3 +-- modules/sd_samplers.py | 4 ++-- modules/shared.py | 1 - 4 files changed, 3 insertions(+), 7 deletions(-) (limited to 'modules') diff --git a/modules/img2img.py b/modules/img2img.py index e60b7e0f..24126774 100644 --- a/modules/img2img.py +++ b/modules/img2img.py @@ -34,8 +34,6 @@ def process_batch(p, input_dir, output_dir, args): state.job = f"{i+1} out of {len(images)}" if state.skipped: state.skipped = False - state.interrupted = False - continue if state.interrupted: break diff --git a/modules/processing.py b/modules/processing.py index 6805039c..3657fe69 100644 --- a/modules/processing.py +++ b/modules/processing.py @@ -357,7 +357,6 @@ def process_images(p: StableDiffusionProcessing) -> Processed: for n in range(p.n_iter): if state.skipped: state.skipped = False - state.interrupted = False if state.interrupted: break @@ -385,7 +384,7 @@ def process_images(p: StableDiffusionProcessing) -> Processed: with devices.autocast(): samples_ddim = p.sample(conditioning=c, unconditional_conditioning=uc, seeds=seeds, subseeds=subseeds, subseed_strength=p.subseed_strength) - if state.interrupted: + if state.interrupted or state.skipped: # if we are interruped, sample returns just noise # use the image collected previously in sampler loop diff --git a/modules/sd_samplers.py b/modules/sd_samplers.py index df17e93c..13a8b322 100644 --- a/modules/sd_samplers.py +++ b/modules/sd_samplers.py @@ -106,7 +106,7 @@ def extended_tdqm(sequence, *args, desc=None, **kwargs): seq = sequence if cmd_opts.disable_console_progressbars else tqdm.tqdm(sequence, *args, desc=state.job, file=shared.progress_print_out, **kwargs) for x in seq: - if state.interrupted: + if state.interrupted or state.skipped: break yield x @@ -254,7 +254,7 @@ def extended_trange(sampler, count, *args, **kwargs): seq = range(count) if cmd_opts.disable_console_progressbars else tqdm.trange(count, *args, desc=state.job, file=shared.progress_print_out, **kwargs) for x in seq: - if state.interrupted: + if state.interrupted or state.skipped: break if sampler.stop_at is not None and x > sampler.stop_at: diff --git a/modules/shared.py b/modules/shared.py index 7f802bd9..ca462628 100644 --- a/modules/shared.py +++ b/modules/shared.py @@ -99,7 +99,6 @@ class State: def skip(self): self.skipped = True - self.interrupted = True def interrupt(self): self.interrupted = True -- cgit v1.2.1 From 4999eb2ef9b30e8c42ca7e4a94d4bbffe4d1f015 Mon Sep 17 00:00:00 2001 From: AUTOMATIC <16777216c@gmail.com> Date: Sat, 8 Oct 2022 14:25:47 +0300 Subject: do not let user choose his own prompt token count limit --- modules/processing.py | 5 ----- modules/sd_hijack.py | 25 ++++++++++++------------- modules/shared.py | 3 --- 3 files changed, 12 insertions(+), 21 deletions(-) (limited to 'modules') diff --git a/modules/processing.py b/modules/processing.py index 3657fe69..d5162ddc 100644 --- a/modules/processing.py +++ b/modules/processing.py @@ -123,7 +123,6 @@ class Processed: self.index_of_first_image = index_of_first_image self.styles = p.styles self.job_timestamp = state.job_timestamp - self.max_prompt_tokens = opts.max_prompt_tokens self.eta = p.eta self.ddim_discretize = p.ddim_discretize @@ -171,7 +170,6 @@ class Processed: "infotexts": self.infotexts, "styles": self.styles, "job_timestamp": self.job_timestamp, - "max_prompt_tokens": self.max_prompt_tokens, } return json.dumps(obj) @@ -269,8 +267,6 @@ def fix_seed(p): def create_infotext(p, all_prompts, all_seeds, all_subseeds, comments, iteration=0, position_in_batch=0): index = position_in_batch + iteration * p.batch_size - max_tokens = getattr(p, 'max_prompt_tokens', opts.max_prompt_tokens) - generation_params = { "Steps": p.steps, "Sampler": sd_samplers.samplers[p.sampler_index].name, @@ -286,7 +282,6 @@ def create_infotext(p, all_prompts, all_seeds, all_subseeds, comments, iteration "Seed resize from": (None if p.seed_resize_from_w == 0 or p.seed_resize_from_h == 0 else f"{p.seed_resize_from_w}x{p.seed_resize_from_h}"), "Denoising strength": getattr(p, 'denoising_strength', None), "Eta": (None if p.sampler is None or p.sampler.eta == p.sampler.default_eta else p.sampler.eta), - "Max tokens": (None if max_tokens == shared.vanilla_max_prompt_tokens else max_tokens) } generation_params.update(p.extra_generation_params) diff --git a/modules/sd_hijack.py b/modules/sd_hijack.py index 340329c0..2c1332c9 100644 --- a/modules/sd_hijack.py +++ b/modules/sd_hijack.py @@ -36,6 +36,13 @@ def undo_optimizations(): ldm.modules.diffusionmodules.model.AttnBlock.forward = diffusionmodules_model_AttnBlock_forward +def get_target_prompt_token_count(token_count): + if token_count < 75: + return 75 + + return math.ceil(token_count / 10) * 10 + + class StableDiffusionModelHijack: fixes = None comments = [] @@ -84,7 +91,7 @@ class StableDiffusionModelHijack: def tokenize(self, text): max_length = opts.max_prompt_tokens - 2 _, remade_batch_tokens, _, _, _, token_count = self.clip.process_text([text]) - return remade_batch_tokens[0], token_count, max_length + return remade_batch_tokens[0], token_count, get_target_prompt_token_count(token_count) class FrozenCLIPEmbedderWithCustomWords(torch.nn.Module): @@ -114,7 +121,6 @@ class FrozenCLIPEmbedderWithCustomWords(torch.nn.Module): def tokenize_line(self, line, used_custom_terms, hijack_comments): id_start = self.wrapped.tokenizer.bos_token_id id_end = self.wrapped.tokenizer.eos_token_id - maxlen = opts.max_prompt_tokens if opts.enable_emphasis: parsed = prompt_parser.parse_prompt_attention(line) @@ -146,19 +152,12 @@ class FrozenCLIPEmbedderWithCustomWords(torch.nn.Module): used_custom_terms.append((embedding.name, embedding.checksum())) i += embedding_length_in_tokens - if len(remade_tokens) > maxlen - 2: - vocab = {v: k for k, v in self.wrapped.tokenizer.get_vocab().items()} - ovf = remade_tokens[maxlen - 2:] - overflowing_words = [vocab.get(int(x), "") for x in ovf] - overflowing_text = self.wrapped.tokenizer.convert_tokens_to_string(''.join(overflowing_words)) - hijack_comments.append(f"Warning: too many input tokens; some ({len(overflowing_words)}) have been truncated:\n{overflowing_text}\n") - token_count = len(remade_tokens) - remade_tokens = remade_tokens + [id_end] * (maxlen - 2 - len(remade_tokens)) - remade_tokens = [id_start] + remade_tokens[0:maxlen - 2] + [id_end] + prompt_target_length = get_target_prompt_token_count(token_count) + tokens_to_add = prompt_target_length - len(remade_tokens) + 1 - multipliers = multipliers + [1.0] * (maxlen - 2 - len(multipliers)) - multipliers = [1.0] + multipliers[0:maxlen - 2] + [1.0] + remade_tokens = [id_start] + remade_tokens + [id_end] * tokens_to_add + multipliers = [1.0] + multipliers + [1.0] * tokens_to_add return remade_tokens, fixes, multipliers, token_count diff --git a/modules/shared.py b/modules/shared.py index ca462628..475d7e52 100644 --- a/modules/shared.py +++ b/modules/shared.py @@ -123,8 +123,6 @@ interrogator = modules.interrogate.InterrogateModels("interrogate") face_restorers = [] -vanilla_max_prompt_tokens = 77 - def realesrgan_models_names(): import modules.realesrgan_model @@ -225,7 +223,6 @@ options_templates.update(options_section(('sd', "Stable Diffusion"), { "use_old_emphasis_implementation": OptionInfo(False, "Use old emphasis implementation. Can be useful to reproduce old seeds."), "enable_batch_seeds": OptionInfo(True, "Make K-diffusion samplers produce same images in a batch as when making a single image"), "filter_nsfw": OptionInfo(False, "Filter NSFW content"), - "max_prompt_tokens": OptionInfo(vanilla_max_prompt_tokens, f"Max prompt token count. Two tokens are reserved for for start and end. Default is {vanilla_max_prompt_tokens}. Setting this to a different value will result in different pictures for same seed.", gr.Number, {"precision": 0}), "random_artist_categories": OptionInfo([], "Allowed categories for random artists selection when using the Roll button", gr.CheckboxGroup, {"choices": artist_db.categories()}), })) -- cgit v1.2.1 From 77f4237d1c3af1756e7dab2699e3dcebad5619d6 Mon Sep 17 00:00:00 2001 From: AUTOMATIC <16777216c@gmail.com> Date: Sat, 8 Oct 2022 15:25:59 +0300 Subject: fix bugs related to variable prompt lengths --- modules/sd_hijack.py | 14 +++++++++----- modules/sd_samplers.py | 35 ++++++++++++++++++++++++++++------- 2 files changed, 37 insertions(+), 12 deletions(-) (limited to 'modules') diff --git a/modules/sd_hijack.py b/modules/sd_hijack.py index 2c1332c9..7e7fde0f 100644 --- a/modules/sd_hijack.py +++ b/modules/sd_hijack.py @@ -89,7 +89,6 @@ class StableDiffusionModelHijack: layer.padding_mode = 'circular' if enable else 'zeros' def tokenize(self, text): - max_length = opts.max_prompt_tokens - 2 _, remade_batch_tokens, _, _, _, token_count = self.clip.process_text([text]) return remade_batch_tokens[0], token_count, get_target_prompt_token_count(token_count) @@ -174,7 +173,8 @@ class FrozenCLIPEmbedderWithCustomWords(torch.nn.Module): if line in cache: remade_tokens, fixes, multipliers = cache[line] else: - remade_tokens, fixes, multipliers, token_count = self.tokenize_line(line, used_custom_terms, hijack_comments) + remade_tokens, fixes, multipliers, current_token_count = self.tokenize_line(line, used_custom_terms, hijack_comments) + token_count = max(current_token_count, token_count) cache[line] = (remade_tokens, fixes, multipliers) @@ -265,15 +265,19 @@ class FrozenCLIPEmbedderWithCustomWords(torch.nn.Module): if len(used_custom_terms) > 0: self.hijack.comments.append("Used embeddings: " + ", ".join([f'{word} [{checksum}]' for word, checksum in used_custom_terms])) - position_ids_array = [min(x, 75) for x in range(len(remade_batch_tokens[0])-1)] + [76] + target_token_count = get_target_prompt_token_count(token_count) + 2 + + position_ids_array = [min(x, 75) for x in range(target_token_count-1)] + [76] position_ids = torch.asarray(position_ids_array, device=devices.device).expand((1, -1)) - tokens = torch.asarray(remade_batch_tokens).to(device) + remade_batch_tokens_of_same_length = [x + [self.wrapped.tokenizer.eos_token_id] * (target_token_count - len(x)) for x in remade_batch_tokens] + tokens = torch.asarray(remade_batch_tokens_of_same_length).to(device) outputs = self.wrapped.transformer(input_ids=tokens, position_ids=position_ids) z = outputs.last_hidden_state # restoring original mean is likely not correct, but it seems to work well to prevent artifacts that happen otherwise - batch_multipliers = torch.asarray(batch_multipliers).to(device) + batch_multipliers_of_same_length = [x + [1.0] * (target_token_count - len(x)) for x in batch_multipliers] + batch_multipliers = torch.asarray(batch_multipliers_of_same_length).to(device) original_mean = z.mean() z *= batch_multipliers.reshape(batch_multipliers.shape + (1,)).expand(z.shape) new_mean = z.mean() diff --git a/modules/sd_samplers.py b/modules/sd_samplers.py index 13a8b322..eade0dbb 100644 --- a/modules/sd_samplers.py +++ b/modules/sd_samplers.py @@ -142,6 +142,16 @@ class VanillaStableDiffusionSampler: assert all([len(conds) == 1 for conds in conds_list]), 'composition via AND is not supported for DDIM/PLMS samplers' cond = tensor + # for DDIM, shapes must match, we can't just process cond and uncond independently; + # filling unconditional_conditioning with repeats of the last vector to match length is + # not 100% correct but should work well enough + if unconditional_conditioning.shape[1] < cond.shape[1]: + last_vector = unconditional_conditioning[:, -1:] + last_vector_repeated = last_vector.repeat([1, cond.shape[1] - unconditional_conditioning.shape[1], 1]) + unconditional_conditioning = torch.hstack([unconditional_conditioning, last_vector_repeated]) + elif unconditional_conditioning.shape[1] > cond.shape[1]: + unconditional_conditioning = unconditional_conditioning[:, :cond.shape[1]] + if self.mask is not None: img_orig = self.sampler.model.q_sample(self.init_latent, ts) x_dec = img_orig * self.mask + self.nmask * x_dec @@ -221,18 +231,29 @@ class CFGDenoiser(torch.nn.Module): x_in = torch.cat([torch.stack([x[i] for _ in range(n)]) for i, n in enumerate(repeats)] + [x]) sigma_in = torch.cat([torch.stack([sigma[i] for _ in range(n)]) for i, n in enumerate(repeats)] + [sigma]) - cond_in = torch.cat([tensor, uncond]) - if shared.batch_cond_uncond: - x_out = self.inner_model(x_in, sigma_in, cond=cond_in) + if tensor.shape[1] == uncond.shape[1]: + cond_in = torch.cat([tensor, uncond]) + + if shared.batch_cond_uncond: + x_out = self.inner_model(x_in, sigma_in, cond=cond_in) + else: + x_out = torch.zeros_like(x_in) + for batch_offset in range(0, x_out.shape[0], batch_size): + a = batch_offset + b = a + batch_size + x_out[a:b] = self.inner_model(x_in[a:b], sigma_in[a:b], cond=cond_in[a:b]) else: x_out = torch.zeros_like(x_in) - for batch_offset in range(0, x_out.shape[0], batch_size): + batch_size = batch_size*2 if shared.batch_cond_uncond else batch_size + for batch_offset in range(0, tensor.shape[0], batch_size): a = batch_offset - b = a + batch_size - x_out[a:b] = self.inner_model(x_in[a:b], sigma_in[a:b], cond=cond_in[a:b]) + b = min(a + batch_size, tensor.shape[0]) + x_out[a:b] = self.inner_model(x_in[a:b], sigma_in[a:b], cond=tensor[a:b]) + + x_out[-uncond.shape[0]:] = self.inner_model(x_in[-uncond.shape[0]:], sigma_in[-uncond.shape[0]:], cond=uncond) - denoised_uncond = x_out[-batch_size:] + denoised_uncond = x_out[-uncond.shape[0]:] denoised = torch.clone(denoised_uncond) for i, conds in enumerate(conds_list): -- cgit v1.2.1 From 7001bffe0247804793dfabb69ac96d832572ccd0 Mon Sep 17 00:00:00 2001 From: AUTOMATIC <16777216c@gmail.com> Date: Sat, 8 Oct 2022 15:43:25 +0300 Subject: fix AND broken for long prompts --- modules/prompt_parser.py | 9 +++++++++ 1 file changed, 9 insertions(+) (limited to 'modules') diff --git a/modules/prompt_parser.py b/modules/prompt_parser.py index f00256f2..15666073 100644 --- a/modules/prompt_parser.py +++ b/modules/prompt_parser.py @@ -239,6 +239,15 @@ def reconstruct_multicond_batch(c: MulticondLearnedConditioning, current_step): conds_list.append(conds_for_batch) + # if prompts have wildly different lengths above the limit we'll get tensors fo different shapes + # and won't be able to torch.stack them. So this fixes that. + token_count = max([x.shape[0] for x in tensors]) + for i in range(len(tensors)): + if tensors[i].shape[0] != token_count: + last_vector = tensors[i][-1:] + last_vector_repeated = last_vector.repeat([token_count - tensors[i].shape[0], 1]) + tensors[i] = torch.vstack([tensors[i], last_vector_repeated]) + return conds_list, torch.stack(tensors).to(device=param.device, dtype=param.dtype) -- cgit v1.2.1 From 772db721a52da374d627b60994222051f26c27a7 Mon Sep 17 00:00:00 2001 From: ddPn08 Date: Fri, 7 Oct 2022 23:02:07 +0900 Subject: fix glob path in hypernetwork.py --- modules/hypernetwork.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) (limited to 'modules') diff --git a/modules/hypernetwork.py b/modules/hypernetwork.py index c7b86682..7f062242 100644 --- a/modules/hypernetwork.py +++ b/modules/hypernetwork.py @@ -43,7 +43,7 @@ class Hypernetwork: def load_hypernetworks(path): res = {} - for filename in glob.iglob(path + '**/*.pt', recursive=True): + for filename in glob.iglob(os.path.join(path, '**/*.pt'), recursive=True): try: hn = Hypernetwork(filename) res[hn.name] = hn -- cgit v1.2.1 From 5f85a74b00c0154bfd559dc67edfa7e30342b7c9 Mon Sep 17 00:00:00 2001 From: MrCheeze Date: Fri, 7 Oct 2022 17:48:34 -0400 Subject: fix bug where when using prompt composition, hijack_comments generated before the final AND will be dropped --- modules/processing.py | 1 + modules/sd_hijack.py | 5 ++++- 2 files changed, 5 insertions(+), 1 deletion(-) (limited to 'modules') diff --git a/modules/processing.py b/modules/processing.py index d5162ddc..8240ee27 100644 --- a/modules/processing.py +++ b/modules/processing.py @@ -313,6 +313,7 @@ def process_images(p: StableDiffusionProcessing) -> Processed: os.makedirs(p.outpath_grids, exist_ok=True) modules.sd_hijack.model_hijack.apply_circular(p.tiling) + modules.sd_hijack.model_hijack.clear_comments() comments = {} diff --git a/modules/sd_hijack.py b/modules/sd_hijack.py index 7e7fde0f..ba808a39 100644 --- a/modules/sd_hijack.py +++ b/modules/sd_hijack.py @@ -88,6 +88,9 @@ class StableDiffusionModelHijack: for layer in [layer for layer in self.layers if type(layer) == torch.nn.Conv2d]: layer.padding_mode = 'circular' if enable else 'zeros' + def clear_comments(self): + self.comments = [] + def tokenize(self, text): _, remade_batch_tokens, _, _, _, token_count = self.clip.process_text([text]) return remade_batch_tokens[0], token_count, get_target_prompt_token_count(token_count) @@ -260,7 +263,7 @@ class FrozenCLIPEmbedderWithCustomWords(torch.nn.Module): batch_multipliers, remade_batch_tokens, used_custom_terms, hijack_comments, hijack_fixes, token_count = self.process_text(text) self.hijack.fixes = hijack_fixes - self.hijack.comments = hijack_comments + self.hijack.comments += hijack_comments if len(used_custom_terms) > 0: self.hijack.comments.append("Used embeddings: " + ", ".join([f'{word} [{checksum}]' for word, checksum in used_custom_terms])) -- cgit v1.2.1 From 26b459a3799c5cdf71ca8ed5315a99f69c69f02c Mon Sep 17 00:00:00 2001 From: C43H66N12O12S2 <36072735+C43H66N12O12S2@users.noreply.github.com> Date: Sat, 8 Oct 2022 16:20:04 +0300 Subject: default to split attention if cuda is available and xformers is not --- modules/sd_hijack.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) (limited to 'modules') diff --git a/modules/sd_hijack.py b/modules/sd_hijack.py index 3da8c8ce..04adcf03 100644 --- a/modules/sd_hijack.py +++ b/modules/sd_hijack.py @@ -21,12 +21,12 @@ diffusionmodules_model_AttnBlock_forward = ldm.modules.diffusionmodules.model.At def apply_optimizations(): ldm.modules.diffusionmodules.model.nonlinearity = silu - if not cmd_opts.disable_opt_xformers_attention and not (cmd_opts.opt_split_attention or torch.version.hip): + if not cmd_opts.disable_opt_xformers_attention and not (cmd_opts.opt_split_attention or torch.version.hip or shared.xformers_available): ldm.modules.attention.CrossAttention.forward = sd_hijack_optimizations.xformers_attention_forward ldm.modules.diffusionmodules.model.AttnBlock.forward = sd_hijack_optimizations.xformers_attnblock_forward elif cmd_opts.opt_split_attention_v1: ldm.modules.attention.CrossAttention.forward = sd_hijack_optimizations.split_cross_attention_forward_v1 - elif cmd_opts.opt_split_attention: + elif cmd_opts.opt_split_attention or torch.cuda.is_available(): ldm.modules.attention_CrossAttention_forward = sd_hijack_optimizations.split_cross_attention_forward ldm.modules.diffusionmodules.model.AttnBlock.forward = sd_hijack_optimizations.cross_attention_attnblock_forward -- cgit v1.2.1 From ddfa9a97865c732193023a71521c5b7b53d8571b Mon Sep 17 00:00:00 2001 From: C43H66N12O12S2 <36072735+C43H66N12O12S2@users.noreply.github.com> Date: Sat, 8 Oct 2022 16:20:41 +0300 Subject: add xformers_available shared variable --- modules/shared.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) (limited to 'modules') diff --git a/modules/shared.py b/modules/shared.py index 8cc3b2fe..6ed4b802 100644 --- a/modules/shared.py +++ b/modules/shared.py @@ -74,7 +74,7 @@ device = devices.device batch_cond_uncond = cmd_opts.always_batch_cond_uncond or not (cmd_opts.lowvram or cmd_opts.medvram) parallel_processing_allowed = not cmd_opts.lowvram and not cmd_opts.medvram - +xformers_available = False config_filename = cmd_opts.ui_settings_file -- cgit v1.2.1 From 69d0053583757ce2942d62de81e8b89e6be07840 Mon Sep 17 00:00:00 2001 From: C43H66N12O12S2 <36072735+C43H66N12O12S2@users.noreply.github.com> Date: Sat, 8 Oct 2022 16:21:40 +0300 Subject: update sd_hijack_opt to respect new env variables --- modules/sd_hijack_optimizations.py | 11 ++++++++--- 1 file changed, 8 insertions(+), 3 deletions(-) (limited to 'modules') diff --git a/modules/sd_hijack_optimizations.py b/modules/sd_hijack_optimizations.py index ee58c7e4..be09ec8f 100644 --- a/modules/sd_hijack_optimizations.py +++ b/modules/sd_hijack_optimizations.py @@ -1,9 +1,14 @@ import math import torch from torch import einsum -import xformers.ops -import functorch -xformers._is_functorch_available=True +try: + import xformers.ops + import functorch + xformers._is_functorch_available = True + shared.xformers_available = True +except: + print('Cannot find xformers, defaulting to split attention. Try setting --xformers in your webui-user file if you wish to install it.') + continue from ldm.util import default from einops import rearrange -- cgit v1.2.1 From 970de9ee6891ff586821d0d80dde01c2f6c681b3 Mon Sep 17 00:00:00 2001 From: C43H66N12O12S2 <36072735+C43H66N12O12S2@users.noreply.github.com> Date: Sat, 8 Oct 2022 16:29:43 +0300 Subject: Update sd_hijack.py --- modules/sd_hijack.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) (limited to 'modules') diff --git a/modules/sd_hijack.py b/modules/sd_hijack.py index 04adcf03..5b30539f 100644 --- a/modules/sd_hijack.py +++ b/modules/sd_hijack.py @@ -21,7 +21,7 @@ diffusionmodules_model_AttnBlock_forward = ldm.modules.diffusionmodules.model.At def apply_optimizations(): ldm.modules.diffusionmodules.model.nonlinearity = silu - if not cmd_opts.disable_opt_xformers_attention and not (cmd_opts.opt_split_attention or torch.version.hip or shared.xformers_available): + if not cmd_opts.disable_opt_xformers_attention and not (cmd_opts.opt_split_attention or torch.version.hip) and shared.xformers_available: ldm.modules.attention.CrossAttention.forward = sd_hijack_optimizations.xformers_attention_forward ldm.modules.diffusionmodules.model.AttnBlock.forward = sd_hijack_optimizations.xformers_attnblock_forward elif cmd_opts.opt_split_attention_v1: -- cgit v1.2.1 From 7ff1170a2e11b6f00f587407326db0b9f8f51adf Mon Sep 17 00:00:00 2001 From: AUTOMATIC <16777216c@gmail.com> Date: Sat, 8 Oct 2022 16:33:39 +0300 Subject: emergency fix for xformers (continue + shared) --- modules/sd_hijack_optimizations.py | 16 ++++++++-------- 1 file changed, 8 insertions(+), 8 deletions(-) (limited to 'modules') diff --git a/modules/sd_hijack_optimizations.py b/modules/sd_hijack_optimizations.py index e43e2c7a..05023b6f 100644 --- a/modules/sd_hijack_optimizations.py +++ b/modules/sd_hijack_optimizations.py @@ -1,19 +1,19 @@ import math import torch from torch import einsum -try: - import xformers.ops - import functorch - xformers._is_functorch_available = True - shared.xformers_available = True -except: - print('Cannot find xformers, defaulting to split attention. Try setting --xformers in your webui-user file if you wish to install it.') - continue + from ldm.util import default from einops import rearrange from modules import shared +try: + import xformers.ops + import functorch + xformers._is_functorch_available = True + shared.xformers_available = True +except Exception: + print('Cannot find xformers, defaulting to split attention. Try adding --xformers commandline argument to your webui-user file if you wish to install it.') # see https://github.com/basujindal/stable-diffusion/pull/117 for discussion def split_cross_attention_forward_v1(self, x, context=None, mask=None): -- cgit v1.2.1 From dc1117233ef8f9b25ff1ac40b158f20b70ba2fcb Mon Sep 17 00:00:00 2001 From: AUTOMATIC <16777216c@gmail.com> Date: Sat, 8 Oct 2022 17:02:18 +0300 Subject: simplify xfrmers options: --xformers to enable and that's it --- modules/sd_hijack.py | 2 +- modules/sd_hijack_optimizations.py | 20 +++++++++++++------- modules/shared.py | 2 +- 3 files changed, 15 insertions(+), 9 deletions(-) (limited to 'modules') diff --git a/modules/sd_hijack.py b/modules/sd_hijack.py index 5d93f7f6..91e98c16 100644 --- a/modules/sd_hijack.py +++ b/modules/sd_hijack.py @@ -22,7 +22,7 @@ def apply_optimizations(): undo_optimizations() ldm.modules.diffusionmodules.model.nonlinearity = silu - if not cmd_opts.disable_opt_xformers_attention and not (cmd_opts.opt_split_attention or torch.version.hip) and shared.xformers_available: + if cmd_opts.xformers and shared.xformers_available and not torch.version.hip: ldm.modules.attention.CrossAttention.forward = sd_hijack_optimizations.xformers_attention_forward ldm.modules.diffusionmodules.model.AttnBlock.forward = sd_hijack_optimizations.xformers_attnblock_forward elif cmd_opts.opt_split_attention_v1: diff --git a/modules/sd_hijack_optimizations.py b/modules/sd_hijack_optimizations.py index 05023b6f..d23d733b 100644 --- a/modules/sd_hijack_optimizations.py +++ b/modules/sd_hijack_optimizations.py @@ -1,4 +1,7 @@ import math +import sys +import traceback + import torch from torch import einsum @@ -7,13 +10,16 @@ from einops import rearrange from modules import shared -try: - import xformers.ops - import functorch - xformers._is_functorch_available = True - shared.xformers_available = True -except Exception: - print('Cannot find xformers, defaulting to split attention. Try adding --xformers commandline argument to your webui-user file if you wish to install it.') +if shared.cmd_opts.xformers: + try: + import xformers.ops + import functorch + xformers._is_functorch_available = True + shared.xformers_available = True + except Exception: + print("Cannot import xformers", file=sys.stderr) + print(traceback.format_exc(), file=sys.stderr) + # see https://github.com/basujindal/stable-diffusion/pull/117 for discussion def split_cross_attention_forward_v1(self, x, context=None, mask=None): diff --git a/modules/shared.py b/modules/shared.py index d68df751..02cb2722 100644 --- a/modules/shared.py +++ b/modules/shared.py @@ -43,7 +43,7 @@ parser.add_argument("--realesrgan-models-path", type=str, help="Path to director parser.add_argument("--scunet-models-path", type=str, help="Path to directory with ScuNET model file(s).", default=os.path.join(models_path, 'ScuNET')) parser.add_argument("--swinir-models-path", type=str, help="Path to directory with SwinIR model file(s).", default=os.path.join(models_path, 'SwinIR')) parser.add_argument("--ldsr-models-path", type=str, help="Path to directory with LDSR model file(s).", default=os.path.join(models_path, 'LDSR')) -parser.add_argument("--disable-opt-xformers-attention", action='store_true', help="force-disables xformers attention optimization") +parser.add_argument("--xformers", action='store_true', help="enable xformers for cross attention layers") parser.add_argument("--opt-split-attention", action='store_true', help="force-enables cross-attention layer optimization. By default, it's on for torch.cuda and off for other torch devices.") parser.add_argument("--disable-opt-split-attention", action='store_true', help="force-disables cross-attention layer optimization") parser.add_argument("--opt-split-attention-v1", action='store_true', help="enable older version of split attention optimization that does not consume all the VRAM it can find") -- cgit v1.2.1 From 27032c47df9c07ac21dd5b89fa7dc247bb8705b6 Mon Sep 17 00:00:00 2001 From: AUTOMATIC <16777216c@gmail.com> Date: Sat, 8 Oct 2022 17:10:05 +0300 Subject: restore old opt_split_attention/disable_opt_split_attention logic --- modules/sd_hijack.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) (limited to 'modules') diff --git a/modules/sd_hijack.py b/modules/sd_hijack.py index 91e98c16..335a2bcf 100644 --- a/modules/sd_hijack.py +++ b/modules/sd_hijack.py @@ -27,7 +27,7 @@ def apply_optimizations(): ldm.modules.diffusionmodules.model.AttnBlock.forward = sd_hijack_optimizations.xformers_attnblock_forward elif cmd_opts.opt_split_attention_v1: ldm.modules.attention.CrossAttention.forward = sd_hijack_optimizations.split_cross_attention_forward_v1 - elif cmd_opts.opt_split_attention or torch.cuda.is_available(): + elif not cmd_opts.disable_opt_split_attention and (cmd_opts.opt_split_attention or torch.cuda.is_available()): ldm.modules.attention_CrossAttention_forward = sd_hijack_optimizations.split_cross_attention_forward ldm.modules.diffusionmodules.model.AttnBlock.forward = sd_hijack_optimizations.cross_attention_attnblock_forward -- cgit v1.2.1 From 4f33289d0fc5aa3a197f4a4c926d03d44f0d597e Mon Sep 17 00:00:00 2001 From: Milly Date: Sat, 8 Oct 2022 22:48:15 +0900 Subject: Fixed typo --- modules/ui.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) (limited to 'modules') diff --git a/modules/ui.py b/modules/ui.py index e3e62fdd..ffd75f6a 100644 --- a/modules/ui.py +++ b/modules/ui.py @@ -946,7 +946,7 @@ def create_ui(wrap_gradio_gpu_call): custom_name = gr.Textbox(label="Custom Name (Optional)") interp_amount = gr.Slider(minimum=0.0, maximum=1.0, step=0.05, label='Interpolation Amount', value=0.3) interp_method = gr.Radio(choices=["Weighted Sum", "Sigmoid", "Inverse Sigmoid"], value="Weighted Sum", label="Interpolation Method") - save_as_half = gr.Checkbox(value=False, label="Safe as float16") + save_as_half = gr.Checkbox(value=False, label="Save as float16") modelmerger_merge = gr.Button(elem_id="modelmerger_merge", label="Merge", variant='primary') with gr.Column(variant='panel'): -- cgit v1.2.1 From cfc33f99d47d1f45af15499e5965834089d11858 Mon Sep 17 00:00:00 2001 From: AUTOMATIC <16777216c@gmail.com> Date: Sat, 8 Oct 2022 17:28:58 +0300 Subject: why did you do this --- modules/sd_hijack.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) (limited to 'modules') diff --git a/modules/sd_hijack.py b/modules/sd_hijack.py index 335a2bcf..ed271976 100644 --- a/modules/sd_hijack.py +++ b/modules/sd_hijack.py @@ -28,7 +28,7 @@ def apply_optimizations(): elif cmd_opts.opt_split_attention_v1: ldm.modules.attention.CrossAttention.forward = sd_hijack_optimizations.split_cross_attention_forward_v1 elif not cmd_opts.disable_opt_split_attention and (cmd_opts.opt_split_attention or torch.cuda.is_available()): - ldm.modules.attention_CrossAttention_forward = sd_hijack_optimizations.split_cross_attention_forward + ldm.modules.attention.CrossAttention.forward = sd_hijack_optimizations.split_cross_attention_forward ldm.modules.diffusionmodules.model.AttnBlock.forward = sd_hijack_optimizations.cross_attention_attnblock_forward -- cgit v1.2.1 From 017b6b8744f0771e498656ec043e12d5cc6969a7 Mon Sep 17 00:00:00 2001 From: C43H66N12O12S2 <36072735+C43H66N12O12S2@users.noreply.github.com> Date: Sat, 8 Oct 2022 17:27:21 +0300 Subject: check for ampere --- modules/sd_hijack.py | 7 ++++--- 1 file changed, 4 insertions(+), 3 deletions(-) (limited to 'modules') diff --git a/modules/sd_hijack.py b/modules/sd_hijack.py index ed271976..5e266d5e 100644 --- a/modules/sd_hijack.py +++ b/modules/sd_hijack.py @@ -22,9 +22,10 @@ def apply_optimizations(): undo_optimizations() ldm.modules.diffusionmodules.model.nonlinearity = silu - if cmd_opts.xformers and shared.xformers_available and not torch.version.hip: - ldm.modules.attention.CrossAttention.forward = sd_hijack_optimizations.xformers_attention_forward - ldm.modules.diffusionmodules.model.AttnBlock.forward = sd_hijack_optimizations.xformers_attnblock_forward + if cmd_opts.xformers and shared.xformers_available and torch.version.cuda: + if torch.cuda.get_device_capability(shared.device) == (8, 6): + ldm.modules.attention.CrossAttention.forward = sd_hijack_optimizations.xformers_attention_forward + ldm.modules.diffusionmodules.model.AttnBlock.forward = sd_hijack_optimizations.xformers_attnblock_forward elif cmd_opts.opt_split_attention_v1: ldm.modules.attention.CrossAttention.forward = sd_hijack_optimizations.split_cross_attention_forward_v1 elif not cmd_opts.disable_opt_split_attention and (cmd_opts.opt_split_attention or torch.cuda.is_available()): -- cgit v1.2.1 From cc0258aea7b6605be3648900063cfa96ed7c5ffa Mon Sep 17 00:00:00 2001 From: C43H66N12O12S2 <36072735+C43H66N12O12S2@users.noreply.github.com> Date: Sat, 8 Oct 2022 17:44:53 +0300 Subject: check for ampere without destroying the optimizations. again. --- modules/sd_hijack.py | 7 +++---- 1 file changed, 3 insertions(+), 4 deletions(-) (limited to 'modules') diff --git a/modules/sd_hijack.py b/modules/sd_hijack.py index 5e266d5e..a3e374f0 100644 --- a/modules/sd_hijack.py +++ b/modules/sd_hijack.py @@ -22,10 +22,9 @@ def apply_optimizations(): undo_optimizations() ldm.modules.diffusionmodules.model.nonlinearity = silu - if cmd_opts.xformers and shared.xformers_available and torch.version.cuda: - if torch.cuda.get_device_capability(shared.device) == (8, 6): - ldm.modules.attention.CrossAttention.forward = sd_hijack_optimizations.xformers_attention_forward - ldm.modules.diffusionmodules.model.AttnBlock.forward = sd_hijack_optimizations.xformers_attnblock_forward + if cmd_opts.xformers and shared.xformers_available and torch.version.cuda and torch.cuda.get_device_capability(shared.device) == (8, 6): + ldm.modules.attention.CrossAttention.forward = sd_hijack_optimizations.xformers_attention_forward + ldm.modules.diffusionmodules.model.AttnBlock.forward = sd_hijack_optimizations.xformers_attnblock_forward elif cmd_opts.opt_split_attention_v1: ldm.modules.attention.CrossAttention.forward = sd_hijack_optimizations.split_cross_attention_forward_v1 elif not cmd_opts.disable_opt_split_attention and (cmd_opts.opt_split_attention or torch.cuda.is_available()): -- cgit v1.2.1 From a5550f0213c3f145b1c984816ebcef92c48853ee Mon Sep 17 00:00:00 2001 From: Artem Zagidulin Date: Wed, 5 Oct 2022 19:10:39 +0300 Subject: alternate prompt --- modules/prompt_parser.py | 9 +++++++-- 1 file changed, 7 insertions(+), 2 deletions(-) (limited to 'modules') diff --git a/modules/prompt_parser.py b/modules/prompt_parser.py index 15666073..919d5d31 100644 --- a/modules/prompt_parser.py +++ b/modules/prompt_parser.py @@ -13,13 +13,14 @@ import lark schedule_parser = lark.Lark(r""" !start: (prompt | /[][():]/+)* -prompt: (emphasized | scheduled | plain | WHITESPACE)* +prompt: (emphasized | scheduled | alternate | plain | WHITESPACE)* !emphasized: "(" prompt ")" | "(" prompt ":" prompt ")" | "[" prompt "]" scheduled: "[" [prompt ":"] prompt ":" [WHITESPACE] NUMBER "]" +alternate: "[" prompt ("|" prompt)+ "]" WHITESPACE: /\s+/ -plain: /([^\\\[\]():]|\\.)+/ +plain: /([^\\\[\]():|]|\\.)+/ %import common.SIGNED_NUMBER -> NUMBER """) @@ -59,6 +60,8 @@ def get_learned_conditioning_prompt_schedules(prompts, steps): tree.children[-1] *= steps tree.children[-1] = min(steps, int(tree.children[-1])) l.append(tree.children[-1]) + def alternate(self, tree): + l.extend(range(1, steps+1)) CollectSteps().visit(tree) return sorted(set(l)) @@ -67,6 +70,8 @@ def get_learned_conditioning_prompt_schedules(prompts, steps): def scheduled(self, args): before, after, _, when = args yield before or () if step <= when else after + def alternate(self, args): + yield next(args[(step - 1)%len(args)]) def start(self, args): def flatten(x): if type(x) == str: -- cgit v1.2.1 From 01f8cb44474e454903c11718e6a4f33dbde34bb8 Mon Sep 17 00:00:00 2001 From: Greendayle Date: Sat, 8 Oct 2022 18:02:56 +0200 Subject: made deepdanbooru optional, added to readme, automatic download of deepbooru model --- modules/deepbooru.py | 20 ++++++++++---------- modules/shared.py | 1 + modules/ui.py | 19 ++++++++++++------- 3 files changed, 23 insertions(+), 17 deletions(-) (limited to 'modules') diff --git a/modules/deepbooru.py b/modules/deepbooru.py index 781b2249..7e3c0618 100644 --- a/modules/deepbooru.py +++ b/modules/deepbooru.py @@ -9,16 +9,16 @@ def _load_tf_and_return_tags(pil_image, threshold): import numpy as np this_folder = os.path.dirname(__file__) - model_path = os.path.join(this_folder, '..', 'models', 'deepbooru', 'deepdanbooru-v3-20211112-sgd-e28') - - model_good = False - for path_candidate in [model_path, os.path.dirname(model_path)]: - if os.path.exists(os.path.join(path_candidate, 'project.json')): - model_path = path_candidate - model_good = True - if not model_good: - return ("Download https://github.com/KichangKim/DeepDanbooru/releases/download/v3-20211112-sgd-e28/" - "deepdanbooru-v3-20211112-sgd-e28.zip unpack and put into models/deepbooru") + model_path = os.path.abspath(os.path.join(this_folder, '..', 'models', 'deepbooru')) + if not os.path.exists(os.path.join(model_path, 'project.json')): + # there is no point importing these every time + import zipfile + from basicsr.utils.download_util import load_file_from_url + load_file_from_url(r"https://github.com/KichangKim/DeepDanbooru/releases/download/v3-20211112-sgd-e28/deepdanbooru-v3-20211112-sgd-e28.zip", + model_path) + with zipfile.ZipFile(os.path.join(model_path, "deepdanbooru-v3-20211112-sgd-e28.zip"), "r") as zip_ref: + zip_ref.extractall(model_path) + os.remove(os.path.join(model_path, "deepdanbooru-v3-20211112-sgd-e28.zip")) tags = dd.project.load_tags_from_project(model_path) model = dd.project.load_model_from_project( diff --git a/modules/shared.py b/modules/shared.py index 02cb2722..c87b726e 100644 --- a/modules/shared.py +++ b/modules/shared.py @@ -44,6 +44,7 @@ parser.add_argument("--scunet-models-path", type=str, help="Path to directory wi parser.add_argument("--swinir-models-path", type=str, help="Path to directory with SwinIR model file(s).", default=os.path.join(models_path, 'SwinIR')) parser.add_argument("--ldsr-models-path", type=str, help="Path to directory with LDSR model file(s).", default=os.path.join(models_path, 'LDSR')) parser.add_argument("--xformers", action='store_true', help="enable xformers for cross attention layers") +parser.add_argument("--deepdanbooru", action='store_true', help="enable deepdanbooru interrogator") parser.add_argument("--opt-split-attention", action='store_true', help="force-enables cross-attention layer optimization. By default, it's on for torch.cuda and off for other torch devices.") parser.add_argument("--disable-opt-split-attention", action='store_true', help="force-disables cross-attention layer optimization") parser.add_argument("--opt-split-attention-v1", action='store_true', help="enable older version of split attention optimization that does not consume all the VRAM it can find") diff --git a/modules/ui.py b/modules/ui.py index 30583fe9..c5c11c3c 100644 --- a/modules/ui.py +++ b/modules/ui.py @@ -23,9 +23,10 @@ import gradio.utils import gradio.routes from modules import sd_hijack -from modules.deepbooru import get_deepbooru_tags from modules.paths import script_path from modules.shared import opts, cmd_opts +if cmd_opts.deepdanbooru: + from modules.deepbooru import get_deepbooru_tags import modules.shared as shared from modules.sd_samplers import samplers, samplers_for_img2img from modules.sd_hijack import model_hijack @@ -437,7 +438,10 @@ def create_toprow(is_img2img): with gr.Row(scale=1): if is_img2img: interrogate = gr.Button('Interrogate\nCLIP', elem_id="interrogate") - deepbooru = gr.Button('Interrogate\nDeepBooru', elem_id="deepbooru") + if cmd_opts.deepdanbooru: + deepbooru = gr.Button('Interrogate\nDeepBooru', elem_id="deepbooru") + else: + deepbooru = None else: interrogate = None deepbooru = None @@ -782,11 +786,12 @@ def create_ui(wrap_gradio_gpu_call): outputs=[img2img_prompt], ) - img2img_deepbooru.click( - fn=interrogate_deepbooru, - inputs=[init_img], - outputs=[img2img_prompt], - ) + if cmd_opts.deepdanbooru: + img2img_deepbooru.click( + fn=interrogate_deepbooru, + inputs=[init_img], + outputs=[img2img_prompt], + ) save.click( fn=wrap_gradio_call(save_files), -- cgit v1.2.1 From f9c5da159245bb1e7603b3c8b9e0703bcb1c2ff5 Mon Sep 17 00:00:00 2001 From: AUTOMATIC <16777216c@gmail.com> Date: Sat, 8 Oct 2022 19:05:19 +0300 Subject: add fallback for xformers_attnblock_forward --- modules/sd_hijack_optimizations.py | 5 ++++- 1 file changed, 4 insertions(+), 1 deletion(-) (limited to 'modules') diff --git a/modules/sd_hijack_optimizations.py b/modules/sd_hijack_optimizations.py index d23d733b..dba21192 100644 --- a/modules/sd_hijack_optimizations.py +++ b/modules/sd_hijack_optimizations.py @@ -211,6 +211,7 @@ def cross_attention_attnblock_forward(self, x): return h3 def xformers_attnblock_forward(self, x): + try: h_ = x h_ = self.norm(h_) q1 = self.q(h_).contiguous() @@ -218,4 +219,6 @@ def xformers_attnblock_forward(self, x): v = self.v(h_).contiguous() out = xformers.ops.memory_efficient_attention(q1, k1, v) out = self.proj_out(out) - return x+out + return x + out + except NotImplementedError: + return cross_attention_attnblock_forward(self, x) -- cgit v1.2.1 From 3061cdb7b610d4ba7f1ea695d9d6364b591e5bc7 Mon Sep 17 00:00:00 2001 From: AUTOMATIC <16777216c@gmail.com> Date: Sat, 8 Oct 2022 19:22:15 +0300 Subject: add --force-enable-xformers option and also add messages to console regarding cross attention optimizations --- modules/sd_hijack.py | 6 +++++- modules/shared.py | 1 + 2 files changed, 6 insertions(+), 1 deletion(-) (limited to 'modules') diff --git a/modules/sd_hijack.py b/modules/sd_hijack.py index a3e374f0..307cc67d 100644 --- a/modules/sd_hijack.py +++ b/modules/sd_hijack.py @@ -22,12 +22,16 @@ def apply_optimizations(): undo_optimizations() ldm.modules.diffusionmodules.model.nonlinearity = silu - if cmd_opts.xformers and shared.xformers_available and torch.version.cuda and torch.cuda.get_device_capability(shared.device) == (8, 6): + + if cmd_opts.force_enable_xformers or (cmd_opts.xformers and shared.xformers_available and torch.version.cuda and torch.cuda.get_device_capability(shared.device) == (8, 6)): + print("Applying xformers cross attention optimization.") ldm.modules.attention.CrossAttention.forward = sd_hijack_optimizations.xformers_attention_forward ldm.modules.diffusionmodules.model.AttnBlock.forward = sd_hijack_optimizations.xformers_attnblock_forward elif cmd_opts.opt_split_attention_v1: + print("Applying v1 cross attention optimization.") ldm.modules.attention.CrossAttention.forward = sd_hijack_optimizations.split_cross_attention_forward_v1 elif not cmd_opts.disable_opt_split_attention and (cmd_opts.opt_split_attention or torch.cuda.is_available()): + print("Applying cross attention optimization.") ldm.modules.attention.CrossAttention.forward = sd_hijack_optimizations.split_cross_attention_forward ldm.modules.diffusionmodules.model.AttnBlock.forward = sd_hijack_optimizations.cross_attention_attnblock_forward diff --git a/modules/shared.py b/modules/shared.py index 02cb2722..8f941226 100644 --- a/modules/shared.py +++ b/modules/shared.py @@ -44,6 +44,7 @@ parser.add_argument("--scunet-models-path", type=str, help="Path to directory wi parser.add_argument("--swinir-models-path", type=str, help="Path to directory with SwinIR model file(s).", default=os.path.join(models_path, 'SwinIR')) parser.add_argument("--ldsr-models-path", type=str, help="Path to directory with LDSR model file(s).", default=os.path.join(models_path, 'LDSR')) parser.add_argument("--xformers", action='store_true', help="enable xformers for cross attention layers") +parser.add_argument("--force-enable-xformers", action='store_true', help="enable xformers for cross attention layers regardless of whether the checking code thinks you can run it; do not make bug reports if this fails to work") parser.add_argument("--opt-split-attention", action='store_true', help="force-enables cross-attention layer optimization. By default, it's on for torch.cuda and off for other torch devices.") parser.add_argument("--disable-opt-split-attention", action='store_true', help="force-disables cross-attention layer optimization") parser.add_argument("--opt-split-attention-v1", action='store_true', help="enable older version of split attention optimization that does not consume all the VRAM it can find") -- cgit v1.2.1 From 15c4278f1a18b8104e135dd82690d10cff39a2e7 Mon Sep 17 00:00:00 2001 From: DepFA <35278260+dfaker@users.noreply.github.com> Date: Sat, 8 Oct 2022 17:50:01 +0100 Subject: TI preprocess wording MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit I had to check the code to work out what splitting was 🤷🏿 --- modules/ui.py | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) (limited to 'modules') diff --git a/modules/ui.py b/modules/ui.py index ffd75f6a..d52d74c6 100644 --- a/modules/ui.py +++ b/modules/ui.py @@ -980,9 +980,9 @@ def create_ui(wrap_gradio_gpu_call): process_dst = gr.Textbox(label='Destination directory') with gr.Row(): - process_flip = gr.Checkbox(label='Flip') - process_split = gr.Checkbox(label='Split into two') - process_caption = gr.Checkbox(label='Add caption') + process_flip = gr.Checkbox(label='Create flipped copies') + process_split = gr.Checkbox(label='Split oversized images into two') + process_caption = gr.Checkbox(label='Use CLIP caption as filename') with gr.Row(): with gr.Column(scale=3): -- cgit v1.2.1 From b458fa48fe5734a872bca83061d702609cb52940 Mon Sep 17 00:00:00 2001 From: DepFA <35278260+dfaker@users.noreply.github.com> Date: Sat, 8 Oct 2022 17:56:28 +0100 Subject: Update ui.py --- modules/ui.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) (limited to 'modules') diff --git a/modules/ui.py b/modules/ui.py index d52d74c6..b09359aa 100644 --- a/modules/ui.py +++ b/modules/ui.py @@ -982,7 +982,7 @@ def create_ui(wrap_gradio_gpu_call): with gr.Row(): process_flip = gr.Checkbox(label='Create flipped copies') process_split = gr.Checkbox(label='Split oversized images into two') - process_caption = gr.Checkbox(label='Use CLIP caption as filename') + process_caption = gr.Checkbox(label='Use BLIP caption as filename') with gr.Row(): with gr.Column(scale=3): -- cgit v1.2.1 From 1371d7608b402d6f15c200ec2f5fde4579836a05 Mon Sep 17 00:00:00 2001 From: Fampai Date: Sat, 8 Oct 2022 14:28:22 -0400 Subject: Added ability to ignore last n layers in FrozenCLIPEmbedder --- modules/sd_hijack.py | 11 +++++++++-- modules/shared.py | 1 + 2 files changed, 10 insertions(+), 2 deletions(-) (limited to 'modules') diff --git a/modules/sd_hijack.py b/modules/sd_hijack.py index 307cc67d..f12a9696 100644 --- a/modules/sd_hijack.py +++ b/modules/sd_hijack.py @@ -281,8 +281,15 @@ class FrozenCLIPEmbedderWithCustomWords(torch.nn.Module): remade_batch_tokens_of_same_length = [x + [self.wrapped.tokenizer.eos_token_id] * (target_token_count - len(x)) for x in remade_batch_tokens] tokens = torch.asarray(remade_batch_tokens_of_same_length).to(device) - outputs = self.wrapped.transformer(input_ids=tokens, position_ids=position_ids) - z = outputs.last_hidden_state + + tmp = -opts.CLIP_ignore_last_layers + if (opts.CLIP_ignore_last_layers == 0): + outputs = self.wrapped.transformer(input_ids=tokens, position_ids=position_ids) + z = outputs.last_hidden_state + else: + outputs = self.wrapped.transformer(input_ids=tokens, position_ids=position_ids, output_hidden_states=tmp) + z = outputs.hidden_states[tmp] + z = self.wrapped.transformer.text_model.final_layer_norm(z) # restoring original mean is likely not correct, but it seems to work well to prevent artifacts that happen otherwise batch_multipliers_of_same_length = [x + [1.0] * (target_token_count - len(x)) for x in batch_multipliers] diff --git a/modules/shared.py b/modules/shared.py index 8f941226..af8dc744 100644 --- a/modules/shared.py +++ b/modules/shared.py @@ -225,6 +225,7 @@ options_templates.update(options_section(('sd', "Stable Diffusion"), { "use_old_emphasis_implementation": OptionInfo(False, "Use old emphasis implementation. Can be useful to reproduce old seeds."), "enable_batch_seeds": OptionInfo(True, "Make K-diffusion samplers produce same images in a batch as when making a single image"), "filter_nsfw": OptionInfo(False, "Filter NSFW content"), + 'CLIP_ignore_last_layers': OptionInfo(0, "Ignore last layers of CLIP model", gr.Slider, {"minimum": 0, "maximum": 5, "step": 1}), "random_artist_categories": OptionInfo([], "Allowed categories for random artists selection when using the Roll button", gr.CheckboxGroup, {"choices": artist_db.categories()}), })) -- cgit v1.2.1 From e6e42f98df2c928c4f49351ad6b466387ce87d42 Mon Sep 17 00:00:00 2001 From: AUTOMATIC <16777216c@gmail.com> Date: Sat, 8 Oct 2022 19:25:10 +0300 Subject: make --force-enable-xformers work without needing --xformers --- modules/sd_hijack_optimizations.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) (limited to 'modules') diff --git a/modules/sd_hijack_optimizations.py b/modules/sd_hijack_optimizations.py index dba21192..c4396bb9 100644 --- a/modules/sd_hijack_optimizations.py +++ b/modules/sd_hijack_optimizations.py @@ -10,7 +10,7 @@ from einops import rearrange from modules import shared -if shared.cmd_opts.xformers: +if shared.cmd_opts.xformers or shared.cmd_opts.force_enable_xformers: try: import xformers.ops import functorch -- cgit v1.2.1 From 3b2141c5fb6a3c2b8ab4b1e759a97ead77260129 Mon Sep 17 00:00:00 2001 From: AUTOMATIC <16777216c@gmail.com> Date: Sat, 8 Oct 2022 22:21:15 +0300 Subject: add 'Ignore last layers of CLIP model' option as a parameter to the infotext --- modules/processing.py | 6 +++++- 1 file changed, 5 insertions(+), 1 deletion(-) (limited to 'modules') diff --git a/modules/processing.py b/modules/processing.py index 8240ee27..515fc91a 100644 --- a/modules/processing.py +++ b/modules/processing.py @@ -123,6 +123,7 @@ class Processed: self.index_of_first_image = index_of_first_image self.styles = p.styles self.job_timestamp = state.job_timestamp + self.clip_skip = opts.CLIP_ignore_last_layers self.eta = p.eta self.ddim_discretize = p.ddim_discretize @@ -141,7 +142,6 @@ class Processed: self.all_subseeds = all_subseeds or [self.subseed] self.infotexts = infotexts or [info] - def js(self): obj = { "prompt": self.prompt, @@ -170,6 +170,7 @@ class Processed: "infotexts": self.infotexts, "styles": self.styles, "job_timestamp": self.job_timestamp, + "clip_skip": self.clip_skip, } return json.dumps(obj) @@ -267,6 +268,8 @@ def fix_seed(p): def create_infotext(p, all_prompts, all_seeds, all_subseeds, comments, iteration=0, position_in_batch=0): index = position_in_batch + iteration * p.batch_size + clip_skip = getattr(p, 'clip_skip', opts.CLIP_ignore_last_layers) + generation_params = { "Steps": p.steps, "Sampler": sd_samplers.samplers[p.sampler_index].name, @@ -282,6 +285,7 @@ def create_infotext(p, all_prompts, all_seeds, all_subseeds, comments, iteration "Seed resize from": (None if p.seed_resize_from_w == 0 or p.seed_resize_from_h == 0 else f"{p.seed_resize_from_w}x{p.seed_resize_from_h}"), "Denoising strength": getattr(p, 'denoising_strength', None), "Eta": (None if p.sampler is None or p.sampler.eta == p.sampler.default_eta else p.sampler.eta), + "Clip skip": None if clip_skip==0 else clip_skip, } generation_params.update(p.extra_generation_params) -- cgit v1.2.1 From 610a7f4e1480c0ffeedb2a07dc27ae86bf03c3a8 Mon Sep 17 00:00:00 2001 From: Edouard Leurent Date: Sat, 8 Oct 2022 16:49:43 +0100 Subject: Break after finding the local directory of stable diffusion Otherwise, we may override it with one of the next two path (. or ..) if it is present there, and then the local paths of other modules (taming transformers, codeformers, etc.) wont be found in sd_path/../. Fix https://github.com/AUTOMATIC1111/stable-diffusion-webui/issues/1085 --- modules/paths.py | 1 + 1 file changed, 1 insertion(+) (limited to 'modules') diff --git a/modules/paths.py b/modules/paths.py index 606f7d66..0519caa0 100644 --- a/modules/paths.py +++ b/modules/paths.py @@ -12,6 +12,7 @@ possible_sd_paths = [os.path.join(script_path, 'repositories/stable-diffusion'), for possible_sd_path in possible_sd_paths: if os.path.exists(os.path.join(possible_sd_path, 'ldm/models/diffusion/ddpm.py')): sd_path = os.path.abspath(possible_sd_path) + break assert sd_path is not None, "Couldn't find Stable Diffusion in any of: " + str(possible_sd_paths) -- cgit v1.2.1 From 432782163ae53e605470bcefc9a6f796c4556912 Mon Sep 17 00:00:00 2001 From: Aidan Holland Date: Sat, 8 Oct 2022 15:12:24 -0400 Subject: chore: Fix typos --- modules/interrogate.py | 4 ++-- modules/processing.py | 2 +- modules/scunet_model_arch.py | 4 ++-- modules/sd_models.py | 4 ++-- modules/sd_samplers.py | 4 ++-- modules/shared.py | 6 +++--- modules/swinir_model_arch.py | 2 +- modules/ui.py | 4 ++-- 8 files changed, 15 insertions(+), 15 deletions(-) (limited to 'modules') diff --git a/modules/interrogate.py b/modules/interrogate.py index eed87144..635e266e 100644 --- a/modules/interrogate.py +++ b/modules/interrogate.py @@ -140,11 +140,11 @@ class InterrogateModels: res = caption - cilp_image = self.clip_preprocess(pil_image).unsqueeze(0).type(self.dtype).to(shared.device) + clip_image = self.clip_preprocess(pil_image).unsqueeze(0).type(self.dtype).to(shared.device) precision_scope = torch.autocast if shared.cmd_opts.precision == "autocast" else contextlib.nullcontext with torch.no_grad(), precision_scope("cuda"): - image_features = self.clip_model.encode_image(cilp_image).type(self.dtype) + image_features = self.clip_model.encode_image(clip_image).type(self.dtype) image_features /= image_features.norm(dim=-1, keepdim=True) diff --git a/modules/processing.py b/modules/processing.py index 515fc91a..31220881 100644 --- a/modules/processing.py +++ b/modules/processing.py @@ -386,7 +386,7 @@ def process_images(p: StableDiffusionProcessing) -> Processed: if state.interrupted or state.skipped: - # if we are interruped, sample returns just noise + # if we are interrupted, sample returns just noise # use the image collected previously in sampler loop samples_ddim = shared.state.current_latent diff --git a/modules/scunet_model_arch.py b/modules/scunet_model_arch.py index 972a2639..43ca8d36 100644 --- a/modules/scunet_model_arch.py +++ b/modules/scunet_model_arch.py @@ -40,7 +40,7 @@ class WMSA(nn.Module): Returns: attn_mask: should be (1 1 w p p), """ - # supporting sqaure. + # supporting square. attn_mask = torch.zeros(h, w, p, p, p, p, dtype=torch.bool, device=self.relative_position_params.device) if self.type == 'W': return attn_mask @@ -65,7 +65,7 @@ class WMSA(nn.Module): x = rearrange(x, 'b (w1 p1) (w2 p2) c -> b w1 w2 p1 p2 c', p1=self.window_size, p2=self.window_size) h_windows = x.size(1) w_windows = x.size(2) - # sqaure validation + # square validation # assert h_windows == w_windows x = rearrange(x, 'b w1 w2 p1 p2 c -> b (w1 w2) (p1 p2) c', p1=self.window_size, p2=self.window_size) diff --git a/modules/sd_models.py b/modules/sd_models.py index 9409d070..a09866ce 100644 --- a/modules/sd_models.py +++ b/modules/sd_models.py @@ -147,7 +147,7 @@ def load_model_weights(model, checkpoint_file, sd_model_hash): model.first_stage_model.load_state_dict(vae_dict) model.sd_model_hash = sd_model_hash - model.sd_model_checkpint = checkpoint_file + model.sd_model_checkpoint = checkpoint_file def load_model(): @@ -175,7 +175,7 @@ def reload_model_weights(sd_model, info=None): from modules import lowvram, devices, sd_hijack checkpoint_info = info or select_checkpoint() - if sd_model.sd_model_checkpint == checkpoint_info.filename: + if sd_model.sd_model_checkpoint == checkpoint_info.filename: return if shared.cmd_opts.lowvram or shared.cmd_opts.medvram: diff --git a/modules/sd_samplers.py b/modules/sd_samplers.py index eade0dbb..6e743f7e 100644 --- a/modules/sd_samplers.py +++ b/modules/sd_samplers.py @@ -181,7 +181,7 @@ class VanillaStableDiffusionSampler: self.initialize(p) - # existing code fails with cetain step counts, like 9 + # existing code fails with certain step counts, like 9 try: self.sampler.make_schedule(ddim_num_steps=steps, ddim_eta=self.eta, ddim_discretize=p.ddim_discretize, verbose=False) except Exception: @@ -204,7 +204,7 @@ class VanillaStableDiffusionSampler: steps = steps or p.steps - # existing code fails with cetin step counts, like 9 + # existing code fails with certain step counts, like 9 try: samples_ddim, _ = self.sampler.sample(S=steps, conditioning=conditioning, batch_size=int(x.shape[0]), shape=x[0].shape, verbose=False, unconditional_guidance_scale=p.cfg_scale, unconditional_conditioning=unconditional_conditioning, x_T=x, eta=self.eta) except Exception: diff --git a/modules/shared.py b/modules/shared.py index af8dc744..2dc092d6 100644 --- a/modules/shared.py +++ b/modules/shared.py @@ -141,9 +141,9 @@ class OptionInfo: self.section = None -def options_section(section_identifer, options_dict): +def options_section(section_identifier, options_dict): for k, v in options_dict.items(): - v.section = section_identifer + v.section = section_identifier return options_dict @@ -246,7 +246,7 @@ options_templates.update(options_section(('ui', "User interface"), { "add_model_hash_to_info": OptionInfo(True, "Add model hash to generation information"), "font": OptionInfo("", "Font for image grids that have text"), "js_modal_lightbox": OptionInfo(True, "Enable full page image viewer"), - "js_modal_lightbox_initialy_zoomed": OptionInfo(True, "Show images zoomed in by default in full page image viewer"), + "js_modal_lightbox_initially_zoomed": OptionInfo(True, "Show images zoomed in by default in full page image viewer"), "show_progress_in_title": OptionInfo(True, "Show generation progress in window title."), })) diff --git a/modules/swinir_model_arch.py b/modules/swinir_model_arch.py index 461fb354..863f42db 100644 --- a/modules/swinir_model_arch.py +++ b/modules/swinir_model_arch.py @@ -166,7 +166,7 @@ class SwinTransformerBlock(nn.Module): Args: dim (int): Number of input channels. - input_resolution (tuple[int]): Input resulotion. + input_resolution (tuple[int]): Input resolution. num_heads (int): Number of attention heads. window_size (int): Window size. shift_size (int): Shift size for SW-MSA. diff --git a/modules/ui.py b/modules/ui.py index b09359aa..b51af121 100644 --- a/modules/ui.py +++ b/modules/ui.py @@ -38,7 +38,7 @@ from modules import prompt_parser from modules.images import save_image import modules.textual_inversion.ui -# this is a fix for Windows users. Without it, javascript files will be served with text/html content-type and the bowser will not show any UI +# this is a fix for Windows users. Without it, javascript files will be served with text/html content-type and the browser will not show any UI mimetypes.init() mimetypes.add_type('application/javascript', '.js') @@ -102,7 +102,7 @@ def save_files(js_data, images, index): import csv filenames = [] - #quick dictionary to class object conversion. Its neccesary due apply_filename_pattern requiring it + #quick dictionary to class object conversion. Its necessary due apply_filename_pattern requiring it class MyObject: def __init__(self, d=None): if d is not None: -- cgit v1.2.1 From 050a6a798cec90ae2f881c2ddd3f0221e69907dc Mon Sep 17 00:00:00 2001 From: AUTOMATIC <16777216c@gmail.com> Date: Sat, 8 Oct 2022 23:26:48 +0300 Subject: support loading .yaml config with same name as model support EMA weights in processing (????) --- modules/processing.py | 2 +- modules/sd_models.py | 30 +++++++++++++++++++++++------- 2 files changed, 24 insertions(+), 8 deletions(-) (limited to 'modules') diff --git a/modules/processing.py b/modules/processing.py index 31220881..4fea6d56 100644 --- a/modules/processing.py +++ b/modules/processing.py @@ -347,7 +347,7 @@ def process_images(p: StableDiffusionProcessing) -> Processed: infotexts = [] output_images = [] - with torch.no_grad(): + with torch.no_grad(), p.sd_model.ema_scope(): with devices.autocast(): p.init(all_prompts, all_seeds, all_subseeds) diff --git a/modules/sd_models.py b/modules/sd_models.py index a09866ce..cb3982b1 100644 --- a/modules/sd_models.py +++ b/modules/sd_models.py @@ -14,7 +14,7 @@ from modules.paths import models_path model_dir = "Stable-diffusion" model_path = os.path.abspath(os.path.join(models_path, model_dir)) -CheckpointInfo = namedtuple("CheckpointInfo", ['filename', 'title', 'hash', 'model_name']) +CheckpointInfo = namedtuple("CheckpointInfo", ['filename', 'title', 'hash', 'model_name', 'config']) checkpoints_list = {} try: @@ -63,14 +63,20 @@ def list_models(): if os.path.exists(cmd_ckpt): h = model_hash(cmd_ckpt) title, short_model_name = modeltitle(cmd_ckpt, h) - checkpoints_list[title] = CheckpointInfo(cmd_ckpt, title, h, short_model_name) + checkpoints_list[title] = CheckpointInfo(cmd_ckpt, title, h, short_model_name, shared.cmd_opts.config) shared.opts.data['sd_model_checkpoint'] = title elif cmd_ckpt is not None and cmd_ckpt != shared.default_sd_model_file: print(f"Checkpoint in --ckpt argument not found (Possible it was moved to {model_path}: {cmd_ckpt}", file=sys.stderr) for filename in model_list: h = model_hash(filename) title, short_model_name = modeltitle(filename, h) - checkpoints_list[title] = CheckpointInfo(filename, title, h, short_model_name) + + basename, _ = os.path.splitext(filename) + config = basename + ".yaml" + if not os.path.exists(config): + config = shared.cmd_opts.config + + checkpoints_list[title] = CheckpointInfo(filename, title, h, short_model_name, config) def get_closet_checkpoint_match(searchString): @@ -116,7 +122,10 @@ def select_checkpoint(): return checkpoint_info -def load_model_weights(model, checkpoint_file, sd_model_hash): +def load_model_weights(model, checkpoint_info): + checkpoint_file = checkpoint_info.filename + sd_model_hash = checkpoint_info.hash + print(f"Loading weights [{sd_model_hash}] from {checkpoint_file}") pl_sd = torch.load(checkpoint_file, map_location="cpu") @@ -148,15 +157,19 @@ def load_model_weights(model, checkpoint_file, sd_model_hash): model.sd_model_hash = sd_model_hash model.sd_model_checkpoint = checkpoint_file + model.sd_checkpoint_info = checkpoint_info def load_model(): from modules import lowvram, sd_hijack checkpoint_info = select_checkpoint() - sd_config = OmegaConf.load(shared.cmd_opts.config) + if checkpoint_info.config != shared.cmd_opts.config: + print(f"Loading config from: {shared.cmd_opts.config}") + + sd_config = OmegaConf.load(checkpoint_info.config) sd_model = instantiate_from_config(sd_config.model) - load_model_weights(sd_model, checkpoint_info.filename, checkpoint_info.hash) + load_model_weights(sd_model, checkpoint_info) if shared.cmd_opts.lowvram or shared.cmd_opts.medvram: lowvram.setup_for_low_vram(sd_model, shared.cmd_opts.medvram) @@ -178,6 +191,9 @@ def reload_model_weights(sd_model, info=None): if sd_model.sd_model_checkpoint == checkpoint_info.filename: return + if sd_model.sd_checkpoint_info.config != checkpoint_info.config: + return load_model() + if shared.cmd_opts.lowvram or shared.cmd_opts.medvram: lowvram.send_everything_to_cpu() else: @@ -185,7 +201,7 @@ def reload_model_weights(sd_model, info=None): sd_hijack.model_hijack.undo_hijack(sd_model) - load_model_weights(sd_model, checkpoint_info.filename, checkpoint_info.hash) + load_model_weights(sd_model, checkpoint_info) sd_hijack.model_hijack.hijack(sd_model) -- cgit v1.2.1 From 5841990b0df04906da7321beef6f7f7902b7d57b Mon Sep 17 00:00:00 2001 From: DepFA <35278260+dfaker@users.noreply.github.com> Date: Sun, 9 Oct 2022 05:38:38 +0100 Subject: Update textual_inversion.py --- modules/textual_inversion/textual_inversion.py | 25 ++++++++++++++++++++++--- 1 file changed, 22 insertions(+), 3 deletions(-) (limited to 'modules') diff --git a/modules/textual_inversion/textual_inversion.py b/modules/textual_inversion/textual_inversion.py index cd9f3498..f6316020 100644 --- a/modules/textual_inversion/textual_inversion.py +++ b/modules/textual_inversion/textual_inversion.py @@ -7,6 +7,9 @@ import tqdm import html import datetime +from PIL import Image, PngImagePlugin +import base64 +from io import BytesIO from modules import shared, devices, sd_hijack, processing, sd_models import modules.textual_inversion.dataset @@ -80,7 +83,15 @@ class EmbeddingDatabase: def process_file(path, filename): name = os.path.splitext(filename)[0] - data = torch.load(path, map_location="cpu") + data = [] + + if filename.upper().endswith('.PNG'): + embed_image = Image.open(path) + if 'sd-embedding' in embed_image.text: + embeddingData = base64.b64decode(embed_image.text['sd-embedding']) + data = torch.load(BytesIO(embeddingData), map_location="cpu") + else: + data = torch.load(path, map_location="cpu") # textual inversion embeddings if 'string_to_param' in data: @@ -156,7 +167,7 @@ def create_embedding(name, num_vectors_per_token, init_text='*'): return fn -def train_embedding(embedding_name, learn_rate, data_root, log_directory, steps, create_image_every, save_embedding_every, template_file): +def train_embedding(embedding_name, learn_rate, data_root, log_directory, steps, create_image_every, save_embedding_every, template_file, save_image_with_stored_embedding): assert embedding_name, 'embedding not selected' shared.state.textinfo = "Initializing textual inversion training..." @@ -244,7 +255,15 @@ def train_embedding(embedding_name, learn_rate, data_root, log_directory, steps, image = processed.images[0] shared.state.current_image = image - image.save(last_saved_image) + + if save_image_with_stored_embedding: + info = PngImagePlugin.PngInfo() + info.add_text("sd-embedding", base64.b64encode(open(last_saved_file,'rb').read())) + image.save(last_saved_image, "PNG", pnginfo=info) + else: + image.save(last_saved_image) + + last_saved_image += f", prompt: {text}" -- cgit v1.2.1 From cd8673bd9b2e59bddefee8d307340d643695fe11 Mon Sep 17 00:00:00 2001 From: DepFA <35278260+dfaker@users.noreply.github.com> Date: Sun, 9 Oct 2022 05:40:57 +0100 Subject: add embed embedding to ui --- modules/ui.py | 4 +++- 1 file changed, 3 insertions(+), 1 deletion(-) (limited to 'modules') diff --git a/modules/ui.py b/modules/ui.py index b51af121..a5983204 100644 --- a/modules/ui.py +++ b/modules/ui.py @@ -1001,7 +1001,8 @@ def create_ui(wrap_gradio_gpu_call): steps = gr.Number(label='Max steps', value=100000, precision=0) create_image_every = gr.Number(label='Save an image to log directory every N steps, 0 to disable', value=500, precision=0) save_embedding_every = gr.Number(label='Save a copy of embedding to log directory every N steps, 0 to disable', value=500, precision=0) - + save_image_with_stored_embedding = gr.Checkbox(label='Save images with embedding in PNG chunks', value=True) + with gr.Row(): with gr.Column(scale=2): gr.HTML(value="") @@ -1063,6 +1064,7 @@ def create_ui(wrap_gradio_gpu_call): create_image_every, save_embedding_every, template_file, + save_image_with_stored_embedding, ], outputs=[ ti_output, -- cgit v1.2.1 From c77c89cc83c618472ad352cf8a28fde28c3a1377 Mon Sep 17 00:00:00 2001 From: AUTOMATIC <16777216c@gmail.com> Date: Sun, 9 Oct 2022 10:23:31 +0300 Subject: make main model loading and model merger use the same code --- modules/extras.py | 6 +++--- modules/sd_models.py | 14 +++++++++----- 2 files changed, 12 insertions(+), 8 deletions(-) (limited to 'modules') diff --git a/modules/extras.py b/modules/extras.py index 1d9e64e5..ef6e6de7 100644 --- a/modules/extras.py +++ b/modules/extras.py @@ -169,9 +169,9 @@ def run_modelmerger(primary_model_name, secondary_model_name, interp_method, int print(f"Loading {secondary_model_info.filename}...") secondary_model = torch.load(secondary_model_info.filename, map_location='cpu') - - theta_0 = primary_model['state_dict'] - theta_1 = secondary_model['state_dict'] + + theta_0 = sd_models.get_state_dict_from_checkpoint(primary_model) + theta_1 = sd_models.get_state_dict_from_checkpoint(secondary_model) theta_funcs = { "Weighted Sum": weighted_sum, diff --git a/modules/sd_models.py b/modules/sd_models.py index cb3982b1..18fb8c2e 100644 --- a/modules/sd_models.py +++ b/modules/sd_models.py @@ -122,6 +122,13 @@ def select_checkpoint(): return checkpoint_info +def get_state_dict_from_checkpoint(pl_sd): + if "state_dict" in pl_sd: + return pl_sd["state_dict"] + + return pl_sd + + def load_model_weights(model, checkpoint_info): checkpoint_file = checkpoint_info.filename sd_model_hash = checkpoint_info.hash @@ -131,11 +138,8 @@ def load_model_weights(model, checkpoint_info): pl_sd = torch.load(checkpoint_file, map_location="cpu") if "global_step" in pl_sd: print(f"Global Step: {pl_sd['global_step']}") - - if "state_dict" in pl_sd: - sd = pl_sd["state_dict"] - else: - sd = pl_sd + + sd = get_state_dict_from_checkpoint(pl_sd) model.load_state_dict(sd, strict=False) -- cgit v1.2.1 From 4e569fd888f8e3c5632a072d51abbb6e4d17abd6 Mon Sep 17 00:00:00 2001 From: AUTOMATIC <16777216c@gmail.com> Date: Sun, 9 Oct 2022 10:31:47 +0300 Subject: fixed incorrect message about loading config; thanks anon! --- modules/sd_models.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) (limited to 'modules') diff --git a/modules/sd_models.py b/modules/sd_models.py index 18fb8c2e..2101b18d 100644 --- a/modules/sd_models.py +++ b/modules/sd_models.py @@ -169,7 +169,7 @@ def load_model(): checkpoint_info = select_checkpoint() if checkpoint_info.config != shared.cmd_opts.config: - print(f"Loading config from: {shared.cmd_opts.config}") + print(f"Loading config from: {checkpoint_info.config}") sd_config = OmegaConf.load(checkpoint_info.config) sd_model = instantiate_from_config(sd_config.model) -- cgit v1.2.1 From 5ab7e88d9b0bb0125af9f7237242a00a93360ce5 Mon Sep 17 00:00:00 2001 From: aoirusann <82883326+aoirusann@users.noreply.github.com> Date: Sat, 8 Oct 2022 13:09:29 +0800 Subject: Add `Download` & `Download as zip` --- modules/ui.py | 39 ++++++++++++++++++++++++++++++++++----- 1 file changed, 34 insertions(+), 5 deletions(-) (limited to 'modules') diff --git a/modules/ui.py b/modules/ui.py index b51af121..fe7f10a7 100644 --- a/modules/ui.py +++ b/modules/ui.py @@ -98,9 +98,10 @@ def send_gradio_gallery_to_image(x): return image_from_url_text(x[0]) -def save_files(js_data, images, index): +def save_files(js_data, images, do_make_zip, index): import csv filenames = [] + fullfns = [] #quick dictionary to class object conversion. Its necessary due apply_filename_pattern requiring it class MyObject: @@ -141,10 +142,22 @@ def save_files(js_data, images, index): filename = os.path.relpath(fullfn, path) filenames.append(filename) + fullfns.append(fullfn) writer.writerow([data["prompt"], data["seed"], data["width"], data["height"], data["sampler"], data["cfg_scale"], data["steps"], filenames[0], data["negative_prompt"]]) - return '', '', plaintext_to_html(f"Saved: {filenames[0]}") + # Make Zip + if do_make_zip: + zip_filepath = os.path.join(path, "images.zip") + + from zipfile import ZipFile + with ZipFile(zip_filepath, "w") as zip_file: + for i in range(len(fullfns)): + with open(fullfns[i], mode="rb") as f: + zip_file.writestr(filenames[i], f.read()) + fullfns.insert(0, zip_filepath) + + return fullfns, '', '', plaintext_to_html(f"Saved: {filenames[0]}") def wrap_gradio_call(func, extra_outputs=None): @@ -521,6 +534,12 @@ def create_ui(wrap_gradio_gpu_call): button_id = "hidden_element" if shared.cmd_opts.hide_ui_dir_config else 'open_folder' open_txt2img_folder = gr.Button(folder_symbol, elem_id=button_id) + with gr.Row(): + do_make_zip = gr.Checkbox(label="Make Zip when Save?", value=False) + + with gr.Row(): + download_files = gr.File(None, file_count="multiple", interactive=False, show_label=False) + with gr.Group(): html_info = gr.HTML() generation_info = gr.Textbox(visible=False) @@ -570,13 +589,15 @@ def create_ui(wrap_gradio_gpu_call): save.click( fn=wrap_gradio_call(save_files), - _js="(x, y, z) => [x, y, selected_gallery_index()]", + _js="(x, y, z, w) => [x, y, z, selected_gallery_index()]", inputs=[ generation_info, txt2img_gallery, + do_make_zip, html_info, ], outputs=[ + download_files, html_info, html_info, html_info, @@ -701,6 +722,12 @@ def create_ui(wrap_gradio_gpu_call): button_id = "hidden_element" if shared.cmd_opts.hide_ui_dir_config else 'open_folder' open_img2img_folder = gr.Button(folder_symbol, elem_id=button_id) + with gr.Row(): + do_make_zip = gr.Checkbox(label="Make Zip when Save?", value=False) + + with gr.Row(): + download_files = gr.File(None, file_count="multiple", interactive=False, show_label=False) + with gr.Group(): html_info = gr.HTML() generation_info = gr.Textbox(visible=False) @@ -776,13 +803,15 @@ def create_ui(wrap_gradio_gpu_call): save.click( fn=wrap_gradio_call(save_files), - _js="(x, y, z) => [x, y, selected_gallery_index()]", + _js="(x, y, z, w) => [x, y, z, selected_gallery_index()]", inputs=[ generation_info, img2img_gallery, - html_info + do_make_zip, + html_info, ], outputs=[ + download_files, html_info, html_info, html_info, -- cgit v1.2.1 From 14192c5b207b16b1ec7a4c9c4ea538d1a6811a4d Mon Sep 17 00:00:00 2001 From: aoirusann Date: Sun, 9 Oct 2022 13:01:10 +0800 Subject: Support `Download` for txt files. --- modules/images.py | 39 +++++++++++++++++++++++++++++++++++++-- modules/ui.py | 5 ++++- 2 files changed, 41 insertions(+), 3 deletions(-) (limited to 'modules') diff --git a/modules/images.py b/modules/images.py index 29c5ee24..c0a90676 100644 --- a/modules/images.py +++ b/modules/images.py @@ -349,6 +349,38 @@ def get_next_sequence_number(path, basename): def save_image(image, path, basename, seed=None, prompt=None, extension='png', info=None, short_filename=False, no_prompt=False, grid=False, pnginfo_section_name='parameters', p=None, existing_info=None, forced_filename=None, suffix="", save_to_dirs=None): + '''Save an image. + + Args: + image (`PIL.Image`): + The image to be saved. + path (`str`): + The directory to save the image. Note, the option `save_to_dirs` will make the image to be saved into a sub directory. + basename (`str`): + The base filename which will be applied to `filename pattern`. + seed, prompt, short_filename, + extension (`str`): + Image file extension, default is `png`. + pngsectionname (`str`): + Specify the name of the section which `info` will be saved in. + info (`str` or `PngImagePlugin.iTXt`): + PNG info chunks. + existing_info (`dict`): + Additional PNG info. `existing_info == {pngsectionname: info, ...}` + no_prompt: + TODO I don't know its meaning. + p (`StableDiffusionProcessing`) + forced_filename (`str`): + If specified, `basename` and filename pattern will be ignored. + save_to_dirs (bool): + If true, the image will be saved into a subdirectory of `path`. + + Returns: (fullfn, txt_fullfn) + fullfn (`str`): + The full path of the saved imaged. + txt_fullfn (`str` or None): + If a text file is saved for this image, this will be its full path. Otherwise None. + ''' if short_filename or prompt is None or seed is None: file_decoration = "" elif opts.save_to_dirs: @@ -424,7 +456,10 @@ def save_image(image, path, basename, seed=None, prompt=None, extension='png', i piexif.insert(exif_bytes(), fullfn_without_extension + ".jpg") if opts.save_txt and info is not None: - with open(f"{fullfn_without_extension}.txt", "w", encoding="utf8") as file: + txt_fullfn = f"{fullfn_without_extension}.txt" + with open(txt_fullfn, "w", encoding="utf8") as file: file.write(info + "\n") + else: + txt_fullfn = None - return fullfn + return fullfn, txt_fullfn diff --git a/modules/ui.py b/modules/ui.py index fe7f10a7..debd8873 100644 --- a/modules/ui.py +++ b/modules/ui.py @@ -138,11 +138,14 @@ def save_files(js_data, images, do_make_zip, index): is_grid = image_index < p.index_of_first_image i = 0 if is_grid else (image_index - p.index_of_first_image) - fullfn = save_image(image, path, "", seed=p.all_seeds[i], prompt=p.all_prompts[i], extension=extension, info=p.infotexts[image_index], grid=is_grid, p=p, save_to_dirs=save_to_dirs) + fullfn, txt_fullfn = save_image(image, path, "", seed=p.all_seeds[i], prompt=p.all_prompts[i], extension=extension, info=p.infotexts[image_index], grid=is_grid, p=p, save_to_dirs=save_to_dirs) filename = os.path.relpath(fullfn, path) filenames.append(filename) fullfns.append(fullfn) + if txt_fullfn: + filenames.append(os.path.basename(txt_fullfn)) + fullfns.append(txt_fullfn) writer.writerow([data["prompt"], data["seed"], data["width"], data["height"], data["sampler"], data["cfg_scale"], data["steps"], filenames[0], data["negative_prompt"]]) -- cgit v1.2.1 From 122d42687b97ec4df4c2a8c335d2de385cd1f1a1 Mon Sep 17 00:00:00 2001 From: Fampai Date: Sat, 8 Oct 2022 22:37:35 -0400 Subject: Fix VRAM Issue by only loading in hypernetwork when selected in settings --- modules/hypernetwork.py | 23 +++++++++++++++-------- modules/sd_hijack_optimizations.py | 6 +++--- modules/shared.py | 7 ++----- 3 files changed, 20 insertions(+), 16 deletions(-) (limited to 'modules') diff --git a/modules/hypernetwork.py b/modules/hypernetwork.py index 7f062242..19f1c227 100644 --- a/modules/hypernetwork.py +++ b/modules/hypernetwork.py @@ -40,18 +40,25 @@ class Hypernetwork: self.layers[size] = (HypernetworkModule(size, sd[0]), HypernetworkModule(size, sd[1])) -def load_hypernetworks(path): +def list_hypernetworks(path): res = {} - for filename in glob.iglob(os.path.join(path, '**/*.pt'), recursive=True): + name = os.path.splitext(os.path.basename(filename))[0] + res[name] = filename + return res + + +def load_hypernetwork(filename): + print(f"Loading hypernetwork {filename}") + path = shared.hypernetworks.get(filename, None) + if (path is not None): try: - hn = Hypernetwork(filename) - res[hn.name] = hn + shared.loaded_hypernetwork = Hypernetwork(path) except Exception: - print(f"Error loading hypernetwork {filename}", file=sys.stderr) + print(f"Error loading hypernetwork {path}", file=sys.stderr) print(traceback.format_exc(), file=sys.stderr) - - return res + else: + shared.loaded_hypernetwork = None def attention_CrossAttention_forward(self, x, context=None, mask=None): @@ -60,7 +67,7 @@ def attention_CrossAttention_forward(self, x, context=None, mask=None): q = self.to_q(x) context = default(context, x) - hypernetwork = shared.selected_hypernetwork() + hypernetwork = shared.loaded_hypernetwork hypernetwork_layers = (hypernetwork.layers if hypernetwork is not None else {}).get(context.shape[2], None) if hypernetwork_layers is not None: diff --git a/modules/sd_hijack_optimizations.py b/modules/sd_hijack_optimizations.py index c4396bb9..634fb4b2 100644 --- a/modules/sd_hijack_optimizations.py +++ b/modules/sd_hijack_optimizations.py @@ -28,7 +28,7 @@ def split_cross_attention_forward_v1(self, x, context=None, mask=None): q_in = self.to_q(x) context = default(context, x) - hypernetwork = shared.selected_hypernetwork() + hypernetwork = shared.loaded_hypernetwork hypernetwork_layers = (hypernetwork.layers if hypernetwork is not None else {}).get(context.shape[2], None) if hypernetwork_layers is not None: @@ -68,7 +68,7 @@ def split_cross_attention_forward(self, x, context=None, mask=None): q_in = self.to_q(x) context = default(context, x) - hypernetwork = shared.selected_hypernetwork() + hypernetwork = shared.loaded_hypernetwork hypernetwork_layers = (hypernetwork.layers if hypernetwork is not None else {}).get(context.shape[2], None) if hypernetwork_layers is not None: @@ -132,7 +132,7 @@ def xformers_attention_forward(self, x, context=None, mask=None): h = self.heads q_in = self.to_q(x) context = default(context, x) - hypernetwork = shared.selected_hypernetwork() + hypernetwork = shared.loaded_hypernetwork hypernetwork_layers = (hypernetwork.layers if hypernetwork is not None else {}).get(context.shape[2], None) if hypernetwork_layers is not None: k_in = self.to_k(hypernetwork_layers[0](context)) diff --git a/modules/shared.py b/modules/shared.py index b2c76a32..9dce6cb7 100644 --- a/modules/shared.py +++ b/modules/shared.py @@ -79,11 +79,8 @@ parallel_processing_allowed = not cmd_opts.lowvram and not cmd_opts.medvram xformers_available = False config_filename = cmd_opts.ui_settings_file -hypernetworks = hypernetwork.load_hypernetworks(os.path.join(models_path, 'hypernetworks')) - - -def selected_hypernetwork(): - return hypernetworks.get(opts.sd_hypernetwork, None) +hypernetworks = hypernetwork.list_hypernetworks(os.path.join(models_path, 'hypernetworks')) +loaded_hypernetwork = None class State: -- cgit v1.2.1 From 03e570886f430f39020e504aba057a95f2e62484 Mon Sep 17 00:00:00 2001 From: frostydad <64224601+Cyberes@users.noreply.github.com> Date: Sat, 8 Oct 2022 18:13:13 -0600 Subject: Fix incorrect sampler name in output --- modules/processing.py | 9 ++++++++- 1 file changed, 8 insertions(+), 1 deletion(-) (limited to 'modules') diff --git a/modules/processing.py b/modules/processing.py index 4fea6d56..6b8664a0 100644 --- a/modules/processing.py +++ b/modules/processing.py @@ -1,3 +1,4 @@ + import json import math import os @@ -46,6 +47,12 @@ def apply_color_correction(correction, image): return image +def get_correct_sampler(p): + if isinstance(p, modules.processing.StableDiffusionProcessingTxt2Img): + return sd_samplers.samplers + elif isinstance(p, modules.processing.StableDiffusionProcessingImg2Img): + return sd_samplers.samplers_for_img2img + class StableDiffusionProcessing: def __init__(self, sd_model=None, outpath_samples=None, outpath_grids=None, prompt="", styles=None, seed=-1, subseed=-1, subseed_strength=0, seed_resize_from_h=-1, seed_resize_from_w=-1, seed_enable_extras=True, sampler_index=0, batch_size=1, n_iter=1, steps=50, cfg_scale=7.0, width=512, height=512, restore_faces=False, tiling=False, do_not_save_samples=False, do_not_save_grid=False, extra_generation_params=None, overlay_images=None, negative_prompt=None, eta=None): self.sd_model = sd_model @@ -272,7 +279,7 @@ def create_infotext(p, all_prompts, all_seeds, all_subseeds, comments, iteration generation_params = { "Steps": p.steps, - "Sampler": sd_samplers.samplers[p.sampler_index].name, + "Sampler": get_correct_sampler(p)[p.sampler_index].name, "CFG scale": p.cfg_scale, "Seed": all_seeds[index], "Face restoration": (opts.face_restoration_model if p.restore_faces else None), -- cgit v1.2.1 From ef93acdc731b7a2b3c13651b6de1bce58af989d4 Mon Sep 17 00:00:00 2001 From: frostydad <64224601+Cyberes@users.noreply.github.com> Date: Sat, 8 Oct 2022 18:15:35 -0600 Subject: remove line break --- modules/processing.py | 1 - 1 file changed, 1 deletion(-) (limited to 'modules') diff --git a/modules/processing.py b/modules/processing.py index 6b8664a0..7fa1144e 100644 --- a/modules/processing.py +++ b/modules/processing.py @@ -1,4 +1,3 @@ - import json import math import os -- cgit v1.2.1 From 1ffeb42d38d9276dc28918189d32f60d593a162c Mon Sep 17 00:00:00 2001 From: Nicolas Noullet Date: Sun, 9 Oct 2022 00:18:45 +0200 Subject: Fix typo --- modules/shared.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) (limited to 'modules') diff --git a/modules/shared.py b/modules/shared.py index 9dce6cb7..dffa0094 100644 --- a/modules/shared.py +++ b/modules/shared.py @@ -238,7 +238,7 @@ options_templates.update(options_section(('interrogate', "Interrogate Options"), options_templates.update(options_section(('ui', "User interface"), { "show_progressbar": OptionInfo(True, "Show progressbar"), - "show_progress_every_n_steps": OptionInfo(0, "Show show image creation progress every N sampling steps. Set 0 to disable.", gr.Slider, {"minimum": 0, "maximum": 32, "step": 1}), + "show_progress_every_n_steps": OptionInfo(0, "Show image creation progress every N sampling steps. Set 0 to disable.", gr.Slider, {"minimum": 0, "maximum": 32, "step": 1}), "return_grid": OptionInfo(True, "Show grid in results for web"), "do_not_show_images": OptionInfo(False, "Do not show any images in results for web"), "add_model_hash_to_info": OptionInfo(True, "Add model hash to generation information"), -- cgit v1.2.1 From e2930f9821c197da94e208b5ae73711002844efc Mon Sep 17 00:00:00 2001 From: Tony Beeman Date: Fri, 7 Oct 2022 17:46:39 -0700 Subject: Fix for Prompts_from_file showing extra textbox. --- modules/scripts.py | 30 ++++++++++++++++++++++++++---- 1 file changed, 26 insertions(+), 4 deletions(-) (limited to 'modules') diff --git a/modules/scripts.py b/modules/scripts.py index 45230f9a..d8f87927 100644 --- a/modules/scripts.py +++ b/modules/scripts.py @@ -1,4 +1,5 @@ import os +from pydoc import visiblename import sys import traceback @@ -31,6 +32,15 @@ class Script: def show(self, is_img2img): return True + + # Called when the ui for this script has been shown. + # Useful for hiding some controls, since the scripts module sets visibility to + # everything to true. The parameters will be the parameters returned by the ui method + # The return value should be gradio updates, similar to what you would return + # from a Gradio event handler. + def on_show(self, *args): + return [ui.gr_show(True)] * len(args) + # This is where the additional processing is implemented. The parameters include # self, the model object "p" (a StableDiffusionProcessing class, see # processing.py), and the parameters returned by the ui method. @@ -125,20 +135,32 @@ class ScriptRunner: inputs += controls script.args_to = len(inputs) - def select_script(script_index): + def select_script(*args): + script_index = args[0] + on_show_updates = [] if 0 < script_index <= len(self.scripts): script = self.scripts[script_index-1] args_from = script.args_from args_to = script.args_to + script_args = args[args_from:args_to] + on_show_updates = wrap_call(script.on_show, script.filename, "on_show", *script_args) else: args_from = 0 args_to = 0 - return [ui.gr_show(True if i == 0 else args_from <= i < args_to) for i in range(len(inputs))] + ret = [ ui.gr_show(True)] # always show the dropdown + for i in range(1, len(inputs)): + if (args_from <= i < args_to): + ret.append( on_show_updates[i - args_from] ) + else: + ret.append(ui.gr_show(False)) + return ret + + # return [ui.gr_show(True if (i == 0) else on_show_updates[i - args_from] if args_from <= i < args_to else False) for i in range(len(inputs))] dropdown.change( fn=select_script, - inputs=[dropdown], + inputs=inputs, outputs=inputs ) @@ -198,4 +220,4 @@ def reload_scripts(basedir): load_scripts(basedir) scripts_txt2img = ScriptRunner() - scripts_img2img = ScriptRunner() + scripts_img2img = ScriptRunner() \ No newline at end of file -- cgit v1.2.1 From 86cb16886f8f48169cee4658ad0c5e5443beed2a Mon Sep 17 00:00:00 2001 From: Tony Beeman Date: Fri, 7 Oct 2022 23:51:50 -0700 Subject: Pull Request Code Review Fixes --- modules/scripts.py | 1 - 1 file changed, 1 deletion(-) (limited to 'modules') diff --git a/modules/scripts.py b/modules/scripts.py index d8f87927..8dfd4de9 100644 --- a/modules/scripts.py +++ b/modules/scripts.py @@ -1,5 +1,4 @@ import os -from pydoc import visiblename import sys import traceback -- cgit v1.2.1 From cbf6dad02d04d98e5a2d5e870777ab99b5796b2d Mon Sep 17 00:00:00 2001 From: Tony Beeman Date: Sat, 8 Oct 2022 10:40:30 -0700 Subject: Handle case where on_show returns the wrong number of arguments --- modules/scripts.py | 9 ++++++--- 1 file changed, 6 insertions(+), 3 deletions(-) (limited to 'modules') diff --git a/modules/scripts.py b/modules/scripts.py index 8dfd4de9..7d89979d 100644 --- a/modules/scripts.py +++ b/modules/scripts.py @@ -143,6 +143,8 @@ class ScriptRunner: args_to = script.args_to script_args = args[args_from:args_to] on_show_updates = wrap_call(script.on_show, script.filename, "on_show", *script_args) + if (len(on_show_updates) != (args_to - args_from)): + print("Error in custom script (" + script.filename + "): on_show() method should return the same number of arguments as ui().", file=sys.stderr) else: args_from = 0 args_to = 0 @@ -150,13 +152,14 @@ class ScriptRunner: ret = [ ui.gr_show(True)] # always show the dropdown for i in range(1, len(inputs)): if (args_from <= i < args_to): - ret.append( on_show_updates[i - args_from] ) + if (i - args_from) < len(on_show_updates): + ret.append( on_show_updates[i - args_from] ) + else: + ret.append(ui.gr_show(True)) else: ret.append(ui.gr_show(False)) return ret - # return [ui.gr_show(True if (i == 0) else on_show_updates[i - args_from] if args_from <= i < args_to else False) for i in range(len(inputs))] - dropdown.change( fn=select_script, inputs=inputs, -- cgit v1.2.1 From ab4fe4f44c3d2675a351269fe2ff1ddeac557aa6 Mon Sep 17 00:00:00 2001 From: AUTOMATIC <16777216c@gmail.com> Date: Sun, 9 Oct 2022 11:59:41 +0300 Subject: hide filenames for save button by default --- modules/ui.py | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) (limited to 'modules') diff --git a/modules/ui.py b/modules/ui.py index 8071b1cb..e1ab2665 100644 --- a/modules/ui.py +++ b/modules/ui.py @@ -162,7 +162,7 @@ def save_files(js_data, images, do_make_zip, index): zip_file.writestr(filenames[i], f.read()) fullfns.insert(0, zip_filepath) - return fullfns, '', '', plaintext_to_html(f"Saved: {filenames[0]}") + return gr.File.update(value=fullfns, visible=True), '', '', plaintext_to_html(f"Saved: {filenames[0]}") def wrap_gradio_call(func, extra_outputs=None): @@ -553,7 +553,7 @@ def create_ui(wrap_gradio_gpu_call): do_make_zip = gr.Checkbox(label="Make Zip when Save?", value=False) with gr.Row(): - download_files = gr.File(None, file_count="multiple", interactive=False, show_label=False) + download_files = gr.File(None, file_count="multiple", interactive=False, show_label=False, visible=False) with gr.Group(): html_info = gr.HTML() @@ -741,7 +741,7 @@ def create_ui(wrap_gradio_gpu_call): do_make_zip = gr.Checkbox(label="Make Zip when Save?", value=False) with gr.Row(): - download_files = gr.File(None, file_count="multiple", interactive=False, show_label=False) + download_files = gr.File(None, file_count="multiple", interactive=False, show_label=False, visible=False) with gr.Group(): html_info = gr.HTML() -- cgit v1.2.1 From 0241d811d23427b99f6b1eda1540bdf8d87963d5 Mon Sep 17 00:00:00 2001 From: AUTOMATIC <16777216c@gmail.com> Date: Sun, 9 Oct 2022 12:04:44 +0300 Subject: Revert "Fix for Prompts_from_file showing extra textbox." This reverts commit e2930f9821c197da94e208b5ae73711002844efc. --- modules/scripts.py | 32 ++++---------------------------- 1 file changed, 4 insertions(+), 28 deletions(-) (limited to 'modules') diff --git a/modules/scripts.py b/modules/scripts.py index 7d89979d..45230f9a 100644 --- a/modules/scripts.py +++ b/modules/scripts.py @@ -31,15 +31,6 @@ class Script: def show(self, is_img2img): return True - - # Called when the ui for this script has been shown. - # Useful for hiding some controls, since the scripts module sets visibility to - # everything to true. The parameters will be the parameters returned by the ui method - # The return value should be gradio updates, similar to what you would return - # from a Gradio event handler. - def on_show(self, *args): - return [ui.gr_show(True)] * len(args) - # This is where the additional processing is implemented. The parameters include # self, the model object "p" (a StableDiffusionProcessing class, see # processing.py), and the parameters returned by the ui method. @@ -134,35 +125,20 @@ class ScriptRunner: inputs += controls script.args_to = len(inputs) - def select_script(*args): - script_index = args[0] - on_show_updates = [] + def select_script(script_index): if 0 < script_index <= len(self.scripts): script = self.scripts[script_index-1] args_from = script.args_from args_to = script.args_to - script_args = args[args_from:args_to] - on_show_updates = wrap_call(script.on_show, script.filename, "on_show", *script_args) - if (len(on_show_updates) != (args_to - args_from)): - print("Error in custom script (" + script.filename + "): on_show() method should return the same number of arguments as ui().", file=sys.stderr) else: args_from = 0 args_to = 0 - ret = [ ui.gr_show(True)] # always show the dropdown - for i in range(1, len(inputs)): - if (args_from <= i < args_to): - if (i - args_from) < len(on_show_updates): - ret.append( on_show_updates[i - args_from] ) - else: - ret.append(ui.gr_show(True)) - else: - ret.append(ui.gr_show(False)) - return ret + return [ui.gr_show(True if i == 0 else args_from <= i < args_to) for i in range(len(inputs))] dropdown.change( fn=select_script, - inputs=inputs, + inputs=[dropdown], outputs=inputs ) @@ -222,4 +198,4 @@ def reload_scripts(basedir): load_scripts(basedir) scripts_txt2img = ScriptRunner() - scripts_img2img = ScriptRunner() \ No newline at end of file + scripts_img2img = ScriptRunner() -- cgit v1.2.1 From 6f6798ddabe10d320fe8ea05edf0fdcef0c51a8e Mon Sep 17 00:00:00 2001 From: AUTOMATIC <16777216c@gmail.com> Date: Sun, 9 Oct 2022 12:33:37 +0300 Subject: prevent a possible code execution error (thanks, RyotaK) --- modules/ui.py | 9 +++++++++ 1 file changed, 9 insertions(+) (limited to 'modules') diff --git a/modules/ui.py b/modules/ui.py index e1ab2665..dad509f3 100644 --- a/modules/ui.py +++ b/modules/ui.py @@ -1153,6 +1153,15 @@ def create_ui(wrap_gradio_gpu_call): component_dict = {} def open_folder(f): + if not os.path.isdir(f): + print(f""" +WARNING +An open_folder request was made with an argument that is not a folder. +This could be an error or a malicious attempt to run code on your computer. +Requested path was: {f} +""", file=sys.stderr) + return + if not shared.cmd_opts.hide_ui_dir_config: path = os.path.normpath(f) if platform.system() == "Windows": -- cgit v1.2.1 From 0609ce06c0778536cb368ac3867292f87c6d9fc7 Mon Sep 17 00:00:00 2001 From: Milly Date: Fri, 7 Oct 2022 03:36:08 +0900 Subject: Removed duplicate definition model_path --- modules/bsrgan_model.py | 2 -- modules/esrgan_model.py | 2 -- modules/ldsr_model.py | 2 -- modules/realesrgan_model.py | 2 -- modules/scunet_model.py | 2 -- modules/swinir_model.py | 2 -- modules/upscaler.py | 7 ++++--- 7 files changed, 4 insertions(+), 15 deletions(-) (limited to 'modules') diff --git a/modules/bsrgan_model.py b/modules/bsrgan_model.py index 3bd80791..737e1a76 100644 --- a/modules/bsrgan_model.py +++ b/modules/bsrgan_model.py @@ -10,13 +10,11 @@ from basicsr.utils.download_util import load_file_from_url import modules.upscaler from modules import devices, modelloader from modules.bsrgan_model_arch import RRDBNet -from modules.paths import models_path class UpscalerBSRGAN(modules.upscaler.Upscaler): def __init__(self, dirname): self.name = "BSRGAN" - self.model_path = os.path.join(models_path, self.name) self.model_name = "BSRGAN 4x" self.model_url = "https://github.com/cszn/KAIR/releases/download/v1.0/BSRGAN.pth" self.user_path = dirname diff --git a/modules/esrgan_model.py b/modules/esrgan_model.py index 28548124..3970e6e4 100644 --- a/modules/esrgan_model.py +++ b/modules/esrgan_model.py @@ -7,7 +7,6 @@ from basicsr.utils.download_util import load_file_from_url import modules.esrgam_model_arch as arch from modules import shared, modelloader, images, devices -from modules.paths import models_path from modules.upscaler import Upscaler, UpscalerData from modules.shared import opts @@ -76,7 +75,6 @@ class UpscalerESRGAN(Upscaler): self.model_name = "ESRGAN_4x" self.scalers = [] self.user_path = dirname - self.model_path = os.path.join(models_path, self.name) super().__init__() model_paths = self.find_models(ext_filter=[".pt", ".pth"]) scalers = [] diff --git a/modules/ldsr_model.py b/modules/ldsr_model.py index 1c1070fc..8c4db44a 100644 --- a/modules/ldsr_model.py +++ b/modules/ldsr_model.py @@ -7,13 +7,11 @@ from basicsr.utils.download_util import load_file_from_url from modules.upscaler import Upscaler, UpscalerData from modules.ldsr_model_arch import LDSR from modules import shared -from modules.paths import models_path class UpscalerLDSR(Upscaler): def __init__(self, user_path): self.name = "LDSR" - self.model_path = os.path.join(models_path, self.name) self.user_path = user_path self.model_url = "https://heibox.uni-heidelberg.de/f/578df07c8fc04ffbadf3/?dl=1" self.yaml_url = "https://heibox.uni-heidelberg.de/f/31a76b13ea27482981b4/?dl=1" diff --git a/modules/realesrgan_model.py b/modules/realesrgan_model.py index dc0123e0..3ac0b97a 100644 --- a/modules/realesrgan_model.py +++ b/modules/realesrgan_model.py @@ -8,14 +8,12 @@ from basicsr.utils.download_util import load_file_from_url from realesrgan import RealESRGANer from modules.upscaler import Upscaler, UpscalerData -from modules.paths import models_path from modules.shared import cmd_opts, opts class UpscalerRealESRGAN(Upscaler): def __init__(self, path): self.name = "RealESRGAN" - self.model_path = os.path.join(models_path, self.name) self.user_path = path super().__init__() try: diff --git a/modules/scunet_model.py b/modules/scunet_model.py index fb64b740..36a996bf 100644 --- a/modules/scunet_model.py +++ b/modules/scunet_model.py @@ -9,14 +9,12 @@ from basicsr.utils.download_util import load_file_from_url import modules.upscaler from modules import devices, modelloader -from modules.paths import models_path from modules.scunet_model_arch import SCUNet as net class UpscalerScuNET(modules.upscaler.Upscaler): def __init__(self, dirname): self.name = "ScuNET" - self.model_path = os.path.join(models_path, self.name) self.model_name = "ScuNET GAN" self.model_name2 = "ScuNET PSNR" self.model_url = "https://github.com/cszn/KAIR/releases/download/v1.0/scunet_color_real_gan.pth" diff --git a/modules/swinir_model.py b/modules/swinir_model.py index 9bd454c6..fbd11f84 100644 --- a/modules/swinir_model.py +++ b/modules/swinir_model.py @@ -8,7 +8,6 @@ from basicsr.utils.download_util import load_file_from_url from tqdm import tqdm from modules import modelloader -from modules.paths import models_path from modules.shared import cmd_opts, opts, device from modules.swinir_model_arch import SwinIR as net from modules.upscaler import Upscaler, UpscalerData @@ -25,7 +24,6 @@ class UpscalerSwinIR(Upscaler): "/003_realSR_BSRGAN_DFOWMFC_s64w8_SwinIR" \ "-L_x4_GAN.pth " self.model_name = "SwinIR 4x" - self.model_path = os.path.join(models_path, self.name) self.user_path = dirname super().__init__() scalers = [] diff --git a/modules/upscaler.py b/modules/upscaler.py index d9d7c5e2..34672be7 100644 --- a/modules/upscaler.py +++ b/modules/upscaler.py @@ -36,10 +36,11 @@ class Upscaler: self.half = not modules.shared.cmd_opts.no_half self.pre_pad = 0 self.mod_scale = None - if self.name is not None and create_dirs: + + if self.model_path is not None and self.name: self.model_path = os.path.join(models_path, self.name) - if not os.path.exists(self.model_path): - os.makedirs(self.model_path) + if self.model_path and create_dirs: + os.makedirs(self.model_path, exist_ok=True) try: import cv2 -- cgit v1.2.1 From bd833409ac7b8337040d521f6b65ced51e1b2ea8 Mon Sep 17 00:00:00 2001 From: AUTOMATIC <16777216c@gmail.com> Date: Sun, 9 Oct 2022 13:10:15 +0300 Subject: additional changes for saving pnginfo for #1803 --- modules/extras.py | 4 ++++ modules/processing.py | 6 ++++-- 2 files changed, 8 insertions(+), 2 deletions(-) (limited to 'modules') diff --git a/modules/extras.py b/modules/extras.py index ef6e6de7..39dd3806 100644 --- a/modules/extras.py +++ b/modules/extras.py @@ -98,6 +98,10 @@ def run_extras(extras_mode, image, image_folder, gfpgan_visibility, codeformer_v no_prompt=True, grid=False, pnginfo_section_name="extras", existing_info=existing_pnginfo, forced_filename=image_name if opts.use_original_name_batch else None) + if opts.enable_pnginfo: + image.info = existing_pnginfo + image.info["extras"] = info + outputs.append(image) devices.torch_gc() diff --git a/modules/processing.py b/modules/processing.py index 7fa1144e..2c991317 100644 --- a/modules/processing.py +++ b/modules/processing.py @@ -451,7 +451,8 @@ def process_images(p: StableDiffusionProcessing) -> Processed: text = infotext(n, i) infotexts.append(text) - image.info["parameters"] = text + if opts.enable_pnginfo: + image.info["parameters"] = text output_images.append(image) del x_samples_ddim @@ -470,7 +471,8 @@ def process_images(p: StableDiffusionProcessing) -> Processed: if opts.return_grid: text = infotext() infotexts.insert(0, text) - grid.info["parameters"] = text + if opts.enable_pnginfo: + grid.info["parameters"] = text output_images.insert(0, grid) index_of_first_image = 1 -- cgit v1.2.1 From f4578b343ded3b8ccd1879ea0c0b3cdadfcc3a5f Mon Sep 17 00:00:00 2001 From: AUTOMATIC <16777216c@gmail.com> Date: Sun, 9 Oct 2022 13:23:30 +0300 Subject: fix model switching not working properly if there is a different yaml config --- modules/sd_models.py | 3 ++- 1 file changed, 2 insertions(+), 1 deletion(-) (limited to 'modules') diff --git a/modules/sd_models.py b/modules/sd_models.py index 2101b18d..d0c74dd8 100644 --- a/modules/sd_models.py +++ b/modules/sd_models.py @@ -196,7 +196,8 @@ def reload_model_weights(sd_model, info=None): return if sd_model.sd_checkpoint_info.config != checkpoint_info.config: - return load_model() + shared.sd_model = load_model() + return shared.sd_model if shared.cmd_opts.lowvram or shared.cmd_opts.medvram: lowvram.send_everything_to_cpu() -- cgit v1.2.1 From 77a719648db515f10136e8b8483d5b16bda2eaeb Mon Sep 17 00:00:00 2001 From: AUTOMATIC <16777216c@gmail.com> Date: Sun, 9 Oct 2022 13:48:04 +0300 Subject: fix logic error in #1832 --- modules/upscaler.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) (limited to 'modules') diff --git a/modules/upscaler.py b/modules/upscaler.py index 34672be7..6ab2fb40 100644 --- a/modules/upscaler.py +++ b/modules/upscaler.py @@ -37,7 +37,7 @@ class Upscaler: self.pre_pad = 0 self.mod_scale = None - if self.model_path is not None and self.name: + if self.model_path is None and self.name: self.model_path = os.path.join(models_path, self.name) if self.model_path and create_dirs: os.makedirs(self.model_path, exist_ok=True) -- cgit v1.2.1 From 542a3d3a4a00c1383fbdaf938ceefef87cf834bb Mon Sep 17 00:00:00 2001 From: AUTOMATIC <16777216c@gmail.com> Date: Sun, 9 Oct 2022 14:33:22 +0300 Subject: fix btoken hypernetworks in XY plot --- modules/hypernetwork.py | 7 +++++-- 1 file changed, 5 insertions(+), 2 deletions(-) (limited to 'modules') diff --git a/modules/hypernetwork.py b/modules/hypernetwork.py index 19f1c227..498bc9d8 100644 --- a/modules/hypernetwork.py +++ b/modules/hypernetwork.py @@ -49,15 +49,18 @@ def list_hypernetworks(path): def load_hypernetwork(filename): - print(f"Loading hypernetwork {filename}") path = shared.hypernetworks.get(filename, None) - if (path is not None): + if path is not None: + print(f"Loading hypernetwork {filename}") try: shared.loaded_hypernetwork = Hypernetwork(path) except Exception: print(f"Error loading hypernetwork {path}", file=sys.stderr) print(traceback.format_exc(), file=sys.stderr) else: + if shared.loaded_hypernetwork is not None: + print(f"Unloading hypernetwork") + shared.loaded_hypernetwork = None -- cgit v1.2.1 From d6d10a37bfd21568e74efb46137f906da96d5fdb Mon Sep 17 00:00:00 2001 From: William Moorehouse Date: Sun, 9 Oct 2022 04:58:40 -0400 Subject: Added extended model details to infotext --- modules/processing.py | 3 +++ modules/sd_models.py | 3 ++- modules/shared.py | 1 + 3 files changed, 6 insertions(+), 1 deletion(-) (limited to 'modules') diff --git a/modules/processing.py b/modules/processing.py index 2c991317..d1bcee4a 100644 --- a/modules/processing.py +++ b/modules/processing.py @@ -284,6 +284,9 @@ def create_infotext(p, all_prompts, all_seeds, all_subseeds, comments, iteration "Face restoration": (opts.face_restoration_model if p.restore_faces else None), "Size": f"{p.width}x{p.height}", "Model hash": getattr(p, 'sd_model_hash', None if not opts.add_model_hash_to_info or not shared.sd_model.sd_model_hash else shared.sd_model.sd_model_hash), + "Model": (None if not opts.add_extended_model_details_to_info or not shared.sd_model.sd_model_name else shared.sd_model.sd_model_name), + "Model VAE": (None if not opts.add_extended_model_details_to_info or not shared.sd_model.sd_model_vae_name else shared.sd_model.sd_model_vae_name), + "Model hypernetwork": (None if not opts.add_extended_model_details_to_info or not opts.sd_hypernetwork else opts.sd_hypernetwork), "Batch size": (None if p.batch_size < 2 else p.batch_size), "Batch pos": (None if p.batch_size < 2 else position_in_batch), "Variation seed": (None if p.subseed_strength == 0 else all_subseeds[index]), diff --git a/modules/sd_models.py b/modules/sd_models.py index d0c74dd8..3fa42329 100644 --- a/modules/sd_models.py +++ b/modules/sd_models.py @@ -4,7 +4,7 @@ import sys from collections import namedtuple import torch from omegaconf import OmegaConf - +from pathlib import Path from ldm.util import instantiate_from_config @@ -158,6 +158,7 @@ def load_model_weights(model, checkpoint_info): vae_dict = {k: v for k, v in vae_ckpt["state_dict"].items() if k[0:4] != "loss"} model.first_stage_model.load_state_dict(vae_dict) + model.sd_model_vae_name = Path(vae_file).stem model.sd_model_hash = sd_model_hash model.sd_model_checkpoint = checkpoint_file diff --git a/modules/shared.py b/modules/shared.py index dffa0094..ca63f7d8 100644 --- a/modules/shared.py +++ b/modules/shared.py @@ -242,6 +242,7 @@ options_templates.update(options_section(('ui', "User interface"), { "return_grid": OptionInfo(True, "Show grid in results for web"), "do_not_show_images": OptionInfo(False, "Do not show any images in results for web"), "add_model_hash_to_info": OptionInfo(True, "Add model hash to generation information"), + "add_extended_model_details_to_info": OptionInfo(False, "Add extended model details to generation information (model name, VAE, hypernetwork)"), "font": OptionInfo("", "Font for image grids that have text"), "js_modal_lightbox": OptionInfo(True, "Enable full page image viewer"), "js_modal_lightbox_initially_zoomed": OptionInfo(True, "Show images zoomed in by default in full page image viewer"), -- cgit v1.2.1 From 006791c13d70e582eee766b7d0499e9821a86bf9 Mon Sep 17 00:00:00 2001 From: William Moorehouse Date: Sun, 9 Oct 2022 05:09:18 -0400 Subject: Fix grabbing the model name for infotext --- modules/processing.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) (limited to 'modules') diff --git a/modules/processing.py b/modules/processing.py index d1bcee4a..c035c990 100644 --- a/modules/processing.py +++ b/modules/processing.py @@ -284,7 +284,7 @@ def create_infotext(p, all_prompts, all_seeds, all_subseeds, comments, iteration "Face restoration": (opts.face_restoration_model if p.restore_faces else None), "Size": f"{p.width}x{p.height}", "Model hash": getattr(p, 'sd_model_hash', None if not opts.add_model_hash_to_info or not shared.sd_model.sd_model_hash else shared.sd_model.sd_model_hash), - "Model": (None if not opts.add_extended_model_details_to_info or not shared.sd_model.sd_model_name else shared.sd_model.sd_model_name), + "Model": (None if not opts.add_extended_model_details_to_info or not shared.sd_model.sd_checkpoint_info.model_name else shared.sd_model.sd_checkpoint_info.model_name), "Model VAE": (None if not opts.add_extended_model_details_to_info or not shared.sd_model.sd_model_vae_name else shared.sd_model.sd_model_vae_name), "Model hypernetwork": (None if not opts.add_extended_model_details_to_info or not opts.sd_hypernetwork else opts.sd_hypernetwork), "Batch size": (None if p.batch_size < 2 else p.batch_size), -- cgit v1.2.1 From 594cbfd8fbe4078b43ceccf01509eeef3d6790c6 Mon Sep 17 00:00:00 2001 From: William Moorehouse Date: Sun, 9 Oct 2022 07:27:11 -0400 Subject: Sanitize infotext output (for now) --- modules/processing.py | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) (limited to 'modules') diff --git a/modules/processing.py b/modules/processing.py index c035c990..049f3769 100644 --- a/modules/processing.py +++ b/modules/processing.py @@ -284,9 +284,9 @@ def create_infotext(p, all_prompts, all_seeds, all_subseeds, comments, iteration "Face restoration": (opts.face_restoration_model if p.restore_faces else None), "Size": f"{p.width}x{p.height}", "Model hash": getattr(p, 'sd_model_hash', None if not opts.add_model_hash_to_info or not shared.sd_model.sd_model_hash else shared.sd_model.sd_model_hash), - "Model": (None if not opts.add_extended_model_details_to_info or not shared.sd_model.sd_checkpoint_info.model_name else shared.sd_model.sd_checkpoint_info.model_name), - "Model VAE": (None if not opts.add_extended_model_details_to_info or not shared.sd_model.sd_model_vae_name else shared.sd_model.sd_model_vae_name), - "Model hypernetwork": (None if not opts.add_extended_model_details_to_info or not opts.sd_hypernetwork else opts.sd_hypernetwork), + "Model": (None if not opts.add_extended_model_details_to_info or not shared.sd_model.sd_checkpoint_info.model_name else shared.sd_model.sd_checkpoint_info.model_name.replace(',', '').replace(':', '')), + "Model VAE": (None if not opts.add_extended_model_details_to_info or not shared.sd_model.sd_model_vae_name else shared.sd_model.sd_model_vae_name.replace(',', '').replace(':', '')), + "Model hypernetwork": (None if not opts.add_extended_model_details_to_info or not opts.sd_hypernetwork else opts.sd_hypernetwork.replace(',', '').replace(':', '')), "Batch size": (None if p.batch_size < 2 else p.batch_size), "Batch pos": (None if p.batch_size < 2 else position_in_batch), "Variation seed": (None if p.subseed_strength == 0 else all_subseeds[index]), -- cgit v1.2.1 From e6e8cabe0c9c335e0d72345602c069b198558b53 Mon Sep 17 00:00:00 2001 From: AUTOMATIC <16777216c@gmail.com> Date: Sun, 9 Oct 2022 14:57:48 +0300 Subject: change up #2056 to make it work how i want it to plus make xy plot write correct values to images --- modules/processing.py | 5 ++--- modules/sd_models.py | 2 -- modules/shared.py | 2 +- 3 files changed, 3 insertions(+), 6 deletions(-) (limited to 'modules') diff --git a/modules/processing.py b/modules/processing.py index 049f3769..04aed989 100644 --- a/modules/processing.py +++ b/modules/processing.py @@ -284,9 +284,8 @@ def create_infotext(p, all_prompts, all_seeds, all_subseeds, comments, iteration "Face restoration": (opts.face_restoration_model if p.restore_faces else None), "Size": f"{p.width}x{p.height}", "Model hash": getattr(p, 'sd_model_hash', None if not opts.add_model_hash_to_info or not shared.sd_model.sd_model_hash else shared.sd_model.sd_model_hash), - "Model": (None if not opts.add_extended_model_details_to_info or not shared.sd_model.sd_checkpoint_info.model_name else shared.sd_model.sd_checkpoint_info.model_name.replace(',', '').replace(':', '')), - "Model VAE": (None if not opts.add_extended_model_details_to_info or not shared.sd_model.sd_model_vae_name else shared.sd_model.sd_model_vae_name.replace(',', '').replace(':', '')), - "Model hypernetwork": (None if not opts.add_extended_model_details_to_info or not opts.sd_hypernetwork else opts.sd_hypernetwork.replace(',', '').replace(':', '')), + "Model": (None if not opts.add_model_name_to_info or not shared.sd_model.sd_checkpoint_info.model_name else shared.sd_model.sd_checkpoint_info.model_name.replace(',', '').replace(':', '')), + "Hypernet": (None if shared.loaded_hypernetwork is None else shared.loaded_hypernetwork.name.replace(',', '').replace(':', '')), "Batch size": (None if p.batch_size < 2 else p.batch_size), "Batch pos": (None if p.batch_size < 2 else position_in_batch), "Variation seed": (None if p.subseed_strength == 0 else all_subseeds[index]), diff --git a/modules/sd_models.py b/modules/sd_models.py index 3fa42329..e63d3c29 100644 --- a/modules/sd_models.py +++ b/modules/sd_models.py @@ -4,7 +4,6 @@ import sys from collections import namedtuple import torch from omegaconf import OmegaConf -from pathlib import Path from ldm.util import instantiate_from_config @@ -158,7 +157,6 @@ def load_model_weights(model, checkpoint_info): vae_dict = {k: v for k, v in vae_ckpt["state_dict"].items() if k[0:4] != "loss"} model.first_stage_model.load_state_dict(vae_dict) - model.sd_model_vae_name = Path(vae_file).stem model.sd_model_hash = sd_model_hash model.sd_model_checkpoint = checkpoint_file diff --git a/modules/shared.py b/modules/shared.py index ca63f7d8..6ecc2503 100644 --- a/modules/shared.py +++ b/modules/shared.py @@ -242,7 +242,7 @@ options_templates.update(options_section(('ui', "User interface"), { "return_grid": OptionInfo(True, "Show grid in results for web"), "do_not_show_images": OptionInfo(False, "Do not show any images in results for web"), "add_model_hash_to_info": OptionInfo(True, "Add model hash to generation information"), - "add_extended_model_details_to_info": OptionInfo(False, "Add extended model details to generation information (model name, VAE, hypernetwork)"), + "add_model_name_to_info": OptionInfo(False, "Add model name to generation information"), "font": OptionInfo("", "Font for image grids that have text"), "js_modal_lightbox": OptionInfo(True, "Enable full page image viewer"), "js_modal_lightbox_initially_zoomed": OptionInfo(True, "Show images zoomed in by default in full page image viewer"), -- cgit v1.2.1 From 9d1138e2940c4ddcd2685bcba12c7d407e9e0ec5 Mon Sep 17 00:00:00 2001 From: AUTOMATIC <16777216c@gmail.com> Date: Sun, 9 Oct 2022 15:08:10 +0300 Subject: fix typo in filename for ESRGAN arch --- modules/esrgam_model_arch.py | 80 -------------------------------------------- modules/esrgan_model.py | 2 +- modules/esrgan_model_arch.py | 80 ++++++++++++++++++++++++++++++++++++++++++++ 3 files changed, 81 insertions(+), 81 deletions(-) delete mode 100644 modules/esrgam_model_arch.py create mode 100644 modules/esrgan_model_arch.py (limited to 'modules') diff --git a/modules/esrgam_model_arch.py b/modules/esrgam_model_arch.py deleted file mode 100644 index e413d36e..00000000 --- a/modules/esrgam_model_arch.py +++ /dev/null @@ -1,80 +0,0 @@ -# this file is taken from https://github.com/xinntao/ESRGAN - -import functools -import torch -import torch.nn as nn -import torch.nn.functional as F - - -def make_layer(block, n_layers): - layers = [] - for _ in range(n_layers): - layers.append(block()) - return nn.Sequential(*layers) - - -class ResidualDenseBlock_5C(nn.Module): - def __init__(self, nf=64, gc=32, bias=True): - super(ResidualDenseBlock_5C, self).__init__() - # gc: growth channel, i.e. intermediate channels - self.conv1 = nn.Conv2d(nf, gc, 3, 1, 1, bias=bias) - self.conv2 = nn.Conv2d(nf + gc, gc, 3, 1, 1, bias=bias) - self.conv3 = nn.Conv2d(nf + 2 * gc, gc, 3, 1, 1, bias=bias) - self.conv4 = nn.Conv2d(nf + 3 * gc, gc, 3, 1, 1, bias=bias) - self.conv5 = nn.Conv2d(nf + 4 * gc, nf, 3, 1, 1, bias=bias) - self.lrelu = nn.LeakyReLU(negative_slope=0.2, inplace=True) - - # initialization - # mutil.initialize_weights([self.conv1, self.conv2, self.conv3, self.conv4, self.conv5], 0.1) - - def forward(self, x): - x1 = self.lrelu(self.conv1(x)) - x2 = self.lrelu(self.conv2(torch.cat((x, x1), 1))) - x3 = self.lrelu(self.conv3(torch.cat((x, x1, x2), 1))) - x4 = self.lrelu(self.conv4(torch.cat((x, x1, x2, x3), 1))) - x5 = self.conv5(torch.cat((x, x1, x2, x3, x4), 1)) - return x5 * 0.2 + x - - -class RRDB(nn.Module): - '''Residual in Residual Dense Block''' - - def __init__(self, nf, gc=32): - super(RRDB, self).__init__() - self.RDB1 = ResidualDenseBlock_5C(nf, gc) - self.RDB2 = ResidualDenseBlock_5C(nf, gc) - self.RDB3 = ResidualDenseBlock_5C(nf, gc) - - def forward(self, x): - out = self.RDB1(x) - out = self.RDB2(out) - out = self.RDB3(out) - return out * 0.2 + x - - -class RRDBNet(nn.Module): - def __init__(self, in_nc, out_nc, nf, nb, gc=32): - super(RRDBNet, self).__init__() - RRDB_block_f = functools.partial(RRDB, nf=nf, gc=gc) - - self.conv_first = nn.Conv2d(in_nc, nf, 3, 1, 1, bias=True) - self.RRDB_trunk = make_layer(RRDB_block_f, nb) - self.trunk_conv = nn.Conv2d(nf, nf, 3, 1, 1, bias=True) - #### upsampling - self.upconv1 = nn.Conv2d(nf, nf, 3, 1, 1, bias=True) - self.upconv2 = nn.Conv2d(nf, nf, 3, 1, 1, bias=True) - self.HRconv = nn.Conv2d(nf, nf, 3, 1, 1, bias=True) - self.conv_last = nn.Conv2d(nf, out_nc, 3, 1, 1, bias=True) - - self.lrelu = nn.LeakyReLU(negative_slope=0.2, inplace=True) - - def forward(self, x): - fea = self.conv_first(x) - trunk = self.trunk_conv(self.RRDB_trunk(fea)) - fea = fea + trunk - - fea = self.lrelu(self.upconv1(F.interpolate(fea, scale_factor=2, mode='nearest'))) - fea = self.lrelu(self.upconv2(F.interpolate(fea, scale_factor=2, mode='nearest'))) - out = self.conv_last(self.lrelu(self.HRconv(fea))) - - return out diff --git a/modules/esrgan_model.py b/modules/esrgan_model.py index 3970e6e4..46ad0da3 100644 --- a/modules/esrgan_model.py +++ b/modules/esrgan_model.py @@ -5,7 +5,7 @@ import torch from PIL import Image from basicsr.utils.download_util import load_file_from_url -import modules.esrgam_model_arch as arch +import modules.esrgan_model_arch as arch from modules import shared, modelloader, images, devices from modules.upscaler import Upscaler, UpscalerData from modules.shared import opts diff --git a/modules/esrgan_model_arch.py b/modules/esrgan_model_arch.py new file mode 100644 index 00000000..e413d36e --- /dev/null +++ b/modules/esrgan_model_arch.py @@ -0,0 +1,80 @@ +# this file is taken from https://github.com/xinntao/ESRGAN + +import functools +import torch +import torch.nn as nn +import torch.nn.functional as F + + +def make_layer(block, n_layers): + layers = [] + for _ in range(n_layers): + layers.append(block()) + return nn.Sequential(*layers) + + +class ResidualDenseBlock_5C(nn.Module): + def __init__(self, nf=64, gc=32, bias=True): + super(ResidualDenseBlock_5C, self).__init__() + # gc: growth channel, i.e. intermediate channels + self.conv1 = nn.Conv2d(nf, gc, 3, 1, 1, bias=bias) + self.conv2 = nn.Conv2d(nf + gc, gc, 3, 1, 1, bias=bias) + self.conv3 = nn.Conv2d(nf + 2 * gc, gc, 3, 1, 1, bias=bias) + self.conv4 = nn.Conv2d(nf + 3 * gc, gc, 3, 1, 1, bias=bias) + self.conv5 = nn.Conv2d(nf + 4 * gc, nf, 3, 1, 1, bias=bias) + self.lrelu = nn.LeakyReLU(negative_slope=0.2, inplace=True) + + # initialization + # mutil.initialize_weights([self.conv1, self.conv2, self.conv3, self.conv4, self.conv5], 0.1) + + def forward(self, x): + x1 = self.lrelu(self.conv1(x)) + x2 = self.lrelu(self.conv2(torch.cat((x, x1), 1))) + x3 = self.lrelu(self.conv3(torch.cat((x, x1, x2), 1))) + x4 = self.lrelu(self.conv4(torch.cat((x, x1, x2, x3), 1))) + x5 = self.conv5(torch.cat((x, x1, x2, x3, x4), 1)) + return x5 * 0.2 + x + + +class RRDB(nn.Module): + '''Residual in Residual Dense Block''' + + def __init__(self, nf, gc=32): + super(RRDB, self).__init__() + self.RDB1 = ResidualDenseBlock_5C(nf, gc) + self.RDB2 = ResidualDenseBlock_5C(nf, gc) + self.RDB3 = ResidualDenseBlock_5C(nf, gc) + + def forward(self, x): + out = self.RDB1(x) + out = self.RDB2(out) + out = self.RDB3(out) + return out * 0.2 + x + + +class RRDBNet(nn.Module): + def __init__(self, in_nc, out_nc, nf, nb, gc=32): + super(RRDBNet, self).__init__() + RRDB_block_f = functools.partial(RRDB, nf=nf, gc=gc) + + self.conv_first = nn.Conv2d(in_nc, nf, 3, 1, 1, bias=True) + self.RRDB_trunk = make_layer(RRDB_block_f, nb) + self.trunk_conv = nn.Conv2d(nf, nf, 3, 1, 1, bias=True) + #### upsampling + self.upconv1 = nn.Conv2d(nf, nf, 3, 1, 1, bias=True) + self.upconv2 = nn.Conv2d(nf, nf, 3, 1, 1, bias=True) + self.HRconv = nn.Conv2d(nf, nf, 3, 1, 1, bias=True) + self.conv_last = nn.Conv2d(nf, out_nc, 3, 1, 1, bias=True) + + self.lrelu = nn.LeakyReLU(negative_slope=0.2, inplace=True) + + def forward(self, x): + fea = self.conv_first(x) + trunk = self.trunk_conv(self.RRDB_trunk(fea)) + fea = fea + trunk + + fea = self.lrelu(self.upconv1(F.interpolate(fea, scale_factor=2, mode='nearest'))) + fea = self.lrelu(self.upconv2(F.interpolate(fea, scale_factor=2, mode='nearest'))) + out = self.conv_last(self.lrelu(self.HRconv(fea))) + + return out -- cgit v1.2.1 From 875ddfeecfaffad9eee24813301637cba310337d Mon Sep 17 00:00:00 2001 From: AUTOMATIC <16777216c@gmail.com> Date: Sun, 9 Oct 2022 17:58:43 +0300 Subject: added guard for torch.load to prevent loading pickles with unknown content --- modules/paths.py | 1 + modules/safe.py | 89 +++++++++++++++++++++++++++++++++++++++++++++++++++++++ modules/shared.py | 1 + 3 files changed, 91 insertions(+) create mode 100644 modules/safe.py (limited to 'modules') diff --git a/modules/paths.py b/modules/paths.py index 0519caa0..1e7a2fbc 100644 --- a/modules/paths.py +++ b/modules/paths.py @@ -1,6 +1,7 @@ import argparse import os import sys +import modules.safe script_path = os.path.dirname(os.path.dirname(os.path.realpath(__file__))) models_path = os.path.join(script_path, "models") diff --git a/modules/safe.py b/modules/safe.py new file mode 100644 index 00000000..2d2c1371 --- /dev/null +++ b/modules/safe.py @@ -0,0 +1,89 @@ +# this code is adapted from the script contributed by anon from /h/ + +import io +import pickle +import collections +import sys +import traceback + +import torch +import numpy +import _codecs +import zipfile + + +def encode(*args): + out = _codecs.encode(*args) + return out + + +class RestrictedUnpickler(pickle.Unpickler): + def persistent_load(self, saved_id): + assert saved_id[0] == 'storage' + return torch.storage._TypedStorage() + + def find_class(self, module, name): + if module == 'collections' and name == 'OrderedDict': + return getattr(collections, name) + if module == 'torch._utils' and name in ['_rebuild_tensor_v2', '_rebuild_parameter']: + return getattr(torch._utils, name) + if module == 'torch' and name in ['FloatStorage', 'HalfStorage', 'IntStorage', 'LongStorage']: + return getattr(torch, name) + if module == 'torch.nn.modules.container' and name in ['ParameterDict']: + return getattr(torch.nn.modules.container, name) + if module == 'numpy.core.multiarray' and name == 'scalar': + return numpy.core.multiarray.scalar + if module == 'numpy' and name == 'dtype': + return numpy.dtype + if module == '_codecs' and name == 'encode': + return encode + if module == "pytorch_lightning.callbacks" and name == 'model_checkpoint': + import pytorch_lightning.callbacks + return pytorch_lightning.callbacks.model_checkpoint + if module == "pytorch_lightning.callbacks.model_checkpoint" and name == 'ModelCheckpoint': + import pytorch_lightning.callbacks.model_checkpoint + return pytorch_lightning.callbacks.model_checkpoint.ModelCheckpoint + if module == "__builtin__" and name == 'set': + return set + + # Forbid everything else. + raise pickle.UnpicklingError(f"global '{module}/{name}' is forbidden") + + +def check_pt(filename): + try: + + # new pytorch format is a zip file + with zipfile.ZipFile(filename) as z: + with z.open('archive/data.pkl') as file: + unpickler = RestrictedUnpickler(file) + unpickler.load() + + except zipfile.BadZipfile: + + # if it's not a zip file, it's an olf pytorch format, with five objects written to pickle + with open(filename, "rb") as file: + unpickler = RestrictedUnpickler(file) + for i in range(5): + unpickler.load() + + +def load(filename, *args, **kwargs): + from modules import shared + + try: + if not shared.cmd_opts.disable_safe_unpickle: + check_pt(filename) + + except Exception: + print(f"Error verifying pickled file from {filename}:", file=sys.stderr) + print(traceback.format_exc(), file=sys.stderr) + print(f"\nThe file may be malicious, so the program is not going to read it.", file=sys.stderr) + print(f"You can skip this check with --disable-safe-unpickle commandline argument.", file=sys.stderr) + return None + + return unsafe_torch_load(filename, *args, **kwargs) + + +unsafe_torch_load = torch.load +torch.load = load diff --git a/modules/shared.py b/modules/shared.py index 6ecc2503..3d7f08e1 100644 --- a/modules/shared.py +++ b/modules/shared.py @@ -65,6 +65,7 @@ parser.add_argument("--autolaunch", action='store_true', help="open the webui UR parser.add_argument("--use-textbox-seed", action='store_true', help="use textbox for seeds in UI (no up/down, but possible to input long seeds)", default=False) parser.add_argument("--disable-console-progressbars", action='store_true', help="do not output progressbars to console", default=False) parser.add_argument("--enable-console-prompts", action='store_true', help="print prompts to console when generating with txt2img and img2img", default=False) +parser.add_argument("--disable-safe-unpickle", action='store_true', help="disable checking pytorch models for malicious code", default=False) cmd_opts = parser.parse_args() -- cgit v1.2.1 From 9ecea0a8d6bdc434755e11128487fd62f1ff130f Mon Sep 17 00:00:00 2001 From: Artem Zagidulin Date: Sun, 9 Oct 2022 16:14:56 +0300 Subject: fix missing png info when Extras Batch Process --- modules/extras.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) (limited to 'modules') diff --git a/modules/extras.py b/modules/extras.py index 39dd3806..41e8612c 100644 --- a/modules/extras.py +++ b/modules/extras.py @@ -29,7 +29,7 @@ def run_extras(extras_mode, image, image_folder, gfpgan_visibility, codeformer_v if extras_mode == 1: #convert file to pillow image for img in image_folder: - image = Image.fromarray(np.array(Image.open(img))) + image = Image.open(img) imageArr.append(image) imageNameArr.append(os.path.splitext(img.orig_name)[0]) else: -- cgit v1.2.1 From 6c383d2e82045fc4475d665f83bdeeac8fd844d9 Mon Sep 17 00:00:00 2001 From: AUTOMATIC <16777216c@gmail.com> Date: Sun, 9 Oct 2022 22:24:07 +0300 Subject: show model selection setting on top of page --- modules/shared.py | 5 +++-- modules/ui.py | 54 +++++++++++++++++++++++++++++++++++++++++++++--------- 2 files changed, 48 insertions(+), 11 deletions(-) (limited to 'modules') diff --git a/modules/shared.py b/modules/shared.py index 3d7f08e1..270fa402 100644 --- a/modules/shared.py +++ b/modules/shared.py @@ -131,13 +131,14 @@ def realesrgan_models_names(): class OptionInfo: - def __init__(self, default=None, label="", component=None, component_args=None, onchange=None): + def __init__(self, default=None, label="", component=None, component_args=None, onchange=None, show_on_main_page=False): self.default = default self.label = label self.component = component self.component_args = component_args self.onchange = onchange self.section = None + self.show_on_main_page = show_on_main_page def options_section(section_identifier, options_dict): @@ -214,7 +215,7 @@ options_templates.update(options_section(('system', "System"), { })) options_templates.update(options_section(('sd', "Stable Diffusion"), { - "sd_model_checkpoint": OptionInfo(None, "Stable Diffusion checkpoint", gr.Dropdown, lambda: {"choices": modules.sd_models.checkpoint_tiles()}), + "sd_model_checkpoint": OptionInfo(None, "Stable Diffusion checkpoint", gr.Dropdown, lambda: {"choices": modules.sd_models.checkpoint_tiles()}, show_on_main_page=True), "sd_hypernetwork": OptionInfo("None", "Stable Diffusion finetune hypernetwork", gr.Dropdown, lambda: {"choices": ["None"] + [x for x in hypernetworks.keys()]}), "img2img_color_correction": OptionInfo(False, "Apply color correction to img2img results to match original colors."), "save_images_before_color_correction": OptionInfo(False, "Save a copy of image before applying color correction to img2img results"), diff --git a/modules/ui.py b/modules/ui.py index dad509f3..2231a8ed 100644 --- a/modules/ui.py +++ b/modules/ui.py @@ -1175,10 +1175,13 @@ Requested path was: {f} changed = 0 for key, value, comp in zip(opts.data_labels.keys(), args, components): - if not opts.same_type(value, opts.data_labels[key].default): - return f"Bad value for setting {key}: {value}; expecting {type(opts.data_labels[key].default).__name__}" + if comp != dummy_component and not opts.same_type(value, opts.data_labels[key].default): + return f"Bad value for setting {key}: {value}; expecting {type(opts.data_labels[key].default).__name__}", opts.dumpjson() for key, value, comp in zip(opts.data_labels.keys(), args, components): + if comp == dummy_component: + continue + comp_args = opts.data_labels[key].component_args if comp_args and isinstance(comp_args, dict) and comp_args.get('visible') is False: continue @@ -1196,6 +1199,21 @@ Requested path was: {f} return f'{changed} settings changed.', opts.dumpjson() + def run_settings_single(value, key): + if not opts.same_type(value, opts.data_labels[key].default): + return gr.update(visible=True), opts.dumpjson() + + oldval = opts.data.get(key, None) + opts.data[key] = value + + if oldval != value: + if opts.data_labels[key].onchange is not None: + opts.data_labels[key].onchange() + + opts.save(shared.config_filename) + + return gr.update(value=value), opts.dumpjson() + with gr.Blocks(analytics_enabled=False) as settings_interface: settings_submit = gr.Button(value="Apply settings", variant='primary') result = gr.HTML() @@ -1203,6 +1221,8 @@ Requested path was: {f} settings_cols = 3 items_per_col = int(len(opts.data_labels) * 0.9 / settings_cols) + quicksettings_list = [] + cols_displayed = 0 items_displayed = 0 previous_section = None @@ -1225,10 +1245,14 @@ Requested path was: {f} gr.HTML(elem_id="settings_header_text_{}".format(item.section[0]), value='

{}

'.format(item.section[1])) - component = create_setting_component(k) - component_dict[k] = component - components.append(component) - items_displayed += 1 + if item.show_on_main_page: + quicksettings_list.append((i, k, item)) + components.append(dummy_component) + else: + component = create_setting_component(k) + component_dict[k] = component + components.append(component) + items_displayed += 1 request_notifications = gr.Button(value='Request browser notifications', elem_id="request_notifications") request_notifications.click( @@ -1242,7 +1266,6 @@ Requested path was: {f} reload_script_bodies = gr.Button(value='Reload custom script bodies (No ui updates, No restart)', variant='secondary') restart_gradio = gr.Button(value='Restart Gradio and Refresh components (Custom Scripts, ui.py, js and css only)', variant='primary') - def reload_scripts(): modules.scripts.reload_script_body_only() @@ -1289,7 +1312,11 @@ Requested path was: {f} css += css_hide_progressbar with gr.Blocks(css=css, analytics_enabled=False, title="Stable Diffusion") as demo: - + with gr.Row(elem_id="quicksettings"): + for i, k, item in quicksettings_list: + component = create_setting_component(k) + component_dict[k] = component + settings_interface.gradio_ref = demo with gr.Tabs() as tabs: @@ -1306,7 +1333,16 @@ Requested path was: {f} inputs=components, outputs=[result, text_settings], ) - + + for i, k, item in quicksettings_list: + component = component_dict[k] + + component.change( + fn=lambda value, k=k: run_settings_single(value, key=k), + inputs=[component], + outputs=[component, text_settings], + ) + def modelmerger(*args): try: results = modules.extras.run_modelmerger(*args) -- cgit v1.2.1 From e59c66c0088422b27f64b401ef42c242f836725a Mon Sep 17 00:00:00 2001 From: Fampai Date: Sat, 8 Oct 2022 16:32:05 -0400 Subject: Optimized code for Ignoring last CLIP layers --- modules/sd_hijack.py | 12 ++++-------- 1 file changed, 4 insertions(+), 8 deletions(-) (limited to 'modules') diff --git a/modules/sd_hijack.py b/modules/sd_hijack.py index f12a9696..4a2d2153 100644 --- a/modules/sd_hijack.py +++ b/modules/sd_hijack.py @@ -282,14 +282,10 @@ class FrozenCLIPEmbedderWithCustomWords(torch.nn.Module): remade_batch_tokens_of_same_length = [x + [self.wrapped.tokenizer.eos_token_id] * (target_token_count - len(x)) for x in remade_batch_tokens] tokens = torch.asarray(remade_batch_tokens_of_same_length).to(device) - tmp = -opts.CLIP_ignore_last_layers - if (opts.CLIP_ignore_last_layers == 0): - outputs = self.wrapped.transformer(input_ids=tokens, position_ids=position_ids) - z = outputs.last_hidden_state - else: - outputs = self.wrapped.transformer(input_ids=tokens, position_ids=position_ids, output_hidden_states=tmp) - z = outputs.hidden_states[tmp] - z = self.wrapped.transformer.text_model.final_layer_norm(z) + tmp = -opts.CLIP_stop_at_last_layers + outputs = self.wrapped.transformer(input_ids=tokens, position_ids=position_ids, output_hidden_states=tmp) + z = outputs.hidden_states[tmp] + z = self.wrapped.transformer.text_model.final_layer_norm(z) # restoring original mean is likely not correct, but it seems to work well to prevent artifacts that happen otherwise batch_multipliers_of_same_length = [x + [1.0] * (target_token_count - len(x)) for x in batch_multipliers] -- cgit v1.2.1 From a14f7bf113a2af9e06a1c4d06c2efa244f9c5730 Mon Sep 17 00:00:00 2001 From: Fampai Date: Sat, 8 Oct 2022 16:33:06 -0400 Subject: Corrected CLIP Layer Ignore description and updated its range to the max possible --- modules/shared.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) (limited to 'modules') diff --git a/modules/shared.py b/modules/shared.py index 270fa402..1995a99a 100644 --- a/modules/shared.py +++ b/modules/shared.py @@ -225,7 +225,7 @@ options_templates.update(options_section(('sd', "Stable Diffusion"), { "use_old_emphasis_implementation": OptionInfo(False, "Use old emphasis implementation. Can be useful to reproduce old seeds."), "enable_batch_seeds": OptionInfo(True, "Make K-diffusion samplers produce same images in a batch as when making a single image"), "filter_nsfw": OptionInfo(False, "Filter NSFW content"), - 'CLIP_ignore_last_layers': OptionInfo(0, "Ignore last layers of CLIP model", gr.Slider, {"minimum": 0, "maximum": 5, "step": 1}), + 'CLIP_stop_at_last_layers': OptionInfo(1, "Stop At last layers of CLIP model", gr.Slider, {"minimum": 1, "maximum": 12, "step": 1}), "random_artist_categories": OptionInfo([], "Allowed categories for random artists selection when using the Roll button", gr.CheckboxGroup, {"choices": artist_db.categories()}), })) -- cgit v1.2.1 From ec2bd9be75865c9f3a8c898163ab381688c03b6e Mon Sep 17 00:00:00 2001 From: Fampai Date: Sat, 8 Oct 2022 17:28:42 -0400 Subject: Fix issues with CLIP ignore option name change --- modules/processing.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) (limited to 'modules') diff --git a/modules/processing.py b/modules/processing.py index 04aed989..92a105a2 100644 --- a/modules/processing.py +++ b/modules/processing.py @@ -129,7 +129,7 @@ class Processed: self.index_of_first_image = index_of_first_image self.styles = p.styles self.job_timestamp = state.job_timestamp - self.clip_skip = opts.CLIP_ignore_last_layers + self.clip_skip = opts.CLIP_stop_at_last_layers self.eta = p.eta self.ddim_discretize = p.ddim_discretize @@ -274,7 +274,7 @@ def fix_seed(p): def create_infotext(p, all_prompts, all_seeds, all_subseeds, comments, iteration=0, position_in_batch=0): index = position_in_batch + iteration * p.batch_size - clip_skip = getattr(p, 'clip_skip', opts.CLIP_ignore_last_layers) + clip_skip = getattr(p, 'clip_skip', opts.CLIP_stop_at_last_layers) generation_params = { "Steps": p.steps, -- cgit v1.2.1 From ad3ae441081155dcd4fde805279e5082ca264695 Mon Sep 17 00:00:00 2001 From: Fampai Date: Sun, 9 Oct 2022 04:32:40 -0400 Subject: Updated code for legibility --- modules/sd_hijack.py | 7 +++++-- 1 file changed, 5 insertions(+), 2 deletions(-) (limited to 'modules') diff --git a/modules/sd_hijack.py b/modules/sd_hijack.py index 4a2d2153..7793d25b 100644 --- a/modules/sd_hijack.py +++ b/modules/sd_hijack.py @@ -284,8 +284,11 @@ class FrozenCLIPEmbedderWithCustomWords(torch.nn.Module): tmp = -opts.CLIP_stop_at_last_layers outputs = self.wrapped.transformer(input_ids=tokens, position_ids=position_ids, output_hidden_states=tmp) - z = outputs.hidden_states[tmp] - z = self.wrapped.transformer.text_model.final_layer_norm(z) + if tmp < -1: + z = outputs.hidden_states[tmp] + z = self.wrapped.transformer.text_model.final_layer_norm(z) + else: + z = outputs.last_hidden_state # restoring original mean is likely not correct, but it seems to work well to prevent artifacts that happen otherwise batch_multipliers_of_same_length = [x + [1.0] * (target_token_count - len(x)) for x in batch_multipliers] -- cgit v1.2.1 From 1824e9ee3ab4f94aee8908a62ea2569a01aeb3d7 Mon Sep 17 00:00:00 2001 From: Fampai Date: Sun, 9 Oct 2022 14:15:43 -0400 Subject: Removed unnecessary tmp variable --- modules/sd_hijack.py | 7 +++---- 1 file changed, 3 insertions(+), 4 deletions(-) (limited to 'modules') diff --git a/modules/sd_hijack.py b/modules/sd_hijack.py index 7793d25b..437acce4 100644 --- a/modules/sd_hijack.py +++ b/modules/sd_hijack.py @@ -282,10 +282,9 @@ class FrozenCLIPEmbedderWithCustomWords(torch.nn.Module): remade_batch_tokens_of_same_length = [x + [self.wrapped.tokenizer.eos_token_id] * (target_token_count - len(x)) for x in remade_batch_tokens] tokens = torch.asarray(remade_batch_tokens_of_same_length).to(device) - tmp = -opts.CLIP_stop_at_last_layers - outputs = self.wrapped.transformer(input_ids=tokens, position_ids=position_ids, output_hidden_states=tmp) - if tmp < -1: - z = outputs.hidden_states[tmp] + outputs = self.wrapped.transformer(input_ids=tokens, position_ids=position_ids, output_hidden_states=-opts.CLIP_stop_at_last_layers) + if opts.CLIP_stop_at_last_layers > 1: + z = outputs.hidden_states[-opts.CLIP_stop_at_last_layers] z = self.wrapped.transformer.text_model.final_layer_norm(z) else: z = outputs.last_hidden_state -- cgit v1.2.1 From 8d340cfb884e1dbff5b6f477f4ecf7d104279115 Mon Sep 17 00:00:00 2001 From: AUTOMATIC <16777216c@gmail.com> Date: Sun, 9 Oct 2022 22:30:59 +0300 Subject: do not add clip skip to parameters if it's 1 or 0 --- modules/processing.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) (limited to 'modules') diff --git a/modules/processing.py b/modules/processing.py index 92a105a2..94d2dd62 100644 --- a/modules/processing.py +++ b/modules/processing.py @@ -293,7 +293,7 @@ def create_infotext(p, all_prompts, all_seeds, all_subseeds, comments, iteration "Seed resize from": (None if p.seed_resize_from_w == 0 or p.seed_resize_from_h == 0 else f"{p.seed_resize_from_w}x{p.seed_resize_from_h}"), "Denoising strength": getattr(p, 'denoising_strength', None), "Eta": (None if p.sampler is None or p.sampler.eta == p.sampler.default_eta else p.sampler.eta), - "Clip skip": None if clip_skip==0 else clip_skip, + "Clip skip": None if clip_skip <= 1 else clip_skip, } generation_params.update(p.extra_generation_params) -- cgit v1.2.1 From fa0c5eb81b72bc1e562d0b9bbd92f30945d78b4e Mon Sep 17 00:00:00 2001 From: DepFA <35278260+dfaker@users.noreply.github.com> Date: Sun, 9 Oct 2022 20:41:22 +0100 Subject: Add pretty image captioning functions --- modules/images.py | 31 +++++++++++++++++++++++++++++++ 1 file changed, 31 insertions(+) (limited to 'modules') diff --git a/modules/images.py b/modules/images.py index 29c5ee24..10963dc7 100644 --- a/modules/images.py +++ b/modules/images.py @@ -428,3 +428,34 @@ def save_image(image, path, basename, seed=None, prompt=None, extension='png', i file.write(info + "\n") return fullfn + +def addCaptionLines(lines,image,initialx,textfont): + draw = ImageDraw.Draw(image) + hstart =initialx + for fill,line in lines: + fontSize = 32 + font = ImageFont.truetype(textfont, fontSize) + _,_,w, h = draw.textbbox((0,0),line,font=font) + fontSize = min( int(fontSize * ((image.size[0]-35)/w) ), 28) + font = ImageFont.truetype(textfont, fontSize) + _,_,w,h = draw.textbbox((0,0),line,font=font) + draw.text(((image.size[0]-w)/2,hstart), line, font=font, fill=fill) + hstart += h + return hstart + +def captionImge(image,prelines,postlines,background=(51, 51, 51),font=None): + if font is None: + try: + font = ImageFont.truetype(opts.font or Roboto, fontsize) + font = opts.font or Roboto + except Exception: + font = Roboto + + sampleImage = image + background = Image.new("RGBA", (sampleImage.size[0],sampleImage.size[1]+1024), background) + hoffset = addCaptionLines(prelines,background,5,font)+16 + background.paste(sampleImage,(0,hoffset)) + hoffset = hoffset+sampleImage.size[1]+8 + hoffset = addCaptionLines(postlines,background,hoffset,font) + background = background.crop((0,0,sampleImage.size[0],hoffset+8)) + return background -- cgit v1.2.1 From a65476718f08a35f527b973ef731e6f488bace5e Mon Sep 17 00:00:00 2001 From: AUTOMATIC <16777216c@gmail.com> Date: Sun, 9 Oct 2022 23:38:49 +0300 Subject: add DoubleStorage to list of allowed classes for pickle --- modules/safe.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) (limited to 'modules') diff --git a/modules/safe.py b/modules/safe.py index 2d2c1371..4d06f2a5 100644 --- a/modules/safe.py +++ b/modules/safe.py @@ -27,7 +27,7 @@ class RestrictedUnpickler(pickle.Unpickler): return getattr(collections, name) if module == 'torch._utils' and name in ['_rebuild_tensor_v2', '_rebuild_parameter']: return getattr(torch._utils, name) - if module == 'torch' and name in ['FloatStorage', 'HalfStorage', 'IntStorage', 'LongStorage']: + if module == 'torch' and name in ['FloatStorage', 'HalfStorage', 'IntStorage', 'LongStorage', 'DoubleStorage']: return getattr(torch, name) if module == 'torch.nn.modules.container' and name in ['ParameterDict']: return getattr(torch.nn.modules.container, name) -- cgit v1.2.1 From 03694e1f9915e34cf7d9a31073f1a1a9def2909f Mon Sep 17 00:00:00 2001 From: DepFA <35278260+dfaker@users.noreply.github.com> Date: Sun, 9 Oct 2022 21:58:14 +0100 Subject: add embedding load and save from b64 json --- modules/textual_inversion/textual_inversion.py | 30 ++++++++++++++++++-------- 1 file changed, 21 insertions(+), 9 deletions(-) (limited to 'modules') diff --git a/modules/textual_inversion/textual_inversion.py b/modules/textual_inversion/textual_inversion.py index f6316020..1b7f8906 100644 --- a/modules/textual_inversion/textual_inversion.py +++ b/modules/textual_inversion/textual_inversion.py @@ -7,9 +7,11 @@ import tqdm import html import datetime -from PIL import Image, PngImagePlugin +from PIL import Image,PngImagePlugin +from ..images import captionImge +import numpy as np import base64 -from io import BytesIO +import json from modules import shared, devices, sd_hijack, processing, sd_models import modules.textual_inversion.dataset @@ -87,9 +89,9 @@ class EmbeddingDatabase: if filename.upper().endswith('.PNG'): embed_image = Image.open(path) - if 'sd-embedding' in embed_image.text: - embeddingData = base64.b64decode(embed_image.text['sd-embedding']) - data = torch.load(BytesIO(embeddingData), map_location="cpu") + if 'sd-ti-embedding' in embed_image.text: + data = embeddingFromB64(embed_image.text['sd-ti-embedding']) + name = data.get('name',name) else: data = torch.load(path, map_location="cpu") @@ -258,13 +260,23 @@ def train_embedding(embedding_name, learn_rate, data_root, log_directory, steps, if save_image_with_stored_embedding: info = PngImagePlugin.PngInfo() - info.add_text("sd-embedding", base64.b64encode(open(last_saved_file,'rb').read())) - image.save(last_saved_image, "PNG", pnginfo=info) + data = torch.load(last_saved_file) + info.add_text("sd-ti-embedding", embeddingToB64(data)) + + pre_lines = [((255, 207, 175),"<{}>".format(data.get('name','???')))] + + caption_checkpoint_hash = data.get('sd_checkpoint','UNK') + caption_checkpoint_hash = caption_checkpoint_hash.upper() if caption_checkpoint_hash else 'UNK' + caption_stepcount = data.get('step',0) + caption_stepcount = caption_stepcount if caption_stepcount else 0 + + post_lines = [((240, 223, 175),"Trained against checkpoint [{}] for {} steps".format(caption_checkpoint_hash, + caption_stepcount))] + captioned_image = captionImge(image,prelines=pre_lines,postlines=post_lines) + captioned_image.save(last_saved_image, "PNG", pnginfo=info) else: image.save(last_saved_image) - - last_saved_image += f", prompt: {text}" shared.state.job_no = embedding.step -- cgit v1.2.1 From 969bd8256e5b4f1007d3cc653723d4ad50a92528 Mon Sep 17 00:00:00 2001 From: DepFA <35278260+dfaker@users.noreply.github.com> Date: Sun, 9 Oct 2022 22:02:28 +0100 Subject: add alternate checkpoint hash source --- modules/textual_inversion/textual_inversion.py | 7 +++++-- 1 file changed, 5 insertions(+), 2 deletions(-) (limited to 'modules') diff --git a/modules/textual_inversion/textual_inversion.py b/modules/textual_inversion/textual_inversion.py index 1b7f8906..d7813084 100644 --- a/modules/textual_inversion/textual_inversion.py +++ b/modules/textual_inversion/textual_inversion.py @@ -265,8 +265,11 @@ def train_embedding(embedding_name, learn_rate, data_root, log_directory, steps, pre_lines = [((255, 207, 175),"<{}>".format(data.get('name','???')))] - caption_checkpoint_hash = data.get('sd_checkpoint','UNK') - caption_checkpoint_hash = caption_checkpoint_hash.upper() if caption_checkpoint_hash else 'UNK' + caption_checkpoint_hash = data.get('sd_checkpoint') + if caption_checkpoint_hash is None: + caption_checkpoint_hash = data.get('hash') + caption_checkpoint_hash = caption_checkpoint_hash.upper() if caption_checkpoint_hash else 'UNKNOWN' + caption_stepcount = data.get('step',0) caption_stepcount = caption_stepcount if caption_stepcount else 0 -- cgit v1.2.1 From 5d12ec82d3e13f5ff4c55db2930e4e10aed7015a Mon Sep 17 00:00:00 2001 From: DepFA <35278260+dfaker@users.noreply.github.com> Date: Sun, 9 Oct 2022 22:05:09 +0100 Subject: add encoder and decoder classes --- modules/textual_inversion/textual_inversion.py | 21 +++++++++++++++++++++ 1 file changed, 21 insertions(+) (limited to 'modules') diff --git a/modules/textual_inversion/textual_inversion.py b/modules/textual_inversion/textual_inversion.py index d7813084..44d4e08b 100644 --- a/modules/textual_inversion/textual_inversion.py +++ b/modules/textual_inversion/textual_inversion.py @@ -16,6 +16,27 @@ import json from modules import shared, devices, sd_hijack, processing, sd_models import modules.textual_inversion.dataset +class EmbeddingEncoder(json.JSONEncoder): + def default(self, obj): + if isinstance(obj, torch.Tensor): + return {'EMBEDDINGTENSOR':obj.cpu().detach().numpy().tolist()} + return json.JSONEncoder.default(self, o) + +class EmbeddingDecoder(json.JSONDecoder): + def __init__(self, *args, **kwargs): + json.JSONDecoder.__init__(self, object_hook=self.object_hook, *args, **kwargs) + def object_hook(self, d): + if 'EMBEDDINGTENSOR' in d: + return torch.from_numpy(np.array(d['EMBEDDINGTENSOR'])) + return d + +def embeddingToB64(data): + d = json.dumps(data,cls=EmbeddingEncoder) + return base64.b64encode(d.encode()) + +def EmbeddingFromB64(data): + d = base64.b64decode(data) + return json.loads(d,cls=EmbeddingDecoder) class Embedding: def __init__(self, vec, name, step=None): -- cgit v1.2.1 From d0184b8f76ce492da699f1926f34b57cd095242e Mon Sep 17 00:00:00 2001 From: DepFA <35278260+dfaker@users.noreply.github.com> Date: Sun, 9 Oct 2022 22:06:12 +0100 Subject: change json tensor key name --- modules/textual_inversion/textual_inversion.py | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) (limited to 'modules') diff --git a/modules/textual_inversion/textual_inversion.py b/modules/textual_inversion/textual_inversion.py index 44d4e08b..ae8d207d 100644 --- a/modules/textual_inversion/textual_inversion.py +++ b/modules/textual_inversion/textual_inversion.py @@ -19,15 +19,15 @@ import modules.textual_inversion.dataset class EmbeddingEncoder(json.JSONEncoder): def default(self, obj): if isinstance(obj, torch.Tensor): - return {'EMBEDDINGTENSOR':obj.cpu().detach().numpy().tolist()} + return {'TORCHTENSOR':obj.cpu().detach().numpy().tolist()} return json.JSONEncoder.default(self, o) class EmbeddingDecoder(json.JSONDecoder): def __init__(self, *args, **kwargs): json.JSONDecoder.__init__(self, object_hook=self.object_hook, *args, **kwargs) def object_hook(self, d): - if 'EMBEDDINGTENSOR' in d: - return torch.from_numpy(np.array(d['EMBEDDINGTENSOR'])) + if 'TORCHTENSOR' in d: + return torch.from_numpy(np.array(d['TORCHTENSOR'])) return d def embeddingToB64(data): -- cgit v1.2.1 From 66846105103cfc282434d0dc2102910160b7a633 Mon Sep 17 00:00:00 2001 From: DepFA <35278260+dfaker@users.noreply.github.com> Date: Sun, 9 Oct 2022 22:06:42 +0100 Subject: correct case on embeddingFromB64 --- modules/textual_inversion/textual_inversion.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) (limited to 'modules') diff --git a/modules/textual_inversion/textual_inversion.py b/modules/textual_inversion/textual_inversion.py index ae8d207d..d2b95fa3 100644 --- a/modules/textual_inversion/textual_inversion.py +++ b/modules/textual_inversion/textual_inversion.py @@ -34,7 +34,7 @@ def embeddingToB64(data): d = json.dumps(data,cls=EmbeddingEncoder) return base64.b64encode(d.encode()) -def EmbeddingFromB64(data): +def embeddingFromB64(data): d = base64.b64decode(data) return json.loads(d,cls=EmbeddingDecoder) -- cgit v1.2.1 From 96f1e6be59316ec640cab2435fa95b3688194906 Mon Sep 17 00:00:00 2001 From: DepFA <35278260+dfaker@users.noreply.github.com> Date: Sun, 9 Oct 2022 22:14:50 +0100 Subject: source checkpoint hash from current checkpoint --- modules/textual_inversion/textual_inversion.py | 6 ++---- 1 file changed, 2 insertions(+), 4 deletions(-) (limited to 'modules') diff --git a/modules/textual_inversion/textual_inversion.py b/modules/textual_inversion/textual_inversion.py index d2b95fa3..b16fa84e 100644 --- a/modules/textual_inversion/textual_inversion.py +++ b/modules/textual_inversion/textual_inversion.py @@ -286,10 +286,8 @@ def train_embedding(embedding_name, learn_rate, data_root, log_directory, steps, pre_lines = [((255, 207, 175),"<{}>".format(data.get('name','???')))] - caption_checkpoint_hash = data.get('sd_checkpoint') - if caption_checkpoint_hash is None: - caption_checkpoint_hash = data.get('hash') - caption_checkpoint_hash = caption_checkpoint_hash.upper() if caption_checkpoint_hash else 'UNKNOWN' + checkpoint = sd_models.select_checkpoint() + caption_checkpoint_hash = checkpoint.hash caption_stepcount = data.get('step',0) caption_stepcount = caption_stepcount if caption_stepcount else 0 -- cgit v1.2.1 From 01fd9cf0d28d8b71a113ab1aa62accfe7f0d9c51 Mon Sep 17 00:00:00 2001 From: DepFA <35278260+dfaker@users.noreply.github.com> Date: Sun, 9 Oct 2022 22:17:02 +0100 Subject: change source of step count --- modules/textual_inversion/textual_inversion.py | 10 ++-------- 1 file changed, 2 insertions(+), 8 deletions(-) (limited to 'modules') diff --git a/modules/textual_inversion/textual_inversion.py b/modules/textual_inversion/textual_inversion.py index b16fa84e..e4f339b8 100644 --- a/modules/textual_inversion/textual_inversion.py +++ b/modules/textual_inversion/textual_inversion.py @@ -285,15 +285,9 @@ def train_embedding(embedding_name, learn_rate, data_root, log_directory, steps, info.add_text("sd-ti-embedding", embeddingToB64(data)) pre_lines = [((255, 207, 175),"<{}>".format(data.get('name','???')))] - checkpoint = sd_models.select_checkpoint() - caption_checkpoint_hash = checkpoint.hash - - caption_stepcount = data.get('step',0) - caption_stepcount = caption_stepcount if caption_stepcount else 0 - - post_lines = [((240, 223, 175),"Trained against checkpoint [{}] for {} steps".format(caption_checkpoint_hash, - caption_stepcount))] + post_lines = [((240, 223, 175),"Trained against checkpoint [{}] for {} steps".format(checkpoint.hash, + embedding.step))] captioned_image = captionImge(image,prelines=pre_lines,postlines=post_lines) captioned_image.save(last_saved_image, "PNG", pnginfo=info) else: -- cgit v1.2.1 From 0ac3a07eecbd7b98f3a19d01dc46f02dcda3443b Mon Sep 17 00:00:00 2001 From: DepFA <35278260+dfaker@users.noreply.github.com> Date: Mon, 10 Oct 2022 00:05:36 +0100 Subject: add caption image with overlay --- modules/images.py | 46 ++++++++++++++++++++++++++++++++++++++++++++++ 1 file changed, 46 insertions(+) (limited to 'modules') diff --git a/modules/images.py b/modules/images.py index 10963dc7..4a4fc977 100644 --- a/modules/images.py +++ b/modules/images.py @@ -459,3 +459,49 @@ def captionImge(image,prelines,postlines,background=(51, 51, 51),font=None): hoffset = addCaptionLines(postlines,background,hoffset,font) background = background.crop((0,0,sampleImage.size[0],hoffset+8)) return background + +def captionImageOverlay(srcimage,title,footerLeft,footerMid,footerRight,textfont=None): + from math import cos + + image = srcimage.copy() + + if textfont is None: + try: + textfont = ImageFont.truetype(opts.font or Roboto, fontsize) + textfont = opts.font or Roboto + except Exception: + textfont = Roboto + + factor = 1.5 + gradient = Image.new('RGBA', (1,image.size[1]), color=(0,0,0,0)) + for y in range(image.size[1]): + mag = 1-cos(y/image.size[1]*factor) + mag = max(mag,1-cos((image.size[1]-y)/image.size[1]*factor*1.1)) + gradient.putpixel((0, y), (0,0,0,int(mag*255))) + image = Image.alpha_composite(image.convert('RGBA'), gradient.resize(image.size)) + + draw = ImageDraw.Draw(image) + fontSize = 32 + font = ImageFont.truetype(textfont, fontSize) + padding = 10 + + _,_,w, h = draw.textbbox((0,0),title,font=font) + fontSize = min( int(fontSize * (((image.size[0]*0.75)-(padding*4))/w) ), 72) + font = ImageFont.truetype(textfont, fontSize) + _,_,w,h = draw.textbbox((0,0),title,font=font) + draw.text((padding,padding), title, anchor='lt', font=font, fill=(255,255,255,230)) + + _,_,w, h = draw.textbbox((0,0),footerLeft,font=font) + fontSizeleft = min( int(fontSize * (((image.size[0]/3)-(padding))/w) ), 72) + _,_,w, h = draw.textbbox((0,0),footerMid,font=font) + fontSizemid = min( int(fontSize * (((image.size[0]/3)-(padding))/w) ), 72) + _,_,w, h = draw.textbbox((0,0),footerRight,font=font) + fontSizeright = min( int(fontSize * (((image.size[0]/3)-(padding))/w) ), 72) + + font = ImageFont.truetype(textfont, min(fontSizeleft,fontSizemid,fontSizeright)) + + draw.text((padding,image.size[1]-padding), footerLeft, anchor='ls', font=font, fill=(255,255,255,230)) + draw.text((image.size[0]/2,image.size[1]-padding), footerMid, anchor='ms', font=font, fill=(255,255,255,230)) + draw.text((image.size[0]-padding,image.size[1]-padding), footerRight, anchor='rs', font=font, fill=(255,255,255,230)) + + return image -- cgit v1.2.1 From d6a599ef9ba18a66ae79b50f2945af5788fdda8f Mon Sep 17 00:00:00 2001 From: DepFA <35278260+dfaker@users.noreply.github.com> Date: Mon, 10 Oct 2022 00:07:52 +0100 Subject: change caption method --- modules/textual_inversion/textual_inversion.py | 30 ++++++++++++++++++-------- 1 file changed, 21 insertions(+), 9 deletions(-) (limited to 'modules') diff --git a/modules/textual_inversion/textual_inversion.py b/modules/textual_inversion/textual_inversion.py index e4f339b8..21596e78 100644 --- a/modules/textual_inversion/textual_inversion.py +++ b/modules/textual_inversion/textual_inversion.py @@ -8,7 +8,7 @@ import html import datetime from PIL import Image,PngImagePlugin -from ..images import captionImge +from ..images import captionImageOverlay import numpy as np import base64 import json @@ -212,6 +212,12 @@ def train_embedding(embedding_name, learn_rate, data_root, log_directory, steps, else: images_dir = None + if create_image_every > 0 and save_image_with_stored_embedding: + images_embeds_dir = os.path.join(log_directory, "image_embeddings") + os.makedirs(images_embeds_dir, exist_ok=True) + else: + images_embeds_dir = None + cond_model = shared.sd_model.cond_stage_model shared.state.textinfo = f"Preparing dataset from {html.escape(data_root)}..." @@ -279,19 +285,25 @@ def train_embedding(embedding_name, learn_rate, data_root, log_directory, steps, shared.state.current_image = image - if save_image_with_stored_embedding: + if save_image_with_stored_embedding and os.path.exists(last_saved_file): + + last_saved_image_chunks = os.path.join(images_embeds_dir, f'{embedding_name}-{embedding.step}.png') + info = PngImagePlugin.PngInfo() data = torch.load(last_saved_file) info.add_text("sd-ti-embedding", embeddingToB64(data)) - pre_lines = [((255, 207, 175),"<{}>".format(data.get('name','???')))] + title = "<{}>".format(data.get('name','???')) checkpoint = sd_models.select_checkpoint() - post_lines = [((240, 223, 175),"Trained against checkpoint [{}] for {} steps".format(checkpoint.hash, - embedding.step))] - captioned_image = captionImge(image,prelines=pre_lines,postlines=post_lines) - captioned_image.save(last_saved_image, "PNG", pnginfo=info) - else: - image.save(last_saved_image) + footer_left = checkpoint.model_name + footer_mid = '[{}]'.format(checkpoint.hash) + footer_right = '[{}]'.format(embedding.step) + + captioned_image = captionImageOverlay(image,title,footer_left,footer_mid,footer_right) + + captioned_image.save(last_saved_image_chunks, "PNG", pnginfo=info) + + image.save(last_saved_image) last_saved_image += f", prompt: {text}" -- cgit v1.2.1 From e2c2925eb4d634b186de2c76798162ec56e2f869 Mon Sep 17 00:00:00 2001 From: DepFA <35278260+dfaker@users.noreply.github.com> Date: Mon, 10 Oct 2022 00:12:53 +0100 Subject: remove braces from steps --- modules/textual_inversion/textual_inversion.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) (limited to 'modules') diff --git a/modules/textual_inversion/textual_inversion.py b/modules/textual_inversion/textual_inversion.py index 21596e78..9a18ee5c 100644 --- a/modules/textual_inversion/textual_inversion.py +++ b/modules/textual_inversion/textual_inversion.py @@ -297,7 +297,7 @@ def train_embedding(embedding_name, learn_rate, data_root, log_directory, steps, checkpoint = sd_models.select_checkpoint() footer_left = checkpoint.model_name footer_mid = '[{}]'.format(checkpoint.hash) - footer_right = '[{}]'.format(embedding.step) + footer_right = '{}'.format(embedding.step) captioned_image = captionImageOverlay(image,title,footer_left,footer_mid,footer_right) -- cgit v1.2.1 From 6435691bb11c5a35703720bfd2a875f24c066f86 Mon Sep 17 00:00:00 2001 From: Justin Maier Date: Sun, 9 Oct 2022 19:26:52 -0600 Subject: Add "Scale to" option to Extras --- modules/extras.py | 28 +++++++++++++++++++++++----- modules/ui.py | 38 +++++++++++++++++++++++++------------- 2 files changed, 48 insertions(+), 18 deletions(-) (limited to 'modules') diff --git a/modules/extras.py b/modules/extras.py index 41e8612c..83ca7049 100644 --- a/modules/extras.py +++ b/modules/extras.py @@ -1,3 +1,4 @@ +import math import os import numpy as np @@ -19,7 +20,7 @@ import gradio as gr cached_images = {} -def run_extras(extras_mode, image, image_folder, gfpgan_visibility, codeformer_visibility, codeformer_weight, upscaling_resize, extras_upscaler_1, extras_upscaler_2, extras_upscaler_2_visibility): +def run_extras(extras_mode, resize_mode, image, image_folder, gfpgan_visibility, codeformer_visibility, codeformer_weight, upscaling_resize, upscaling_resize_w, upscaling_resize_h, upscaling_crop, extras_upscaler_1, extras_upscaler_2, extras_upscaler_2_visibility): devices.torch_gc() imageArr = [] @@ -67,8 +68,23 @@ def run_extras(extras_mode, image, image_folder, gfpgan_visibility, codeformer_v info += f"CodeFormer w: {round(codeformer_weight, 2)}, CodeFormer visibility:{round(codeformer_visibility, 2)}\n" image = res + if resize_mode == 1: + upscaling_resize = max(upscaling_resize_w/image.width, upscaling_resize_h/image.height) + crop_info = " (crop)" if upscaling_crop else "" + info += f"Resize to: {upscaling_resize_w:g}x{upscaling_resize_h:g}{crop_info}\n" + + def crop_upscaled_center(image, resize_w, resize_h): + left = int(math.ceil((image.width - resize_w) / 2)) + right = image.width - int(math.floor((image.width - resize_w) / 2)) + top = int(math.ceil((image.height - resize_h) / 2)) + bottom = image.height - int(math.floor((image.height - resize_h) / 2)) + + image = image.crop((left, top, right, bottom)) + return image + + if upscaling_resize != 1.0: - def upscale(image, scaler_index, resize): + def upscale(image, scaler_index, resize, mode, resize_w, resize_h, crop): small = image.crop((image.width // 2, image.height // 2, image.width // 2 + 10, image.height // 2 + 10)) pixels = tuple(np.array(small).flatten().tolist()) key = (resize, scaler_index, image.width, image.height, gfpgan_visibility, codeformer_visibility, codeformer_weight) + pixels @@ -77,15 +93,17 @@ def run_extras(extras_mode, image, image_folder, gfpgan_visibility, codeformer_v if c is None: upscaler = shared.sd_upscalers[scaler_index] c = upscaler.scaler.upscale(image, resize, upscaler.data_path) + if mode == 1 and crop: + c = crop_upscaled_center(c, resize_w, resize_h) cached_images[key] = c return c info += f"Upscale: {round(upscaling_resize, 3)}, model:{shared.sd_upscalers[extras_upscaler_1].name}\n" - res = upscale(image, extras_upscaler_1, upscaling_resize) + res = upscale(image, extras_upscaler_1, upscaling_resize, resize_mode, upscaling_resize_w, upscaling_resize_h, upscaling_crop) if extras_upscaler_2 != 0 and extras_upscaler_2_visibility > 0: - res2 = upscale(image, extras_upscaler_2, upscaling_resize) + res2 = upscale(image, extras_upscaler_2, upscaling_resize, resize_mode, upscaling_resize_w, upscaling_resize_h, upscaling_crop) info += f"Upscale: {round(upscaling_resize, 3)}, visibility: {round(extras_upscaler_2_visibility, 3)}, model:{shared.sd_upscalers[extras_upscaler_2].name}\n" res = Image.blend(res, res2, extras_upscaler_2_visibility) @@ -190,7 +208,7 @@ def run_modelmerger(primary_model_name, secondary_model_name, interp_method, int theta_0[key] = theta_func(theta_0[key], theta_1[key], (float(1.0) - interp_amount)) # Need to reverse the interp_amount to match the desired mix ration in the merged checkpoint if save_as_half: theta_0[key] = theta_0[key].half() - + for key in theta_1.keys(): if 'model' in key and key not in theta_0: theta_0[key] = theta_1[key] diff --git a/modules/ui.py b/modules/ui.py index 2231a8ed..4bb2892b 100644 --- a/modules/ui.py +++ b/modules/ui.py @@ -101,7 +101,7 @@ def send_gradio_gallery_to_image(x): def save_files(js_data, images, do_make_zip, index): - import csv + import csv filenames = [] fullfns = [] @@ -551,7 +551,7 @@ def create_ui(wrap_gradio_gpu_call): with gr.Row(): do_make_zip = gr.Checkbox(label="Make Zip when Save?", value=False) - + with gr.Row(): download_files = gr.File(None, file_count="multiple", interactive=False, show_label=False, visible=False) @@ -739,7 +739,7 @@ def create_ui(wrap_gradio_gpu_call): with gr.Row(): do_make_zip = gr.Checkbox(label="Make Zip when Save?", value=False) - + with gr.Row(): download_files = gr.File(None, file_count="multiple", interactive=False, show_label=False, visible=False) @@ -903,7 +903,15 @@ def create_ui(wrap_gradio_gpu_call): with gr.TabItem('Batch Process'): image_batch = gr.File(label="Batch Process", file_count="multiple", interactive=True, type="file") - upscaling_resize = gr.Slider(minimum=1.0, maximum=4.0, step=0.05, label="Resize", value=2) + with gr.Tabs(elem_id="extras_resize_mode"): + with gr.TabItem('Scale by'): + upscaling_resize = gr.Slider(minimum=1.0, maximum=4.0, step=0.05, label="Resize", value=2) + with gr.TabItem('Scale to'): + with gr.Group(): + with gr.Row(): + upscaling_resize_w = gr.Number(label="Width", value=512) + upscaling_resize_h = gr.Number(label="Height", value=512) + upscaling_crop = gr.Checkbox(label='Crop to fit', value=True) with gr.Group(): extras_upscaler_1 = gr.Radio(label='Upscaler 1', choices=[x.name for x in shared.sd_upscalers], value=shared.sd_upscalers[0].name, type="index") @@ -934,6 +942,7 @@ def create_ui(wrap_gradio_gpu_call): fn=wrap_gradio_gpu_call(modules.extras.run_extras), _js="get_extras_tab_index", inputs=[ + dummy_component, dummy_component, extras_image, image_batch, @@ -941,6 +950,9 @@ def create_ui(wrap_gradio_gpu_call): codeformer_visibility, codeformer_weight, upscaling_resize, + upscaling_resize_w, + upscaling_resize_h, + upscaling_crop, extras_upscaler_1, extras_upscaler_2, extras_upscaler_2_visibility, @@ -951,14 +963,14 @@ def create_ui(wrap_gradio_gpu_call): html_info, ] ) - + extras_send_to_img2img.click( fn=lambda x: image_from_url_text(x), _js="extract_image_from_gallery_img2img", inputs=[result_images], outputs=[init_img], ) - + extras_send_to_inpaint.click( fn=lambda x: image_from_url_text(x), _js="extract_image_from_gallery_img2img", @@ -1286,7 +1298,7 @@ Requested path was: {f} outputs=[], _js='function(){restart_reload()}' ) - + if column is not None: column.__exit__() @@ -1318,12 +1330,12 @@ Requested path was: {f} component_dict[k] = component settings_interface.gradio_ref = demo - + with gr.Tabs() as tabs: for interface, label, ifid in interfaces: with gr.TabItem(label, id=ifid): interface.render() - + if os.path.exists(os.path.join(script_path, "notification.mp3")): audio_notification = gr.Audio(interactive=False, value=os.path.join(script_path, "notification.mp3"), elem_id="audio_notification", visible=False) @@ -1456,10 +1468,10 @@ Requested path was: {f} if getattr(obj,'custom_script_source',None) is not None: key = 'customscript/' + obj.custom_script_source + '/' + key - + if getattr(obj, 'do_not_save_to_config', False): return - + saved_value = ui_settings.get(key, None) if saved_value is None: ui_settings[key] = getattr(obj, field) @@ -1483,10 +1495,10 @@ Requested path was: {f} if type(x) == gr.Textbox: apply_field(x, 'value') - + if type(x) == gr.Number: apply_field(x, 'value') - + visit(txt2img_interface, loadsave, "txt2img") visit(img2img_interface, loadsave, "img2img") visit(extras_interface, loadsave, "extras") -- cgit v1.2.1 From cc92dc1f8d73dd4d574c4c8ccab78b7fc61e440b Mon Sep 17 00:00:00 2001 From: ssysm Date: Sun, 9 Oct 2022 23:17:29 -0400 Subject: add vae path args --- modules/sd_models.py | 2 +- modules/shared.py | 2 +- 2 files changed, 2 insertions(+), 2 deletions(-) (limited to 'modules') diff --git a/modules/sd_models.py b/modules/sd_models.py index cb3982b1..b6979432 100644 --- a/modules/sd_models.py +++ b/modules/sd_models.py @@ -147,7 +147,7 @@ def load_model_weights(model, checkpoint_info): devices.dtype = torch.float32 if shared.cmd_opts.no_half else torch.float16 - vae_file = os.path.splitext(checkpoint_file)[0] + ".vae.pt" + vae_file = shared.cmd_opts.vae_path or os.path.splitext(checkpoint_file)[0] + ".vae.pt" if os.path.exists(vae_file): print(f"Loading VAE weights from: {vae_file}") vae_ckpt = torch.load(vae_file, map_location="cpu") diff --git a/modules/shared.py b/modules/shared.py index 2dc092d6..52ccfa6e 100644 --- a/modules/shared.py +++ b/modules/shared.py @@ -64,7 +64,7 @@ parser.add_argument("--autolaunch", action='store_true', help="open the webui UR parser.add_argument("--use-textbox-seed", action='store_true', help="use textbox for seeds in UI (no up/down, but possible to input long seeds)", default=False) parser.add_argument("--disable-console-progressbars", action='store_true', help="do not output progressbars to console", default=False) parser.add_argument("--enable-console-prompts", action='store_true', help="print prompts to console when generating with txt2img and img2img", default=False) - +parser.add_argument('--vae-path', type=str, help='Path to Variational Autoencoders model', default=None) cmd_opts = parser.parse_args() -- cgit v1.2.1 From 1f92336be768d235c18a82acb2195b7135101ae7 Mon Sep 17 00:00:00 2001 From: JC_Array Date: Sun, 9 Oct 2022 23:58:18 -0500 Subject: refactored the deepbooru module to improve speed on running multiple interogations in a row. Added the option to generate deepbooru tags for textual inversion preproccessing. --- modules/deepbooru.py | 84 +++++++++++++++++++++++++-------- modules/textual_inversion/preprocess.py | 22 ++++++++- modules/ui.py | 52 ++++++++++++++------ 3 files changed, 122 insertions(+), 36 deletions(-) (limited to 'modules') diff --git a/modules/deepbooru.py b/modules/deepbooru.py index 7e3c0618..cee4a3b4 100644 --- a/modules/deepbooru.py +++ b/modules/deepbooru.py @@ -1,21 +1,74 @@ import os.path from concurrent.futures import ProcessPoolExecutor -from multiprocessing import get_context +import multiprocessing -def _load_tf_and_return_tags(pil_image, threshold): +def get_deepbooru_tags(pil_image, threshold=0.5): + """ + This method is for running only one image at a time for simple use. Used to the img2img interrogate. + """ + from modules import shared # prevents circular reference + create_deepbooru_process(threshold) + shared.deepbooru_process_return["value"] = -1 + shared.deepbooru_process_queue.put(pil_image) + while shared.deepbooru_process_return["value"] == -1: + time.sleep(0.2) + release_process() + return ret + + +def deepbooru_process(queue, deepbooru_process_return, threshold): + model, tags = get_deepbooru_tags_model() + while True: # while process is running, keep monitoring queue for new image + pil_image = queue.get() + if pil_image == "QUIT": + break + else: + deepbooru_process_return["value"] = get_deepbooru_tags_from_model(model, tags, pil_image, threshold) + + +def create_deepbooru_process(threshold=0.5): + """ + Creates deepbooru process. A queue is created to send images into the process. This enables multiple images + to be processed in a row without reloading the model or creating a new process. To return the data, a shared + dictionary is created to hold the tags created. To wait for tags to be returned, a value of -1 is assigned + to the dictionary and the method adding the image to the queue should wait for this value to be updated with + the tags. + """ + from modules import shared # prevents circular reference + shared.deepbooru_process_manager = multiprocessing.Manager() + shared.deepbooru_process_queue = shared.deepbooru_process_manager.Queue() + shared.deepbooru_process_return = shared.deepbooru_process_manager.dict() + shared.deepbooru_process_return["value"] = -1 + shared.deepbooru_process = multiprocessing.Process(target=deepbooru_process, args=(shared.deepbooru_process_queue, shared.deepbooru_process_return, threshold)) + shared.deepbooru_process.start() + + +def release_process(): + """ + Stops the deepbooru process to return used memory + """ + from modules import shared # prevents circular reference + shared.deepbooru_process_queue.put("QUIT") + shared.deepbooru_process.join() + shared.deepbooru_process_queue = None + shared.deepbooru_process = None + shared.deepbooru_process_return = None + shared.deepbooru_process_manager = None + +def get_deepbooru_tags_model(): import deepdanbooru as dd import tensorflow as tf import numpy as np - this_folder = os.path.dirname(__file__) model_path = os.path.abspath(os.path.join(this_folder, '..', 'models', 'deepbooru')) if not os.path.exists(os.path.join(model_path, 'project.json')): # there is no point importing these every time import zipfile from basicsr.utils.download_util import load_file_from_url - load_file_from_url(r"https://github.com/KichangKim/DeepDanbooru/releases/download/v3-20211112-sgd-e28/deepdanbooru-v3-20211112-sgd-e28.zip", - model_path) + load_file_from_url( + r"https://github.com/KichangKim/DeepDanbooru/releases/download/v3-20211112-sgd-e28/deepdanbooru-v3-20211112-sgd-e28.zip", + model_path) with zipfile.ZipFile(os.path.join(model_path, "deepdanbooru-v3-20211112-sgd-e28.zip"), "r") as zip_ref: zip_ref.extractall(model_path) os.remove(os.path.join(model_path, "deepdanbooru-v3-20211112-sgd-e28.zip")) @@ -24,7 +77,13 @@ def _load_tf_and_return_tags(pil_image, threshold): model = dd.project.load_model_from_project( model_path, compile_model=True ) + return model, tags + +def get_deepbooru_tags_from_model(model, tags, pil_image, threshold=0.5): + import deepdanbooru as dd + import tensorflow as tf + import numpy as np width = model.input_shape[2] height = model.input_shape[1] image = np.array(pil_image) @@ -57,17 +116,4 @@ def _load_tf_and_return_tags(pil_image, threshold): print('\n'.join(sorted(result_tags_print, reverse=True))) - return ', '.join(result_tags_out).replace('_', ' ').replace(':', ' ') - - -def subprocess_init_no_cuda(): - import os - os.environ["CUDA_VISIBLE_DEVICES"] = "-1" - - -def get_deepbooru_tags(pil_image, threshold=0.5): - context = get_context('spawn') - with ProcessPoolExecutor(initializer=subprocess_init_no_cuda, mp_context=context) as executor: - f = executor.submit(_load_tf_and_return_tags, pil_image, threshold, ) - ret = f.result() # will rethrow any exceptions - return ret \ No newline at end of file + return ', '.join(result_tags_out).replace('_', ' ').replace(':', ' ') \ No newline at end of file diff --git a/modules/textual_inversion/preprocess.py b/modules/textual_inversion/preprocess.py index f1c002a2..9f63c9a4 100644 --- a/modules/textual_inversion/preprocess.py +++ b/modules/textual_inversion/preprocess.py @@ -3,11 +3,14 @@ from PIL import Image, ImageOps import platform import sys import tqdm +import time from modules import shared, images +from modules.shared import opts, cmd_opts +if cmd_opts.deepdanbooru: + import modules.deepbooru as deepbooru - -def preprocess(process_src, process_dst, process_flip, process_split, process_caption): +def preprocess(process_src, process_dst, process_flip, process_split, process_caption, process_caption_deepbooru=False): size = 512 src = os.path.abspath(process_src) dst = os.path.abspath(process_dst) @@ -24,10 +27,21 @@ def preprocess(process_src, process_dst, process_flip, process_split, process_ca if process_caption: shared.interrogator.load() + if process_caption_deepbooru: + deepbooru.create_deepbooru_process() + def save_pic_with_caption(image, index): if process_caption: caption = "-" + shared.interrogator.generate_caption(image) caption = sanitize_caption(os.path.join(dst, f"{index:05}-{subindex[0]}"), caption, ".png") + elif process_caption_deepbooru: + shared.deepbooru_process_return["value"] = -1 + shared.deepbooru_process_queue.put(image) + while shared.deepbooru_process_return["value"] == -1: + time.sleep(0.2) + caption = "-" + shared.deepbooru_process_return["value"] + caption = sanitize_caption(os.path.join(dst, f"{index:05}-{subindex[0]}"), caption, ".png") + shared.deepbooru_process_return["value"] = -1 else: caption = filename caption = os.path.splitext(caption)[0] @@ -79,6 +93,10 @@ def preprocess(process_src, process_dst, process_flip, process_split, process_ca if process_caption: shared.interrogator.send_blip_to_ram() + if process_caption_deepbooru: + deepbooru.release_process() + + def sanitize_caption(base_path, original_caption, suffix): operating_system = platform.system().lower() if (operating_system == "windows"): diff --git a/modules/ui.py b/modules/ui.py index 2231a8ed..179e3a83 100644 --- a/modules/ui.py +++ b/modules/ui.py @@ -1034,6 +1034,9 @@ def create_ui(wrap_gradio_gpu_call): process_flip = gr.Checkbox(label='Create flipped copies') process_split = gr.Checkbox(label='Split oversized images into two') process_caption = gr.Checkbox(label='Use BLIP caption as filename') + if cmd_opts.deepdanbooru: + process_caption_deepbooru = gr.Checkbox(label='Use deepbooru caption as filename') + with gr.Row(): with gr.Column(scale=3): @@ -1086,21 +1089,40 @@ def create_ui(wrap_gradio_gpu_call): ] ) - run_preprocess.click( - fn=wrap_gradio_gpu_call(modules.textual_inversion.ui.preprocess, extra_outputs=[gr.update()]), - _js="start_training_textual_inversion", - inputs=[ - process_src, - process_dst, - process_flip, - process_split, - process_caption, - ], - outputs=[ - ti_output, - ti_outcome, - ], - ) + if cmd_opts.deepdanbooru: + # if process_caption_deepbooru is None, it will cause an error, as a result only include it if it is enabled + run_preprocess.click( + fn=wrap_gradio_gpu_call(modules.textual_inversion.ui.preprocess, extra_outputs=[gr.update()]), + _js="start_training_textual_inversion", + inputs=[ + process_src, + process_dst, + process_flip, + process_split, + process_caption, + process_caption_deepbooru, + ], + outputs=[ + ti_output, + ti_outcome, + ], + ) + else: + run_preprocess.click( + fn=wrap_gradio_gpu_call(modules.textual_inversion.ui.preprocess, extra_outputs=[gr.update()]), + _js="start_training_textual_inversion", + inputs=[ + process_src, + process_dst, + process_flip, + process_split, + process_caption, + ], + outputs=[ + ti_output, + ti_outcome, + ], + ) train_embedding.click( fn=wrap_gradio_gpu_call(modules.textual_inversion.ui.train_embedding, extra_outputs=[gr.update()]), -- cgit v1.2.1 From 8acc901ba3a252dc6ab4fabcb41644cf64d1774c Mon Sep 17 00:00:00 2001 From: brkirch Date: Mon, 10 Oct 2022 00:38:55 -0400 Subject: Newer versions of PyTorch use TypedStorage instead Pytorch 1.13 and later will rename _TypedStorage to TypedStorage, so check for TypedStorage and use _TypedStorage if it is not available. Currently this is needed so that nightly builds of PyTorch work correctly. --- modules/safe.py | 6 +++++- 1 file changed, 5 insertions(+), 1 deletion(-) (limited to 'modules') diff --git a/modules/safe.py b/modules/safe.py index 4d06f2a5..05917463 100644 --- a/modules/safe.py +++ b/modules/safe.py @@ -12,6 +12,10 @@ import _codecs import zipfile +# PyTorch 1.13 and later have _TypedStorage renamed to TypedStorage +TypedStorage = torch.storage.TypedStorage if hasattr(torch.storage, 'TypedStorage') else torch.storage._TypedStorage + + def encode(*args): out = _codecs.encode(*args) return out @@ -20,7 +24,7 @@ def encode(*args): class RestrictedUnpickler(pickle.Unpickler): def persistent_load(self, saved_id): assert saved_id[0] == 'storage' - return torch.storage._TypedStorage() + return TypedStorage() def find_class(self, module, name): if module == 'collections' and name == 'OrderedDict': -- cgit v1.2.1 From 8a7c07a2140c98bceca858087525d77fd0352fda Mon Sep 17 00:00:00 2001 From: yfszzx Date: Mon, 10 Oct 2022 15:39:39 +0800 Subject: show image history --- modules/images_history.py | 90 +++++++++++++++++++++++++++++++++++++++++++++++ modules/ui.py | 11 ++++-- 2 files changed, 99 insertions(+), 2 deletions(-) create mode 100644 modules/images_history.py (limited to 'modules') diff --git a/modules/images_history.py b/modules/images_history.py new file mode 100644 index 00000000..23d83557 --- /dev/null +++ b/modules/images_history.py @@ -0,0 +1,90 @@ +import os +def get_recent_images(is_img2img, dir_name, page_index, step): + page_index = int(page_index) + f_list = os.listdir(dir_name) + file_list = [] + for file in f_list: + if file[-4:] == ".txt": + continue + file_list.append(file) + file_list = sorted(file_list, key=lambda file: -os.path.getctime(os.path.join(dir_name, file))) + num = 24 + max_page_index = len(file_list) // num + 1 + page_index = max_page_index if page_index == -1 else page_index + step + page_index = 1 if page_index < 1 else page_index + page_index = max_page_index if page_index > max_page_index else page_index + idx_frm = (page_index - 1) * num + file_list = file_list[idx_frm:idx_frm + num] + print(f"Loading history page {page_index}") + return [os.path.join(dir_name, file) for file in file_list], page_index, file_list +def first_page_click(is_img2img, dir_name): + return get_recent_images(is_img2img, dir_name, 1, 0) +def end_page_click(is_img2img, dir_name): + return get_recent_images(is_img2img, dir_name, -1, 0) +def prev_page_click(is_img2img, dir_name, page_index): + return get_recent_images(is_img2img, dir_name, page_index, -1) +def next_page_click(is_img2img, dir_name, page_index): + return get_recent_images(is_img2img, dir_name, page_index, 1) +def page_index_change(is_img2img, dir_name, page_index): + return get_recent_images(is_img2img, dir_name, page_index, 0) +def show_image_info(num, filenames): + return filenames[int(num)] +def delete_image(is_img2img, dir_name, name, page_index, filenames): + path = os.path.join(dir_name, name) + if os.path.exists(path): + print(f"Delete file {path}") + os.remove(path) + i = 0 + for f in filenames: + if f == name: + break + i += 1 + images, page_index, file_list = get_recent_images(is_img2img, dir_name, page_index, 0) + current_file = file_list[i] if i < len(file_list) else None + return images, page_index, file_list, current_file + + +def show_images_history(gr, opts, is_img2img): + def id_name(is_img2img, name): + return ("img2img" if is_img2img else "txt2img") + "_" + name + with gr.Row(): + if is_img2img: + dir_name = opts.outdir_img2img_samples + else: + dir_name = opts.outdir_txt2img_samples + first_page = gr.Button('First Page', elem_id=id_name(is_img2img,"images_history_first_page")) + prev_page = gr.Button('Prev Page') + page_index = gr.Number(value=1) + next_page = gr.Button('Next Page') + end_page = gr.Button('End Page') + with gr.Row(): + delete = gr.Button('Delete') + Send = gr.Button('Send') + with gr.Row(): + with gr.Column(elem_id=id_name(is_img2img,"images_history")): + history_gallery = gr.Gallery(label="Images history").style(grid=6) + img_file_name = gr.Textbox() + img_file_info = gr.Textbox(dir_name) + img_path = gr.Textbox(dir_name, visible=False) + set_index = gr.Button('set_index', elem_id=id_name(is_img2img,"images_history_set_index")) + is_img2img_flag = gr.Checkbox(is_img2img, visible=False) + filenames = gr.State() + first_page.click(first_page_click, inputs=[is_img2img_flag, img_path], outputs=[history_gallery, page_index, filenames]) + next_page.click(next_page_click, inputs=[is_img2img_flag, img_path, page_index], outputs=[history_gallery, page_index, filenames]) + prev_page.click(prev_page_click, inputs=[is_img2img_flag, img_path, page_index], outputs=[history_gallery, page_index, filenames]) + end_page.click(end_page_click, inputs=[is_img2img_flag, img_path], outputs=[history_gallery, page_index, filenames]) + page_index.submit(page_index_change, inputs=[is_img2img_flag, img_path, page_index], outputs=[history_gallery, page_index, filenames]) + set_index.click(show_image_info, _js="images_history_get_current_img",inputs=[is_img2img_flag, filenames], outputs=img_file_name) + delete.click(delete_image, inputs=[is_img2img_flag, img_path, img_file_name, page_index, filenames], outputs=[history_gallery, page_index, filenames,img_file_name]) + #page_index.change(page_index_change, inputs=[is_img2img_flag, img_path, page_index], outputs=[history_gallery, page_index]) + +def create_history_tabs(gr, opts): + with gr.Blocks(analytics_enabled=False) as images_history: + with gr.Tabs() as tabs: + with gr.Tab("txt2img history", id="images_history_txt2img"): + with gr.Blocks(analytics_enabled=False) as images_history_txt2img: + show_images_history(gr, opts, is_img2img=False) + with gr.Tab("img2img history", id="images_history_img2img"): + with gr.Blocks(analytics_enabled=False) as images_history_img2img: + show_images_history(gr, opts, is_img2img=True) + return images_history diff --git a/modules/ui.py b/modules/ui.py index 4f18126f..8762fcf5 100644 --- a/modules/ui.py +++ b/modules/ui.py @@ -37,6 +37,7 @@ import modules.generation_parameters_copypaste from modules import prompt_parser from modules.images import save_image import modules.textual_inversion.ui +import modules.images_history as img_his # this is a fix for Windows users. Without it, javascript files will be served with text/html content-type and the bowser will not show any UI mimetypes.init() @@ -499,7 +500,6 @@ def create_ui(wrap_gradio_gpu_call): custom_inputs = modules.scripts.scripts_txt2img.setup_ui(is_img2img=False) with gr.Column(variant='panel'): - with gr.Group(): txt2img_preview = gr.Image(elem_id='txt2img_preview', visible=False) txt2img_gallery = gr.Gallery(label='Output', show_label=False, elem_id='txt2img_gallery').style(grid=4) @@ -516,6 +516,7 @@ def create_ui(wrap_gradio_gpu_call): with gr.Group(): html_info = gr.HTML() generation_info = gr.Textbox(visible=False) + connect_reuse_seed(seed, reuse_seed, generation_info, dummy_component, is_subseed=False) connect_reuse_seed(subseed, reuse_subseed, generation_info, dummy_component, is_subseed=True) @@ -607,6 +608,7 @@ def create_ui(wrap_gradio_gpu_call): ] modules.generation_parameters_copypaste.connect_paste(paste, txt2img_paste_fields, txt2img_prompt) token_button.click(fn=update_token_counter, inputs=[txt2img_prompt, steps], outputs=[token_counter]) + with gr.Blocks(analytics_enabled=False) as img2img_interface: img2img_prompt, roll, img2img_prompt_style, img2img_negative_prompt, img2img_prompt_style2, submit, img2img_interrogate, img2img_prompt_style_apply, img2img_save_style, paste, token_counter, token_button = create_toprow(is_img2img=True) @@ -696,6 +698,7 @@ def create_ui(wrap_gradio_gpu_call): with gr.Group(): html_info = gr.HTML() generation_info = gr.Textbox(visible=False) + connect_reuse_seed(seed, reuse_seed, generation_info, dummy_component, is_subseed=False) connect_reuse_seed(subseed, reuse_subseed, generation_info, dummy_component, is_subseed=True) @@ -1126,8 +1129,10 @@ def create_ui(wrap_gradio_gpu_call): opts.save(shared.config_filename) - return f'{changed} settings changed.', opts.dumpjson() + return f'{changed} settings changed.', opts.dumpjson() + + images_history = img_his.create_history_tabs(gr, opts) with gr.Blocks(analytics_enabled=False) as settings_interface: settings_submit = gr.Button(value="Apply settings", variant='primary') result = gr.HTML() @@ -1206,7 +1211,9 @@ def create_ui(wrap_gradio_gpu_call): (pnginfo_interface, "PNG Info", "pnginfo"), (modelmerger_interface, "Checkpoint Merger", "modelmerger"), (textual_inversion_interface, "Textual inversion", "ti"), + (images_history, "History", "images_history"), (settings_interface, "Settings", "settings"), + ] with open(os.path.join(script_path, "style.css"), "r", encoding="utf8") as file: -- cgit v1.2.1 From 3110f895b2718a3a25aae419fdf5c87c177ec9f4 Mon Sep 17 00:00:00 2001 From: alg-wiki Date: Mon, 10 Oct 2022 17:07:46 +0900 Subject: Textual Inversion: Added custom training image size and number of repeats per input image in a single epoch --- modules/textual_inversion/dataset.py | 6 +++--- modules/textual_inversion/preprocess.py | 4 ++-- modules/textual_inversion/textual_inversion.py | 15 ++++++++++++--- modules/ui.py | 8 +++++++- 4 files changed, 24 insertions(+), 9 deletions(-) (limited to 'modules') diff --git a/modules/textual_inversion/dataset.py b/modules/textual_inversion/dataset.py index 7c44ea5b..acc4ce59 100644 --- a/modules/textual_inversion/dataset.py +++ b/modules/textual_inversion/dataset.py @@ -15,13 +15,13 @@ re_tag = re.compile(r"[a-zA-Z][_\w\d()]+") class PersonalizedBase(Dataset): - def __init__(self, data_root, size=None, repeats=100, flip_p=0.5, placeholder_token="*", width=512, height=512, model=None, device=None, template_file=None): + def __init__(self, data_root, size, repeats, flip_p=0.5, placeholder_token="*", model=None, device=None, template_file=None): self.placeholder_token = placeholder_token self.size = size - self.width = width - self.height = height + self.width = size + self.height = size self.flip = transforms.RandomHorizontalFlip(p=flip_p) self.dataset = [] diff --git a/modules/textual_inversion/preprocess.py b/modules/textual_inversion/preprocess.py index f1c002a2..b3de6fd7 100644 --- a/modules/textual_inversion/preprocess.py +++ b/modules/textual_inversion/preprocess.py @@ -7,8 +7,8 @@ import tqdm from modules import shared, images -def preprocess(process_src, process_dst, process_flip, process_split, process_caption): - size = 512 +def preprocess(process_src, process_dst, process_size, process_flip, process_split, process_caption): + size = process_size src = os.path.abspath(process_src) dst = os.path.abspath(process_dst) diff --git a/modules/textual_inversion/textual_inversion.py b/modules/textual_inversion/textual_inversion.py index cd9f3498..e34dc2e8 100644 --- a/modules/textual_inversion/textual_inversion.py +++ b/modules/textual_inversion/textual_inversion.py @@ -6,6 +6,7 @@ import torch import tqdm import html import datetime +import math from modules import shared, devices, sd_hijack, processing, sd_models @@ -156,7 +157,7 @@ def create_embedding(name, num_vectors_per_token, init_text='*'): return fn -def train_embedding(embedding_name, learn_rate, data_root, log_directory, steps, create_image_every, save_embedding_every, template_file): +def train_embedding(embedding_name, learn_rate, data_root, log_directory, training_size, steps, num_repeats, create_image_every, save_embedding_every, template_file): assert embedding_name, 'embedding not selected' shared.state.textinfo = "Initializing textual inversion training..." @@ -182,7 +183,7 @@ def train_embedding(embedding_name, learn_rate, data_root, log_directory, steps, shared.state.textinfo = f"Preparing dataset from {html.escape(data_root)}..." with torch.autocast("cuda"): - ds = modules.textual_inversion.dataset.PersonalizedBase(data_root=data_root, size=512, placeholder_token=embedding_name, model=shared.sd_model, device=devices.device, template_file=template_file) + ds = modules.textual_inversion.dataset.PersonalizedBase(data_root=data_root, size=training_size, repeats=num_repeats, placeholder_token=embedding_name, model=shared.sd_model, device=devices.device, template_file=template_file) hijack = sd_hijack.model_hijack @@ -200,6 +201,9 @@ def train_embedding(embedding_name, learn_rate, data_root, log_directory, steps, if ititial_step > steps: return embedding, filename + tr_img_len = len([os.path.join(data_root, file_path) for file_path in os.listdir(data_root)]) + epoch_len = (tr_img_len * num_repeats) + tr_img_len + pbar = tqdm.tqdm(enumerate(ds), total=steps-ititial_step) for i, (x, text) in pbar: embedding.step = i + ititial_step @@ -223,7 +227,10 @@ def train_embedding(embedding_name, learn_rate, data_root, log_directory, steps, loss.backward() optimizer.step() - pbar.set_description(f"loss: {losses.mean():.7f}") + epoch_num = math.floor(embedding.step / epoch_len) + epoch_step = embedding.step - (epoch_num * epoch_len) + + pbar.set_description(f"[Epoch {epoch_num}: {epoch_step}/{epoch_len}]loss: {losses.mean():.7f}") if embedding.step > 0 and embedding_dir is not None and embedding.step % save_embedding_every == 0: last_saved_file = os.path.join(embedding_dir, f'{embedding_name}-{embedding.step}.pt') @@ -236,6 +243,8 @@ def train_embedding(embedding_name, learn_rate, data_root, log_directory, steps, sd_model=shared.sd_model, prompt=text, steps=20, + height=training_size, + width=training_size, do_not_save_grid=True, do_not_save_samples=True, ) diff --git a/modules/ui.py b/modules/ui.py index 2231a8ed..f821fd8d 100644 --- a/modules/ui.py +++ b/modules/ui.py @@ -1029,6 +1029,7 @@ def create_ui(wrap_gradio_gpu_call): process_src = gr.Textbox(label='Source directory') process_dst = gr.Textbox(label='Destination directory') + process_size = gr.Slider(minimum=64, maximum=2048, step=64, label="Size (width and height)", value=512) with gr.Row(): process_flip = gr.Checkbox(label='Create flipped copies') @@ -1043,13 +1044,15 @@ def create_ui(wrap_gradio_gpu_call): run_preprocess = gr.Button(value="Preprocess", variant='primary') with gr.Group(): - gr.HTML(value="

Train an embedding; must specify a directory with a set of 512x512 images

") + gr.HTML(value="

Train an embedding; must specify a directory with a set of 1:1 ratio images

") train_embedding_name = gr.Dropdown(label='Embedding', choices=sorted(sd_hijack.model_hijack.embedding_db.word_embeddings.keys())) learn_rate = gr.Number(label='Learning rate', value=5.0e-03) dataset_directory = gr.Textbox(label='Dataset directory', placeholder="Path to directory with input images") log_directory = gr.Textbox(label='Log directory', placeholder="Path to directory where to write outputs", value="textual_inversion") template_file = gr.Textbox(label='Prompt template file', value=os.path.join(script_path, "textual_inversion_templates", "style_filewords.txt")) + training_size = gr.Slider(minimum=64, maximum=2048, step=64, label="Size (width and height)", value=512) steps = gr.Number(label='Max steps', value=100000, precision=0) + num_repeats = gr.Number(label='Number of repeats for a single input image per epoch', value=100, precision=0) create_image_every = gr.Number(label='Save an image to log directory every N steps, 0 to disable', value=500, precision=0) save_embedding_every = gr.Number(label='Save a copy of embedding to log directory every N steps, 0 to disable', value=500, precision=0) @@ -1092,6 +1095,7 @@ def create_ui(wrap_gradio_gpu_call): inputs=[ process_src, process_dst, + process_size, process_flip, process_split, process_caption, @@ -1110,7 +1114,9 @@ def create_ui(wrap_gradio_gpu_call): learn_rate, dataset_directory, log_directory, + training_size, steps, + num_repeats, create_image_every, save_embedding_every, template_file, -- cgit v1.2.1 From 8ec069e64df48f8f202f8b93a08e91b69448eb39 Mon Sep 17 00:00:00 2001 From: JC_Array Date: Mon, 10 Oct 2022 03:23:24 -0500 Subject: removed duplicate run_preprocess.click by creating run_preprocess_inputs list and appending deepbooru variable to input list if in scope --- modules/ui.py | 49 +++++++++++++++++-------------------------------- 1 file changed, 17 insertions(+), 32 deletions(-) (limited to 'modules') diff --git a/modules/ui.py b/modules/ui.py index 179e3a83..22ca74c2 100644 --- a/modules/ui.py +++ b/modules/ui.py @@ -1089,40 +1089,25 @@ def create_ui(wrap_gradio_gpu_call): ] ) + run_preprocess_inputs = [ + process_src, + process_dst, + process_flip, + process_split, + process_caption, + ] if cmd_opts.deepdanbooru: # if process_caption_deepbooru is None, it will cause an error, as a result only include it if it is enabled - run_preprocess.click( - fn=wrap_gradio_gpu_call(modules.textual_inversion.ui.preprocess, extra_outputs=[gr.update()]), - _js="start_training_textual_inversion", - inputs=[ - process_src, - process_dst, - process_flip, - process_split, - process_caption, - process_caption_deepbooru, - ], - outputs=[ - ti_output, - ti_outcome, - ], - ) - else: - run_preprocess.click( - fn=wrap_gradio_gpu_call(modules.textual_inversion.ui.preprocess, extra_outputs=[gr.update()]), - _js="start_training_textual_inversion", - inputs=[ - process_src, - process_dst, - process_flip, - process_split, - process_caption, - ], - outputs=[ - ti_output, - ti_outcome, - ], - ) + run_preprocess_inputs.append(process_caption_deepbooru) + run_preprocess.click( + fn=wrap_gradio_gpu_call(modules.textual_inversion.ui.preprocess, extra_outputs=[gr.update()]), + _js="start_training_textual_inversion", + inputs=run_preprocess_inputs, + outputs=[ + ti_output, + ti_outcome, + ], + ) train_embedding.click( fn=wrap_gradio_gpu_call(modules.textual_inversion.ui.train_embedding, extra_outputs=[gr.update()]), -- cgit v1.2.1 From 4ee7519fc2e459ce8eff1f61f1655afba393357c Mon Sep 17 00:00:00 2001 From: alg-wiki Date: Mon, 10 Oct 2022 17:31:33 +0900 Subject: Fixed progress bar output for epoch --- modules/textual_inversion/textual_inversion.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) (limited to 'modules') diff --git a/modules/textual_inversion/textual_inversion.py b/modules/textual_inversion/textual_inversion.py index e34dc2e8..769682ea 100644 --- a/modules/textual_inversion/textual_inversion.py +++ b/modules/textual_inversion/textual_inversion.py @@ -228,7 +228,7 @@ def train_embedding(embedding_name, learn_rate, data_root, log_directory, traini optimizer.step() epoch_num = math.floor(embedding.step / epoch_len) - epoch_step = embedding.step - (epoch_num * epoch_len) + epoch_step = embedding.step - (epoch_num * epoch_len) + 1 pbar.set_description(f"[Epoch {epoch_num}: {epoch_step}/{epoch_len}]loss: {losses.mean():.7f}") -- cgit v1.2.1 From 2f94331df2cb1181439adecc28cfd758049f6501 Mon Sep 17 00:00:00 2001 From: JC_Array Date: Mon, 10 Oct 2022 03:34:00 -0500 Subject: removed change in last commit, simplified to adding the visible argument to process_caption_deepbooru and it set to False if deepdanbooru argument is not set --- modules/ui.py | 22 ++++++++++------------ 1 file changed, 10 insertions(+), 12 deletions(-) (limited to 'modules') diff --git a/modules/ui.py b/modules/ui.py index 22ca74c2..f8adafb3 100644 --- a/modules/ui.py +++ b/modules/ui.py @@ -1036,7 +1036,8 @@ def create_ui(wrap_gradio_gpu_call): process_caption = gr.Checkbox(label='Use BLIP caption as filename') if cmd_opts.deepdanbooru: process_caption_deepbooru = gr.Checkbox(label='Use deepbooru caption as filename') - + else: + process_caption_deepbooru = gr.Checkbox(label='Use deepbooru caption as filename', visible=False) with gr.Row(): with gr.Column(scale=3): @@ -1089,20 +1090,17 @@ def create_ui(wrap_gradio_gpu_call): ] ) - run_preprocess_inputs = [ - process_src, - process_dst, - process_flip, - process_split, - process_caption, - ] - if cmd_opts.deepdanbooru: - # if process_caption_deepbooru is None, it will cause an error, as a result only include it if it is enabled - run_preprocess_inputs.append(process_caption_deepbooru) run_preprocess.click( fn=wrap_gradio_gpu_call(modules.textual_inversion.ui.preprocess, extra_outputs=[gr.update()]), _js="start_training_textual_inversion", - inputs=run_preprocess_inputs, + inputs=[ + process_src, + process_dst, + process_flip, + process_split, + process_caption, + process_caption_deepbooru + ], outputs=[ ti_output, ti_outcome, -- cgit v1.2.1 From 23f2989799ee3911d2959cfceb74b921f20c9a51 Mon Sep 17 00:00:00 2001 From: yfszzx Date: Mon, 10 Oct 2022 18:33:49 +0800 Subject: images history over --- modules/images_history.py | 141 +++++++++++++++++++++++++++------------------- modules/ui.py | 9 ++- 2 files changed, 92 insertions(+), 58 deletions(-) (limited to 'modules') diff --git a/modules/images_history.py b/modules/images_history.py index 23d83557..0e0a48f3 100644 --- a/modules/images_history.py +++ b/modules/images_history.py @@ -1,5 +1,6 @@ import os -def get_recent_images(is_img2img, dir_name, page_index, step): +def get_recent_images(dir_name, page_index, step, image_index): + print(image_index) page_index = int(page_index) f_list = os.listdir(dir_name) file_list = [] @@ -8,7 +9,7 @@ def get_recent_images(is_img2img, dir_name, page_index, step): continue file_list.append(file) file_list = sorted(file_list, key=lambda file: -os.path.getctime(os.path.join(dir_name, file))) - num = 24 + num = 48 max_page_index = len(file_list) // num + 1 page_index = max_page_index if page_index == -1 else page_index + step page_index = 1 if page_index < 1 else page_index @@ -16,75 +17,101 @@ def get_recent_images(is_img2img, dir_name, page_index, step): idx_frm = (page_index - 1) * num file_list = file_list[idx_frm:idx_frm + num] print(f"Loading history page {page_index}") - return [os.path.join(dir_name, file) for file in file_list], page_index, file_list -def first_page_click(is_img2img, dir_name): - return get_recent_images(is_img2img, dir_name, 1, 0) -def end_page_click(is_img2img, dir_name): - return get_recent_images(is_img2img, dir_name, -1, 0) -def prev_page_click(is_img2img, dir_name, page_index): - return get_recent_images(is_img2img, dir_name, page_index, -1) -def next_page_click(is_img2img, dir_name, page_index): - return get_recent_images(is_img2img, dir_name, page_index, 1) -def page_index_change(is_img2img, dir_name, page_index): - return get_recent_images(is_img2img, dir_name, page_index, 0) -def show_image_info(num, filenames): - return filenames[int(num)] -def delete_image(is_img2img, dir_name, name, page_index, filenames): + image_index = int(image_index) + if image_index < 0 or image_index > len(file_list) - 1: + current_file = None + hide_image = None + else: + current_file = file_list[int(image_index)] + hide_image = os.path.join(dir_name, current_file) + return [os.path.join(dir_name, file) for file in file_list], page_index, file_list, current_file, hide_image +def first_page_click(dir_name, page_index, image_index): + return get_recent_images(dir_name, 1, 0, image_index) +def end_page_click(dir_name, page_index, image_index): + return get_recent_images(dir_name, -1, 0, image_index) +def prev_page_click(dir_name, page_index, image_index): + return get_recent_images(dir_name, page_index, -1, image_index) +def next_page_click(dir_name, page_index, image_index): + return get_recent_images(dir_name, page_index, 1, image_index) +def page_index_change(dir_name, page_index, image_index): + return get_recent_images(dir_name, page_index, 0, image_index) + +def show_image_info(num, image_path, filenames): + file = filenames[int(num)] + return file, num, os.path.join(image_path, file) +def delete_image(is_img2img, dir_name, name, page_index, filenames, image_index): + print("filename", name) path = os.path.join(dir_name, name) if os.path.exists(path): print(f"Delete file {path}") os.remove(path) - i = 0 - for f in filenames: - if f == name: - break - i += 1 - images, page_index, file_list = get_recent_images(is_img2img, dir_name, page_index, 0) - current_file = file_list[i] if i < len(file_list) else None - return images, page_index, file_list, current_file + images, page_index, file_list, current_file, hide_image = get_recent_images(dir_name, page_index, 0, image_index) + return images, page_index, file_list, current_file, hide_image -def show_images_history(gr, opts, is_img2img): +def show_images_history(gr, opts, is_img2img, run_pnginfo, switch_dict): def id_name(is_img2img, name): return ("img2img" if is_img2img else "txt2img") + "_" + name - with gr.Row(): - if is_img2img: - dir_name = opts.outdir_img2img_samples - else: - dir_name = opts.outdir_txt2img_samples - first_page = gr.Button('First Page', elem_id=id_name(is_img2img,"images_history_first_page")) - prev_page = gr.Button('Prev Page') - page_index = gr.Number(value=1) - next_page = gr.Button('Next Page') - end_page = gr.Button('End Page') - with gr.Row(): - delete = gr.Button('Delete') - Send = gr.Button('Send') - with gr.Row(): - with gr.Column(elem_id=id_name(is_img2img,"images_history")): - history_gallery = gr.Gallery(label="Images history").style(grid=6) - img_file_name = gr.Textbox() - img_file_info = gr.Textbox(dir_name) - img_path = gr.Textbox(dir_name, visible=False) - set_index = gr.Button('set_index', elem_id=id_name(is_img2img,"images_history_set_index")) - is_img2img_flag = gr.Checkbox(is_img2img, visible=False) - filenames = gr.State() - first_page.click(first_page_click, inputs=[is_img2img_flag, img_path], outputs=[history_gallery, page_index, filenames]) - next_page.click(next_page_click, inputs=[is_img2img_flag, img_path, page_index], outputs=[history_gallery, page_index, filenames]) - prev_page.click(prev_page_click, inputs=[is_img2img_flag, img_path, page_index], outputs=[history_gallery, page_index, filenames]) - end_page.click(end_page_click, inputs=[is_img2img_flag, img_path], outputs=[history_gallery, page_index, filenames]) - page_index.submit(page_index_change, inputs=[is_img2img_flag, img_path, page_index], outputs=[history_gallery, page_index, filenames]) - set_index.click(show_image_info, _js="images_history_get_current_img",inputs=[is_img2img_flag, filenames], outputs=img_file_name) - delete.click(delete_image, inputs=[is_img2img_flag, img_path, img_file_name, page_index, filenames], outputs=[history_gallery, page_index, filenames,img_file_name]) + if is_img2img: + dir_name = opts.outdir_img2img_samples + else: + dir_name = opts.outdir_txt2img_samples + with gr.Row(): + first_page = gr.Button('First', elem_id=id_name(is_img2img,"images_history_first_page")) + prev_page = gr.Button('Prev') + page_index = gr.Number(value=1, label="Page Index") + next_page = gr.Button('Next') + end_page = gr.Button('End') + with gr.Row(elem_id=id_name(is_img2img,"images_history")): + with gr.Row(): + with gr.Column(): + history_gallery = gr.Gallery(show_label=False).style(grid=6) + with gr.Column(): + with gr.Row(): + delete = gr.Button('Delete') + pnginfo_send_to_txt2img = gr.Button('Send to txt2img') + pnginfo_send_to_img2img = gr.Button('Send to img2img') + with gr.Row(): + with gr.Column(): + img_file_info = gr.Textbox(dir_name, label="Generate Info") + img_file_name = gr.Textbox(label="File Name") + with gr.Row(): + # hiden items + img_path = gr.Textbox(dir_name, visible=False) + is_img2img_flag = gr.Checkbox(is_img2img, visible=False) + image_index = gr.Textbox(value=-1, visible=False) + set_index = gr.Button('set_index', elem_id=id_name(is_img2img,"images_history_set_index")) + filenames = gr.State() + hide_image = gr.Image(visible=False, type="pil") + info1 = gr.Textbox(visible=False) + info2 = gr.Textbox(visible=False) + + + # turn pages + gallery_inputs = [img_path, page_index, image_index] + gallery_outputs = [history_gallery, page_index, filenames, img_file_name, hide_image] + first_page.click(first_page_click, inputs=gallery_inputs, outputs=gallery_outputs) + next_page.click(next_page_click, inputs=gallery_inputs, outputs=gallery_outputs) + prev_page.click(prev_page_click, inputs=gallery_inputs, outputs=gallery_outputs) + end_page.click(end_page_click, inputs=gallery_inputs, outputs=gallery_outputs) + page_index.submit(page_index_change, inputs=gallery_inputs, outputs=gallery_outputs) #page_index.change(page_index_change, inputs=[is_img2img_flag, img_path, page_index], outputs=[history_gallery, page_index]) + + #other funcitons + set_index.click(show_image_info, _js="images_history_get_current_img", inputs=[is_img2img_flag, img_path, filenames], outputs=[img_file_name, image_index, hide_image]) + delete.click(delete_image, inputs=[is_img2img_flag, img_path, img_file_name, page_index, filenames, image_index], outputs=gallery_outputs) + hide_image.change(fn=run_pnginfo, inputs=[hide_image], outputs=[info1, img_file_info, info2]) + switch_dict["fn"](pnginfo_send_to_txt2img, switch_dict["t2i"], img_file_info, 'switch_to_txt2img') + switch_dict["fn"](pnginfo_send_to_img2img, switch_dict["i2i"], img_file_info, 'switch_to_img2img_img2img') + -def create_history_tabs(gr, opts): +def create_history_tabs(gr, opts, run_pnginfo, switch_dict): with gr.Blocks(analytics_enabled=False) as images_history: with gr.Tabs() as tabs: with gr.Tab("txt2img history", id="images_history_txt2img"): with gr.Blocks(analytics_enabled=False) as images_history_txt2img: - show_images_history(gr, opts, is_img2img=False) + show_images_history(gr, opts, False, run_pnginfo, switch_dict) with gr.Tab("img2img history", id="images_history_img2img"): with gr.Blocks(analytics_enabled=False) as images_history_img2img: - show_images_history(gr, opts, is_img2img=True) + show_images_history(gr, opts, True, run_pnginfo, switch_dict) return images_history diff --git a/modules/ui.py b/modules/ui.py index 8762fcf5..21c9236b 100644 --- a/modules/ui.py +++ b/modules/ui.py @@ -1131,8 +1131,15 @@ def create_ui(wrap_gradio_gpu_call): return f'{changed} settings changed.', opts.dumpjson() + #images history + images_history_switch_dict = { + "fn":modules.generation_parameters_copypaste.connect_paste, + "t2i":txt2img_paste_fields, + "i2i":img2img_paste_fields + } + images_history = img_his.create_history_tabs(gr, opts, wrap_gradio_call(modules.extras.run_pnginfo), images_history_switch_dict) - images_history = img_his.create_history_tabs(gr, opts) + with gr.Blocks(analytics_enabled=False) as settings_interface: settings_submit = gr.Button(value="Apply settings", variant='primary') result = gr.HTML() -- cgit v1.2.1 From 7349088d32b080f64058b6e5de5f0380a71ecd09 Mon Sep 17 00:00:00 2001 From: AUTOMATIC <16777216c@gmail.com> Date: Mon, 10 Oct 2022 16:11:14 +0300 Subject: --no-half-vae --- modules/devices.py | 6 +++++- modules/processing.py | 11 +++++++++-- modules/sd_models.py | 3 +++ modules/sd_samplers.py | 4 ++-- modules/shared.py | 1 + 5 files changed, 20 insertions(+), 5 deletions(-) (limited to 'modules') diff --git a/modules/devices.py b/modules/devices.py index 0158b11f..03ef58f1 100644 --- a/modules/devices.py +++ b/modules/devices.py @@ -36,6 +36,7 @@ errors.run(enable_tf32, "Enabling TF32") device = device_gfpgan = device_bsrgan = device_esrgan = device_scunet = device_codeformer = get_optimal_device() dtype = torch.float16 +dtype_vae = torch.float16 def randn(seed, shape): # Pytorch currently doesn't handle setting randomness correctly when the metal backend is used. @@ -59,9 +60,12 @@ def randn_without_seed(shape): return torch.randn(shape, device=device) -def autocast(): +def autocast(disable=False): from modules import shared + if disable: + return contextlib.nullcontext() + if dtype == torch.float32 or shared.cmd_opts.precision == "full": return contextlib.nullcontext() diff --git a/modules/processing.py b/modules/processing.py index 94d2dd62..ec8651ae 100644 --- a/modules/processing.py +++ b/modules/processing.py @@ -259,6 +259,13 @@ def create_random_tensors(shape, seeds, subseeds=None, subseed_strength=0.0, see return x +def decode_first_stage(model, x): + with devices.autocast(disable=x.dtype == devices.dtype_vae): + x = model.decode_first_stage(x) + + return x + + def get_fixed_seed(seed): if seed is None or seed == '' or seed == -1: return int(random.randrange(4294967294)) @@ -400,7 +407,7 @@ def process_images(p: StableDiffusionProcessing) -> Processed: samples_ddim = samples_ddim.to(devices.dtype) - x_samples_ddim = p.sd_model.decode_first_stage(samples_ddim) + x_samples_ddim = decode_first_stage(p.sd_model, samples_ddim) x_samples_ddim = torch.clamp((x_samples_ddim + 1.0) / 2.0, min=0.0, max=1.0) del samples_ddim @@ -533,7 +540,7 @@ class StableDiffusionProcessingTxt2Img(StableDiffusionProcessing): if self.scale_latent: samples = torch.nn.functional.interpolate(samples, size=(self.height // opt_f, self.width // opt_f), mode="bilinear") else: - decoded_samples = self.sd_model.decode_first_stage(samples) + decoded_samples = decode_first_stage(self.sd_model, samples) if opts.upscaler_for_img2img is None or opts.upscaler_for_img2img == "None": decoded_samples = torch.nn.functional.interpolate(decoded_samples, size=(self.height, self.width), mode="bilinear") diff --git a/modules/sd_models.py b/modules/sd_models.py index e63d3c29..2cdcd84f 100644 --- a/modules/sd_models.py +++ b/modules/sd_models.py @@ -149,6 +149,7 @@ def load_model_weights(model, checkpoint_info): model.half() devices.dtype = torch.float32 if shared.cmd_opts.no_half else torch.float16 + devices.dtype_vae = torch.float32 if shared.cmd_opts.no_half or shared.cmd_opts.no_half_vae else torch.float16 vae_file = os.path.splitext(checkpoint_file)[0] + ".vae.pt" if os.path.exists(vae_file): @@ -158,6 +159,8 @@ def load_model_weights(model, checkpoint_info): model.first_stage_model.load_state_dict(vae_dict) + model.first_stage_model.to(devices.dtype_vae) + model.sd_model_hash = sd_model_hash model.sd_model_checkpoint = checkpoint_file model.sd_checkpoint_info = checkpoint_info diff --git a/modules/sd_samplers.py b/modules/sd_samplers.py index 6e743f7e..d168b938 100644 --- a/modules/sd_samplers.py +++ b/modules/sd_samplers.py @@ -7,7 +7,7 @@ import inspect import k_diffusion.sampling import ldm.models.diffusion.ddim import ldm.models.diffusion.plms -from modules import prompt_parser +from modules import prompt_parser, devices, processing from modules.shared import opts, cmd_opts, state import modules.shared as shared @@ -83,7 +83,7 @@ def setup_img2img_steps(p, steps=None): def sample_to_image(samples): - x_sample = shared.sd_model.decode_first_stage(samples[0:1].type(shared.sd_model.dtype))[0] + x_sample = processing.decode_first_stage(shared.sd_model, samples[0:1])[0] x_sample = torch.clamp((x_sample + 1.0) / 2.0, min=0.0, max=1.0) x_sample = 255. * np.moveaxis(x_sample.cpu().numpy(), 0, 2) x_sample = x_sample.astype(np.uint8) diff --git a/modules/shared.py b/modules/shared.py index 1995a99a..5dfc344c 100644 --- a/modules/shared.py +++ b/modules/shared.py @@ -25,6 +25,7 @@ parser.add_argument("--ckpt-dir", type=str, default=None, help="Path to director parser.add_argument("--gfpgan-dir", type=str, help="GFPGAN directory", default=('./src/gfpgan' if os.path.exists('./src/gfpgan') else './GFPGAN')) parser.add_argument("--gfpgan-model", type=str, help="GFPGAN model file name", default=None) parser.add_argument("--no-half", action='store_true', help="do not switch the model to 16-bit floats") +parser.add_argument("--no-half-vae", action='store_true', help="do not switch the VAE model to 16-bit floats") parser.add_argument("--no-progressbar-hiding", action='store_true', help="do not hide progressbar in gradio UI (we hide it because it slows down ML if you have hardware acceleration in browser)") parser.add_argument("--max-batch-count", type=int, default=16, help="maximum batch count value for the UI") parser.add_argument("--embeddings-dir", type=str, default=os.path.join(script_path, 'embeddings'), help="embeddings directory for textual inversion (default: embeddings)") -- cgit v1.2.1 From 04c745ea4f81518999927fee5f78500560c25e29 Mon Sep 17 00:00:00 2001 From: alg-wiki Date: Mon, 10 Oct 2022 22:35:35 +0900 Subject: Custom Width and Height --- modules/textual_inversion/dataset.py | 7 +++---- modules/textual_inversion/preprocess.py | 19 ++++++++++--------- modules/textual_inversion/textual_inversion.py | 11 +++++------ modules/ui.py | 12 ++++++++---- 4 files changed, 26 insertions(+), 23 deletions(-) (limited to 'modules') diff --git a/modules/textual_inversion/dataset.py b/modules/textual_inversion/dataset.py index acc4ce59..bcf772d2 100644 --- a/modules/textual_inversion/dataset.py +++ b/modules/textual_inversion/dataset.py @@ -15,13 +15,12 @@ re_tag = re.compile(r"[a-zA-Z][_\w\d()]+") class PersonalizedBase(Dataset): - def __init__(self, data_root, size, repeats, flip_p=0.5, placeholder_token="*", model=None, device=None, template_file=None): + def __init__(self, data_root, width, height, repeats, flip_p=0.5, placeholder_token="*", model=None, device=None, template_file=None): self.placeholder_token = placeholder_token - self.size = size - self.width = size - self.height = size + self.width = width + self.height = height self.flip = transforms.RandomHorizontalFlip(p=flip_p) self.dataset = [] diff --git a/modules/textual_inversion/preprocess.py b/modules/textual_inversion/preprocess.py index b3de6fd7..d7efdef2 100644 --- a/modules/textual_inversion/preprocess.py +++ b/modules/textual_inversion/preprocess.py @@ -7,8 +7,9 @@ import tqdm from modules import shared, images -def preprocess(process_src, process_dst, process_size, process_flip, process_split, process_caption): - size = process_size +def preprocess(process_src, process_dst, process_width, process_height, process_flip, process_split, process_caption): + width = process_width + height = process_height src = os.path.abspath(process_src) dst = os.path.abspath(process_dst) @@ -55,23 +56,23 @@ def preprocess(process_src, process_dst, process_size, process_flip, process_spl is_wide = ratio < 1 / 1.35 if process_split and is_tall: - img = img.resize((size, size * img.height // img.width)) + img = img.resize((width, height * img.height // img.width)) - top = img.crop((0, 0, size, size)) + top = img.crop((0, 0, width, height)) save_pic(top, index) - bot = img.crop((0, img.height - size, size, img.height)) + bot = img.crop((0, img.height - height, width, img.height)) save_pic(bot, index) elif process_split and is_wide: - img = img.resize((size * img.width // img.height, size)) + img = img.resize((width * img.width // img.height, height)) - left = img.crop((0, 0, size, size)) + left = img.crop((0, 0, width, height)) save_pic(left, index) - right = img.crop((img.width - size, 0, img.width, size)) + right = img.crop((img.width - width, 0, img.width, height)) save_pic(right, index) else: - img = images.resize_image(1, img, size, size) + img = images.resize_image(1, img, width, height) save_pic(img, index) shared.state.nextjob() diff --git a/modules/textual_inversion/textual_inversion.py b/modules/textual_inversion/textual_inversion.py index 769682ea..5965c5a0 100644 --- a/modules/textual_inversion/textual_inversion.py +++ b/modules/textual_inversion/textual_inversion.py @@ -6,7 +6,6 @@ import torch import tqdm import html import datetime -import math from modules import shared, devices, sd_hijack, processing, sd_models @@ -157,7 +156,7 @@ def create_embedding(name, num_vectors_per_token, init_text='*'): return fn -def train_embedding(embedding_name, learn_rate, data_root, log_directory, training_size, steps, num_repeats, create_image_every, save_embedding_every, template_file): +def train_embedding(embedding_name, learn_rate, data_root, log_directory, training_width, training_height, steps, num_repeats, create_image_every, save_embedding_every, template_file): assert embedding_name, 'embedding not selected' shared.state.textinfo = "Initializing textual inversion training..." @@ -183,7 +182,7 @@ def train_embedding(embedding_name, learn_rate, data_root, log_directory, traini shared.state.textinfo = f"Preparing dataset from {html.escape(data_root)}..." with torch.autocast("cuda"): - ds = modules.textual_inversion.dataset.PersonalizedBase(data_root=data_root, size=training_size, repeats=num_repeats, placeholder_token=embedding_name, model=shared.sd_model, device=devices.device, template_file=template_file) + ds = modules.textual_inversion.dataset.PersonalizedBase(data_root=data_root, width=training_width, height=training_height, repeats=num_repeats, placeholder_token=embedding_name, model=shared.sd_model, device=devices.device, template_file=template_file) hijack = sd_hijack.model_hijack @@ -227,7 +226,7 @@ def train_embedding(embedding_name, learn_rate, data_root, log_directory, traini loss.backward() optimizer.step() - epoch_num = math.floor(embedding.step / epoch_len) + epoch_num = embedding.step // epoch_len epoch_step = embedding.step - (epoch_num * epoch_len) + 1 pbar.set_description(f"[Epoch {epoch_num}: {epoch_step}/{epoch_len}]loss: {losses.mean():.7f}") @@ -243,8 +242,8 @@ def train_embedding(embedding_name, learn_rate, data_root, log_directory, traini sd_model=shared.sd_model, prompt=text, steps=20, - height=training_size, - width=training_size, + height=training_height, + width=training_width, do_not_save_grid=True, do_not_save_samples=True, ) diff --git a/modules/ui.py b/modules/ui.py index f821fd8d..8c06ad7c 100644 --- a/modules/ui.py +++ b/modules/ui.py @@ -1029,7 +1029,8 @@ def create_ui(wrap_gradio_gpu_call): process_src = gr.Textbox(label='Source directory') process_dst = gr.Textbox(label='Destination directory') - process_size = gr.Slider(minimum=64, maximum=2048, step=64, label="Size (width and height)", value=512) + process_width = gr.Slider(minimum=64, maximum=2048, step=64, label="Width", value=512) + process_height = gr.Slider(minimum=64, maximum=2048, step=64, label="Height", value=512) with gr.Row(): process_flip = gr.Checkbox(label='Create flipped copies') @@ -1050,7 +1051,8 @@ def create_ui(wrap_gradio_gpu_call): dataset_directory = gr.Textbox(label='Dataset directory', placeholder="Path to directory with input images") log_directory = gr.Textbox(label='Log directory', placeholder="Path to directory where to write outputs", value="textual_inversion") template_file = gr.Textbox(label='Prompt template file', value=os.path.join(script_path, "textual_inversion_templates", "style_filewords.txt")) - training_size = gr.Slider(minimum=64, maximum=2048, step=64, label="Size (width and height)", value=512) + training_width = gr.Slider(minimum=64, maximum=2048, step=64, label="Width", value=512) + training_height = gr.Slider(minimum=64, maximum=2048, step=64, label="Height", value=512) steps = gr.Number(label='Max steps', value=100000, precision=0) num_repeats = gr.Number(label='Number of repeats for a single input image per epoch', value=100, precision=0) create_image_every = gr.Number(label='Save an image to log directory every N steps, 0 to disable', value=500, precision=0) @@ -1095,7 +1097,8 @@ def create_ui(wrap_gradio_gpu_call): inputs=[ process_src, process_dst, - process_size, + process_width, + process_height, process_flip, process_split, process_caption, @@ -1114,7 +1117,8 @@ def create_ui(wrap_gradio_gpu_call): learn_rate, dataset_directory, log_directory, - training_size, + training_width, + training_height, steps, num_repeats, create_image_every, -- cgit v1.2.1 From 8f1efdc130cf7ff47cb8d3722cdfc0dbeba3069e Mon Sep 17 00:00:00 2001 From: AUTOMATIC <16777216c@gmail.com> Date: Mon, 10 Oct 2022 17:03:45 +0300 Subject: --no-half-vae pt2 --- modules/processing.py | 3 +-- 1 file changed, 1 insertion(+), 2 deletions(-) (limited to 'modules') diff --git a/modules/processing.py b/modules/processing.py index ec8651ae..50ba4fc5 100644 --- a/modules/processing.py +++ b/modules/processing.py @@ -405,8 +405,7 @@ def process_images(p: StableDiffusionProcessing) -> Processed: # use the image collected previously in sampler loop samples_ddim = shared.state.current_latent - samples_ddim = samples_ddim.to(devices.dtype) - + samples_ddim = samples_ddim.to(devices.dtype_vae) x_samples_ddim = decode_first_stage(p.sd_model, samples_ddim) x_samples_ddim = torch.clamp((x_samples_ddim + 1.0) / 2.0, min=0.0, max=1.0) -- cgit v1.2.1 From ea00c1624bbb0dcb5be07f59c9509061baddf5b1 Mon Sep 17 00:00:00 2001 From: alg-wiki Date: Mon, 10 Oct 2022 17:07:46 +0900 Subject: Textual Inversion: Added custom training image size and number of repeats per input image in a single epoch --- modules/textual_inversion/dataset.py | 6 +++--- modules/textual_inversion/preprocess.py | 4 ++-- modules/textual_inversion/textual_inversion.py | 15 ++++++++++++--- modules/ui.py | 8 +++++++- 4 files changed, 24 insertions(+), 9 deletions(-) (limited to 'modules') diff --git a/modules/textual_inversion/dataset.py b/modules/textual_inversion/dataset.py index 7c44ea5b..acc4ce59 100644 --- a/modules/textual_inversion/dataset.py +++ b/modules/textual_inversion/dataset.py @@ -15,13 +15,13 @@ re_tag = re.compile(r"[a-zA-Z][_\w\d()]+") class PersonalizedBase(Dataset): - def __init__(self, data_root, size=None, repeats=100, flip_p=0.5, placeholder_token="*", width=512, height=512, model=None, device=None, template_file=None): + def __init__(self, data_root, size, repeats, flip_p=0.5, placeholder_token="*", model=None, device=None, template_file=None): self.placeholder_token = placeholder_token self.size = size - self.width = width - self.height = height + self.width = size + self.height = size self.flip = transforms.RandomHorizontalFlip(p=flip_p) self.dataset = [] diff --git a/modules/textual_inversion/preprocess.py b/modules/textual_inversion/preprocess.py index f1c002a2..b3de6fd7 100644 --- a/modules/textual_inversion/preprocess.py +++ b/modules/textual_inversion/preprocess.py @@ -7,8 +7,8 @@ import tqdm from modules import shared, images -def preprocess(process_src, process_dst, process_flip, process_split, process_caption): - size = 512 +def preprocess(process_src, process_dst, process_size, process_flip, process_split, process_caption): + size = process_size src = os.path.abspath(process_src) dst = os.path.abspath(process_dst) diff --git a/modules/textual_inversion/textual_inversion.py b/modules/textual_inversion/textual_inversion.py index cd9f3498..e34dc2e8 100644 --- a/modules/textual_inversion/textual_inversion.py +++ b/modules/textual_inversion/textual_inversion.py @@ -6,6 +6,7 @@ import torch import tqdm import html import datetime +import math from modules import shared, devices, sd_hijack, processing, sd_models @@ -156,7 +157,7 @@ def create_embedding(name, num_vectors_per_token, init_text='*'): return fn -def train_embedding(embedding_name, learn_rate, data_root, log_directory, steps, create_image_every, save_embedding_every, template_file): +def train_embedding(embedding_name, learn_rate, data_root, log_directory, training_size, steps, num_repeats, create_image_every, save_embedding_every, template_file): assert embedding_name, 'embedding not selected' shared.state.textinfo = "Initializing textual inversion training..." @@ -182,7 +183,7 @@ def train_embedding(embedding_name, learn_rate, data_root, log_directory, steps, shared.state.textinfo = f"Preparing dataset from {html.escape(data_root)}..." with torch.autocast("cuda"): - ds = modules.textual_inversion.dataset.PersonalizedBase(data_root=data_root, size=512, placeholder_token=embedding_name, model=shared.sd_model, device=devices.device, template_file=template_file) + ds = modules.textual_inversion.dataset.PersonalizedBase(data_root=data_root, size=training_size, repeats=num_repeats, placeholder_token=embedding_name, model=shared.sd_model, device=devices.device, template_file=template_file) hijack = sd_hijack.model_hijack @@ -200,6 +201,9 @@ def train_embedding(embedding_name, learn_rate, data_root, log_directory, steps, if ititial_step > steps: return embedding, filename + tr_img_len = len([os.path.join(data_root, file_path) for file_path in os.listdir(data_root)]) + epoch_len = (tr_img_len * num_repeats) + tr_img_len + pbar = tqdm.tqdm(enumerate(ds), total=steps-ititial_step) for i, (x, text) in pbar: embedding.step = i + ititial_step @@ -223,7 +227,10 @@ def train_embedding(embedding_name, learn_rate, data_root, log_directory, steps, loss.backward() optimizer.step() - pbar.set_description(f"loss: {losses.mean():.7f}") + epoch_num = math.floor(embedding.step / epoch_len) + epoch_step = embedding.step - (epoch_num * epoch_len) + + pbar.set_description(f"[Epoch {epoch_num}: {epoch_step}/{epoch_len}]loss: {losses.mean():.7f}") if embedding.step > 0 and embedding_dir is not None and embedding.step % save_embedding_every == 0: last_saved_file = os.path.join(embedding_dir, f'{embedding_name}-{embedding.step}.pt') @@ -236,6 +243,8 @@ def train_embedding(embedding_name, learn_rate, data_root, log_directory, steps, sd_model=shared.sd_model, prompt=text, steps=20, + height=training_size, + width=training_size, do_not_save_grid=True, do_not_save_samples=True, ) diff --git a/modules/ui.py b/modules/ui.py index 2231a8ed..f821fd8d 100644 --- a/modules/ui.py +++ b/modules/ui.py @@ -1029,6 +1029,7 @@ def create_ui(wrap_gradio_gpu_call): process_src = gr.Textbox(label='Source directory') process_dst = gr.Textbox(label='Destination directory') + process_size = gr.Slider(minimum=64, maximum=2048, step=64, label="Size (width and height)", value=512) with gr.Row(): process_flip = gr.Checkbox(label='Create flipped copies') @@ -1043,13 +1044,15 @@ def create_ui(wrap_gradio_gpu_call): run_preprocess = gr.Button(value="Preprocess", variant='primary') with gr.Group(): - gr.HTML(value="

Train an embedding; must specify a directory with a set of 512x512 images

") + gr.HTML(value="

Train an embedding; must specify a directory with a set of 1:1 ratio images

") train_embedding_name = gr.Dropdown(label='Embedding', choices=sorted(sd_hijack.model_hijack.embedding_db.word_embeddings.keys())) learn_rate = gr.Number(label='Learning rate', value=5.0e-03) dataset_directory = gr.Textbox(label='Dataset directory', placeholder="Path to directory with input images") log_directory = gr.Textbox(label='Log directory', placeholder="Path to directory where to write outputs", value="textual_inversion") template_file = gr.Textbox(label='Prompt template file', value=os.path.join(script_path, "textual_inversion_templates", "style_filewords.txt")) + training_size = gr.Slider(minimum=64, maximum=2048, step=64, label="Size (width and height)", value=512) steps = gr.Number(label='Max steps', value=100000, precision=0) + num_repeats = gr.Number(label='Number of repeats for a single input image per epoch', value=100, precision=0) create_image_every = gr.Number(label='Save an image to log directory every N steps, 0 to disable', value=500, precision=0) save_embedding_every = gr.Number(label='Save a copy of embedding to log directory every N steps, 0 to disable', value=500, precision=0) @@ -1092,6 +1095,7 @@ def create_ui(wrap_gradio_gpu_call): inputs=[ process_src, process_dst, + process_size, process_flip, process_split, process_caption, @@ -1110,7 +1114,9 @@ def create_ui(wrap_gradio_gpu_call): learn_rate, dataset_directory, log_directory, + training_size, steps, + num_repeats, create_image_every, save_embedding_every, template_file, -- cgit v1.2.1 From 6ad3a53e368d36535de1a4fca73b3bb78fd40654 Mon Sep 17 00:00:00 2001 From: alg-wiki Date: Mon, 10 Oct 2022 17:31:33 +0900 Subject: Fixed progress bar output for epoch --- modules/textual_inversion/textual_inversion.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) (limited to 'modules') diff --git a/modules/textual_inversion/textual_inversion.py b/modules/textual_inversion/textual_inversion.py index e34dc2e8..769682ea 100644 --- a/modules/textual_inversion/textual_inversion.py +++ b/modules/textual_inversion/textual_inversion.py @@ -228,7 +228,7 @@ def train_embedding(embedding_name, learn_rate, data_root, log_directory, traini optimizer.step() epoch_num = math.floor(embedding.step / epoch_len) - epoch_step = embedding.step - (epoch_num * epoch_len) + epoch_step = embedding.step - (epoch_num * epoch_len) + 1 pbar.set_description(f"[Epoch {epoch_num}: {epoch_step}/{epoch_len}]loss: {losses.mean():.7f}") -- cgit v1.2.1 From 7a20f914eddfdf09c0ccced157ec108205bc3d0f Mon Sep 17 00:00:00 2001 From: alg-wiki Date: Mon, 10 Oct 2022 22:35:35 +0900 Subject: Custom Width and Height --- modules/textual_inversion/dataset.py | 7 +++---- modules/textual_inversion/preprocess.py | 19 ++++++++++--------- modules/textual_inversion/textual_inversion.py | 11 +++++------ modules/ui.py | 12 ++++++++---- 4 files changed, 26 insertions(+), 23 deletions(-) (limited to 'modules') diff --git a/modules/textual_inversion/dataset.py b/modules/textual_inversion/dataset.py index acc4ce59..bcf772d2 100644 --- a/modules/textual_inversion/dataset.py +++ b/modules/textual_inversion/dataset.py @@ -15,13 +15,12 @@ re_tag = re.compile(r"[a-zA-Z][_\w\d()]+") class PersonalizedBase(Dataset): - def __init__(self, data_root, size, repeats, flip_p=0.5, placeholder_token="*", model=None, device=None, template_file=None): + def __init__(self, data_root, width, height, repeats, flip_p=0.5, placeholder_token="*", model=None, device=None, template_file=None): self.placeholder_token = placeholder_token - self.size = size - self.width = size - self.height = size + self.width = width + self.height = height self.flip = transforms.RandomHorizontalFlip(p=flip_p) self.dataset = [] diff --git a/modules/textual_inversion/preprocess.py b/modules/textual_inversion/preprocess.py index b3de6fd7..d7efdef2 100644 --- a/modules/textual_inversion/preprocess.py +++ b/modules/textual_inversion/preprocess.py @@ -7,8 +7,9 @@ import tqdm from modules import shared, images -def preprocess(process_src, process_dst, process_size, process_flip, process_split, process_caption): - size = process_size +def preprocess(process_src, process_dst, process_width, process_height, process_flip, process_split, process_caption): + width = process_width + height = process_height src = os.path.abspath(process_src) dst = os.path.abspath(process_dst) @@ -55,23 +56,23 @@ def preprocess(process_src, process_dst, process_size, process_flip, process_spl is_wide = ratio < 1 / 1.35 if process_split and is_tall: - img = img.resize((size, size * img.height // img.width)) + img = img.resize((width, height * img.height // img.width)) - top = img.crop((0, 0, size, size)) + top = img.crop((0, 0, width, height)) save_pic(top, index) - bot = img.crop((0, img.height - size, size, img.height)) + bot = img.crop((0, img.height - height, width, img.height)) save_pic(bot, index) elif process_split and is_wide: - img = img.resize((size * img.width // img.height, size)) + img = img.resize((width * img.width // img.height, height)) - left = img.crop((0, 0, size, size)) + left = img.crop((0, 0, width, height)) save_pic(left, index) - right = img.crop((img.width - size, 0, img.width, size)) + right = img.crop((img.width - width, 0, img.width, height)) save_pic(right, index) else: - img = images.resize_image(1, img, size, size) + img = images.resize_image(1, img, width, height) save_pic(img, index) shared.state.nextjob() diff --git a/modules/textual_inversion/textual_inversion.py b/modules/textual_inversion/textual_inversion.py index 769682ea..5965c5a0 100644 --- a/modules/textual_inversion/textual_inversion.py +++ b/modules/textual_inversion/textual_inversion.py @@ -6,7 +6,6 @@ import torch import tqdm import html import datetime -import math from modules import shared, devices, sd_hijack, processing, sd_models @@ -157,7 +156,7 @@ def create_embedding(name, num_vectors_per_token, init_text='*'): return fn -def train_embedding(embedding_name, learn_rate, data_root, log_directory, training_size, steps, num_repeats, create_image_every, save_embedding_every, template_file): +def train_embedding(embedding_name, learn_rate, data_root, log_directory, training_width, training_height, steps, num_repeats, create_image_every, save_embedding_every, template_file): assert embedding_name, 'embedding not selected' shared.state.textinfo = "Initializing textual inversion training..." @@ -183,7 +182,7 @@ def train_embedding(embedding_name, learn_rate, data_root, log_directory, traini shared.state.textinfo = f"Preparing dataset from {html.escape(data_root)}..." with torch.autocast("cuda"): - ds = modules.textual_inversion.dataset.PersonalizedBase(data_root=data_root, size=training_size, repeats=num_repeats, placeholder_token=embedding_name, model=shared.sd_model, device=devices.device, template_file=template_file) + ds = modules.textual_inversion.dataset.PersonalizedBase(data_root=data_root, width=training_width, height=training_height, repeats=num_repeats, placeholder_token=embedding_name, model=shared.sd_model, device=devices.device, template_file=template_file) hijack = sd_hijack.model_hijack @@ -227,7 +226,7 @@ def train_embedding(embedding_name, learn_rate, data_root, log_directory, traini loss.backward() optimizer.step() - epoch_num = math.floor(embedding.step / epoch_len) + epoch_num = embedding.step // epoch_len epoch_step = embedding.step - (epoch_num * epoch_len) + 1 pbar.set_description(f"[Epoch {epoch_num}: {epoch_step}/{epoch_len}]loss: {losses.mean():.7f}") @@ -243,8 +242,8 @@ def train_embedding(embedding_name, learn_rate, data_root, log_directory, traini sd_model=shared.sd_model, prompt=text, steps=20, - height=training_size, - width=training_size, + height=training_height, + width=training_width, do_not_save_grid=True, do_not_save_samples=True, ) diff --git a/modules/ui.py b/modules/ui.py index f821fd8d..8c06ad7c 100644 --- a/modules/ui.py +++ b/modules/ui.py @@ -1029,7 +1029,8 @@ def create_ui(wrap_gradio_gpu_call): process_src = gr.Textbox(label='Source directory') process_dst = gr.Textbox(label='Destination directory') - process_size = gr.Slider(minimum=64, maximum=2048, step=64, label="Size (width and height)", value=512) + process_width = gr.Slider(minimum=64, maximum=2048, step=64, label="Width", value=512) + process_height = gr.Slider(minimum=64, maximum=2048, step=64, label="Height", value=512) with gr.Row(): process_flip = gr.Checkbox(label='Create flipped copies') @@ -1050,7 +1051,8 @@ def create_ui(wrap_gradio_gpu_call): dataset_directory = gr.Textbox(label='Dataset directory', placeholder="Path to directory with input images") log_directory = gr.Textbox(label='Log directory', placeholder="Path to directory where to write outputs", value="textual_inversion") template_file = gr.Textbox(label='Prompt template file', value=os.path.join(script_path, "textual_inversion_templates", "style_filewords.txt")) - training_size = gr.Slider(minimum=64, maximum=2048, step=64, label="Size (width and height)", value=512) + training_width = gr.Slider(minimum=64, maximum=2048, step=64, label="Width", value=512) + training_height = gr.Slider(minimum=64, maximum=2048, step=64, label="Height", value=512) steps = gr.Number(label='Max steps', value=100000, precision=0) num_repeats = gr.Number(label='Number of repeats for a single input image per epoch', value=100, precision=0) create_image_every = gr.Number(label='Save an image to log directory every N steps, 0 to disable', value=500, precision=0) @@ -1095,7 +1097,8 @@ def create_ui(wrap_gradio_gpu_call): inputs=[ process_src, process_dst, - process_size, + process_width, + process_height, process_flip, process_split, process_caption, @@ -1114,7 +1117,8 @@ def create_ui(wrap_gradio_gpu_call): learn_rate, dataset_directory, log_directory, - training_size, + training_width, + training_height, steps, num_repeats, create_image_every, -- cgit v1.2.1 From 707a431100362645e914042bb344d08439f48ac8 Mon Sep 17 00:00:00 2001 From: DepFA <35278260+dfaker@users.noreply.github.com> Date: Mon, 10 Oct 2022 15:34:49 +0100 Subject: add pixel data footer --- modules/textual_inversion/textual_inversion.py | 48 ++++++++++++++++++++++++-- 1 file changed, 46 insertions(+), 2 deletions(-) (limited to 'modules') diff --git a/modules/textual_inversion/textual_inversion.py b/modules/textual_inversion/textual_inversion.py index 7a24192e..6fb64691 100644 --- a/modules/textual_inversion/textual_inversion.py +++ b/modules/textual_inversion/textual_inversion.py @@ -12,6 +12,7 @@ from ..images import captionImageOverlay import numpy as np import base64 import json +import zlib from modules import shared, devices, sd_hijack, processing, sd_models import modules.textual_inversion.dataset @@ -20,7 +21,7 @@ class EmbeddingEncoder(json.JSONEncoder): def default(self, obj): if isinstance(obj, torch.Tensor): return {'TORCHTENSOR':obj.cpu().detach().numpy().tolist()} - return json.JSONEncoder.default(self, o) + return json.JSONEncoder.default(self, obj) class EmbeddingDecoder(json.JSONDecoder): def __init__(self, *args, **kwargs): @@ -38,6 +39,45 @@ def embeddingFromB64(data): d = base64.b64decode(data) return json.loads(d,cls=EmbeddingDecoder) +def appendImageDataFooter(image,data): + d = 3 + data_compressed = zlib.compress( json.dumps(data,cls=EmbeddingEncoder).encode(),level=9) + dnp = np.frombuffer(data_compressed,np.uint8).copy() + w = image.size[0] + next_size = dnp.shape[0] + (w-(dnp.shape[0]%w)) + next_size = next_size + ((w*d)-(next_size%(w*d))) + dnp.resize(next_size) + dnp = dnp.reshape((-1,w,d)) + print(dnp.shape) + im = Image.fromarray(dnp,mode='RGB') + background = Image.new('RGB',(image.size[0],image.size[1]+im.size[1]+1),(0,0,0)) + background.paste(image,(0,0)) + background.paste(im,(0,image.size[1]+1)) + return background + +def crop_black(img,tol=0): + mask = (img>tol).all(2) + mask0,mask1 = mask.any(0),mask.any(1) + col_start,col_end = mask0.argmax(),mask.shape[1]-mask0[::-1].argmax() + row_start,row_end = mask1.argmax(),mask.shape[0]-mask1[::-1].argmax() + return img[row_start:row_end,col_start:col_end] + +def extractImageDataFooter(image): + d=3 + outarr = crop_black(np.array(image.getdata()).reshape(image.size[1],image.size[0],d ).astype(np.uint8) ) + lastRow = np.where( np.sum(outarr, axis=(1,2))==0) + if lastRow[0].shape[0] == 0: + print('Image data block not found.') + return None + lastRow = lastRow[0] + + lastRow = lastRow.max() + + dataBlock = outarr[lastRow+1::].astype(np.uint8).flatten().tobytes() + print(lastRow) + data = zlib.decompress(dataBlock) + return json.loads(data,cls=EmbeddingDecoder) + class Embedding: def __init__(self, vec, name, step=None): self.vec = vec @@ -113,6 +153,9 @@ class EmbeddingDatabase: if 'sd-ti-embedding' in embed_image.text: data = embeddingFromB64(embed_image.text['sd-ti-embedding']) name = data.get('name',name) + else: + data = extractImageDataFooter(embed_image) + name = data.get('name',name) else: data = torch.load(path, map_location="cpu") @@ -190,7 +233,7 @@ def create_embedding(name, num_vectors_per_token, init_text='*'): return fn -def train_embedding(embedding_name, learn_rate, data_root, log_directory, training_width, training_height, steps, num_repeats, create_image_every, save_embedding_every, template_file): +def train_embedding(embedding_name, learn_rate, data_root, log_directory, training_width, training_height, steps, num_repeats, create_image_every, save_embedding_every, template_file, save_image_with_stored_embedding): assert embedding_name, 'embedding not selected' shared.state.textinfo = "Initializing textual inversion training..." @@ -308,6 +351,7 @@ def train_embedding(embedding_name, learn_rate, data_root, log_directory, traini footer_right = '{}'.format(embedding.step) captioned_image = captionImageOverlay(image,title,footer_left,footer_mid,footer_right) + captioned_image = appendImageDataFooter(captioned_image,data) captioned_image.save(last_saved_image_chunks, "PNG", pnginfo=info) -- cgit v1.2.1 From df6d0d9286279c41c4c67460c3158fa268697524 Mon Sep 17 00:00:00 2001 From: DepFA <35278260+dfaker@users.noreply.github.com> Date: Mon, 10 Oct 2022 15:43:09 +0100 Subject: convert back to rgb as some hosts add alpha --- modules/textual_inversion/textual_inversion.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) (limited to 'modules') diff --git a/modules/textual_inversion/textual_inversion.py b/modules/textual_inversion/textual_inversion.py index 6fb64691..667a7cf2 100644 --- a/modules/textual_inversion/textual_inversion.py +++ b/modules/textual_inversion/textual_inversion.py @@ -64,7 +64,7 @@ def crop_black(img,tol=0): def extractImageDataFooter(image): d=3 - outarr = crop_black(np.array(image.getdata()).reshape(image.size[1],image.size[0],d ).astype(np.uint8) ) + outarr = crop_black(np.array(image.convert('RGB').getdata()).reshape(image.size[1],image.size[0],d ).astype(np.uint8) ) lastRow = np.where( np.sum(outarr, axis=(1,2))==0) if lastRow[0].shape[0] == 0: print('Image data block not found.') -- cgit v1.2.1 From f347ddfd808c56bb1bacdec0c4bedf826ff85cd8 Mon Sep 17 00:00:00 2001 From: RW21 Date: Mon, 10 Oct 2022 10:44:11 +0900 Subject: Remove max_batch_count from ui.py --- modules/ui.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) (limited to 'modules') diff --git a/modules/ui.py b/modules/ui.py index 8c06ad7c..8ba84911 100644 --- a/modules/ui.py +++ b/modules/ui.py @@ -524,7 +524,7 @@ def create_ui(wrap_gradio_gpu_call): denoising_strength = gr.Slider(minimum=0.0, maximum=1.0, step=0.01, label='Denoising strength', value=0.7) with gr.Row(): - batch_count = gr.Slider(minimum=1, maximum=cmd_opts.max_batch_count, step=1, label='Batch count', value=1) + batch_count = gr.Slider(minimum=1, step=1, label='Batch count', value=1) batch_size = gr.Slider(minimum=1, maximum=8, step=1, label='Batch size', value=1) cfg_scale = gr.Slider(minimum=1.0, maximum=30.0, step=0.5, label='CFG Scale', value=7.0) @@ -710,7 +710,7 @@ def create_ui(wrap_gradio_gpu_call): tiling = gr.Checkbox(label='Tiling', value=False) with gr.Row(): - batch_count = gr.Slider(minimum=1, maximum=cmd_opts.max_batch_count, step=1, label='Batch count', value=1) + batch_count = gr.Slider(minimum=1, step=1, label='Batch count', value=1) batch_size = gr.Slider(minimum=1, maximum=8, step=1, label='Batch size', value=1) with gr.Group(): -- cgit v1.2.1 From b340439586d844e76782149ca1857c8de35773ec Mon Sep 17 00:00:00 2001 From: hentailord85ez <112723046+hentailord85ez@users.noreply.github.com> Date: Mon, 10 Oct 2022 05:28:06 +0100 Subject: Unlimited Token Works Unlimited tokens actually work now. Works with textual inversion too. Replaces the previous not-so-much-working implementation. --- modules/sd_hijack.py | 69 ++++++++++++++++++++++++++++++++++------------------ 1 file changed, 46 insertions(+), 23 deletions(-) (limited to 'modules') diff --git a/modules/sd_hijack.py b/modules/sd_hijack.py index 437acce4..8d5c77d8 100644 --- a/modules/sd_hijack.py +++ b/modules/sd_hijack.py @@ -43,10 +43,7 @@ def undo_optimizations(): def get_target_prompt_token_count(token_count): - if token_count < 75: - return 75 - - return math.ceil(token_count / 10) * 10 + return math.ceil(max(token_count, 1) / 75) * 75 class StableDiffusionModelHijack: @@ -127,7 +124,6 @@ class FrozenCLIPEmbedderWithCustomWords(torch.nn.Module): self.token_mults[ident] = mult def tokenize_line(self, line, used_custom_terms, hijack_comments): - id_start = self.wrapped.tokenizer.bos_token_id id_end = self.wrapped.tokenizer.eos_token_id if opts.enable_emphasis: @@ -154,7 +150,8 @@ class FrozenCLIPEmbedderWithCustomWords(torch.nn.Module): i += 1 else: emb_len = int(embedding.vec.shape[0]) - fixes.append((len(remade_tokens), embedding)) + iteration = len(remade_tokens) // 75 + fixes.append((iteration, (len(remade_tokens) % 75, embedding))) remade_tokens += [0] * emb_len multipliers += [weight] * emb_len used_custom_terms.append((embedding.name, embedding.checksum())) @@ -162,10 +159,10 @@ class FrozenCLIPEmbedderWithCustomWords(torch.nn.Module): token_count = len(remade_tokens) prompt_target_length = get_target_prompt_token_count(token_count) - tokens_to_add = prompt_target_length - len(remade_tokens) + 1 + tokens_to_add = prompt_target_length - len(remade_tokens) - remade_tokens = [id_start] + remade_tokens + [id_end] * tokens_to_add - multipliers = [1.0] + multipliers + [1.0] * tokens_to_add + remade_tokens = remade_tokens + [id_end] * tokens_to_add + multipliers = multipliers + [1.0] * tokens_to_add return remade_tokens, fixes, multipliers, token_count @@ -260,29 +257,55 @@ class FrozenCLIPEmbedderWithCustomWords(torch.nn.Module): hijack_fixes.append(fixes) batch_multipliers.append(multipliers) return batch_multipliers, remade_batch_tokens, used_custom_terms, hijack_comments, hijack_fixes, token_count - + def forward(self, text): - - if opts.use_old_emphasis_implementation: + use_old = opts.use_old_emphasis_implementation + if use_old: batch_multipliers, remade_batch_tokens, used_custom_terms, hijack_comments, hijack_fixes, token_count = self.process_text_old(text) else: batch_multipliers, remade_batch_tokens, used_custom_terms, hijack_comments, hijack_fixes, token_count = self.process_text(text) - self.hijack.fixes = hijack_fixes self.hijack.comments += hijack_comments if len(used_custom_terms) > 0: self.hijack.comments.append("Used embeddings: " + ", ".join([f'{word} [{checksum}]' for word, checksum in used_custom_terms])) + + if use_old: + self.hijack.fixes = hijack_fixes + return self.process_tokens(remade_batch_tokens, batch_multipliers) + + z = None + i = 0 + while max(map(len, remade_batch_tokens)) != 0: + rem_tokens = [x[75:] for x in remade_batch_tokens] + rem_multipliers = [x[75:] for x in batch_multipliers] + + self.hijack.fixes = [] + for unfiltered in hijack_fixes: + fixes = [] + for fix in unfiltered: + if fix[0] == i: + fixes.append(fix[1]) + self.hijack.fixes.append(fixes) + + z1 = self.process_tokens([x[:75] for x in remade_batch_tokens], [x[:75] for x in batch_multipliers]) + z = z1 if z is None else torch.cat((z, z1), axis=-2) + + remade_batch_tokens = rem_tokens + batch_multipliers = rem_multipliers + i += 1 + + return z + + + def process_tokens(self, remade_batch_tokens, batch_multipliers): + if not opts.use_old_emphasis_implementation: + remade_batch_tokens = [[self.wrapped.tokenizer.bos_token_id] + x[:75] + [self.wrapped.tokenizer.eos_token_id] for x in remade_batch_tokens] + batch_multipliers = [[1.0] + x[:75] + [1.0] for x in batch_multipliers] + + tokens = torch.asarray(remade_batch_tokens).to(device) + outputs = self.wrapped.transformer(input_ids=tokens) - target_token_count = get_target_prompt_token_count(token_count) + 2 - - position_ids_array = [min(x, 75) for x in range(target_token_count-1)] + [76] - position_ids = torch.asarray(position_ids_array, device=devices.device).expand((1, -1)) - - remade_batch_tokens_of_same_length = [x + [self.wrapped.tokenizer.eos_token_id] * (target_token_count - len(x)) for x in remade_batch_tokens] - tokens = torch.asarray(remade_batch_tokens_of_same_length).to(device) - - outputs = self.wrapped.transformer(input_ids=tokens, position_ids=position_ids, output_hidden_states=-opts.CLIP_stop_at_last_layers) if opts.CLIP_stop_at_last_layers > 1: z = outputs.hidden_states[-opts.CLIP_stop_at_last_layers] z = self.wrapped.transformer.text_model.final_layer_norm(z) @@ -290,7 +313,7 @@ class FrozenCLIPEmbedderWithCustomWords(torch.nn.Module): z = outputs.last_hidden_state # restoring original mean is likely not correct, but it seems to work well to prevent artifacts that happen otherwise - batch_multipliers_of_same_length = [x + [1.0] * (target_token_count - len(x)) for x in batch_multipliers] + batch_multipliers_of_same_length = [x + [1.0] * (75 - len(x)) for x in batch_multipliers] batch_multipliers = torch.asarray(batch_multipliers_of_same_length).to(device) original_mean = z.mean() z *= batch_multipliers.reshape(batch_multipliers.shape + (1,)).expand(z.shape) -- cgit v1.2.1 From 460bbae58726c177beddfcddf351f27e205d3fb2 Mon Sep 17 00:00:00 2001 From: hentailord85ez <112723046+hentailord85ez@users.noreply.github.com> Date: Mon, 10 Oct 2022 16:09:06 +0100 Subject: Pad beginning of textual inversion embedding --- modules/sd_hijack.py | 5 +++++ 1 file changed, 5 insertions(+) (limited to 'modules') diff --git a/modules/sd_hijack.py b/modules/sd_hijack.py index 8d5c77d8..3a60cd63 100644 --- a/modules/sd_hijack.py +++ b/modules/sd_hijack.py @@ -151,6 +151,11 @@ class FrozenCLIPEmbedderWithCustomWords(torch.nn.Module): else: emb_len = int(embedding.vec.shape[0]) iteration = len(remade_tokens) // 75 + if (len(remade_tokens) + emb_len) // 75 != iteration: + rem = (75 * (iteration + 1) - len(remade_tokens)) + remade_tokens += [id_end] * rem + multipliers += [1.0] * rem + iteration += 1 fixes.append((iteration, (len(remade_tokens) % 75, embedding))) remade_tokens += [0] * emb_len multipliers += [weight] * emb_len -- cgit v1.2.1 From d5c14365fd468dbf89fa12a68bea5b217077273c Mon Sep 17 00:00:00 2001 From: hentailord85ez <112723046+hentailord85ez@users.noreply.github.com> Date: Mon, 10 Oct 2022 16:13:47 +0100 Subject: Add back in output hidden states parameter --- modules/sd_hijack.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) (limited to 'modules') diff --git a/modules/sd_hijack.py b/modules/sd_hijack.py index 3a60cd63..3edc0e9d 100644 --- a/modules/sd_hijack.py +++ b/modules/sd_hijack.py @@ -309,7 +309,7 @@ class FrozenCLIPEmbedderWithCustomWords(torch.nn.Module): batch_multipliers = [[1.0] + x[:75] + [1.0] for x in batch_multipliers] tokens = torch.asarray(remade_batch_tokens).to(device) - outputs = self.wrapped.transformer(input_ids=tokens) + outputs = self.wrapped.transformer(input_ids=tokens, output_hidden_states=-opts.CLIP_stop_at_last_layers) if opts.CLIP_stop_at_last_layers > 1: z = outputs.hidden_states[-opts.CLIP_stop_at_last_layers] -- cgit v1.2.1 From 9d33baba587637815d818e5e641d8f8b74c4900d Mon Sep 17 00:00:00 2001 From: Vladimir Repin <32306715+mezotaken@users.noreply.github.com> Date: Mon, 10 Oct 2022 18:46:48 +0300 Subject: Always show previous mask and fix extras_send dest --- modules/ui.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) (limited to 'modules') diff --git a/modules/ui.py b/modules/ui.py index 8ba84911..e8039d76 100644 --- a/modules/ui.py +++ b/modules/ui.py @@ -961,7 +961,7 @@ def create_ui(wrap_gradio_gpu_call): extras_send_to_inpaint.click( fn=lambda x: image_from_url_text(x), - _js="extract_image_from_gallery_img2img", + _js="extract_image_from_gallery_inpaint", inputs=[result_images], outputs=[init_img_with_mask], ) -- cgit v1.2.1 From 623251ce2b8d152e242011f62984a8247a14a389 Mon Sep 17 00:00:00 2001 From: C43H66N12O12S2 <36072735+C43H66N12O12S2@users.noreply.github.com> Date: Mon, 10 Oct 2022 17:45:38 +0300 Subject: allow pascal onwards --- modules/sd_hijack.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) (limited to 'modules') diff --git a/modules/sd_hijack.py b/modules/sd_hijack.py index 3edc0e9d..827bf304 100644 --- a/modules/sd_hijack.py +++ b/modules/sd_hijack.py @@ -23,7 +23,7 @@ def apply_optimizations(): ldm.modules.diffusionmodules.model.nonlinearity = silu - if cmd_opts.force_enable_xformers or (cmd_opts.xformers and shared.xformers_available and torch.version.cuda and torch.cuda.get_device_capability(shared.device) == (8, 6)): + if cmd_opts.force_enable_xformers or (cmd_opts.xformers and shared.xformers_available and torch.version.cuda and (6, 0) <= torch.cuda.get_device_capability(shared.device) <= (8, 6)): print("Applying xformers cross attention optimization.") ldm.modules.attention.CrossAttention.forward = sd_hijack_optimizations.xformers_attention_forward ldm.modules.diffusionmodules.model.AttnBlock.forward = sd_hijack_optimizations.xformers_attnblock_forward -- cgit v1.2.1 From 3e7a981194ed9c454e951365846e4eba66fa7095 Mon Sep 17 00:00:00 2001 From: C43H66N12O12S2 <36072735+C43H66N12O12S2@users.noreply.github.com> Date: Mon, 10 Oct 2022 17:51:05 +0300 Subject: remove functorch --- modules/sd_hijack_optimizations.py | 2 -- 1 file changed, 2 deletions(-) (limited to 'modules') diff --git a/modules/sd_hijack_optimizations.py b/modules/sd_hijack_optimizations.py index 634fb4b2..18408e62 100644 --- a/modules/sd_hijack_optimizations.py +++ b/modules/sd_hijack_optimizations.py @@ -13,8 +13,6 @@ from modules import shared if shared.cmd_opts.xformers or shared.cmd_opts.force_enable_xformers: try: import xformers.ops - import functorch - xformers._is_functorch_available = True shared.xformers_available = True except Exception: print("Cannot import xformers", file=sys.stderr) -- cgit v1.2.1 From ece27fe98933eb0eda8ea94dc496dd7554f3a08f Mon Sep 17 00:00:00 2001 From: C43H66N12O12S2 <36072735+C43H66N12O12S2@users.noreply.github.com> Date: Sun, 9 Oct 2022 18:55:33 +0300 Subject: Add files via upload --- modules/swinir_model_arch_v2.py | 1017 +++++++++++++++++++++++++++++++++++++++ 1 file changed, 1017 insertions(+) create mode 100644 modules/swinir_model_arch_v2.py (limited to 'modules') diff --git a/modules/swinir_model_arch_v2.py b/modules/swinir_model_arch_v2.py new file mode 100644 index 00000000..0e28ae6e --- /dev/null +++ b/modules/swinir_model_arch_v2.py @@ -0,0 +1,1017 @@ +# ----------------------------------------------------------------------------------- +# Swin2SR: Swin2SR: SwinV2 Transformer for Compressed Image Super-Resolution and Restoration, https://arxiv.org/abs/ +# Written by Conde and Choi et al. +# ----------------------------------------------------------------------------------- + +import math +import numpy as np +import torch +import torch.nn as nn +import torch.nn.functional as F +import torch.utils.checkpoint as checkpoint +from timm.models.layers import DropPath, to_2tuple, trunc_normal_ + + +class Mlp(nn.Module): + def __init__(self, in_features, hidden_features=None, out_features=None, act_layer=nn.GELU, drop=0.): + super().__init__() + out_features = out_features or in_features + hidden_features = hidden_features or in_features + self.fc1 = nn.Linear(in_features, hidden_features) + self.act = act_layer() + self.fc2 = nn.Linear(hidden_features, out_features) + self.drop = nn.Dropout(drop) + + def forward(self, x): + x = self.fc1(x) + x = self.act(x) + x = self.drop(x) + x = self.fc2(x) + x = self.drop(x) + return x + + +def window_partition(x, window_size): + """ + Args: + x: (B, H, W, C) + window_size (int): window size + Returns: + windows: (num_windows*B, window_size, window_size, C) + """ + B, H, W, C = x.shape + x = x.view(B, H // window_size, window_size, W // window_size, window_size, C) + windows = x.permute(0, 1, 3, 2, 4, 5).contiguous().view(-1, window_size, window_size, C) + return windows + + +def window_reverse(windows, window_size, H, W): + """ + Args: + windows: (num_windows*B, window_size, window_size, C) + window_size (int): Window size + H (int): Height of image + W (int): Width of image + Returns: + x: (B, H, W, C) + """ + B = int(windows.shape[0] / (H * W / window_size / window_size)) + x = windows.view(B, H // window_size, W // window_size, window_size, window_size, -1) + x = x.permute(0, 1, 3, 2, 4, 5).contiguous().view(B, H, W, -1) + return x + +class WindowAttention(nn.Module): + r""" Window based multi-head self attention (W-MSA) module with relative position bias. + It supports both of shifted and non-shifted window. + Args: + dim (int): Number of input channels. + window_size (tuple[int]): The height and width of the window. + num_heads (int): Number of attention heads. + qkv_bias (bool, optional): If True, add a learnable bias to query, key, value. Default: True + attn_drop (float, optional): Dropout ratio of attention weight. Default: 0.0 + proj_drop (float, optional): Dropout ratio of output. Default: 0.0 + pretrained_window_size (tuple[int]): The height and width of the window in pre-training. + """ + + def __init__(self, dim, window_size, num_heads, qkv_bias=True, attn_drop=0., proj_drop=0., + pretrained_window_size=[0, 0]): + + super().__init__() + self.dim = dim + self.window_size = window_size # Wh, Ww + self.pretrained_window_size = pretrained_window_size + self.num_heads = num_heads + + self.logit_scale = nn.Parameter(torch.log(10 * torch.ones((num_heads, 1, 1))), requires_grad=True) + + # mlp to generate continuous relative position bias + self.cpb_mlp = nn.Sequential(nn.Linear(2, 512, bias=True), + nn.ReLU(inplace=True), + nn.Linear(512, num_heads, bias=False)) + + # get relative_coords_table + relative_coords_h = torch.arange(-(self.window_size[0] - 1), self.window_size[0], dtype=torch.float32) + relative_coords_w = torch.arange(-(self.window_size[1] - 1), self.window_size[1], dtype=torch.float32) + relative_coords_table = torch.stack( + torch.meshgrid([relative_coords_h, + relative_coords_w])).permute(1, 2, 0).contiguous().unsqueeze(0) # 1, 2*Wh-1, 2*Ww-1, 2 + if pretrained_window_size[0] > 0: + relative_coords_table[:, :, :, 0] /= (pretrained_window_size[0] - 1) + relative_coords_table[:, :, :, 1] /= (pretrained_window_size[1] - 1) + else: + relative_coords_table[:, :, :, 0] /= (self.window_size[0] - 1) + relative_coords_table[:, :, :, 1] /= (self.window_size[1] - 1) + relative_coords_table *= 8 # normalize to -8, 8 + relative_coords_table = torch.sign(relative_coords_table) * torch.log2( + torch.abs(relative_coords_table) + 1.0) / np.log2(8) + + self.register_buffer("relative_coords_table", relative_coords_table) + + # get pair-wise relative position index for each token inside the window + coords_h = torch.arange(self.window_size[0]) + coords_w = torch.arange(self.window_size[1]) + coords = torch.stack(torch.meshgrid([coords_h, coords_w])) # 2, Wh, Ww + coords_flatten = torch.flatten(coords, 1) # 2, Wh*Ww + relative_coords = coords_flatten[:, :, None] - coords_flatten[:, None, :] # 2, Wh*Ww, Wh*Ww + relative_coords = relative_coords.permute(1, 2, 0).contiguous() # Wh*Ww, Wh*Ww, 2 + relative_coords[:, :, 0] += self.window_size[0] - 1 # shift to start from 0 + relative_coords[:, :, 1] += self.window_size[1] - 1 + relative_coords[:, :, 0] *= 2 * self.window_size[1] - 1 + relative_position_index = relative_coords.sum(-1) # Wh*Ww, Wh*Ww + self.register_buffer("relative_position_index", relative_position_index) + + self.qkv = nn.Linear(dim, dim * 3, bias=False) + if qkv_bias: + self.q_bias = nn.Parameter(torch.zeros(dim)) + self.v_bias = nn.Parameter(torch.zeros(dim)) + else: + self.q_bias = None + self.v_bias = None + self.attn_drop = nn.Dropout(attn_drop) + self.proj = nn.Linear(dim, dim) + self.proj_drop = nn.Dropout(proj_drop) + self.softmax = nn.Softmax(dim=-1) + + def forward(self, x, mask=None): + """ + Args: + x: input features with shape of (num_windows*B, N, C) + mask: (0/-inf) mask with shape of (num_windows, Wh*Ww, Wh*Ww) or None + """ + B_, N, C = x.shape + qkv_bias = None + if self.q_bias is not None: + qkv_bias = torch.cat((self.q_bias, torch.zeros_like(self.v_bias, requires_grad=False), self.v_bias)) + qkv = F.linear(input=x, weight=self.qkv.weight, bias=qkv_bias) + qkv = qkv.reshape(B_, N, 3, self.num_heads, -1).permute(2, 0, 3, 1, 4) + q, k, v = qkv[0], qkv[1], qkv[2] # make torchscript happy (cannot use tensor as tuple) + + # cosine attention + attn = (F.normalize(q, dim=-1) @ F.normalize(k, dim=-1).transpose(-2, -1)) + logit_scale = torch.clamp(self.logit_scale, max=torch.log(torch.tensor(1. / 0.01)).to(self.logit_scale.device)).exp() + attn = attn * logit_scale + + relative_position_bias_table = self.cpb_mlp(self.relative_coords_table).view(-1, self.num_heads) + relative_position_bias = relative_position_bias_table[self.relative_position_index.view(-1)].view( + self.window_size[0] * self.window_size[1], self.window_size[0] * self.window_size[1], -1) # Wh*Ww,Wh*Ww,nH + relative_position_bias = relative_position_bias.permute(2, 0, 1).contiguous() # nH, Wh*Ww, Wh*Ww + relative_position_bias = 16 * torch.sigmoid(relative_position_bias) + attn = attn + relative_position_bias.unsqueeze(0) + + if mask is not None: + nW = mask.shape[0] + attn = attn.view(B_ // nW, nW, self.num_heads, N, N) + mask.unsqueeze(1).unsqueeze(0) + attn = attn.view(-1, self.num_heads, N, N) + attn = self.softmax(attn) + else: + attn = self.softmax(attn) + + attn = self.attn_drop(attn) + + x = (attn @ v).transpose(1, 2).reshape(B_, N, C) + x = self.proj(x) + x = self.proj_drop(x) + return x + + def extra_repr(self) -> str: + return f'dim={self.dim}, window_size={self.window_size}, ' \ + f'pretrained_window_size={self.pretrained_window_size}, num_heads={self.num_heads}' + + def flops(self, N): + # calculate flops for 1 window with token length of N + flops = 0 + # qkv = self.qkv(x) + flops += N * self.dim * 3 * self.dim + # attn = (q @ k.transpose(-2, -1)) + flops += self.num_heads * N * (self.dim // self.num_heads) * N + # x = (attn @ v) + flops += self.num_heads * N * N * (self.dim // self.num_heads) + # x = self.proj(x) + flops += N * self.dim * self.dim + return flops + +class SwinTransformerBlock(nn.Module): + r""" Swin Transformer Block. + Args: + dim (int): Number of input channels. + input_resolution (tuple[int]): Input resulotion. + num_heads (int): Number of attention heads. + window_size (int): Window size. + shift_size (int): Shift size for SW-MSA. + mlp_ratio (float): Ratio of mlp hidden dim to embedding dim. + qkv_bias (bool, optional): If True, add a learnable bias to query, key, value. Default: True + drop (float, optional): Dropout rate. Default: 0.0 + attn_drop (float, optional): Attention dropout rate. Default: 0.0 + drop_path (float, optional): Stochastic depth rate. Default: 0.0 + act_layer (nn.Module, optional): Activation layer. Default: nn.GELU + norm_layer (nn.Module, optional): Normalization layer. Default: nn.LayerNorm + pretrained_window_size (int): Window size in pre-training. + """ + + def __init__(self, dim, input_resolution, num_heads, window_size=7, shift_size=0, + mlp_ratio=4., qkv_bias=True, drop=0., attn_drop=0., drop_path=0., + act_layer=nn.GELU, norm_layer=nn.LayerNorm, pretrained_window_size=0): + super().__init__() + self.dim = dim + self.input_resolution = input_resolution + self.num_heads = num_heads + self.window_size = window_size + self.shift_size = shift_size + self.mlp_ratio = mlp_ratio + if min(self.input_resolution) <= self.window_size: + # if window size is larger than input resolution, we don't partition windows + self.shift_size = 0 + self.window_size = min(self.input_resolution) + assert 0 <= self.shift_size < self.window_size, "shift_size must in 0-window_size" + + self.norm1 = norm_layer(dim) + self.attn = WindowAttention( + dim, window_size=to_2tuple(self.window_size), num_heads=num_heads, + qkv_bias=qkv_bias, attn_drop=attn_drop, proj_drop=drop, + pretrained_window_size=to_2tuple(pretrained_window_size)) + + self.drop_path = DropPath(drop_path) if drop_path > 0. else nn.Identity() + self.norm2 = norm_layer(dim) + mlp_hidden_dim = int(dim * mlp_ratio) + self.mlp = Mlp(in_features=dim, hidden_features=mlp_hidden_dim, act_layer=act_layer, drop=drop) + + if self.shift_size > 0: + attn_mask = self.calculate_mask(self.input_resolution) + else: + attn_mask = None + + self.register_buffer("attn_mask", attn_mask) + + def calculate_mask(self, x_size): + # calculate attention mask for SW-MSA + H, W = x_size + img_mask = torch.zeros((1, H, W, 1)) # 1 H W 1 + h_slices = (slice(0, -self.window_size), + slice(-self.window_size, -self.shift_size), + slice(-self.shift_size, None)) + w_slices = (slice(0, -self.window_size), + slice(-self.window_size, -self.shift_size), + slice(-self.shift_size, None)) + cnt = 0 + for h in h_slices: + for w in w_slices: + img_mask[:, h, w, :] = cnt + cnt += 1 + + mask_windows = window_partition(img_mask, self.window_size) # nW, window_size, window_size, 1 + mask_windows = mask_windows.view(-1, self.window_size * self.window_size) + attn_mask = mask_windows.unsqueeze(1) - mask_windows.unsqueeze(2) + attn_mask = attn_mask.masked_fill(attn_mask != 0, float(-100.0)).masked_fill(attn_mask == 0, float(0.0)) + + return attn_mask + + def forward(self, x, x_size): + H, W = x_size + B, L, C = x.shape + #assert L == H * W, "input feature has wrong size" + + shortcut = x + x = x.view(B, H, W, C) + + # cyclic shift + if self.shift_size > 0: + shifted_x = torch.roll(x, shifts=(-self.shift_size, -self.shift_size), dims=(1, 2)) + else: + shifted_x = x + + # partition windows + x_windows = window_partition(shifted_x, self.window_size) # nW*B, window_size, window_size, C + x_windows = x_windows.view(-1, self.window_size * self.window_size, C) # nW*B, window_size*window_size, C + + # W-MSA/SW-MSA (to be compatible for testing on images whose shapes are the multiple of window size + if self.input_resolution == x_size: + attn_windows = self.attn(x_windows, mask=self.attn_mask) # nW*B, window_size*window_size, C + else: + attn_windows = self.attn(x_windows, mask=self.calculate_mask(x_size).to(x.device)) + + # merge windows + attn_windows = attn_windows.view(-1, self.window_size, self.window_size, C) + shifted_x = window_reverse(attn_windows, self.window_size, H, W) # B H' W' C + + # reverse cyclic shift + if self.shift_size > 0: + x = torch.roll(shifted_x, shifts=(self.shift_size, self.shift_size), dims=(1, 2)) + else: + x = shifted_x + x = x.view(B, H * W, C) + x = shortcut + self.drop_path(self.norm1(x)) + + # FFN + x = x + self.drop_path(self.norm2(self.mlp(x))) + + return x + + def extra_repr(self) -> str: + return f"dim={self.dim}, input_resolution={self.input_resolution}, num_heads={self.num_heads}, " \ + f"window_size={self.window_size}, shift_size={self.shift_size}, mlp_ratio={self.mlp_ratio}" + + def flops(self): + flops = 0 + H, W = self.input_resolution + # norm1 + flops += self.dim * H * W + # W-MSA/SW-MSA + nW = H * W / self.window_size / self.window_size + flops += nW * self.attn.flops(self.window_size * self.window_size) + # mlp + flops += 2 * H * W * self.dim * self.dim * self.mlp_ratio + # norm2 + flops += self.dim * H * W + return flops + +class PatchMerging(nn.Module): + r""" Patch Merging Layer. + Args: + input_resolution (tuple[int]): Resolution of input feature. + dim (int): Number of input channels. + norm_layer (nn.Module, optional): Normalization layer. Default: nn.LayerNorm + """ + + def __init__(self, input_resolution, dim, norm_layer=nn.LayerNorm): + super().__init__() + self.input_resolution = input_resolution + self.dim = dim + self.reduction = nn.Linear(4 * dim, 2 * dim, bias=False) + self.norm = norm_layer(2 * dim) + + def forward(self, x): + """ + x: B, H*W, C + """ + H, W = self.input_resolution + B, L, C = x.shape + assert L == H * W, "input feature has wrong size" + assert H % 2 == 0 and W % 2 == 0, f"x size ({H}*{W}) are not even." + + x = x.view(B, H, W, C) + + x0 = x[:, 0::2, 0::2, :] # B H/2 W/2 C + x1 = x[:, 1::2, 0::2, :] # B H/2 W/2 C + x2 = x[:, 0::2, 1::2, :] # B H/2 W/2 C + x3 = x[:, 1::2, 1::2, :] # B H/2 W/2 C + x = torch.cat([x0, x1, x2, x3], -1) # B H/2 W/2 4*C + x = x.view(B, -1, 4 * C) # B H/2*W/2 4*C + + x = self.reduction(x) + x = self.norm(x) + + return x + + def extra_repr(self) -> str: + return f"input_resolution={self.input_resolution}, dim={self.dim}" + + def flops(self): + H, W = self.input_resolution + flops = (H // 2) * (W // 2) * 4 * self.dim * 2 * self.dim + flops += H * W * self.dim // 2 + return flops + +class BasicLayer(nn.Module): + """ A basic Swin Transformer layer for one stage. + Args: + dim (int): Number of input channels. + input_resolution (tuple[int]): Input resolution. + depth (int): Number of blocks. + num_heads (int): Number of attention heads. + window_size (int): Local window size. + mlp_ratio (float): Ratio of mlp hidden dim to embedding dim. + qkv_bias (bool, optional): If True, add a learnable bias to query, key, value. Default: True + drop (float, optional): Dropout rate. Default: 0.0 + attn_drop (float, optional): Attention dropout rate. Default: 0.0 + drop_path (float | tuple[float], optional): Stochastic depth rate. Default: 0.0 + norm_layer (nn.Module, optional): Normalization layer. Default: nn.LayerNorm + downsample (nn.Module | None, optional): Downsample layer at the end of the layer. Default: None + use_checkpoint (bool): Whether to use checkpointing to save memory. Default: False. + pretrained_window_size (int): Local window size in pre-training. + """ + + def __init__(self, dim, input_resolution, depth, num_heads, window_size, + mlp_ratio=4., qkv_bias=True, drop=0., attn_drop=0., + drop_path=0., norm_layer=nn.LayerNorm, downsample=None, use_checkpoint=False, + pretrained_window_size=0): + + super().__init__() + self.dim = dim + self.input_resolution = input_resolution + self.depth = depth + self.use_checkpoint = use_checkpoint + + # build blocks + self.blocks = nn.ModuleList([ + SwinTransformerBlock(dim=dim, input_resolution=input_resolution, + num_heads=num_heads, window_size=window_size, + shift_size=0 if (i % 2 == 0) else window_size // 2, + mlp_ratio=mlp_ratio, + qkv_bias=qkv_bias, + drop=drop, attn_drop=attn_drop, + drop_path=drop_path[i] if isinstance(drop_path, list) else drop_path, + norm_layer=norm_layer, + pretrained_window_size=pretrained_window_size) + for i in range(depth)]) + + # patch merging layer + if downsample is not None: + self.downsample = downsample(input_resolution, dim=dim, norm_layer=norm_layer) + else: + self.downsample = None + + def forward(self, x, x_size): + for blk in self.blocks: + if self.use_checkpoint: + x = checkpoint.checkpoint(blk, x, x_size) + else: + x = blk(x, x_size) + if self.downsample is not None: + x = self.downsample(x) + return x + + def extra_repr(self) -> str: + return f"dim={self.dim}, input_resolution={self.input_resolution}, depth={self.depth}" + + def flops(self): + flops = 0 + for blk in self.blocks: + flops += blk.flops() + if self.downsample is not None: + flops += self.downsample.flops() + return flops + + def _init_respostnorm(self): + for blk in self.blocks: + nn.init.constant_(blk.norm1.bias, 0) + nn.init.constant_(blk.norm1.weight, 0) + nn.init.constant_(blk.norm2.bias, 0) + nn.init.constant_(blk.norm2.weight, 0) + +class PatchEmbed(nn.Module): + r""" Image to Patch Embedding + Args: + img_size (int): Image size. Default: 224. + patch_size (int): Patch token size. Default: 4. + in_chans (int): Number of input image channels. Default: 3. + embed_dim (int): Number of linear projection output channels. Default: 96. + norm_layer (nn.Module, optional): Normalization layer. Default: None + """ + + def __init__(self, img_size=224, patch_size=4, in_chans=3, embed_dim=96, norm_layer=None): + super().__init__() + img_size = to_2tuple(img_size) + patch_size = to_2tuple(patch_size) + patches_resolution = [img_size[0] // patch_size[0], img_size[1] // patch_size[1]] + self.img_size = img_size + self.patch_size = patch_size + self.patches_resolution = patches_resolution + self.num_patches = patches_resolution[0] * patches_resolution[1] + + self.in_chans = in_chans + self.embed_dim = embed_dim + + self.proj = nn.Conv2d(in_chans, embed_dim, kernel_size=patch_size, stride=patch_size) + if norm_layer is not None: + self.norm = norm_layer(embed_dim) + else: + self.norm = None + + def forward(self, x): + B, C, H, W = x.shape + # FIXME look at relaxing size constraints + # assert H == self.img_size[0] and W == self.img_size[1], + # f"Input image size ({H}*{W}) doesn't match model ({self.img_size[0]}*{self.img_size[1]})." + x = self.proj(x).flatten(2).transpose(1, 2) # B Ph*Pw C + if self.norm is not None: + x = self.norm(x) + return x + + def flops(self): + Ho, Wo = self.patches_resolution + flops = Ho * Wo * self.embed_dim * self.in_chans * (self.patch_size[0] * self.patch_size[1]) + if self.norm is not None: + flops += Ho * Wo * self.embed_dim + return flops + +class RSTB(nn.Module): + """Residual Swin Transformer Block (RSTB). + + Args: + dim (int): Number of input channels. + input_resolution (tuple[int]): Input resolution. + depth (int): Number of blocks. + num_heads (int): Number of attention heads. + window_size (int): Local window size. + mlp_ratio (float): Ratio of mlp hidden dim to embedding dim. + qkv_bias (bool, optional): If True, add a learnable bias to query, key, value. Default: True + drop (float, optional): Dropout rate. Default: 0.0 + attn_drop (float, optional): Attention dropout rate. Default: 0.0 + drop_path (float | tuple[float], optional): Stochastic depth rate. Default: 0.0 + norm_layer (nn.Module, optional): Normalization layer. Default: nn.LayerNorm + downsample (nn.Module | None, optional): Downsample layer at the end of the layer. Default: None + use_checkpoint (bool): Whether to use checkpointing to save memory. Default: False. + img_size: Input image size. + patch_size: Patch size. + resi_connection: The convolutional block before residual connection. + """ + + def __init__(self, dim, input_resolution, depth, num_heads, window_size, + mlp_ratio=4., qkv_bias=True, drop=0., attn_drop=0., + drop_path=0., norm_layer=nn.LayerNorm, downsample=None, use_checkpoint=False, + img_size=224, patch_size=4, resi_connection='1conv'): + super(RSTB, self).__init__() + + self.dim = dim + self.input_resolution = input_resolution + + self.residual_group = BasicLayer(dim=dim, + input_resolution=input_resolution, + depth=depth, + num_heads=num_heads, + window_size=window_size, + mlp_ratio=mlp_ratio, + qkv_bias=qkv_bias, + drop=drop, attn_drop=attn_drop, + drop_path=drop_path, + norm_layer=norm_layer, + downsample=downsample, + use_checkpoint=use_checkpoint) + + if resi_connection == '1conv': + self.conv = nn.Conv2d(dim, dim, 3, 1, 1) + elif resi_connection == '3conv': + # to save parameters and memory + self.conv = nn.Sequential(nn.Conv2d(dim, dim // 4, 3, 1, 1), nn.LeakyReLU(negative_slope=0.2, inplace=True), + nn.Conv2d(dim // 4, dim // 4, 1, 1, 0), + nn.LeakyReLU(negative_slope=0.2, inplace=True), + nn.Conv2d(dim // 4, dim, 3, 1, 1)) + + self.patch_embed = PatchEmbed( + img_size=img_size, patch_size=patch_size, in_chans=dim, embed_dim=dim, + norm_layer=None) + + self.patch_unembed = PatchUnEmbed( + img_size=img_size, patch_size=patch_size, in_chans=dim, embed_dim=dim, + norm_layer=None) + + def forward(self, x, x_size): + return self.patch_embed(self.conv(self.patch_unembed(self.residual_group(x, x_size), x_size))) + x + + def flops(self): + flops = 0 + flops += self.residual_group.flops() + H, W = self.input_resolution + flops += H * W * self.dim * self.dim * 9 + flops += self.patch_embed.flops() + flops += self.patch_unembed.flops() + + return flops + +class PatchUnEmbed(nn.Module): + r""" Image to Patch Unembedding + + Args: + img_size (int): Image size. Default: 224. + patch_size (int): Patch token size. Default: 4. + in_chans (int): Number of input image channels. Default: 3. + embed_dim (int): Number of linear projection output channels. Default: 96. + norm_layer (nn.Module, optional): Normalization layer. Default: None + """ + + def __init__(self, img_size=224, patch_size=4, in_chans=3, embed_dim=96, norm_layer=None): + super().__init__() + img_size = to_2tuple(img_size) + patch_size = to_2tuple(patch_size) + patches_resolution = [img_size[0] // patch_size[0], img_size[1] // patch_size[1]] + self.img_size = img_size + self.patch_size = patch_size + self.patches_resolution = patches_resolution + self.num_patches = patches_resolution[0] * patches_resolution[1] + + self.in_chans = in_chans + self.embed_dim = embed_dim + + def forward(self, x, x_size): + B, HW, C = x.shape + x = x.transpose(1, 2).view(B, self.embed_dim, x_size[0], x_size[1]) # B Ph*Pw C + return x + + def flops(self): + flops = 0 + return flops + + +class Upsample(nn.Sequential): + """Upsample module. + + Args: + scale (int): Scale factor. Supported scales: 2^n and 3. + num_feat (int): Channel number of intermediate features. + """ + + def __init__(self, scale, num_feat): + m = [] + if (scale & (scale - 1)) == 0: # scale = 2^n + for _ in range(int(math.log(scale, 2))): + m.append(nn.Conv2d(num_feat, 4 * num_feat, 3, 1, 1)) + m.append(nn.PixelShuffle(2)) + elif scale == 3: + m.append(nn.Conv2d(num_feat, 9 * num_feat, 3, 1, 1)) + m.append(nn.PixelShuffle(3)) + else: + raise ValueError(f'scale {scale} is not supported. ' 'Supported scales: 2^n and 3.') + super(Upsample, self).__init__(*m) + +class Upsample_hf(nn.Sequential): + """Upsample module. + + Args: + scale (int): Scale factor. Supported scales: 2^n and 3. + num_feat (int): Channel number of intermediate features. + """ + + def __init__(self, scale, num_feat): + m = [] + if (scale & (scale - 1)) == 0: # scale = 2^n + for _ in range(int(math.log(scale, 2))): + m.append(nn.Conv2d(num_feat, 4 * num_feat, 3, 1, 1)) + m.append(nn.PixelShuffle(2)) + elif scale == 3: + m.append(nn.Conv2d(num_feat, 9 * num_feat, 3, 1, 1)) + m.append(nn.PixelShuffle(3)) + else: + raise ValueError(f'scale {scale} is not supported. ' 'Supported scales: 2^n and 3.') + super(Upsample_hf, self).__init__(*m) + + +class UpsampleOneStep(nn.Sequential): + """UpsampleOneStep module (the difference with Upsample is that it always only has 1conv + 1pixelshuffle) + Used in lightweight SR to save parameters. + + Args: + scale (int): Scale factor. Supported scales: 2^n and 3. + num_feat (int): Channel number of intermediate features. + + """ + + def __init__(self, scale, num_feat, num_out_ch, input_resolution=None): + self.num_feat = num_feat + self.input_resolution = input_resolution + m = [] + m.append(nn.Conv2d(num_feat, (scale ** 2) * num_out_ch, 3, 1, 1)) + m.append(nn.PixelShuffle(scale)) + super(UpsampleOneStep, self).__init__(*m) + + def flops(self): + H, W = self.input_resolution + flops = H * W * self.num_feat * 3 * 9 + return flops + + + +class Swin2SR(nn.Module): + r""" Swin2SR + A PyTorch impl of : `Swin2SR: SwinV2 Transformer for Compressed Image Super-Resolution and Restoration`. + + Args: + img_size (int | tuple(int)): Input image size. Default 64 + patch_size (int | tuple(int)): Patch size. Default: 1 + in_chans (int): Number of input image channels. Default: 3 + embed_dim (int): Patch embedding dimension. Default: 96 + depths (tuple(int)): Depth of each Swin Transformer layer. + num_heads (tuple(int)): Number of attention heads in different layers. + window_size (int): Window size. Default: 7 + mlp_ratio (float): Ratio of mlp hidden dim to embedding dim. Default: 4 + qkv_bias (bool): If True, add a learnable bias to query, key, value. Default: True + drop_rate (float): Dropout rate. Default: 0 + attn_drop_rate (float): Attention dropout rate. Default: 0 + drop_path_rate (float): Stochastic depth rate. Default: 0.1 + norm_layer (nn.Module): Normalization layer. Default: nn.LayerNorm. + ape (bool): If True, add absolute position embedding to the patch embedding. Default: False + patch_norm (bool): If True, add normalization after patch embedding. Default: True + use_checkpoint (bool): Whether to use checkpointing to save memory. Default: False + upscale: Upscale factor. 2/3/4/8 for image SR, 1 for denoising and compress artifact reduction + img_range: Image range. 1. or 255. + upsampler: The reconstruction reconstruction module. 'pixelshuffle'/'pixelshuffledirect'/'nearest+conv'/None + resi_connection: The convolutional block before residual connection. '1conv'/'3conv' + """ + + def __init__(self, img_size=64, patch_size=1, in_chans=3, + embed_dim=96, depths=[6, 6, 6, 6], num_heads=[6, 6, 6, 6], + window_size=7, mlp_ratio=4., qkv_bias=True, + drop_rate=0., attn_drop_rate=0., drop_path_rate=0.1, + norm_layer=nn.LayerNorm, ape=False, patch_norm=True, + use_checkpoint=False, upscale=2, img_range=1., upsampler='', resi_connection='1conv', + **kwargs): + super(Swin2SR, self).__init__() + num_in_ch = in_chans + num_out_ch = in_chans + num_feat = 64 + self.img_range = img_range + if in_chans == 3: + rgb_mean = (0.4488, 0.4371, 0.4040) + self.mean = torch.Tensor(rgb_mean).view(1, 3, 1, 1) + else: + self.mean = torch.zeros(1, 1, 1, 1) + self.upscale = upscale + self.upsampler = upsampler + self.window_size = window_size + + ##################################################################################################### + ################################### 1, shallow feature extraction ################################### + self.conv_first = nn.Conv2d(num_in_ch, embed_dim, 3, 1, 1) + + ##################################################################################################### + ################################### 2, deep feature extraction ###################################### + self.num_layers = len(depths) + self.embed_dim = embed_dim + self.ape = ape + self.patch_norm = patch_norm + self.num_features = embed_dim + self.mlp_ratio = mlp_ratio + + # split image into non-overlapping patches + self.patch_embed = PatchEmbed( + img_size=img_size, patch_size=patch_size, in_chans=embed_dim, embed_dim=embed_dim, + norm_layer=norm_layer if self.patch_norm else None) + num_patches = self.patch_embed.num_patches + patches_resolution = self.patch_embed.patches_resolution + self.patches_resolution = patches_resolution + + # merge non-overlapping patches into image + self.patch_unembed = PatchUnEmbed( + img_size=img_size, patch_size=patch_size, in_chans=embed_dim, embed_dim=embed_dim, + norm_layer=norm_layer if self.patch_norm else None) + + # absolute position embedding + if self.ape: + self.absolute_pos_embed = nn.Parameter(torch.zeros(1, num_patches, embed_dim)) + trunc_normal_(self.absolute_pos_embed, std=.02) + + self.pos_drop = nn.Dropout(p=drop_rate) + + # stochastic depth + dpr = [x.item() for x in torch.linspace(0, drop_path_rate, sum(depths))] # stochastic depth decay rule + + # build Residual Swin Transformer blocks (RSTB) + self.layers = nn.ModuleList() + for i_layer in range(self.num_layers): + layer = RSTB(dim=embed_dim, + input_resolution=(patches_resolution[0], + patches_resolution[1]), + depth=depths[i_layer], + num_heads=num_heads[i_layer], + window_size=window_size, + mlp_ratio=self.mlp_ratio, + qkv_bias=qkv_bias, + drop=drop_rate, attn_drop=attn_drop_rate, + drop_path=dpr[sum(depths[:i_layer]):sum(depths[:i_layer + 1])], # no impact on SR results + norm_layer=norm_layer, + downsample=None, + use_checkpoint=use_checkpoint, + img_size=img_size, + patch_size=patch_size, + resi_connection=resi_connection + + ) + self.layers.append(layer) + + if self.upsampler == 'pixelshuffle_hf': + self.layers_hf = nn.ModuleList() + for i_layer in range(self.num_layers): + layer = RSTB(dim=embed_dim, + input_resolution=(patches_resolution[0], + patches_resolution[1]), + depth=depths[i_layer], + num_heads=num_heads[i_layer], + window_size=window_size, + mlp_ratio=self.mlp_ratio, + qkv_bias=qkv_bias, + drop=drop_rate, attn_drop=attn_drop_rate, + drop_path=dpr[sum(depths[:i_layer]):sum(depths[:i_layer + 1])], # no impact on SR results + norm_layer=norm_layer, + downsample=None, + use_checkpoint=use_checkpoint, + img_size=img_size, + patch_size=patch_size, + resi_connection=resi_connection + + ) + self.layers_hf.append(layer) + + self.norm = norm_layer(self.num_features) + + # build the last conv layer in deep feature extraction + if resi_connection == '1conv': + self.conv_after_body = nn.Conv2d(embed_dim, embed_dim, 3, 1, 1) + elif resi_connection == '3conv': + # to save parameters and memory + self.conv_after_body = nn.Sequential(nn.Conv2d(embed_dim, embed_dim // 4, 3, 1, 1), + nn.LeakyReLU(negative_slope=0.2, inplace=True), + nn.Conv2d(embed_dim // 4, embed_dim // 4, 1, 1, 0), + nn.LeakyReLU(negative_slope=0.2, inplace=True), + nn.Conv2d(embed_dim // 4, embed_dim, 3, 1, 1)) + + ##################################################################################################### + ################################ 3, high quality image reconstruction ################################ + if self.upsampler == 'pixelshuffle': + # for classical SR + self.conv_before_upsample = nn.Sequential(nn.Conv2d(embed_dim, num_feat, 3, 1, 1), + nn.LeakyReLU(inplace=True)) + self.upsample = Upsample(upscale, num_feat) + self.conv_last = nn.Conv2d(num_feat, num_out_ch, 3, 1, 1) + elif self.upsampler == 'pixelshuffle_aux': + self.conv_bicubic = nn.Conv2d(num_in_ch, num_feat, 3, 1, 1) + self.conv_before_upsample = nn.Sequential( + nn.Conv2d(embed_dim, num_feat, 3, 1, 1), + nn.LeakyReLU(inplace=True)) + self.conv_aux = nn.Conv2d(num_feat, num_out_ch, 3, 1, 1) + self.conv_after_aux = nn.Sequential( + nn.Conv2d(3, num_feat, 3, 1, 1), + nn.LeakyReLU(inplace=True)) + self.upsample = Upsample(upscale, num_feat) + self.conv_last = nn.Conv2d(num_feat, num_out_ch, 3, 1, 1) + + elif self.upsampler == 'pixelshuffle_hf': + self.conv_before_upsample = nn.Sequential(nn.Conv2d(embed_dim, num_feat, 3, 1, 1), + nn.LeakyReLU(inplace=True)) + self.upsample = Upsample(upscale, num_feat) + self.upsample_hf = Upsample_hf(upscale, num_feat) + self.conv_last = nn.Conv2d(num_feat, num_out_ch, 3, 1, 1) + self.conv_first_hf = nn.Sequential(nn.Conv2d(num_feat, embed_dim, 3, 1, 1), + nn.LeakyReLU(inplace=True)) + self.conv_after_body_hf = nn.Conv2d(embed_dim, embed_dim, 3, 1, 1) + self.conv_before_upsample_hf = nn.Sequential( + nn.Conv2d(embed_dim, num_feat, 3, 1, 1), + nn.LeakyReLU(inplace=True)) + self.conv_last_hf = nn.Conv2d(num_feat, num_out_ch, 3, 1, 1) + + elif self.upsampler == 'pixelshuffledirect': + # for lightweight SR (to save parameters) + self.upsample = UpsampleOneStep(upscale, embed_dim, num_out_ch, + (patches_resolution[0], patches_resolution[1])) + elif self.upsampler == 'nearest+conv': + # for real-world SR (less artifacts) + assert self.upscale == 4, 'only support x4 now.' + self.conv_before_upsample = nn.Sequential(nn.Conv2d(embed_dim, num_feat, 3, 1, 1), + nn.LeakyReLU(inplace=True)) + self.conv_up1 = nn.Conv2d(num_feat, num_feat, 3, 1, 1) + self.conv_up2 = nn.Conv2d(num_feat, num_feat, 3, 1, 1) + self.conv_hr = nn.Conv2d(num_feat, num_feat, 3, 1, 1) + self.conv_last = nn.Conv2d(num_feat, num_out_ch, 3, 1, 1) + self.lrelu = nn.LeakyReLU(negative_slope=0.2, inplace=True) + else: + # for image denoising and JPEG compression artifact reduction + self.conv_last = nn.Conv2d(embed_dim, num_out_ch, 3, 1, 1) + + self.apply(self._init_weights) + + def _init_weights(self, m): + if isinstance(m, nn.Linear): + trunc_normal_(m.weight, std=.02) + if isinstance(m, nn.Linear) and m.bias is not None: + nn.init.constant_(m.bias, 0) + elif isinstance(m, nn.LayerNorm): + nn.init.constant_(m.bias, 0) + nn.init.constant_(m.weight, 1.0) + + @torch.jit.ignore + def no_weight_decay(self): + return {'absolute_pos_embed'} + + @torch.jit.ignore + def no_weight_decay_keywords(self): + return {'relative_position_bias_table'} + + def check_image_size(self, x): + _, _, h, w = x.size() + mod_pad_h = (self.window_size - h % self.window_size) % self.window_size + mod_pad_w = (self.window_size - w % self.window_size) % self.window_size + x = F.pad(x, (0, mod_pad_w, 0, mod_pad_h), 'reflect') + return x + + def forward_features(self, x): + x_size = (x.shape[2], x.shape[3]) + x = self.patch_embed(x) + if self.ape: + x = x + self.absolute_pos_embed + x = self.pos_drop(x) + + for layer in self.layers: + x = layer(x, x_size) + + x = self.norm(x) # B L C + x = self.patch_unembed(x, x_size) + + return x + + def forward_features_hf(self, x): + x_size = (x.shape[2], x.shape[3]) + x = self.patch_embed(x) + if self.ape: + x = x + self.absolute_pos_embed + x = self.pos_drop(x) + + for layer in self.layers_hf: + x = layer(x, x_size) + + x = self.norm(x) # B L C + x = self.patch_unembed(x, x_size) + + return x + + def forward(self, x): + H, W = x.shape[2:] + x = self.check_image_size(x) + + self.mean = self.mean.type_as(x) + x = (x - self.mean) * self.img_range + + if self.upsampler == 'pixelshuffle': + # for classical SR + x = self.conv_first(x) + x = self.conv_after_body(self.forward_features(x)) + x + x = self.conv_before_upsample(x) + x = self.conv_last(self.upsample(x)) + elif self.upsampler == 'pixelshuffle_aux': + bicubic = F.interpolate(x, size=(H * self.upscale, W * self.upscale), mode='bicubic', align_corners=False) + bicubic = self.conv_bicubic(bicubic) + x = self.conv_first(x) + x = self.conv_after_body(self.forward_features(x)) + x + x = self.conv_before_upsample(x) + aux = self.conv_aux(x) # b, 3, LR_H, LR_W + x = self.conv_after_aux(aux) + x = self.upsample(x)[:, :, :H * self.upscale, :W * self.upscale] + bicubic[:, :, :H * self.upscale, :W * self.upscale] + x = self.conv_last(x) + aux = aux / self.img_range + self.mean + elif self.upsampler == 'pixelshuffle_hf': + # for classical SR with HF + x = self.conv_first(x) + x = self.conv_after_body(self.forward_features(x)) + x + x_before = self.conv_before_upsample(x) + x_out = self.conv_last(self.upsample(x_before)) + + x_hf = self.conv_first_hf(x_before) + x_hf = self.conv_after_body_hf(self.forward_features_hf(x_hf)) + x_hf + x_hf = self.conv_before_upsample_hf(x_hf) + x_hf = self.conv_last_hf(self.upsample_hf(x_hf)) + x = x_out + x_hf + x_hf = x_hf / self.img_range + self.mean + + elif self.upsampler == 'pixelshuffledirect': + # for lightweight SR + x = self.conv_first(x) + x = self.conv_after_body(self.forward_features(x)) + x + x = self.upsample(x) + elif self.upsampler == 'nearest+conv': + # for real-world SR + x = self.conv_first(x) + x = self.conv_after_body(self.forward_features(x)) + x + x = self.conv_before_upsample(x) + x = self.lrelu(self.conv_up1(torch.nn.functional.interpolate(x, scale_factor=2, mode='nearest'))) + x = self.lrelu(self.conv_up2(torch.nn.functional.interpolate(x, scale_factor=2, mode='nearest'))) + x = self.conv_last(self.lrelu(self.conv_hr(x))) + else: + # for image denoising and JPEG compression artifact reduction + x_first = self.conv_first(x) + res = self.conv_after_body(self.forward_features(x_first)) + x_first + x = x + self.conv_last(res) + + x = x / self.img_range + self.mean + if self.upsampler == "pixelshuffle_aux": + return x[:, :, :H*self.upscale, :W*self.upscale], aux + + elif self.upsampler == "pixelshuffle_hf": + x_out = x_out / self.img_range + self.mean + return x_out[:, :, :H*self.upscale, :W*self.upscale], x[:, :, :H*self.upscale, :W*self.upscale], x_hf[:, :, :H*self.upscale, :W*self.upscale] + + else: + return x[:, :, :H*self.upscale, :W*self.upscale] + + def flops(self): + flops = 0 + H, W = self.patches_resolution + flops += H * W * 3 * self.embed_dim * 9 + flops += self.patch_embed.flops() + for i, layer in enumerate(self.layers): + flops += layer.flops() + flops += H * W * 3 * self.embed_dim * self.embed_dim + flops += self.upsample.flops() + return flops + + +if __name__ == '__main__': + upscale = 4 + window_size = 8 + height = (1024 // upscale // window_size + 1) * window_size + width = (720 // upscale // window_size + 1) * window_size + model = Swin2SR(upscale=2, img_size=(height, width), + window_size=window_size, img_range=1., depths=[6, 6, 6, 6], + embed_dim=60, num_heads=[6, 6, 6, 6], mlp_ratio=2, upsampler='pixelshuffledirect') + print(model) + print(height, width, model.flops() / 1e9) + + x = torch.randn((1, 3, height, width)) + x = model(x) + print(x.shape) \ No newline at end of file -- cgit v1.2.1 From ed769977f0d0f201d8e361d365102f18775fc62c Mon Sep 17 00:00:00 2001 From: C43H66N12O12S2 <36072735+C43H66N12O12S2@users.noreply.github.com> Date: Sun, 9 Oct 2022 18:56:59 +0300 Subject: add swinir v2 support --- modules/swinir_model.py | 35 ++++++++++++++++++++++++++++------- 1 file changed, 28 insertions(+), 7 deletions(-) (limited to 'modules') diff --git a/modules/swinir_model.py b/modules/swinir_model.py index fbd11f84..baa02e3d 100644 --- a/modules/swinir_model.py +++ b/modules/swinir_model.py @@ -10,6 +10,7 @@ from tqdm import tqdm from modules import modelloader from modules.shared import cmd_opts, opts, device from modules.swinir_model_arch import SwinIR as net +from modules.swinir_model_arch_v2 import Swin2SR as net2 from modules.upscaler import Upscaler, UpscalerData precision_scope = ( @@ -57,22 +58,42 @@ class UpscalerSwinIR(Upscaler): filename = path if filename is None or not os.path.exists(filename): return None - model = net( + if filename.endswith(".v2.pth"): + model = net2( upscale=scale, in_chans=3, img_size=64, window_size=8, img_range=1.0, - depths=[6, 6, 6, 6, 6, 6, 6, 6, 6], - embed_dim=240, - num_heads=[8, 8, 8, 8, 8, 8, 8, 8, 8], + depths=[6, 6, 6, 6, 6, 6], + embed_dim=180, + num_heads=[6, 6, 6, 6, 6, 6], mlp_ratio=2, upsampler="nearest+conv", - resi_connection="3conv", - ) + resi_connection="1conv", + ) + params = None + else: + model = net( + upscale=scale, + in_chans=3, + img_size=64, + window_size=8, + img_range=1.0, + depths=[6, 6, 6, 6, 6, 6, 6, 6, 6], + embed_dim=240, + num_heads=[8, 8, 8, 8, 8, 8, 8, 8, 8], + mlp_ratio=2, + upsampler="nearest+conv", + resi_connection="3conv", + ) + params = "params_ema" pretrained_model = torch.load(filename) - model.load_state_dict(pretrained_model["params_ema"], strict=True) + if params is not None: + model.load_state_dict(pretrained_model[params], strict=True) + else: + model.load_state_dict(pretrained_model, strict=True) if not cmd_opts.no_half: model = model.half() return model -- cgit v1.2.1 From af62ad4d25dcd0454944368f4925d83101cdedbc Mon Sep 17 00:00:00 2001 From: ssysm Date: Mon, 10 Oct 2022 13:25:28 -0400 Subject: change vae loading method --- modules/sd_models.py | 11 +++++++++-- 1 file changed, 9 insertions(+), 2 deletions(-) (limited to 'modules') diff --git a/modules/sd_models.py b/modules/sd_models.py index b0e1d8bd..7a42d924 100644 --- a/modules/sd_models.py +++ b/modules/sd_models.py @@ -150,9 +150,16 @@ def load_model_weights(model, checkpoint_info): devices.dtype = torch.float32 if shared.cmd_opts.no_half else torch.float16 - vae_file = shared.cmd_opts.vae_path or os.path.splitext(checkpoint_file)[0] + ".vae.pt" + vae_file = os.path.splitext(checkpoint_file)[0] + ".vae.pt" + if os.path.exists(vae_file): + print(f"Found VAE Weights: {vae_file}") + elif shared.cmd_opts.vae_path != None: + vae_file = shared.cmd_opts.vae_path + print(f'No VAE found for inside the model folder. Using CLI specified : {vae_file}') + else: + print("No VAE found for inside the model folder. Passing.") + if os.path.exists(vae_file): - print(f"Loading VAE weights from: {vae_file}") vae_ckpt = torch.load(vae_file, map_location="cpu") vae_dict = {k: v for k, v in vae_ckpt["state_dict"].items() if k[0:4] != "loss"} -- cgit v1.2.1 From 39919c40dd18f5a14ae21403efea1b0f819756c7 Mon Sep 17 00:00:00 2001 From: AUTOMATIC <16777216c@gmail.com> Date: Mon, 10 Oct 2022 20:32:37 +0300 Subject: add eta noise seed delta option --- modules/processing.py | 6 +++++- modules/shared.py | 1 + 2 files changed, 6 insertions(+), 1 deletion(-) (limited to 'modules') diff --git a/modules/processing.py b/modules/processing.py index 50ba4fc5..698b3069 100644 --- a/modules/processing.py +++ b/modules/processing.py @@ -207,7 +207,7 @@ def create_random_tensors(shape, seeds, subseeds=None, subseed_strength=0.0, see # enables the generation of additional tensors with noise that the sampler will use during its processing. # Using those pre-generated tensors instead of simple torch.randn allows a batch with seeds [100, 101] to # produce the same images as with two batches [100], [101]. - if p is not None and p.sampler is not None and len(seeds) > 1 and opts.enable_batch_seeds: + if p is not None and p.sampler is not None and (len(seeds) > 1 and opts.enable_batch_seeds or opts.eta_noise_seed_delta > 0): sampler_noises = [[] for _ in range(p.sampler.number_of_needed_noises(p))] else: sampler_noises = None @@ -247,6 +247,9 @@ def create_random_tensors(shape, seeds, subseeds=None, subseed_strength=0.0, see if sampler_noises is not None: cnt = p.sampler.number_of_needed_noises(p) + if opts.eta_noise_seed_delta > 0: + torch.manual_seed(seed + opts.eta_noise_seed_delta) + for j in range(cnt): sampler_noises[j].append(devices.randn_without_seed(tuple(noise_shape))) @@ -301,6 +304,7 @@ def create_infotext(p, all_prompts, all_seeds, all_subseeds, comments, iteration "Denoising strength": getattr(p, 'denoising_strength', None), "Eta": (None if p.sampler is None or p.sampler.eta == p.sampler.default_eta else p.sampler.eta), "Clip skip": None if clip_skip <= 1 else clip_skip, + "ENSD": None if opts.eta_noise_seed_delta == 0 else opts.eta_noise_seed_delta, } generation_params.update(p.extra_generation_params) diff --git a/modules/shared.py b/modules/shared.py index 5dfc344c..b1c65ecf 100644 --- a/modules/shared.py +++ b/modules/shared.py @@ -260,6 +260,7 @@ options_templates.update(options_section(('sampler-params', "Sampler parameters" 's_churn': OptionInfo(0.0, "sigma churn", gr.Slider, {"minimum": 0.0, "maximum": 1.0, "step": 0.01}), 's_tmin': OptionInfo(0.0, "sigma tmin", gr.Slider, {"minimum": 0.0, "maximum": 1.0, "step": 0.01}), 's_noise': OptionInfo(1.0, "sigma noise", gr.Slider, {"minimum": 0.0, "maximum": 1.0, "step": 0.01}), + 'eta_noise_seed_delta': OptionInfo(0, "Eta noise seed delta", gr.Number, {"precision": 0}), })) -- cgit v1.2.1 From 727e4d108674dc2813507e2a973a733ef21e8d53 Mon Sep 17 00:00:00 2001 From: AUTOMATIC <16777216c@gmail.com> Date: Mon, 10 Oct 2022 20:46:55 +0300 Subject: no to different messages plus fix using != to compare to None --- modules/sd_models.py | 9 +++------ 1 file changed, 3 insertions(+), 6 deletions(-) (limited to 'modules') diff --git a/modules/sd_models.py b/modules/sd_models.py index 4c06051e..0a55b4c3 100644 --- a/modules/sd_models.py +++ b/modules/sd_models.py @@ -152,15 +152,12 @@ def load_model_weights(model, checkpoint_info): devices.dtype_vae = torch.float32 if shared.cmd_opts.no_half or shared.cmd_opts.no_half_vae else torch.float16 vae_file = os.path.splitext(checkpoint_file)[0] + ".vae.pt" - if os.path.exists(vae_file): - print(f"Found VAE Weights: {vae_file}") - elif shared.cmd_opts.vae_path != None: + + if not os.path.exists(vae_file) and shared.cmd_opts.vae_path is not None: vae_file = shared.cmd_opts.vae_path - print(f'No VAE found for inside the model folder. Using CLI specified : {vae_file}') - else: - print("No VAE found for inside the model folder. Passing.") if os.path.exists(vae_file): + print(f"Loading VAE weights from: {vae_file}") vae_ckpt = torch.load(vae_file, map_location="cpu") vae_dict = {k: v for k, v in vae_ckpt["state_dict"].items() if k[0:4] != "loss"} -- cgit v1.2.1 From 1d64976dbc5a0f3124567b91fadd5014a9d93c5f Mon Sep 17 00:00:00 2001 From: Justin Maier Date: Mon, 10 Oct 2022 12:04:21 -0600 Subject: Simplify crop logic --- modules/extras.py | 14 +++----------- modules/ui.py | 4 ++-- 2 files changed, 5 insertions(+), 13 deletions(-) (limited to 'modules') diff --git a/modules/extras.py b/modules/extras.py index 83ca7049..b24d7de3 100644 --- a/modules/extras.py +++ b/modules/extras.py @@ -73,16 +73,6 @@ def run_extras(extras_mode, resize_mode, image, image_folder, gfpgan_visibility, crop_info = " (crop)" if upscaling_crop else "" info += f"Resize to: {upscaling_resize_w:g}x{upscaling_resize_h:g}{crop_info}\n" - def crop_upscaled_center(image, resize_w, resize_h): - left = int(math.ceil((image.width - resize_w) / 2)) - right = image.width - int(math.floor((image.width - resize_w) / 2)) - top = int(math.ceil((image.height - resize_h) / 2)) - bottom = image.height - int(math.floor((image.height - resize_h) / 2)) - - image = image.crop((left, top, right, bottom)) - return image - - if upscaling_resize != 1.0: def upscale(image, scaler_index, resize, mode, resize_w, resize_h, crop): small = image.crop((image.width // 2, image.height // 2, image.width // 2 + 10, image.height // 2 + 10)) @@ -94,7 +84,9 @@ def run_extras(extras_mode, resize_mode, image, image_folder, gfpgan_visibility, upscaler = shared.sd_upscalers[scaler_index] c = upscaler.scaler.upscale(image, resize, upscaler.data_path) if mode == 1 and crop: - c = crop_upscaled_center(c, resize_w, resize_h) + cropped = Image.new("RGB", (resize_w, resize_h)) + cropped.paste(c, box=(resize_w // 2 - c.width // 2, resize_h // 2 - c.height // 2)) + c = cropped cached_images[key] = c return c diff --git a/modules/ui.py b/modules/ui.py index 4bb2892b..1aabe18d 100644 --- a/modules/ui.py +++ b/modules/ui.py @@ -909,8 +909,8 @@ def create_ui(wrap_gradio_gpu_call): with gr.TabItem('Scale to'): with gr.Group(): with gr.Row(): - upscaling_resize_w = gr.Number(label="Width", value=512) - upscaling_resize_h = gr.Number(label="Height", value=512) + upscaling_resize_w = gr.Number(label="Width", value=512, precision=0) + upscaling_resize_h = gr.Number(label="Height", value=512, precision=0) upscaling_crop = gr.Checkbox(label='Crop to fit', value=True) with gr.Group(): -- cgit v1.2.1 From bc3e183b739913e7be91213a256f038b10eb71e9 Mon Sep 17 00:00:00 2001 From: alg-wiki Date: Tue, 11 Oct 2022 04:30:13 +0900 Subject: Textual Inversion: Preprocess and Training will only pick-up image files --- modules/textual_inversion/dataset.py | 3 ++- modules/textual_inversion/preprocess.py | 3 ++- modules/textual_inversion/textual_inversion.py | 3 ++- 3 files changed, 6 insertions(+), 3 deletions(-) (limited to 'modules') diff --git a/modules/textual_inversion/dataset.py b/modules/textual_inversion/dataset.py index bcf772d2..d4baf066 100644 --- a/modules/textual_inversion/dataset.py +++ b/modules/textual_inversion/dataset.py @@ -22,6 +22,7 @@ class PersonalizedBase(Dataset): self.width = width self.height = height self.flip = transforms.RandomHorizontalFlip(p=flip_p) + self.extns = [".jpg",".jpeg",".png"] self.dataset = [] @@ -32,7 +33,7 @@ class PersonalizedBase(Dataset): assert data_root, 'dataset directory not specified' - self.image_paths = [os.path.join(data_root, file_path) for file_path in os.listdir(data_root)] + self.image_paths = [os.path.join(data_root, file_path) for file_path in os.listdir(data_root) if os.path.splitext(file_path.casefold())[1] in self.extns] print("Preparing dataset...") for path in tqdm.tqdm(self.image_paths): image = Image.open(path) diff --git a/modules/textual_inversion/preprocess.py b/modules/textual_inversion/preprocess.py index d7efdef2..b6c78cf8 100644 --- a/modules/textual_inversion/preprocess.py +++ b/modules/textual_inversion/preprocess.py @@ -12,12 +12,13 @@ def preprocess(process_src, process_dst, process_width, process_height, process_ height = process_height src = os.path.abspath(process_src) dst = os.path.abspath(process_dst) + extns = [".jpg",".jpeg",".png"] assert src != dst, 'same directory specified as source and destination' os.makedirs(dst, exist_ok=True) - files = os.listdir(src) + files = [i for i in os.listdir(src) if os.path.splitext(i.casefold())[1] in extns] shared.state.textinfo = "Preprocessing..." shared.state.job_count = len(files) diff --git a/modules/textual_inversion/textual_inversion.py b/modules/textual_inversion/textual_inversion.py index 5965c5a0..45397be9 100644 --- a/modules/textual_inversion/textual_inversion.py +++ b/modules/textual_inversion/textual_inversion.py @@ -161,6 +161,7 @@ def train_embedding(embedding_name, learn_rate, data_root, log_directory, traini shared.state.textinfo = "Initializing textual inversion training..." shared.state.job_count = steps + extns = [".jpg",".jpeg",".png"] filename = os.path.join(shared.cmd_opts.embeddings_dir, f'{embedding_name}.pt') @@ -200,7 +201,7 @@ def train_embedding(embedding_name, learn_rate, data_root, log_directory, traini if ititial_step > steps: return embedding, filename - tr_img_len = len([os.path.join(data_root, file_path) for file_path in os.listdir(data_root)]) + tr_img_len = len([os.path.join(data_root, file_path) for file_path in os.listdir(data_root) if os.path.splitext(file_path.casefold())[1] in extns]) epoch_len = (tr_img_len * num_repeats) + tr_img_len pbar = tqdm.tqdm(enumerate(ds), total=steps-ititial_step) -- cgit v1.2.1 From f98338faa84ecce503e68d8ba13d5f7bbae52730 Mon Sep 17 00:00:00 2001 From: AUTOMATIC <16777216c@gmail.com> Date: Mon, 10 Oct 2022 23:15:48 +0300 Subject: add an option to not add watermark to created images --- modules/shared.py | 1 + 1 file changed, 1 insertion(+) (limited to 'modules') diff --git a/modules/shared.py b/modules/shared.py index da389f9c..ecd15ef5 100644 --- a/modules/shared.py +++ b/modules/shared.py @@ -173,6 +173,7 @@ options_templates.update(options_section(('saving-images', "Saving images/grids" "use_original_name_batch": OptionInfo(False, "Use original name for output filename during batch process in extras tab"), "save_selected_only": OptionInfo(True, "When using 'Save' button, only save a single selected image"), + "do_not_add_watermark": OptionInfo(False, "Do not add watermark to images"), })) options_templates.update(options_section(('saving-paths', "Paths for saving"), { -- cgit v1.2.1 From 2536ecbb1790da2af0d61b6a26f38732cba665cd Mon Sep 17 00:00:00 2001 From: Fampai <> Date: Mon, 10 Oct 2022 17:10:29 -0400 Subject: Refactored learning rate code --- modules/textual_inversion/textual_inversion.py | 51 ++++++++++++++++++++++++-- modules/ui.py | 2 +- 2 files changed, 48 insertions(+), 5 deletions(-) (limited to 'modules') diff --git a/modules/textual_inversion/textual_inversion.py b/modules/textual_inversion/textual_inversion.py index 5965c5a0..c64a4598 100644 --- a/modules/textual_inversion/textual_inversion.py +++ b/modules/textual_inversion/textual_inversion.py @@ -189,8 +189,6 @@ def train_embedding(embedding_name, learn_rate, data_root, log_directory, traini embedding = hijack.embedding_db.word_embeddings[embedding_name] embedding.vec.requires_grad = True - optimizer = torch.optim.AdamW([embedding.vec], lr=learn_rate) - losses = torch.zeros((32,)) last_saved_file = "" @@ -203,12 +201,24 @@ def train_embedding(embedding_name, learn_rate, data_root, log_directory, traini tr_img_len = len([os.path.join(data_root, file_path) for file_path in os.listdir(data_root)]) epoch_len = (tr_img_len * num_repeats) + tr_img_len + scheduleIter = iter(LearnSchedule(learn_rate, steps, ititial_step)) + (learn_rate, end_step) = next(scheduleIter) + print(f'Training at rate of {learn_rate} until step {end_step}') + + optimizer = torch.optim.AdamW([embedding.vec], lr=learn_rate) + pbar = tqdm.tqdm(enumerate(ds), total=steps-ititial_step) for i, (x, text) in pbar: embedding.step = i + ititial_step - if embedding.step > steps: - break + if embedding.step > end_step: + try: + (learn_rate, end_step) = next(scheduleIter) + except: + break + tqdm.tqdm.write(f'Training at rate of {learn_rate} until step {end_step}') + for pg in optimizer.param_groups: + pg['lr'] = learn_rate if shared.state.interrupted: break @@ -277,3 +287,36 @@ Last saved image: {html.escape(last_saved_image)}
return embedding, filename +class LearnSchedule: + def __init__(self, learn_rate, max_steps, cur_step=0): + pairs = learn_rate.split(',') + self.rates = [] + self.it = 0 + self.maxit = 0 + for i, pair in enumerate(pairs): + tmp = pair.split(':') + if len(tmp) == 2: + step = int(tmp[1]) + if step > cur_step: + self.rates.append((float(tmp[0]), min(step, max_steps))) + self.maxit += 1 + if step > max_steps: + return + elif step == -1: + self.rates.append((float(tmp[0]), max_steps)) + self.maxit += 1 + return + else: + self.rates.append((float(tmp[0]), max_steps)) + self.maxit += 1 + return + + def __iter__(self): + return self + + def __next__(self): + if self.it < self.maxit: + self.it += 1 + return self.rates[self.it - 1] + else: + raise StopIteration diff --git a/modules/ui.py b/modules/ui.py index 8c06ad7c..c9e8355b 100644 --- a/modules/ui.py +++ b/modules/ui.py @@ -1047,7 +1047,7 @@ def create_ui(wrap_gradio_gpu_call): with gr.Group(): gr.HTML(value="

Train an embedding; must specify a directory with a set of 1:1 ratio images

") train_embedding_name = gr.Dropdown(label='Embedding', choices=sorted(sd_hijack.model_hijack.embedding_db.word_embeddings.keys())) - learn_rate = gr.Number(label='Learning rate', value=5.0e-03) + learn_rate = gr.Textbox(label='Learning rate', placeholder="Learning rate", value = "5.0e-03") dataset_directory = gr.Textbox(label='Dataset directory', placeholder="Path to directory with input images") log_directory = gr.Textbox(label='Log directory', placeholder="Path to directory where to write outputs", value="textual_inversion") template_file = gr.Textbox(label='Prompt template file', value=os.path.join(script_path, "textual_inversion_templates", "style_filewords.txt")) -- cgit v1.2.1 From 907a88b2d0be320575c2129d8d6a1d4f3a68f9eb Mon Sep 17 00:00:00 2001 From: alg-wiki Date: Tue, 11 Oct 2022 06:33:08 +0900 Subject: Added .webp .bmp --- modules/textual_inversion/dataset.py | 2 +- modules/textual_inversion/preprocess.py | 2 +- modules/textual_inversion/textual_inversion.py | 2 +- 3 files changed, 3 insertions(+), 3 deletions(-) (limited to 'modules') diff --git a/modules/textual_inversion/dataset.py b/modules/textual_inversion/dataset.py index d4baf066..0dc54fb7 100644 --- a/modules/textual_inversion/dataset.py +++ b/modules/textual_inversion/dataset.py @@ -22,7 +22,7 @@ class PersonalizedBase(Dataset): self.width = width self.height = height self.flip = transforms.RandomHorizontalFlip(p=flip_p) - self.extns = [".jpg",".jpeg",".png"] + self.extns = [".jpg",".jpeg",".png",".webp",".bmp"] self.dataset = [] diff --git a/modules/textual_inversion/preprocess.py b/modules/textual_inversion/preprocess.py index b6c78cf8..8290abe8 100644 --- a/modules/textual_inversion/preprocess.py +++ b/modules/textual_inversion/preprocess.py @@ -12,7 +12,7 @@ def preprocess(process_src, process_dst, process_width, process_height, process_ height = process_height src = os.path.abspath(process_src) dst = os.path.abspath(process_dst) - extns = [".jpg",".jpeg",".png"] + extns = [".jpg",".jpeg",".png",".webp",".bmp"] assert src != dst, 'same directory specified as source and destination' diff --git a/modules/textual_inversion/textual_inversion.py b/modules/textual_inversion/textual_inversion.py index a03b299c..33c923d1 100644 --- a/modules/textual_inversion/textual_inversion.py +++ b/modules/textual_inversion/textual_inversion.py @@ -161,7 +161,7 @@ def train_embedding(embedding_name, learn_rate, data_root, log_directory, traini shared.state.textinfo = "Initializing textual inversion training..." shared.state.job_count = steps - extns = [".jpg",".jpeg",".png"] + extns = [".jpg",".jpeg",".png",".webp",".bmp"] filename = os.path.join(shared.cmd_opts.embeddings_dir, f'{embedding_name}.pt') -- cgit v1.2.1 From a1a05ad2d13d0b995dbf8ecead6315f17837ef81 Mon Sep 17 00:00:00 2001 From: JC_Array Date: Mon, 10 Oct 2022 16:47:58 -0500 Subject: import time missing, added to deepbooru fixxing error on get_deepbooru_tags --- modules/deepbooru.py | 1 + 1 file changed, 1 insertion(+) (limited to 'modules') diff --git a/modules/deepbooru.py b/modules/deepbooru.py index cee4a3b4..12555b2e 100644 --- a/modules/deepbooru.py +++ b/modules/deepbooru.py @@ -1,6 +1,7 @@ import os.path from concurrent.futures import ProcessPoolExecutor import multiprocessing +import time def get_deepbooru_tags(pil_image, threshold=0.5): -- cgit v1.2.1 From b980e7188c671fc55b26557f097076fb5c976ba0 Mon Sep 17 00:00:00 2001 From: JC_Array Date: Mon, 10 Oct 2022 16:52:54 -0500 Subject: corrected tag return in get_deepbooru_tags --- modules/deepbooru.py | 1 - 1 file changed, 1 deletion(-) (limited to 'modules') diff --git a/modules/deepbooru.py b/modules/deepbooru.py index 12555b2e..ebdba5e0 100644 --- a/modules/deepbooru.py +++ b/modules/deepbooru.py @@ -15,7 +15,6 @@ def get_deepbooru_tags(pil_image, threshold=0.5): while shared.deepbooru_process_return["value"] == -1: time.sleep(0.2) release_process() - return ret def deepbooru_process(queue, deepbooru_process_return, threshold): -- cgit v1.2.1 From 315d5a8ed975c88f670bc484f40a23fbf3a77b63 Mon Sep 17 00:00:00 2001 From: DepFA <35278260+dfaker@users.noreply.github.com> Date: Mon, 10 Oct 2022 23:14:44 +0100 Subject: update data dis[play style --- modules/textual_inversion/textual_inversion.py | 88 +++++++++++++++++++------- 1 file changed, 65 insertions(+), 23 deletions(-) (limited to 'modules') diff --git a/modules/textual_inversion/textual_inversion.py b/modules/textual_inversion/textual_inversion.py index 667a7cf2..95eebea7 100644 --- a/modules/textual_inversion/textual_inversion.py +++ b/modules/textual_inversion/textual_inversion.py @@ -39,20 +39,59 @@ def embeddingFromB64(data): d = base64.b64decode(data) return json.loads(d,cls=EmbeddingDecoder) -def appendImageDataFooter(image,data): +def xorBlock(block): + return np.bitwise_xor(block.astype(np.uint8), + ((np.random.RandomState(0xDEADBEEF).random(block.shape)*255).astype(np.uint8)) & 0x0F ) + +def styleBlock(block,sequence): + im = Image.new('RGB',(block.shape[1],block.shape[0])) + draw = ImageDraw.Draw(im) + i=0 + for x in range(-6,im.size[0],8): + for yi,y in enumerate(range(-6,im.size[1],8)): + offset=0 + if yi%2==0: + offset=4 + shade = sequence[i%len(sequence)] + i+=1 + draw.ellipse((x+offset, y, x+6+offset, y+6), fill =(shade,shade,shade) ) + + fg = np.array(im).astype(np.uint8) & 0xF0 + return block ^ fg + +def insertImageDataEmbed(image,data): d = 3 data_compressed = zlib.compress( json.dumps(data,cls=EmbeddingEncoder).encode(),level=9) dnp = np.frombuffer(data_compressed,np.uint8).copy() - w = image.size[0] - next_size = dnp.shape[0] + (w-(dnp.shape[0]%w)) - next_size = next_size + ((w*d)-(next_size%(w*d))) - dnp.resize(next_size) - dnp = dnp.reshape((-1,w,d)) - print(dnp.shape) - im = Image.fromarray(dnp,mode='RGB') - background = Image.new('RGB',(image.size[0],image.size[1]+im.size[1]+1),(0,0,0)) - background.paste(image,(0,0)) - background.paste(im,(0,image.size[1]+1)) + dnphigh = dnp >> 4 + dnplow = dnp & 0x0F + + h = image.size[1] + next_size = dnplow.shape[0] + (h-(dnplow.shape[0]%h)) + next_size = next_size + ((h*d)-(next_size%(h*d))) + + dnplow.resize(next_size) + dnplow = dnplow.reshape((h,-1,d)) + + dnphigh.resize(next_size) + dnphigh = dnphigh.reshape((h,-1,d)) + + edgeStyleWeights = list(data['string_to_param'].values())[0].cpu().detach().numpy().tolist()[0][:1024] + edgeStyleWeights = (np.abs(edgeStyleWeights)/np.max(np.abs(edgeStyleWeights))*255).astype(np.uint8) + + dnplow = styleBlock(dnplow,sequence=edgeStyleWeights) + dnplow = xorBlock(dnplow) + dnphigh = styleBlock(dnphigh,sequence=edgeStyleWeights[::-1]) + dnphigh = xorBlock(dnphigh) + + imlow = Image.fromarray(dnplow,mode='RGB') + imhigh = Image.fromarray(dnphigh,mode='RGB') + + background = Image.new('RGB',(image.size[0]+imlow.size[0]+imhigh.size[0]+2,image.size[1]),(0,0,0)) + background.paste(imlow,(0,0)) + background.paste(image,(imlow.size[0]+1,0)) + background.paste(imhigh,(imlow.size[0]+1+image.size[0]+1,0)) + return background def crop_black(img,tol=0): @@ -62,19 +101,22 @@ def crop_black(img,tol=0): row_start,row_end = mask1.argmax(),mask.shape[0]-mask1[::-1].argmax() return img[row_start:row_end,col_start:col_end] -def extractImageDataFooter(image): +def extractImageDataEmbed(image): d=3 - outarr = crop_black(np.array(image.convert('RGB').getdata()).reshape(image.size[1],image.size[0],d ).astype(np.uint8) ) - lastRow = np.where( np.sum(outarr, axis=(1,2))==0) - if lastRow[0].shape[0] == 0: - print('Image data block not found.') + outarr = crop_black(np.array(image.getdata()).reshape(image.size[1],image.size[0],d ).astype(np.uint8) ) & 0x0F + blackCols = np.where( np.sum(outarr, axis=(0,2))==0) + if blackCols[0].shape[0] < 2: + print('No Image data blocks found.') return None - lastRow = lastRow[0] - - lastRow = lastRow.max() - dataBlock = outarr[lastRow+1::].astype(np.uint8).flatten().tobytes() - print(lastRow) + dataBlocklower = outarr[:,:blackCols[0].min(),:].astype(np.uint8) + dataBlockupper = outarr[:,blackCols[0].max()+1:,:].astype(np.uint8) + + dataBlocklower = xorBlock(dataBlocklower) + dataBlockupper = xorBlock(dataBlockupper) + + dataBlock = (dataBlockupper << 4) | (dataBlocklower) + dataBlock = dataBlock.flatten().tobytes() data = zlib.decompress(dataBlock) return json.loads(data,cls=EmbeddingDecoder) @@ -154,7 +196,7 @@ class EmbeddingDatabase: data = embeddingFromB64(embed_image.text['sd-ti-embedding']) name = data.get('name',name) else: - data = extractImageDataFooter(embed_image) + data = extractImageDataEmbed(embed_image) name = data.get('name',name) else: data = torch.load(path, map_location="cpu") @@ -351,7 +393,7 @@ def train_embedding(embedding_name, learn_rate, data_root, log_directory, traini footer_right = '{}'.format(embedding.step) captioned_image = captionImageOverlay(image,title,footer_left,footer_mid,footer_right) - captioned_image = appendImageDataFooter(captioned_image,data) + captioned_image = insertImageDataEmbed(captioned_image,data) captioned_image.save(last_saved_image_chunks, "PNG", pnginfo=info) -- cgit v1.2.1 From 767202a4c324f9b49f63ab4dabbb5736fe9df6e5 Mon Sep 17 00:00:00 2001 From: DepFA <35278260+dfaker@users.noreply.github.com> Date: Mon, 10 Oct 2022 23:20:52 +0100 Subject: add dependency --- modules/textual_inversion/textual_inversion.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) (limited to 'modules') diff --git a/modules/textual_inversion/textual_inversion.py b/modules/textual_inversion/textual_inversion.py index 95eebea7..f3cacaa0 100644 --- a/modules/textual_inversion/textual_inversion.py +++ b/modules/textual_inversion/textual_inversion.py @@ -7,7 +7,7 @@ import tqdm import html import datetime -from PIL import Image,PngImagePlugin +from PIL import Image,PngImagePlugin,ImageDraw from ..images import captionImageOverlay import numpy as np import base64 -- cgit v1.2.1 From e0fbe6d27e7b4505766c8cb5a4264e1114cf3721 Mon Sep 17 00:00:00 2001 From: DepFA <35278260+dfaker@users.noreply.github.com> Date: Mon, 10 Oct 2022 23:26:24 +0100 Subject: colour depth conversion fix --- modules/textual_inversion/textual_inversion.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) (limited to 'modules') diff --git a/modules/textual_inversion/textual_inversion.py b/modules/textual_inversion/textual_inversion.py index f3cacaa0..ae807268 100644 --- a/modules/textual_inversion/textual_inversion.py +++ b/modules/textual_inversion/textual_inversion.py @@ -103,7 +103,7 @@ def crop_black(img,tol=0): def extractImageDataEmbed(image): d=3 - outarr = crop_black(np.array(image.getdata()).reshape(image.size[1],image.size[0],d ).astype(np.uint8) ) & 0x0F + outarr = crop_black(np.array(image.convert('RGB').getdata()).reshape(image.size[1],image.size[0],d ).astype(np.uint8) ) & 0x0F blackCols = np.where( np.sum(outarr, axis=(0,2))==0) if blackCols[0].shape[0] < 2: print('No Image data blocks found.') -- cgit v1.2.1 From 76ef3d75f61253516c024553335d9083d9660a8a Mon Sep 17 00:00:00 2001 From: JC_Array Date: Mon, 10 Oct 2022 18:01:49 -0500 Subject: added deepbooru settings (threshold and sort by alpha or likelyhood) --- modules/deepbooru.py | 36 +++++++++++++++++++++++++----------- modules/shared.py | 6 ++++++ 2 files changed, 31 insertions(+), 11 deletions(-) (limited to 'modules') diff --git a/modules/deepbooru.py b/modules/deepbooru.py index ebdba5e0..e31e92c0 100644 --- a/modules/deepbooru.py +++ b/modules/deepbooru.py @@ -3,31 +3,32 @@ from concurrent.futures import ProcessPoolExecutor import multiprocessing import time - -def get_deepbooru_tags(pil_image, threshold=0.5): +def get_deepbooru_tags(pil_image): """ This method is for running only one image at a time for simple use. Used to the img2img interrogate. """ from modules import shared # prevents circular reference - create_deepbooru_process(threshold) + create_deepbooru_process(shared.opts.deepbooru_threshold, shared.opts.deepbooru_sort_alpha) shared.deepbooru_process_return["value"] = -1 shared.deepbooru_process_queue.put(pil_image) while shared.deepbooru_process_return["value"] == -1: time.sleep(0.2) + tags = shared.deepbooru_process_return["value"] release_process() + return tags -def deepbooru_process(queue, deepbooru_process_return, threshold): +def deepbooru_process(queue, deepbooru_process_return, threshold, alpha_sort): model, tags = get_deepbooru_tags_model() while True: # while process is running, keep monitoring queue for new image pil_image = queue.get() if pil_image == "QUIT": break else: - deepbooru_process_return["value"] = get_deepbooru_tags_from_model(model, tags, pil_image, threshold) + deepbooru_process_return["value"] = get_deepbooru_tags_from_model(model, tags, pil_image, threshold, alpha_sort) -def create_deepbooru_process(threshold=0.5): +def create_deepbooru_process(threshold, alpha_sort): """ Creates deepbooru process. A queue is created to send images into the process. This enables multiple images to be processed in a row without reloading the model or creating a new process. To return the data, a shared @@ -40,7 +41,7 @@ def create_deepbooru_process(threshold=0.5): shared.deepbooru_process_queue = shared.deepbooru_process_manager.Queue() shared.deepbooru_process_return = shared.deepbooru_process_manager.dict() shared.deepbooru_process_return["value"] = -1 - shared.deepbooru_process = multiprocessing.Process(target=deepbooru_process, args=(shared.deepbooru_process_queue, shared.deepbooru_process_return, threshold)) + shared.deepbooru_process = multiprocessing.Process(target=deepbooru_process, args=(shared.deepbooru_process_queue, shared.deepbooru_process_return, threshold, alpha_sort)) shared.deepbooru_process.start() @@ -80,7 +81,7 @@ def get_deepbooru_tags_model(): return model, tags -def get_deepbooru_tags_from_model(model, tags, pil_image, threshold=0.5): +def get_deepbooru_tags_from_model(model, tags, pil_image, threshold, alpha_sort): import deepdanbooru as dd import tensorflow as tf import numpy as np @@ -105,15 +106,28 @@ def get_deepbooru_tags_from_model(model, tags, pil_image, threshold=0.5): for i, tag in enumerate(tags): result_dict[tag] = y[i] - result_tags_out = [] + + unsorted_tags_in_theshold = [] result_tags_print = [] for tag in tags: if result_dict[tag] >= threshold: if tag.startswith("rating:"): continue - result_tags_out.append(tag) + unsorted_tags_in_theshold.append((result_dict[tag], tag)) result_tags_print.append(f'{result_dict[tag]} {tag}') + # sort tags + result_tags_out = [] + sort_ndx = 0 + print(alpha_sort) + if alpha_sort: + sort_ndx = 1 + + # sort by reverse by likelihood and normal for alpha + unsorted_tags_in_theshold.sort(key=lambda y: y[sort_ndx], reverse=(not alpha_sort)) + for weight, tag in unsorted_tags_in_theshold: + result_tags_out.append(tag) + print('\n'.join(sorted(result_tags_print, reverse=True))) - return ', '.join(result_tags_out).replace('_', ' ').replace(':', ' ') \ No newline at end of file + return ', '.join(result_tags_out).replace('_', ' ').replace(':', ' ') diff --git a/modules/shared.py b/modules/shared.py index 1995a99a..2e307809 100644 --- a/modules/shared.py +++ b/modules/shared.py @@ -261,6 +261,12 @@ options_templates.update(options_section(('sampler-params', "Sampler parameters" 's_noise': OptionInfo(1.0, "sigma noise", gr.Slider, {"minimum": 0.0, "maximum": 1.0, "step": 0.01}), })) +if cmd_opts.deepdanbooru: + options_templates.update(options_section(('deepbooru-params', "DeepBooru parameters"), { + "deepbooru_sort_alpha": OptionInfo(True, "Sort Alphabetical", gr.Checkbox), + 'deepbooru_threshold': OptionInfo(0.5, "Threshold", gr.Slider, {"minimum": 0.0, "maximum": 1.0, "step": 0.01}), + })) + class Options: data = None -- cgit v1.2.1 From bb932dbf9faf43ba918daa4791873078797b2a48 Mon Sep 17 00:00:00 2001 From: JC_Array Date: Mon, 10 Oct 2022 18:37:52 -0500 Subject: added alpha sort and threshold variables to create process method in preprocessing --- modules/textual_inversion/preprocess.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) (limited to 'modules') diff --git a/modules/textual_inversion/preprocess.py b/modules/textual_inversion/preprocess.py index 4a2194da..c0af729b 100644 --- a/modules/textual_inversion/preprocess.py +++ b/modules/textual_inversion/preprocess.py @@ -29,7 +29,7 @@ def preprocess(process_src, process_dst, process_width, process_height, process_ shared.interrogator.load() if process_caption_deepbooru: - deepbooru.create_deepbooru_process() + deepbooru.create_deepbooru_process(opts.deepbooru_threshold, opts.deepbooru_sort_alpha) def save_pic_with_caption(image, index): if process_caption: -- cgit v1.2.1 From 1add3cff84b7e2436d69b1e97ae689281e4a7c33 Mon Sep 17 00:00:00 2001 From: papuSpartan Date: Mon, 10 Oct 2022 19:57:43 -0500 Subject: Refresh list of models/ckpts upon hitting restart gradio in the settings pane --- modules/ui.py | 4 ++++ 1 file changed, 4 insertions(+) (limited to 'modules') diff --git a/modules/ui.py b/modules/ui.py index e8039d76..06ff118f 100644 --- a/modules/ui.py +++ b/modules/ui.py @@ -39,6 +39,7 @@ import modules.generation_parameters_copypaste from modules import prompt_parser from modules.images import save_image import modules.textual_inversion.ui +from modules.sd_models import list_models # this is a fix for Windows users. Without it, javascript files will be served with text/html content-type and the browser will not show any UI mimetypes.init() @@ -1290,6 +1291,9 @@ Requested path was: {f} shared.state.interrupt() settings_interface.gradio_ref.do_restart = True + # refresh models so that new models/.ckpt's show up on reload + list_models() + restart_gradio.click( fn=request_restart, inputs=[], -- cgit v1.2.1 From 7aa8fcac1e45c3ad9c6a40df0e44a346afcd5032 Mon Sep 17 00:00:00 2001 From: DepFA <35278260+dfaker@users.noreply.github.com> Date: Tue, 11 Oct 2022 04:17:36 +0100 Subject: use simple lcg in xor --- modules/textual_inversion/textual_inversion.py | 10 ++++++++-- 1 file changed, 8 insertions(+), 2 deletions(-) (limited to 'modules') diff --git a/modules/textual_inversion/textual_inversion.py b/modules/textual_inversion/textual_inversion.py index ae807268..13416a08 100644 --- a/modules/textual_inversion/textual_inversion.py +++ b/modules/textual_inversion/textual_inversion.py @@ -39,9 +39,15 @@ def embeddingFromB64(data): d = base64.b64decode(data) return json.loads(d,cls=EmbeddingDecoder) +def lcg(m=2**32, a=1664525, c=1013904223, seed=0): + while True: + seed = (a * seed + c) % m + yield seed + def xorBlock(block): - return np.bitwise_xor(block.astype(np.uint8), - ((np.random.RandomState(0xDEADBEEF).random(block.shape)*255).astype(np.uint8)) & 0x0F ) + g = lcg() + randblock = np.array([next(g) for _ in range(np.product(block.shape))]).astype(np.uint8).reshape(block.shape) + return np.bitwise_xor(block.astype(np.uint8),randblock & 0x0F) def styleBlock(block,sequence): im = Image.new('RGB',(block.shape[1],block.shape[0])) -- cgit v1.2.1 From 8b7d3f1bef47bbe048f644ed0d8dd3ad46554045 Mon Sep 17 00:00:00 2001 From: Jairo Correa Date: Tue, 11 Oct 2022 02:22:46 -0300 Subject: Make the ctrl+enter shortcut use the generate button on the current tab --- modules/ui.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) (limited to 'modules') diff --git a/modules/ui.py b/modules/ui.py index e8039d76..cafda884 100644 --- a/modules/ui.py +++ b/modules/ui.py @@ -1331,7 +1331,7 @@ Requested path was: {f} with gr.Tabs() as tabs: for interface, label, ifid in interfaces: - with gr.TabItem(label, id=ifid): + with gr.TabItem(label, id=ifid, elem_id='tab_' + ifid): interface.render() if os.path.exists(os.path.join(script_path, "notification.mp3")): -- cgit v1.2.1 From 8617396c6df71074c7fd3d39419802026874712a Mon Sep 17 00:00:00 2001 From: Kenneth Date: Mon, 10 Oct 2022 17:23:07 -0600 Subject: Added slider for deepbooru score threshold in settings --- modules/shared.py | 1 + modules/ui.py | 2 +- 2 files changed, 2 insertions(+), 1 deletion(-) (limited to 'modules') diff --git a/modules/shared.py b/modules/shared.py index ecd15ef5..e0830e28 100644 --- a/modules/shared.py +++ b/modules/shared.py @@ -239,6 +239,7 @@ options_templates.update(options_section(('interrogate', "Interrogate Options"), "interrogate_clip_min_length": OptionInfo(24, "Interrogate: minimum description length (excluding artists, etc..)", gr.Slider, {"minimum": 1, "maximum": 128, "step": 1}), "interrogate_clip_max_length": OptionInfo(48, "Interrogate: maximum description length", gr.Slider, {"minimum": 1, "maximum": 256, "step": 1}), "interrogate_clip_dict_limit": OptionInfo(1500, "Interrogate: maximum number of lines in text file (0 = No limit)"), + "interrogate_deepbooru_score_threshold": OptionInfo(0.5, "Interrogate: deepbooru score threshold", gr.Slider, {"minimum": 0, "maximum": 1, "step": 0.01}), })) options_templates.update(options_section(('ui', "User interface"), { diff --git a/modules/ui.py b/modules/ui.py index cafda884..ca3151c4 100644 --- a/modules/ui.py +++ b/modules/ui.py @@ -311,7 +311,7 @@ def interrogate(image): def interrogate_deepbooru(image): - prompt = get_deepbooru_tags(image) + prompt = get_deepbooru_tags(image, opts.interrogate_deepbooru_score_threshold) return gr_show(True) if prompt is None else prompt -- cgit v1.2.1 From 5e2627a1a63e4c9f87e6e604ecc24e9936f149de Mon Sep 17 00:00:00 2001 From: hentailord85ez <112723046+hentailord85ez@users.noreply.github.com> Date: Tue, 11 Oct 2022 07:55:28 +0100 Subject: Comma backtrack padding (#2192) Comma backtrack padding --- modules/sd_hijack.py | 19 ++++++++++++++++++- modules/shared.py | 1 + 2 files changed, 19 insertions(+), 1 deletion(-) (limited to 'modules') diff --git a/modules/sd_hijack.py b/modules/sd_hijack.py index 827bf304..aa4d2cbc 100644 --- a/modules/sd_hijack.py +++ b/modules/sd_hijack.py @@ -107,6 +107,8 @@ class FrozenCLIPEmbedderWithCustomWords(torch.nn.Module): self.tokenizer = wrapped.tokenizer self.token_mults = {} + self.comma_token = [v for k, v in self.tokenizer.get_vocab().items() if k == ','][0] + tokens_with_parens = [(k, v) for k, v in self.tokenizer.get_vocab().items() if '(' in k or ')' in k or '[' in k or ']' in k] for text, ident in tokens_with_parens: mult = 1.0 @@ -136,6 +138,7 @@ class FrozenCLIPEmbedderWithCustomWords(torch.nn.Module): fixes = [] remade_tokens = [] multipliers = [] + last_comma = -1 for tokens, (text, weight) in zip(tokenized, parsed): i = 0 @@ -144,6 +147,20 @@ class FrozenCLIPEmbedderWithCustomWords(torch.nn.Module): embedding, embedding_length_in_tokens = self.hijack.embedding_db.find_embedding_at_position(tokens, i) + if token == self.comma_token: + last_comma = len(remade_tokens) + elif opts.comma_padding_backtrack != 0 and max(len(remade_tokens), 1) % 75 == 0 and last_comma != -1 and len(remade_tokens) - last_comma <= opts.comma_padding_backtrack: + last_comma += 1 + reloc_tokens = remade_tokens[last_comma:] + reloc_mults = multipliers[last_comma:] + + remade_tokens = remade_tokens[:last_comma] + length = len(remade_tokens) + + rem = int(math.ceil(length / 75)) * 75 - length + remade_tokens += [id_end] * rem + reloc_tokens + multipliers = multipliers[:last_comma] + [1.0] * rem + reloc_mults + if embedding is None: remade_tokens.append(token) multipliers.append(weight) @@ -284,7 +301,7 @@ class FrozenCLIPEmbedderWithCustomWords(torch.nn.Module): while max(map(len, remade_batch_tokens)) != 0: rem_tokens = [x[75:] for x in remade_batch_tokens] rem_multipliers = [x[75:] for x in batch_multipliers] - + self.hijack.fixes = [] for unfiltered in hijack_fixes: fixes = [] diff --git a/modules/shared.py b/modules/shared.py index e0830e28..14b40d70 100644 --- a/modules/shared.py +++ b/modules/shared.py @@ -227,6 +227,7 @@ options_templates.update(options_section(('sd', "Stable Diffusion"), { "enable_emphasis": OptionInfo(True, "Emphasis: use (text) to make model pay more attention to text and [text] to make it pay less attention"), "use_old_emphasis_implementation": OptionInfo(False, "Use old emphasis implementation. Can be useful to reproduce old seeds."), "enable_batch_seeds": OptionInfo(True, "Make K-diffusion samplers produce same images in a batch as when making a single image"), + "comma_padding_backtrack": OptionInfo(20, "Increase coherency by padding from the last comma within n tokens when using more than 75 tokens", gr.Slider, {"minimum": 0, "maximum": 74, "step": 1 }), "filter_nsfw": OptionInfo(False, "Filter NSFW content"), 'CLIP_stop_at_last_layers': OptionInfo(1, "Stop At last layers of CLIP model", gr.Slider, {"minimum": 1, "maximum": 12, "step": 1}), "random_artist_categories": OptionInfo([], "Allowed categories for random artists selection when using the Roll button", gr.CheckboxGroup, {"choices": artist_db.categories()}), -- cgit v1.2.1 From 948533950c9db5069a874d925fadd50bac00fdb5 Mon Sep 17 00:00:00 2001 From: AUTOMATIC <16777216c@gmail.com> Date: Tue, 11 Oct 2022 11:09:51 +0300 Subject: replace duplicate code with a function --- modules/hypernetwork.py | 23 ++++++++++++-------- modules/sd_hijack_optimizations.py | 44 +++++++++++++------------------------- 2 files changed, 29 insertions(+), 38 deletions(-) (limited to 'modules') diff --git a/modules/hypernetwork.py b/modules/hypernetwork.py index 498bc9d8..7bbc443e 100644 --- a/modules/hypernetwork.py +++ b/modules/hypernetwork.py @@ -64,21 +64,26 @@ def load_hypernetwork(filename): shared.loaded_hypernetwork = None +def apply_hypernetwork(hypernetwork, context): + hypernetwork_layers = (hypernetwork.layers if hypernetwork is not None else {}).get(context.shape[2], None) + + if hypernetwork_layers is None: + return context, context + + context_k = hypernetwork_layers[0](context) + context_v = hypernetwork_layers[1](context) + return context_k, context_v + + def attention_CrossAttention_forward(self, x, context=None, mask=None): h = self.heads q = self.to_q(x) context = default(context, x) - hypernetwork = shared.loaded_hypernetwork - hypernetwork_layers = (hypernetwork.layers if hypernetwork is not None else {}).get(context.shape[2], None) - - if hypernetwork_layers is not None: - k = self.to_k(hypernetwork_layers[0](context)) - v = self.to_v(hypernetwork_layers[1](context)) - else: - k = self.to_k(context) - v = self.to_v(context) + context_k, context_v = apply_hypernetwork(shared.loaded_hypernetwork, context) + k = self.to_k(context_k) + v = self.to_v(context_v) q, k, v = map(lambda t: rearrange(t, 'b n (h d) -> (b h) n d', h=h), (q, k, v)) diff --git a/modules/sd_hijack_optimizations.py b/modules/sd_hijack_optimizations.py index 18408e62..25cb67a4 100644 --- a/modules/sd_hijack_optimizations.py +++ b/modules/sd_hijack_optimizations.py @@ -8,7 +8,8 @@ from torch import einsum from ldm.util import default from einops import rearrange -from modules import shared +from modules import shared, hypernetwork + if shared.cmd_opts.xformers or shared.cmd_opts.force_enable_xformers: try: @@ -26,16 +27,10 @@ def split_cross_attention_forward_v1(self, x, context=None, mask=None): q_in = self.to_q(x) context = default(context, x) - hypernetwork = shared.loaded_hypernetwork - hypernetwork_layers = (hypernetwork.layers if hypernetwork is not None else {}).get(context.shape[2], None) - - if hypernetwork_layers is not None: - k_in = self.to_k(hypernetwork_layers[0](context)) - v_in = self.to_v(hypernetwork_layers[1](context)) - else: - k_in = self.to_k(context) - v_in = self.to_v(context) - del context, x + context_k, context_v = hypernetwork.apply_hypernetwork(shared.loaded_hypernetwork, context) + k_in = self.to_k(context_k) + v_in = self.to_v(context_v) + del context, context_k, context_v, x q, k, v = map(lambda t: rearrange(t, 'b n (h d) -> (b h) n d', h=h), (q_in, k_in, v_in)) del q_in, k_in, v_in @@ -59,22 +54,16 @@ def split_cross_attention_forward_v1(self, x, context=None, mask=None): return self.to_out(r2) -# taken from https://github.com/Doggettx/stable-diffusion +# taken from https://github.com/Doggettx/stable-diffusion and modified def split_cross_attention_forward(self, x, context=None, mask=None): h = self.heads q_in = self.to_q(x) context = default(context, x) - hypernetwork = shared.loaded_hypernetwork - hypernetwork_layers = (hypernetwork.layers if hypernetwork is not None else {}).get(context.shape[2], None) - - if hypernetwork_layers is not None: - k_in = self.to_k(hypernetwork_layers[0](context)) - v_in = self.to_v(hypernetwork_layers[1](context)) - else: - k_in = self.to_k(context) - v_in = self.to_v(context) + context_k, context_v = hypernetwork.apply_hypernetwork(shared.loaded_hypernetwork, context) + k_in = self.to_k(context_k) + v_in = self.to_v(context_v) k_in *= self.scale @@ -130,14 +119,11 @@ def xformers_attention_forward(self, x, context=None, mask=None): h = self.heads q_in = self.to_q(x) context = default(context, x) - hypernetwork = shared.loaded_hypernetwork - hypernetwork_layers = (hypernetwork.layers if hypernetwork is not None else {}).get(context.shape[2], None) - if hypernetwork_layers is not None: - k_in = self.to_k(hypernetwork_layers[0](context)) - v_in = self.to_v(hypernetwork_layers[1](context)) - else: - k_in = self.to_k(context) - v_in = self.to_v(context) + + context_k, context_v = hypernetwork.apply_hypernetwork(shared.loaded_hypernetwork, context) + k_in = self.to_k(context_k) + v_in = self.to_v(context_v) + q, k, v = map(lambda t: rearrange(t, 'b n (h d) -> b n h d', h=h), (q_in, k_in, v_in)) del q_in, k_in, v_in out = xformers.ops.memory_efficient_attention(q, k, v, attn_bias=None) -- cgit v1.2.1 From b2368a3bce663f19a7209d9cb38617e635ca6e3c Mon Sep 17 00:00:00 2001 From: alg-wiki Date: Tue, 11 Oct 2022 17:32:46 +0900 Subject: Switched to exception handling --- modules/textual_inversion/dataset.py | 10 +++++----- modules/textual_inversion/preprocess.py | 8 +++++--- modules/textual_inversion/textual_inversion.py | 18 ++++++++---------- 3 files changed, 18 insertions(+), 18 deletions(-) (limited to 'modules') diff --git a/modules/textual_inversion/dataset.py b/modules/textual_inversion/dataset.py index 0dc54fb7..4d006366 100644 --- a/modules/textual_inversion/dataset.py +++ b/modules/textual_inversion/dataset.py @@ -22,7 +22,6 @@ class PersonalizedBase(Dataset): self.width = width self.height = height self.flip = transforms.RandomHorizontalFlip(p=flip_p) - self.extns = [".jpg",".jpeg",".png",".webp",".bmp"] self.dataset = [] @@ -33,12 +32,13 @@ class PersonalizedBase(Dataset): assert data_root, 'dataset directory not specified' - self.image_paths = [os.path.join(data_root, file_path) for file_path in os.listdir(data_root) if os.path.splitext(file_path.casefold())[1] in self.extns] + self.image_paths = [os.path.join(data_root, file_path) for file_path in os.listdir(data_root)] print("Preparing dataset...") for path in tqdm.tqdm(self.image_paths): - image = Image.open(path) - image = image.convert('RGB') - image = image.resize((self.width, self.height), PIL.Image.BICUBIC) + try: + image = Image.open(path).convert('RGB').resize((self.width, self.height), PIL.Image.BICUBIC) + except Exception: + continue filename = os.path.basename(path) filename_tokens = os.path.splitext(filename)[0] diff --git a/modules/textual_inversion/preprocess.py b/modules/textual_inversion/preprocess.py index 8290abe8..1a672725 100644 --- a/modules/textual_inversion/preprocess.py +++ b/modules/textual_inversion/preprocess.py @@ -12,13 +12,12 @@ def preprocess(process_src, process_dst, process_width, process_height, process_ height = process_height src = os.path.abspath(process_src) dst = os.path.abspath(process_dst) - extns = [".jpg",".jpeg",".png",".webp",".bmp"] assert src != dst, 'same directory specified as source and destination' os.makedirs(dst, exist_ok=True) - files = [i for i in os.listdir(src) if os.path.splitext(i.casefold())[1] in extns] + files = os.listdir(src) shared.state.textinfo = "Preprocessing..." shared.state.job_count = len(files) @@ -47,7 +46,10 @@ def preprocess(process_src, process_dst, process_width, process_height, process_ for index, imagefile in enumerate(tqdm.tqdm(files)): subindex = [0] filename = os.path.join(src, imagefile) - img = Image.open(filename).convert("RGB") + try: + img = Image.open(filename).convert("RGB") + except Exception: + continue if shared.state.interrupted: break diff --git a/modules/textual_inversion/textual_inversion.py b/modules/textual_inversion/textual_inversion.py index 33c923d1..91cde04b 100644 --- a/modules/textual_inversion/textual_inversion.py +++ b/modules/textual_inversion/textual_inversion.py @@ -161,7 +161,6 @@ def train_embedding(embedding_name, learn_rate, data_root, log_directory, traini shared.state.textinfo = "Initializing textual inversion training..." shared.state.job_count = steps - extns = [".jpg",".jpeg",".png",".webp",".bmp"] filename = os.path.join(shared.cmd_opts.embeddings_dir, f'{embedding_name}.pt') @@ -201,10 +200,6 @@ def train_embedding(embedding_name, learn_rate, data_root, log_directory, traini if ititial_step > steps: return embedding, filename - tr_img_len = len([os.path.join(data_root, file_path) for file_path in os.listdir(data_root) if os.path.splitext(file_path.casefold())[1] in extns]) - - epoch_len = (tr_img_len * num_repeats) + tr_img_len - pbar = tqdm.tqdm(enumerate(ds), total=steps-ititial_step) for i, (x, text) in pbar: embedding.step = i + ititial_step @@ -228,10 +223,10 @@ def train_embedding(embedding_name, learn_rate, data_root, log_directory, traini loss.backward() optimizer.step() - epoch_num = embedding.step // epoch_len - epoch_step = embedding.step - (epoch_num * epoch_len) + 1 + epoch_num = embedding.step // len(ds) + epoch_step = embedding.step - (epoch_num * len(ds)) + 1 - pbar.set_description(f"[Epoch {epoch_num}: {epoch_step}/{epoch_len}]loss: {losses.mean():.7f}") + pbar.set_description(f"[Epoch {epoch_num}: {epoch_step}/{len(ds)}]loss: {losses.mean():.7f}") if embedding.step > 0 and embedding_dir is not None and embedding.step % save_embedding_every == 0: last_saved_file = os.path.join(embedding_dir, f'{embedding_name}-{embedding.step}.pt') @@ -243,9 +238,12 @@ def train_embedding(embedding_name, learn_rate, data_root, log_directory, traini p = processing.StableDiffusionProcessingTxt2Img( sd_model=shared.sd_model, prompt=text, - steps=20, - height=training_height, + steps=28, + height=768, width=training_width, + negative_prompt="lowres, bad anatomy, bad hands, text, error, missing fingers, extra digit, fewer digits, cropped, worst quality, low quality, normal quality, jpeg artifacts,signature, watermark, username, blurry, artist name", + cfg_scale=7.0, + sampler_index=0, do_not_save_grid=True, do_not_save_samples=True, ) -- cgit v1.2.1 From 8bacbca0a1ab9aabcb0ad0cbf070e0006991e98a Mon Sep 17 00:00:00 2001 From: alg-wiki Date: Tue, 11 Oct 2022 17:35:09 +0900 Subject: Removed my local edits to checkpoint image generation --- modules/textual_inversion/textual_inversion.py | 7 ++----- 1 file changed, 2 insertions(+), 5 deletions(-) (limited to 'modules') diff --git a/modules/textual_inversion/textual_inversion.py b/modules/textual_inversion/textual_inversion.py index 91cde04b..e9ff80c2 100644 --- a/modules/textual_inversion/textual_inversion.py +++ b/modules/textual_inversion/textual_inversion.py @@ -238,12 +238,9 @@ def train_embedding(embedding_name, learn_rate, data_root, log_directory, traini p = processing.StableDiffusionProcessingTxt2Img( sd_model=shared.sd_model, prompt=text, - steps=28, - height=768, + steps=20, + height=training_height, width=training_width, - negative_prompt="lowres, bad anatomy, bad hands, text, error, missing fingers, extra digit, fewer digits, cropped, worst quality, low quality, normal quality, jpeg artifacts,signature, watermark, username, blurry, artist name", - cfg_scale=7.0, - sampler_index=0, do_not_save_grid=True, do_not_save_samples=True, ) -- cgit v1.2.1 From 530103b586109c11fd068eb70ef09503ec6a4caf Mon Sep 17 00:00:00 2001 From: AUTOMATIC <16777216c@gmail.com> Date: Tue, 11 Oct 2022 14:53:02 +0300 Subject: fixes related to merge --- modules/hypernetwork.py | 103 ------------------------- modules/hypernetwork/hypernetwork.py | 74 +++++++++++------- modules/hypernetwork/ui.py | 10 +-- modules/sd_hijack_optimizations.py | 3 +- modules/shared.py | 13 +++- modules/textual_inversion/textual_inversion.py | 12 +-- modules/ui.py | 5 +- 7 files changed, 73 insertions(+), 147 deletions(-) delete mode 100644 modules/hypernetwork.py (limited to 'modules') diff --git a/modules/hypernetwork.py b/modules/hypernetwork.py deleted file mode 100644 index 7bbc443e..00000000 --- a/modules/hypernetwork.py +++ /dev/null @@ -1,103 +0,0 @@ -import glob -import os -import sys -import traceback - -import torch - -from ldm.util import default -from modules import devices, shared -import torch -from torch import einsum -from einops import rearrange, repeat - - -class HypernetworkModule(torch.nn.Module): - def __init__(self, dim, state_dict): - super().__init__() - - self.linear1 = torch.nn.Linear(dim, dim * 2) - self.linear2 = torch.nn.Linear(dim * 2, dim) - - self.load_state_dict(state_dict, strict=True) - self.to(devices.device) - - def forward(self, x): - return x + (self.linear2(self.linear1(x))) - - -class Hypernetwork: - filename = None - name = None - - def __init__(self, filename): - self.filename = filename - self.name = os.path.splitext(os.path.basename(filename))[0] - self.layers = {} - - state_dict = torch.load(filename, map_location='cpu') - for size, sd in state_dict.items(): - self.layers[size] = (HypernetworkModule(size, sd[0]), HypernetworkModule(size, sd[1])) - - -def list_hypernetworks(path): - res = {} - for filename in glob.iglob(os.path.join(path, '**/*.pt'), recursive=True): - name = os.path.splitext(os.path.basename(filename))[0] - res[name] = filename - return res - - -def load_hypernetwork(filename): - path = shared.hypernetworks.get(filename, None) - if path is not None: - print(f"Loading hypernetwork {filename}") - try: - shared.loaded_hypernetwork = Hypernetwork(path) - except Exception: - print(f"Error loading hypernetwork {path}", file=sys.stderr) - print(traceback.format_exc(), file=sys.stderr) - else: - if shared.loaded_hypernetwork is not None: - print(f"Unloading hypernetwork") - - shared.loaded_hypernetwork = None - - -def apply_hypernetwork(hypernetwork, context): - hypernetwork_layers = (hypernetwork.layers if hypernetwork is not None else {}).get(context.shape[2], None) - - if hypernetwork_layers is None: - return context, context - - context_k = hypernetwork_layers[0](context) - context_v = hypernetwork_layers[1](context) - return context_k, context_v - - -def attention_CrossAttention_forward(self, x, context=None, mask=None): - h = self.heads - - q = self.to_q(x) - context = default(context, x) - - context_k, context_v = apply_hypernetwork(shared.loaded_hypernetwork, context) - k = self.to_k(context_k) - v = self.to_v(context_v) - - q, k, v = map(lambda t: rearrange(t, 'b n (h d) -> (b h) n d', h=h), (q, k, v)) - - sim = einsum('b i d, b j d -> b i j', q, k) * self.scale - - if mask is not None: - mask = rearrange(mask, 'b ... -> b (...)') - max_neg_value = -torch.finfo(sim.dtype).max - mask = repeat(mask, 'b j -> (b h) () j', h=h) - sim.masked_fill_(~mask, max_neg_value) - - # attention, what we cannot get enough of - attn = sim.softmax(dim=-1) - - out = einsum('b i j, b j d -> b i d', attn, v) - out = rearrange(out, '(b h) n d -> b n (h d)', h=h) - return self.to_out(out) diff --git a/modules/hypernetwork/hypernetwork.py b/modules/hypernetwork/hypernetwork.py index a3d6a47e..aa701bda 100644 --- a/modules/hypernetwork/hypernetwork.py +++ b/modules/hypernetwork/hypernetwork.py @@ -26,10 +26,11 @@ class HypernetworkModule(torch.nn.Module): if state_dict is not None: self.load_state_dict(state_dict, strict=True) else: - self.linear1.weight.data.fill_(0.0001) - self.linear1.bias.data.fill_(0.0001) - self.linear2.weight.data.fill_(0.0001) - self.linear2.bias.data.fill_(0.0001) + + self.linear1.weight.data.normal_(mean=0.0, std=0.01) + self.linear1.bias.data.zero_() + self.linear2.weight.data.normal_(mean=0.0, std=0.01) + self.linear2.bias.data.zero_() self.to(devices.device) @@ -92,41 +93,54 @@ class Hypernetwork: self.sd_checkpoint_name = state_dict.get('sd_checkpoint_name', None) -def load_hypernetworks(path): +def list_hypernetworks(path): res = {} + for filename in glob.iglob(os.path.join(path, '**/*.pt'), recursive=True): + name = os.path.splitext(os.path.basename(filename))[0] + res[name] = filename + return res - for filename in glob.iglob(path + '**/*.pt', recursive=True): + +def load_hypernetwork(filename): + path = shared.hypernetworks.get(filename, None) + if path is not None: + print(f"Loading hypernetwork {filename}") try: - hn = Hypernetwork() - hn.load(filename) - res[hn.name] = hn + shared.loaded_hypernetwork = Hypernetwork() + shared.loaded_hypernetwork.load(path) + except Exception: - print(f"Error loading hypernetwork {filename}", file=sys.stderr) + print(f"Error loading hypernetwork {path}", file=sys.stderr) print(traceback.format_exc(), file=sys.stderr) + else: + if shared.loaded_hypernetwork is not None: + print(f"Unloading hypernetwork") - return res + shared.loaded_hypernetwork = None -def attention_CrossAttention_forward(self, x, context=None, mask=None): - h = self.heads +def apply_hypernetwork(hypernetwork, context, layer=None): + hypernetwork_layers = (hypernetwork.layers if hypernetwork is not None else {}).get(context.shape[2], None) - q = self.to_q(x) - context = default(context, x) + if hypernetwork_layers is None: + return context, context - hypernetwork_layers = (shared.hypernetwork.layers if shared.hypernetwork is not None else {}).get(context.shape[2], None) + if layer is not None: + layer.hyper_k = hypernetwork_layers[0] + layer.hyper_v = hypernetwork_layers[1] - if hypernetwork_layers is not None: - hypernetwork_k, hypernetwork_v = hypernetwork_layers + context_k = hypernetwork_layers[0](context) + context_v = hypernetwork_layers[1](context) + return context_k, context_v - self.hypernetwork_k = hypernetwork_k - self.hypernetwork_v = hypernetwork_v - context_k = hypernetwork_k(context) - context_v = hypernetwork_v(context) - else: - context_k = context - context_v = context +def attention_CrossAttention_forward(self, x, context=None, mask=None): + h = self.heads + + q = self.to_q(x) + context = default(context, x) + context_k, context_v = apply_hypernetwork(shared.loaded_hypernetwork, context, self) k = self.to_k(context_k) v = self.to_v(context_v) @@ -151,7 +165,9 @@ def attention_CrossAttention_forward(self, x, context=None, mask=None): def train_hypernetwork(hypernetwork_name, learn_rate, data_root, log_directory, steps, create_image_every, save_hypernetwork_every, template_file, preview_image_prompt): assert hypernetwork_name, 'embedding not selected' - shared.hypernetwork = shared.hypernetworks[hypernetwork_name] + path = shared.hypernetworks.get(hypernetwork_name, None) + shared.loaded_hypernetwork = Hypernetwork() + shared.loaded_hypernetwork.load(path) shared.state.textinfo = "Initializing hypernetwork training..." shared.state.job_count = steps @@ -176,9 +192,9 @@ def train_hypernetwork(hypernetwork_name, learn_rate, data_root, log_directory, shared.state.textinfo = f"Preparing dataset from {html.escape(data_root)}..." with torch.autocast("cuda"): - ds = modules.textual_inversion.dataset.PersonalizedBase(data_root=data_root, size=512, placeholder_token=hypernetwork_name, model=shared.sd_model, device=devices.device, template_file=template_file) + ds = modules.textual_inversion.dataset.PersonalizedBase(data_root=data_root, width=512, height=512, repeats=1, placeholder_token=hypernetwork_name, model=shared.sd_model, device=devices.device, template_file=template_file) - hypernetwork = shared.hypernetworks[hypernetwork_name] + hypernetwork = shared.loaded_hypernetwork weights = hypernetwork.weights() for weight in weights: weight.requires_grad = True @@ -194,7 +210,7 @@ def train_hypernetwork(hypernetwork_name, learn_rate, data_root, log_directory, if ititial_step > steps: return hypernetwork, filename - pbar = tqdm.tqdm(enumerate(ds), total=steps-ititial_step) + pbar = tqdm.tqdm(enumerate(ds), total=steps - ititial_step) for i, (x, text) in pbar: hypernetwork.step = i + ititial_step diff --git a/modules/hypernetwork/ui.py b/modules/hypernetwork/ui.py index 525f978c..f6d1d0a3 100644 --- a/modules/hypernetwork/ui.py +++ b/modules/hypernetwork/ui.py @@ -6,24 +6,24 @@ import gradio as gr import modules.textual_inversion.textual_inversion import modules.textual_inversion.preprocess from modules import sd_hijack, shared +from modules.hypernetwork import hypernetwork def create_hypernetwork(name): fn = os.path.join(shared.cmd_opts.hypernetwork_dir, f"{name}.pt") assert not os.path.exists(fn), f"file {fn} already exists" - hypernetwork = modules.hypernetwork.hypernetwork.Hypernetwork(name=name) - hypernetwork.save(fn) + hypernet = modules.hypernetwork.hypernetwork.Hypernetwork(name=name) + hypernet.save(fn) shared.reload_hypernetworks() - shared.hypernetwork = shared.hypernetworks.get(shared.opts.sd_hypernetwork, None) return gr.Dropdown.update(choices=sorted([x for x in shared.hypernetworks.keys()])), f"Created: {fn}", "" def train_hypernetwork(*args): - initial_hypernetwork = shared.hypernetwork + initial_hypernetwork = shared.loaded_hypernetwork try: sd_hijack.undo_optimizations() @@ -38,6 +38,6 @@ Hypernetwork saved to {html.escape(filename)} except Exception: raise finally: - shared.hypernetwork = initial_hypernetwork + shared.loaded_hypernetwork = initial_hypernetwork sd_hijack.apply_optimizations() diff --git a/modules/sd_hijack_optimizations.py b/modules/sd_hijack_optimizations.py index 25cb67a4..27e571fc 100644 --- a/modules/sd_hijack_optimizations.py +++ b/modules/sd_hijack_optimizations.py @@ -8,7 +8,8 @@ from torch import einsum from ldm.util import default from einops import rearrange -from modules import shared, hypernetwork +from modules import shared +from modules.hypernetwork import hypernetwork if shared.cmd_opts.xformers or shared.cmd_opts.force_enable_xformers: diff --git a/modules/shared.py b/modules/shared.py index 14b40d70..8753015e 100644 --- a/modules/shared.py +++ b/modules/shared.py @@ -13,7 +13,8 @@ import modules.memmon import modules.sd_models import modules.styles import modules.devices as devices -from modules import sd_samplers, hypernetwork +from modules import sd_samplers +from modules.hypernetwork import hypernetwork from modules.paths import models_path, script_path, sd_path sd_model_file = os.path.join(script_path, 'model.ckpt') @@ -29,6 +30,7 @@ parser.add_argument("--no-half-vae", action='store_true', help="do not switch th parser.add_argument("--no-progressbar-hiding", action='store_true', help="do not hide progressbar in gradio UI (we hide it because it slows down ML if you have hardware acceleration in browser)") parser.add_argument("--max-batch-count", type=int, default=16, help="maximum batch count value for the UI") parser.add_argument("--embeddings-dir", type=str, default=os.path.join(script_path, 'embeddings'), help="embeddings directory for textual inversion (default: embeddings)") +parser.add_argument("--hypernetwork-dir", type=str, default=os.path.join(models_path, 'hypernetworks'), help="hypernetwork directory") parser.add_argument("--allow-code", action='store_true', help="allow custom script execution from webui") parser.add_argument("--medvram", action='store_true', help="enable stable diffusion model optimizations for sacrificing a little speed for low VRM usage") parser.add_argument("--lowvram", action='store_true', help="enable stable diffusion model optimizations for sacrificing a lot of speed for very low VRM usage") @@ -82,10 +84,17 @@ parallel_processing_allowed = not cmd_opts.lowvram and not cmd_opts.medvram xformers_available = False config_filename = cmd_opts.ui_settings_file -hypernetworks = hypernetwork.list_hypernetworks(os.path.join(models_path, 'hypernetworks')) +hypernetworks = hypernetwork.list_hypernetworks(cmd_opts.hypernetwork_dir) loaded_hypernetwork = None +def reload_hypernetworks(): + global hypernetworks + + hypernetworks = hypernetwork.list_hypernetworks(cmd_opts.hypernetwork_dir) + hypernetwork.load_hypernetwork(opts.sd_hypernetwork) + + class State: skipped = False interrupted = False diff --git a/modules/textual_inversion/textual_inversion.py b/modules/textual_inversion/textual_inversion.py index 5965c5a0..d6977950 100644 --- a/modules/textual_inversion/textual_inversion.py +++ b/modules/textual_inversion/textual_inversion.py @@ -156,7 +156,7 @@ def create_embedding(name, num_vectors_per_token, init_text='*'): return fn -def train_embedding(embedding_name, learn_rate, data_root, log_directory, training_width, training_height, steps, num_repeats, create_image_every, save_embedding_every, template_file): +def train_embedding(embedding_name, learn_rate, data_root, log_directory, training_width, training_height, steps, num_repeats, create_image_every, save_embedding_every, template_file, preview_image_prompt): assert embedding_name, 'embedding not selected' shared.state.textinfo = "Initializing textual inversion training..." @@ -238,12 +238,14 @@ def train_embedding(embedding_name, learn_rate, data_root, log_directory, traini if embedding.step > 0 and images_dir is not None and embedding.step % create_image_every == 0: last_saved_image = os.path.join(images_dir, f'{embedding_name}-{embedding.step}.png') + preview_text = text if preview_image_prompt == "" else preview_image_prompt + p = processing.StableDiffusionProcessingTxt2Img( sd_model=shared.sd_model, - prompt=text, + prompt=preview_text, steps=20, - height=training_height, - width=training_width, + height=training_height, + width=training_width, do_not_save_grid=True, do_not_save_samples=True, ) @@ -254,7 +256,7 @@ def train_embedding(embedding_name, learn_rate, data_root, log_directory, traini shared.state.current_image = image image.save(last_saved_image) - last_saved_image += f", prompt: {text}" + last_saved_image += f", prompt: {preview_text}" shared.state.job_no = embedding.step diff --git a/modules/ui.py b/modules/ui.py index 10b1ee3a..df653059 100644 --- a/modules/ui.py +++ b/modules/ui.py @@ -1023,7 +1023,7 @@ def create_ui(wrap_gradio_gpu_call): gr.HTML(value="") with gr.Column(): - create_embedding = gr.Button(value="Create", variant='primary') + create_embedding = gr.Button(value="Create embedding", variant='primary') with gr.Group(): gr.HTML(value="

Create a new hypernetwork

") @@ -1035,7 +1035,7 @@ def create_ui(wrap_gradio_gpu_call): gr.HTML(value="") with gr.Column(): - create_hypernetwork = gr.Button(value="Create", variant='primary') + create_hypernetwork = gr.Button(value="Create hypernetwork", variant='primary') with gr.Group(): gr.HTML(value="

Preprocess images

") @@ -1147,6 +1147,7 @@ def create_ui(wrap_gradio_gpu_call): create_image_every, save_embedding_every, template_file, + preview_image_prompt, ], outputs=[ ti_output, -- cgit v1.2.1 From 7b1db45e1fda8603d4617affd976066be5e5b821 Mon Sep 17 00:00:00 2001 From: yfszzx Date: Tue, 11 Oct 2022 20:17:27 +0800 Subject: images history improvement --- modules/images_history.py | 229 ++++++++++++++++++++++++---------------------- 1 file changed, 121 insertions(+), 108 deletions(-) (limited to 'modules') diff --git a/modules/images_history.py b/modules/images_history.py index 0e0a48f3..01d11a01 100644 --- a/modules/images_history.py +++ b/modules/images_history.py @@ -1,117 +1,130 @@ import os def get_recent_images(dir_name, page_index, step, image_index): - print(image_index) - page_index = int(page_index) - f_list = os.listdir(dir_name) - file_list = [] - for file in f_list: - if file[-4:] == ".txt": - continue - file_list.append(file) - file_list = sorted(file_list, key=lambda file: -os.path.getctime(os.path.join(dir_name, file))) - num = 48 - max_page_index = len(file_list) // num + 1 - page_index = max_page_index if page_index == -1 else page_index + step - page_index = 1 if page_index < 1 else page_index - page_index = max_page_index if page_index > max_page_index else page_index - idx_frm = (page_index - 1) * num - file_list = file_list[idx_frm:idx_frm + num] - print(f"Loading history page {page_index}") - image_index = int(image_index) - if image_index < 0 or image_index > len(file_list) - 1: - current_file = None - hide_image = None - else: - current_file = file_list[int(image_index)] - hide_image = os.path.join(dir_name, current_file) - return [os.path.join(dir_name, file) for file in file_list], page_index, file_list, current_file, hide_image -def first_page_click(dir_name, page_index, image_index): - return get_recent_images(dir_name, 1, 0, image_index) -def end_page_click(dir_name, page_index, image_index): - return get_recent_images(dir_name, -1, 0, image_index) -def prev_page_click(dir_name, page_index, image_index): - return get_recent_images(dir_name, page_index, -1, image_index) -def next_page_click(dir_name, page_index, image_index): - return get_recent_images(dir_name, page_index, 1, image_index) -def page_index_change(dir_name, page_index, image_index): - return get_recent_images(dir_name, page_index, 0, image_index) + #print(image_index) + page_index = int(page_index) + f_list = os.listdir(dir_name) + file_list = [] + for file in f_list: + if file[-4:] == ".txt": + continue + file_list.append(file) + file_list = sorted(file_list, key=lambda file: -os.path.getctime(os.path.join(dir_name, file))) + num = 48 + max_page_index = len(file_list) // num + 1 + page_index = max_page_index if page_index == -1 else page_index + step + page_index = 1 if page_index < 1 else page_index + page_index = max_page_index if page_index > max_page_index else page_index + idx_frm = (page_index - 1) * num + file_list = file_list[idx_frm:idx_frm + num] + #print(f"Loading history page {page_index}") + image_index = int(image_index) + if image_index < 0 or image_index > len(file_list) - 1: + current_file = None + hide_image = None + else: + current_file = file_list[int(image_index)] + hide_image = os.path.join(dir_name, current_file) + return [os.path.join(dir_name, file) for file in file_list], page_index, file_list, current_file, hide_image +def first_page_click(dir_name, page_index, image_index, tabname): + return get_recent_images(dir_name, 1, 0, image_index) +def end_page_click(dir_name, page_index, image_index, tabname): + return get_recent_images(dir_name, -1, 0, image_index) +def prev_page_click(dir_name, page_index, image_index, tabname): + return get_recent_images(dir_name, page_index, -1, image_index) +def next_page_click(dir_name, page_index, image_index, tabname): + return get_recent_images(dir_name, page_index, 1, image_index) +def page_index_change(dir_name, page_index, image_index, tabname): + return get_recent_images(dir_name, page_index, 0, image_index) def show_image_info(num, image_path, filenames): - file = filenames[int(num)] - return file, num, os.path.join(image_path, file) -def delete_image(is_img2img, dir_name, name, page_index, filenames, image_index): - print("filename", name) - path = os.path.join(dir_name, name) - if os.path.exists(path): - print(f"Delete file {path}") - os.remove(path) - images, page_index, file_list, current_file, hide_image = get_recent_images(dir_name, page_index, 0, image_index) - return images, page_index, file_list, current_file, hide_image + #print("set img",num) + file = filenames[int(num)] + return file, num, os.path.join(image_path, file) +def delete_image(tabname, dir_name, name, page_index, filenames, image_index): + #print("filename", name) + path = os.path.join(dir_name, name) + if os.path.exists(path): + print(f"Delete file {path}") + os.remove(path) + new_file_list = [] + for f in filenames: + if f == name: + continue + new_file_list.append(f) + else: + print(f"Not exists file {path}") + new_file_list = filenames + return page_index, new_file_list +def show_images_history(gr, opts, tabname, run_pnginfo, switch_dict): + if tabname == "txt2img": + dir_name = opts.outdir_txt2img_samples + elif tabname == "img2img": + dir_name = opts.outdir_img2img_samples + elif tabname == "extras": + dir_name = opts.outdir_extras_samples + with gr.Row(): + renew_page = gr.Button('Renew', elem_id=tabname + "_images_history_renew_page") + first_page = gr.Button('First', elem_id=tabname + "_images_history_first_page") + prev_page = gr.Button('Prev') + page_index = gr.Number(value=1, label="Page Index") + next_page = gr.Button('Next', elem_id=tabname + "_images_history_next_page") + end_page = gr.Button('End') + with gr.Row(elem_id=tabname + "_images_history"): + with gr.Row(): + with gr.Column(): + history_gallery = gr.Gallery(show_label=False).style(grid=6) + with gr.Column(): + with gr.Row(): + delete = gr.Button('Delete') + pnginfo_send_to_txt2img = gr.Button('Send to txt2img') + pnginfo_send_to_img2img = gr.Button('Send to img2img') + with gr.Row(): + with gr.Column(): + img_file_info = gr.Textbox(label="Generate Info") + img_file_name = gr.Textbox(label="File Name") + with gr.Row(): + # hiden items + img_path = gr.Textbox(dir_name, visible=False) + tabname_box = gr.Textbox(tabname, visible=False) + image_index = gr.Textbox(value=-1, visible=False) + set_index = gr.Button('set_index', elem_id=tabname + "_images_history_set_index", visible=False) + filenames = gr.State() + hide_image = gr.Image(visible=False, type="pil") + info1 = gr.Textbox(visible=False) + info2 = gr.Textbox(visible=False) -def show_images_history(gr, opts, is_img2img, run_pnginfo, switch_dict): - def id_name(is_img2img, name): - return ("img2img" if is_img2img else "txt2img") + "_" + name - if is_img2img: - dir_name = opts.outdir_img2img_samples - else: - dir_name = opts.outdir_txt2img_samples - with gr.Row(): - first_page = gr.Button('First', elem_id=id_name(is_img2img,"images_history_first_page")) - prev_page = gr.Button('Prev') - page_index = gr.Number(value=1, label="Page Index") - next_page = gr.Button('Next') - end_page = gr.Button('End') - with gr.Row(elem_id=id_name(is_img2img,"images_history")): - with gr.Row(): - with gr.Column(): - history_gallery = gr.Gallery(show_label=False).style(grid=6) - with gr.Column(): - with gr.Row(): - delete = gr.Button('Delete') - pnginfo_send_to_txt2img = gr.Button('Send to txt2img') - pnginfo_send_to_img2img = gr.Button('Send to img2img') - with gr.Row(): - with gr.Column(): - img_file_info = gr.Textbox(dir_name, label="Generate Info") - img_file_name = gr.Textbox(label="File Name") - with gr.Row(): - # hiden items - img_path = gr.Textbox(dir_name, visible=False) - is_img2img_flag = gr.Checkbox(is_img2img, visible=False) - image_index = gr.Textbox(value=-1, visible=False) - set_index = gr.Button('set_index', elem_id=id_name(is_img2img,"images_history_set_index")) - filenames = gr.State() - hide_image = gr.Image(visible=False, type="pil") - info1 = gr.Textbox(visible=False) - info2 = gr.Textbox(visible=False) + + # turn pages + gallery_inputs = [img_path, page_index, image_index, tabname_box] + gallery_outputs = [history_gallery, page_index, filenames, img_file_name, hide_image] - - # turn pages - gallery_inputs = [img_path, page_index, image_index] - gallery_outputs = [history_gallery, page_index, filenames, img_file_name, hide_image] - first_page.click(first_page_click, inputs=gallery_inputs, outputs=gallery_outputs) - next_page.click(next_page_click, inputs=gallery_inputs, outputs=gallery_outputs) - prev_page.click(prev_page_click, inputs=gallery_inputs, outputs=gallery_outputs) - end_page.click(end_page_click, inputs=gallery_inputs, outputs=gallery_outputs) - page_index.submit(page_index_change, inputs=gallery_inputs, outputs=gallery_outputs) - #page_index.change(page_index_change, inputs=[is_img2img_flag, img_path, page_index], outputs=[history_gallery, page_index]) + first_page.click(first_page_click, _js="images_history_turnpage", inputs=gallery_inputs, outputs=gallery_outputs) + next_page.click(next_page_click, _js="images_history_turnpage", inputs=gallery_inputs, outputs=gallery_outputs) + prev_page.click(prev_page_click, _js="images_history_turnpage", inputs=gallery_inputs, outputs=gallery_outputs) + end_page.click(end_page_click, _js="images_history_turnpage", inputs=gallery_inputs, outputs=gallery_outputs) + page_index.submit(page_index_change, _js="images_history_turnpage", inputs=gallery_inputs, outputs=gallery_outputs) + renew_page.click(page_index_change, _js="images_history_turnpage", inputs=gallery_inputs, outputs=gallery_outputs) + #page_index.change(page_index_change, inputs=[tabname_box, img_path, page_index], outputs=[history_gallery, page_index]) - #other funcitons - set_index.click(show_image_info, _js="images_history_get_current_img", inputs=[is_img2img_flag, img_path, filenames], outputs=[img_file_name, image_index, hide_image]) - delete.click(delete_image, inputs=[is_img2img_flag, img_path, img_file_name, page_index, filenames, image_index], outputs=gallery_outputs) - hide_image.change(fn=run_pnginfo, inputs=[hide_image], outputs=[info1, img_file_info, info2]) - switch_dict["fn"](pnginfo_send_to_txt2img, switch_dict["t2i"], img_file_info, 'switch_to_txt2img') - switch_dict["fn"](pnginfo_send_to_img2img, switch_dict["i2i"], img_file_info, 'switch_to_img2img_img2img') - - + #other funcitons + set_index.click(show_image_info, _js="images_history_get_current_img", inputs=[tabname_box, img_path, filenames], outputs=[img_file_name, image_index, hide_image]) + delete.click(delete_image,_js="images_history_delete", inputs=[tabname_box, img_path, img_file_name, page_index, filenames, image_index], outputs=[page_index, filenames]) + hide_image.change(fn=run_pnginfo, inputs=[hide_image], outputs=[info1, img_file_info, info2]) + switch_dict["fn"](pnginfo_send_to_txt2img, switch_dict["t2i"], img_file_info, 'switch_to_txt2img') + switch_dict["fn"](pnginfo_send_to_img2img, switch_dict["i2i"], img_file_info, 'switch_to_img2img_img2img') + + def create_history_tabs(gr, opts, run_pnginfo, switch_dict): - with gr.Blocks(analytics_enabled=False) as images_history: - with gr.Tabs() as tabs: - with gr.Tab("txt2img history", id="images_history_txt2img"): - with gr.Blocks(analytics_enabled=False) as images_history_txt2img: - show_images_history(gr, opts, False, run_pnginfo, switch_dict) - with gr.Tab("img2img history", id="images_history_img2img"): - with gr.Blocks(analytics_enabled=False) as images_history_img2img: - show_images_history(gr, opts, True, run_pnginfo, switch_dict) - return images_history + with gr.Blocks(analytics_enabled=False) as images_history: + with gr.Tabs() as tabs: + with gr.Tab("txt2img history"): + with gr.Blocks(analytics_enabled=False) as images_history_txt2img: + show_images_history(gr, opts, "txt2img", run_pnginfo, switch_dict) + with gr.Tab("img2img history"): + with gr.Blocks(analytics_enabled=False) as images_history_img2img: + show_images_history(gr, opts, "img2img", run_pnginfo, switch_dict) + with gr.Tab("extras history"): + with gr.Blocks(analytics_enabled=False) as images_history_img2img: + show_images_history(gr, opts, "extras", run_pnginfo, switch_dict) + return images_history -- cgit v1.2.1 From 92d7a138857b308c97a8d009848f642aeb93d6c8 Mon Sep 17 00:00:00 2001 From: Martin Cairns Date: Tue, 11 Oct 2022 00:02:44 +0100 Subject: Handle different parameters for DPM fast & adaptive --- modules/sd_samplers.py | 25 ++++++++++++++++++------- 1 file changed, 18 insertions(+), 7 deletions(-) (limited to 'modules') diff --git a/modules/sd_samplers.py b/modules/sd_samplers.py index d168b938..eee52e7d 100644 --- a/modules/sd_samplers.py +++ b/modules/sd_samplers.py @@ -57,7 +57,7 @@ def set_samplers(): global samplers, samplers_for_img2img hidden = set(opts.hide_samplers) - hidden_img2img = set(opts.hide_samplers + ['PLMS', 'DPM fast', 'DPM adaptive']) + hidden_img2img = set(opts.hide_samplers + ['PLMS']) samplers = [x for x in all_samplers if x.name not in hidden] samplers_for_img2img = [x for x in all_samplers if x.name not in hidden_img2img] @@ -365,16 +365,27 @@ class KDiffusionSampler: else: sigmas = self.model_wrap.get_sigmas(steps) - noise = noise * sigmas[steps - t_enc - 1] - xi = x + noise - - extra_params_kwargs = self.initialize(p) - sigma_sched = sigmas[steps - t_enc - 1:] + print('check values same', sigmas[steps - t_enc - 1] , sigma_sched[0], sigmas[steps - t_enc - 1] - sigma_sched[0]) + xi = x + noise * sigma_sched[0] + + extra_params_kwargs = self.initialize(p) + if 'sigma_min' in inspect.signature(self.func).parameters: + ## last sigma is zero which is allowed by DPM Fast & Adaptive so taking value before last + extra_params_kwargs['sigma_min'] = sigma_sched[-2] + if 'sigma_max' in inspect.signature(self.func).parameters: + extra_params_kwargs['sigma_max'] = sigma_sched[0] + if 'n' in inspect.signature(self.func).parameters: + extra_params_kwargs['n'] = len(sigma_sched) - 1 + if 'sigma_sched' in inspect.signature(self.func).parameters: + extra_params_kwargs['sigma_sched'] = sigma_sched + if 'sigmas' in inspect.signature(self.func).parameters: + extra_params_kwargs['sigmas'] = sigma_sched self.model_wrap_cfg.init_latent = x - return self.func(self.model_wrap_cfg, xi, sigma_sched, extra_args={'cond': conditioning, 'uncond': unconditional_conditioning, 'cond_scale': p.cfg_scale}, disable=False, callback=self.callback_state, **extra_params_kwargs) + return self.func(self.model_wrap_cfg, xi, extra_args={'cond': conditioning, 'uncond': unconditional_conditioning, 'cond_scale': p.cfg_scale}, disable=False, callback=self.callback_state, **extra_params_kwargs) + def sample(self, p, x, conditioning, unconditional_conditioning, steps=None): steps = steps or p.steps -- cgit v1.2.1 From 1eae3076078f00ecc5d0fac3c77fffb85cd2eb77 Mon Sep 17 00:00:00 2001 From: Martin Cairns Date: Tue, 11 Oct 2022 00:04:06 +0100 Subject: Remove debug code for checking that first sigma value is same after code cleanup --- modules/sd_samplers.py | 1 - 1 file changed, 1 deletion(-) (limited to 'modules') diff --git a/modules/sd_samplers.py b/modules/sd_samplers.py index eee52e7d..32272916 100644 --- a/modules/sd_samplers.py +++ b/modules/sd_samplers.py @@ -366,7 +366,6 @@ class KDiffusionSampler: sigmas = self.model_wrap.get_sigmas(steps) sigma_sched = sigmas[steps - t_enc - 1:] - print('check values same', sigmas[steps - t_enc - 1] , sigma_sched[0], sigmas[steps - t_enc - 1] - sigma_sched[0]) xi = x + noise * sigma_sched[0] extra_params_kwargs = self.initialize(p) -- cgit v1.2.1 From eacc03b16730bcc5be95cda2d7c966ff1b4a8263 Mon Sep 17 00:00:00 2001 From: Martin Cairns Date: Tue, 11 Oct 2022 00:36:00 +0100 Subject: Fix typo in comments --- modules/sd_samplers.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) (limited to 'modules') diff --git a/modules/sd_samplers.py b/modules/sd_samplers.py index 32272916..20309e06 100644 --- a/modules/sd_samplers.py +++ b/modules/sd_samplers.py @@ -370,7 +370,7 @@ class KDiffusionSampler: extra_params_kwargs = self.initialize(p) if 'sigma_min' in inspect.signature(self.func).parameters: - ## last sigma is zero which is allowed by DPM Fast & Adaptive so taking value before last + ## last sigma is zero which isn't allowed by DPM Fast & Adaptive so taking value before last extra_params_kwargs['sigma_min'] = sigma_sched[-2] if 'sigma_max' in inspect.signature(self.func).parameters: extra_params_kwargs['sigma_max'] = sigma_sched[0] -- cgit v1.2.1 From 87d63bbab5c973ac5cec777ef7304d28f1ab3f24 Mon Sep 17 00:00:00 2001 From: yfszzx Date: Tue, 11 Oct 2022 20:37:03 +0800 Subject: images history improvement --- modules/images_history.py | 30 +++++++++++++++--------------- 1 file changed, 15 insertions(+), 15 deletions(-) (limited to 'modules') diff --git a/modules/images_history.py b/modules/images_history.py index 01d11a01..23f55b30 100644 --- a/modules/images_history.py +++ b/modules/images_history.py @@ -64,12 +64,12 @@ def show_images_history(gr, opts, tabname, run_pnginfo, switch_dict): elif tabname == "extras": dir_name = opts.outdir_extras_samples with gr.Row(): - renew_page = gr.Button('Renew', elem_id=tabname + "_images_history_renew_page") - first_page = gr.Button('First', elem_id=tabname + "_images_history_first_page") - prev_page = gr.Button('Prev') - page_index = gr.Number(value=1, label="Page Index") - next_page = gr.Button('Next', elem_id=tabname + "_images_history_next_page") - end_page = gr.Button('End') + renew_page = gr.Button('Renew', elem_id=tabname + "_images_history_renew_page") + first_page = gr.Button('First', elem_id=tabname + "_images_history_first_page") + prev_page = gr.Button('Prev') + page_index = gr.Number(value=1, label="Page Index") + next_page = gr.Button('Next', elem_id=tabname + "_images_history_next_page") + end_page = gr.Button('End') with gr.Row(elem_id=tabname + "_images_history"): with gr.Row(): with gr.Column(): @@ -84,15 +84,15 @@ def show_images_history(gr, opts, tabname, run_pnginfo, switch_dict): img_file_info = gr.Textbox(label="Generate Info") img_file_name = gr.Textbox(label="File Name") with gr.Row(): - # hiden items - img_path = gr.Textbox(dir_name, visible=False) - tabname_box = gr.Textbox(tabname, visible=False) - image_index = gr.Textbox(value=-1, visible=False) - set_index = gr.Button('set_index', elem_id=tabname + "_images_history_set_index", visible=False) - filenames = gr.State() - hide_image = gr.Image(visible=False, type="pil") - info1 = gr.Textbox(visible=False) - info2 = gr.Textbox(visible=False) + # hiden items + img_path = gr.Textbox(dir_name, visible=False) + tabname_box = gr.Textbox(tabname, visible=False) + image_index = gr.Textbox(value=-1, visible=False) + set_index = gr.Button('set_index', elem_id=tabname + "_images_history_set_index", visible=False) + filenames = gr.State() + hide_image = gr.Image(visible=False, type="pil") + info1 = gr.Textbox(visible=False) + info2 = gr.Textbox(visible=False) # turn pages -- cgit v1.2.1 From 87b77cad5f3017c952a7dfec0e7904a9df5b72fd Mon Sep 17 00:00:00 2001 From: Ben <110583491+TheLastBen@users.noreply.github.com> Date: Mon, 10 Oct 2022 19:37:16 +0100 Subject: Layout fix --- modules/ui.py | 28 ++++++++++++++-------------- 1 file changed, 14 insertions(+), 14 deletions(-) (limited to 'modules') diff --git a/modules/ui.py b/modules/ui.py index df653059..de4cd7f2 100644 --- a/modules/ui.py +++ b/modules/ui.py @@ -550,15 +550,15 @@ def create_ui(wrap_gradio_gpu_call): button_id = "hidden_element" if shared.cmd_opts.hide_ui_dir_config else 'open_folder' open_txt2img_folder = gr.Button(folder_symbol, elem_id=button_id) - with gr.Row(): - do_make_zip = gr.Checkbox(label="Make Zip when Save?", value=False) + with gr.Row(): + do_make_zip = gr.Checkbox(label="Make Zip when Save?", value=False) - with gr.Row(): - download_files = gr.File(None, file_count="multiple", interactive=False, show_label=False, visible=False) + with gr.Row(): + download_files = gr.File(None, file_count="multiple", interactive=False, show_label=False, visible=False) - with gr.Group(): - html_info = gr.HTML() - generation_info = gr.Textbox(visible=False) + with gr.Group(): + html_info = gr.HTML() + generation_info = gr.Textbox(visible=False) connect_reuse_seed(seed, reuse_seed, generation_info, dummy_component, is_subseed=False) connect_reuse_seed(subseed, reuse_subseed, generation_info, dummy_component, is_subseed=True) @@ -738,15 +738,15 @@ def create_ui(wrap_gradio_gpu_call): button_id = "hidden_element" if shared.cmd_opts.hide_ui_dir_config else 'open_folder' open_img2img_folder = gr.Button(folder_symbol, elem_id=button_id) - with gr.Row(): - do_make_zip = gr.Checkbox(label="Make Zip when Save?", value=False) + with gr.Row(): + do_make_zip = gr.Checkbox(label="Make Zip when Save?", value=False) - with gr.Row(): - download_files = gr.File(None, file_count="multiple", interactive=False, show_label=False, visible=False) + with gr.Row(): + download_files = gr.File(None, file_count="multiple", interactive=False, show_label=False, visible=False) - with gr.Group(): - html_info = gr.HTML() - generation_info = gr.Textbox(visible=False) + with gr.Group(): + html_info = gr.HTML() + generation_info = gr.Textbox(visible=False) connect_reuse_seed(seed, reuse_seed, generation_info, dummy_component, is_subseed=False) connect_reuse_seed(subseed, reuse_subseed, generation_info, dummy_component, is_subseed=True) -- cgit v1.2.1 From 861297cefe2bb663f4e09dd4778a4cb93ebe8ff1 Mon Sep 17 00:00:00 2001 From: Ben <110583491+TheLastBen@users.noreply.github.com> Date: Tue, 11 Oct 2022 08:08:45 +0100 Subject: add a space holder --- modules/ui.py | 5 ++++- 1 file changed, 4 insertions(+), 1 deletion(-) (limited to 'modules') diff --git a/modules/ui.py b/modules/ui.py index de4cd7f2..fc0f3d3c 100644 --- a/modules/ui.py +++ b/modules/ui.py @@ -429,7 +429,10 @@ def create_toprow(is_img2img): with gr.Row(): with gr.Column(scale=8): - negative_prompt = gr.Textbox(label="Negative prompt", elem_id="negative_prompt", show_label=False, placeholder="Negative prompt", lines=2) + with gr.Row(): + negative_prompt = gr.Textbox(label="Negative prompt", elem_id="negative_prompt", show_label=False, placeholder="Negative prompt", lines=2) + with gr.Column(scale=1, elem_id="roll_col"): + sh = gr.Button(elem_id="sh", visible=True) with gr.Column(scale=1, elem_id="style_neg_col"): prompt_style2 = gr.Dropdown(label="Style 2", elem_id=f"{id_part}_style2_index", choices=[k for k, v in shared.prompt_styles.styles.items()], value=next(iter(shared.prompt_styles.styles.keys())), visible=len(shared.prompt_styles.styles) > 1) -- cgit v1.2.1 From 59925644480b6fd84f6bb84b4df7d4fbc6a0cce8 Mon Sep 17 00:00:00 2001 From: JamnedZ Date: Tue, 11 Oct 2022 16:40:27 +0700 Subject: Cleaned ngrok integration --- modules/ngrok.py | 15 +++++++++++++++ modules/shared.py | 1 + modules/ui.py | 5 +++++ 3 files changed, 21 insertions(+) create mode 100644 modules/ngrok.py (limited to 'modules') diff --git a/modules/ngrok.py b/modules/ngrok.py new file mode 100644 index 00000000..17e6976f --- /dev/null +++ b/modules/ngrok.py @@ -0,0 +1,15 @@ +from pyngrok import ngrok, conf, exception + + +def connect(token, port): + if token == None: + token = 'None' + conf.get_default().auth_token = token + try: + public_url = ngrok.connect(port).public_url + except exception.PyngrokNgrokError: + print(f'Invalid ngrok authtoken, ngrok connection aborted.\n' + f'Your token: {token}, get the right one on https://dashboard.ngrok.com/get-started/your-authtoken') + else: + print(f'ngrok connected to localhost:{port}! URL: {public_url}\n' + 'You can use this link after the launch is complete.') \ No newline at end of file diff --git a/modules/shared.py b/modules/shared.py index 8753015e..375e3afb 100644 --- a/modules/shared.py +++ b/modules/shared.py @@ -38,6 +38,7 @@ parser.add_argument("--always-batch-cond-uncond", action='store_true', help="dis parser.add_argument("--unload-gfpgan", action='store_true', help="does not do anything.") parser.add_argument("--precision", type=str, help="evaluate at this precision", choices=["full", "autocast"], default="autocast") parser.add_argument("--share", action='store_true', help="use share=True for gradio and make the UI accessible through their site (doesn't work for me but you might have better luck)") +parser.add_argument("--ngrok", type=str, help="ngrok authtoken, alternative to gradio --share", default=None) parser.add_argument("--codeformer-models-path", type=str, help="Path to directory with codeformer model file(s).", default=os.path.join(models_path, 'Codeformer')) parser.add_argument("--gfpgan-models-path", type=str, help="Path to directory with GFPGAN model file(s).", default=os.path.join(models_path, 'GFPGAN')) parser.add_argument("--esrgan-models-path", type=str, help="Path to directory with ESRGAN model file(s).", default=os.path.join(models_path, 'ESRGAN')) diff --git a/modules/ui.py b/modules/ui.py index fc0f3d3c..f57f32db 100644 --- a/modules/ui.py +++ b/modules/ui.py @@ -51,6 +51,11 @@ if not cmd_opts.share and not cmd_opts.listen: gradio.utils.version_check = lambda: None gradio.utils.get_local_ip_address = lambda: '127.0.0.1' +if cmd_opts.ngrok != None: + import modules.ngrok as ngrok + print('ngrok authtoken detected, trying to connect...') + ngrok.connect(cmd_opts.ngrok, cmd_opts.port if cmd_opts.port != None else 7860) + def gr_show(visible=True): return {"visible": visible, "__type__": "update"} -- cgit v1.2.1 From a004d1a855311b0d7ff2976a4e31b0247ad9d1f6 Mon Sep 17 00:00:00 2001 From: JamnedZ Date: Tue, 11 Oct 2022 16:48:27 +0700 Subject: Added new line at the end of ngrok.py --- modules/ngrok.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) (limited to 'modules') diff --git a/modules/ngrok.py b/modules/ngrok.py index 17e6976f..7d03a6df 100644 --- a/modules/ngrok.py +++ b/modules/ngrok.py @@ -12,4 +12,4 @@ def connect(token, port): f'Your token: {token}, get the right one on https://dashboard.ngrok.com/get-started/your-authtoken') else: print(f'ngrok connected to localhost:{port}! URL: {public_url}\n' - 'You can use this link after the launch is complete.') \ No newline at end of file + 'You can use this link after the launch is complete.') -- cgit v1.2.1 From 873efeed49bb5197a42da18272115b326c5d68f3 Mon Sep 17 00:00:00 2001 From: AUTOMATIC <16777216c@gmail.com> Date: Tue, 11 Oct 2022 15:51:22 +0300 Subject: rename hypernetwork dir to hypernetworks to prevent clash with an old filename that people who use zip instead of git clone will have --- modules/hypernetwork/hypernetwork.py | 283 ---------------------------------- modules/hypernetwork/ui.py | 43 ------ modules/hypernetworks/hypernetwork.py | 283 ++++++++++++++++++++++++++++++++++ modules/hypernetworks/ui.py | 43 ++++++ modules/sd_hijack.py | 2 +- modules/sd_hijack_optimizations.py | 2 +- modules/shared.py | 2 +- modules/ui.py | 2 +- 8 files changed, 330 insertions(+), 330 deletions(-) delete mode 100644 modules/hypernetwork/hypernetwork.py delete mode 100644 modules/hypernetwork/ui.py create mode 100644 modules/hypernetworks/hypernetwork.py create mode 100644 modules/hypernetworks/ui.py (limited to 'modules') diff --git a/modules/hypernetwork/hypernetwork.py b/modules/hypernetwork/hypernetwork.py deleted file mode 100644 index aa701bda..00000000 --- a/modules/hypernetwork/hypernetwork.py +++ /dev/null @@ -1,283 +0,0 @@ -import datetime -import glob -import html -import os -import sys -import traceback -import tqdm - -import torch - -from ldm.util import default -from modules import devices, shared, processing, sd_models -import torch -from torch import einsum -from einops import rearrange, repeat -import modules.textual_inversion.dataset - - -class HypernetworkModule(torch.nn.Module): - def __init__(self, dim, state_dict=None): - super().__init__() - - self.linear1 = torch.nn.Linear(dim, dim * 2) - self.linear2 = torch.nn.Linear(dim * 2, dim) - - if state_dict is not None: - self.load_state_dict(state_dict, strict=True) - else: - - self.linear1.weight.data.normal_(mean=0.0, std=0.01) - self.linear1.bias.data.zero_() - self.linear2.weight.data.normal_(mean=0.0, std=0.01) - self.linear2.bias.data.zero_() - - self.to(devices.device) - - def forward(self, x): - return x + (self.linear2(self.linear1(x))) - - -class Hypernetwork: - filename = None - name = None - - def __init__(self, name=None): - self.filename = None - self.name = name - self.layers = {} - self.step = 0 - self.sd_checkpoint = None - self.sd_checkpoint_name = None - - for size in [320, 640, 768, 1280]: - self.layers[size] = (HypernetworkModule(size), HypernetworkModule(size)) - - def weights(self): - res = [] - - for k, layers in self.layers.items(): - for layer in layers: - layer.train() - res += [layer.linear1.weight, layer.linear1.bias, layer.linear2.weight, layer.linear2.bias] - - return res - - def save(self, filename): - state_dict = {} - - for k, v in self.layers.items(): - state_dict[k] = (v[0].state_dict(), v[1].state_dict()) - - state_dict['step'] = self.step - state_dict['name'] = self.name - state_dict['sd_checkpoint'] = self.sd_checkpoint - state_dict['sd_checkpoint_name'] = self.sd_checkpoint_name - - torch.save(state_dict, filename) - - def load(self, filename): - self.filename = filename - if self.name is None: - self.name = os.path.splitext(os.path.basename(filename))[0] - - state_dict = torch.load(filename, map_location='cpu') - - for size, sd in state_dict.items(): - if type(size) == int: - self.layers[size] = (HypernetworkModule(size, sd[0]), HypernetworkModule(size, sd[1])) - - self.name = state_dict.get('name', self.name) - self.step = state_dict.get('step', 0) - self.sd_checkpoint = state_dict.get('sd_checkpoint', None) - self.sd_checkpoint_name = state_dict.get('sd_checkpoint_name', None) - - -def list_hypernetworks(path): - res = {} - for filename in glob.iglob(os.path.join(path, '**/*.pt'), recursive=True): - name = os.path.splitext(os.path.basename(filename))[0] - res[name] = filename - return res - - -def load_hypernetwork(filename): - path = shared.hypernetworks.get(filename, None) - if path is not None: - print(f"Loading hypernetwork {filename}") - try: - shared.loaded_hypernetwork = Hypernetwork() - shared.loaded_hypernetwork.load(path) - - except Exception: - print(f"Error loading hypernetwork {path}", file=sys.stderr) - print(traceback.format_exc(), file=sys.stderr) - else: - if shared.loaded_hypernetwork is not None: - print(f"Unloading hypernetwork") - - shared.loaded_hypernetwork = None - - -def apply_hypernetwork(hypernetwork, context, layer=None): - hypernetwork_layers = (hypernetwork.layers if hypernetwork is not None else {}).get(context.shape[2], None) - - if hypernetwork_layers is None: - return context, context - - if layer is not None: - layer.hyper_k = hypernetwork_layers[0] - layer.hyper_v = hypernetwork_layers[1] - - context_k = hypernetwork_layers[0](context) - context_v = hypernetwork_layers[1](context) - return context_k, context_v - - -def attention_CrossAttention_forward(self, x, context=None, mask=None): - h = self.heads - - q = self.to_q(x) - context = default(context, x) - - context_k, context_v = apply_hypernetwork(shared.loaded_hypernetwork, context, self) - k = self.to_k(context_k) - v = self.to_v(context_v) - - q, k, v = map(lambda t: rearrange(t, 'b n (h d) -> (b h) n d', h=h), (q, k, v)) - - sim = einsum('b i d, b j d -> b i j', q, k) * self.scale - - if mask is not None: - mask = rearrange(mask, 'b ... -> b (...)') - max_neg_value = -torch.finfo(sim.dtype).max - mask = repeat(mask, 'b j -> (b h) () j', h=h) - sim.masked_fill_(~mask, max_neg_value) - - # attention, what we cannot get enough of - attn = sim.softmax(dim=-1) - - out = einsum('b i j, b j d -> b i d', attn, v) - out = rearrange(out, '(b h) n d -> b n (h d)', h=h) - return self.to_out(out) - - -def train_hypernetwork(hypernetwork_name, learn_rate, data_root, log_directory, steps, create_image_every, save_hypernetwork_every, template_file, preview_image_prompt): - assert hypernetwork_name, 'embedding not selected' - - path = shared.hypernetworks.get(hypernetwork_name, None) - shared.loaded_hypernetwork = Hypernetwork() - shared.loaded_hypernetwork.load(path) - - shared.state.textinfo = "Initializing hypernetwork training..." - shared.state.job_count = steps - - filename = os.path.join(shared.cmd_opts.hypernetwork_dir, f'{hypernetwork_name}.pt') - - log_directory = os.path.join(log_directory, datetime.datetime.now().strftime("%Y-%m-%d"), hypernetwork_name) - - if save_hypernetwork_every > 0: - hypernetwork_dir = os.path.join(log_directory, "hypernetworks") - os.makedirs(hypernetwork_dir, exist_ok=True) - else: - hypernetwork_dir = None - - if create_image_every > 0: - images_dir = os.path.join(log_directory, "images") - os.makedirs(images_dir, exist_ok=True) - else: - images_dir = None - - cond_model = shared.sd_model.cond_stage_model - - shared.state.textinfo = f"Preparing dataset from {html.escape(data_root)}..." - with torch.autocast("cuda"): - ds = modules.textual_inversion.dataset.PersonalizedBase(data_root=data_root, width=512, height=512, repeats=1, placeholder_token=hypernetwork_name, model=shared.sd_model, device=devices.device, template_file=template_file) - - hypernetwork = shared.loaded_hypernetwork - weights = hypernetwork.weights() - for weight in weights: - weight.requires_grad = True - - optimizer = torch.optim.AdamW(weights, lr=learn_rate) - - losses = torch.zeros((32,)) - - last_saved_file = "" - last_saved_image = "" - - ititial_step = hypernetwork.step or 0 - if ititial_step > steps: - return hypernetwork, filename - - pbar = tqdm.tqdm(enumerate(ds), total=steps - ititial_step) - for i, (x, text) in pbar: - hypernetwork.step = i + ititial_step - - if hypernetwork.step > steps: - break - - if shared.state.interrupted: - break - - with torch.autocast("cuda"): - c = cond_model([text]) - - x = x.to(devices.device) - loss = shared.sd_model(x.unsqueeze(0), c)[0] - del x - - losses[hypernetwork.step % losses.shape[0]] = loss.item() - - optimizer.zero_grad() - loss.backward() - optimizer.step() - - pbar.set_description(f"loss: {losses.mean():.7f}") - - if hypernetwork.step > 0 and hypernetwork_dir is not None and hypernetwork.step % save_hypernetwork_every == 0: - last_saved_file = os.path.join(hypernetwork_dir, f'{hypernetwork_name}-{hypernetwork.step}.pt') - hypernetwork.save(last_saved_file) - - if hypernetwork.step > 0 and images_dir is not None and hypernetwork.step % create_image_every == 0: - last_saved_image = os.path.join(images_dir, f'{hypernetwork_name}-{hypernetwork.step}.png') - - preview_text = text if preview_image_prompt == "" else preview_image_prompt - - p = processing.StableDiffusionProcessingTxt2Img( - sd_model=shared.sd_model, - prompt=preview_text, - steps=20, - do_not_save_grid=True, - do_not_save_samples=True, - ) - - processed = processing.process_images(p) - image = processed.images[0] - - shared.state.current_image = image - image.save(last_saved_image) - - last_saved_image += f", prompt: {preview_text}" - - shared.state.job_no = hypernetwork.step - - shared.state.textinfo = f""" -

-Loss: {losses.mean():.7f}
-Step: {hypernetwork.step}
-Last prompt: {html.escape(text)}
-Last saved embedding: {html.escape(last_saved_file)}
-Last saved image: {html.escape(last_saved_image)}
-

-""" - - checkpoint = sd_models.select_checkpoint() - - hypernetwork.sd_checkpoint = checkpoint.hash - hypernetwork.sd_checkpoint_name = checkpoint.model_name - hypernetwork.save(filename) - - return hypernetwork, filename - - diff --git a/modules/hypernetwork/ui.py b/modules/hypernetwork/ui.py deleted file mode 100644 index f6d1d0a3..00000000 --- a/modules/hypernetwork/ui.py +++ /dev/null @@ -1,43 +0,0 @@ -import html -import os - -import gradio as gr - -import modules.textual_inversion.textual_inversion -import modules.textual_inversion.preprocess -from modules import sd_hijack, shared -from modules.hypernetwork import hypernetwork - - -def create_hypernetwork(name): - fn = os.path.join(shared.cmd_opts.hypernetwork_dir, f"{name}.pt") - assert not os.path.exists(fn), f"file {fn} already exists" - - hypernet = modules.hypernetwork.hypernetwork.Hypernetwork(name=name) - hypernet.save(fn) - - shared.reload_hypernetworks() - - return gr.Dropdown.update(choices=sorted([x for x in shared.hypernetworks.keys()])), f"Created: {fn}", "" - - -def train_hypernetwork(*args): - - initial_hypernetwork = shared.loaded_hypernetwork - - try: - sd_hijack.undo_optimizations() - - hypernetwork, filename = modules.hypernetwork.hypernetwork.train_hypernetwork(*args) - - res = f""" -Training {'interrupted' if shared.state.interrupted else 'finished'} at {hypernetwork.step} steps. -Hypernetwork saved to {html.escape(filename)} -""" - return res, "" - except Exception: - raise - finally: - shared.loaded_hypernetwork = initial_hypernetwork - sd_hijack.apply_optimizations() - diff --git a/modules/hypernetworks/hypernetwork.py b/modules/hypernetworks/hypernetwork.py new file mode 100644 index 00000000..aa701bda --- /dev/null +++ b/modules/hypernetworks/hypernetwork.py @@ -0,0 +1,283 @@ +import datetime +import glob +import html +import os +import sys +import traceback +import tqdm + +import torch + +from ldm.util import default +from modules import devices, shared, processing, sd_models +import torch +from torch import einsum +from einops import rearrange, repeat +import modules.textual_inversion.dataset + + +class HypernetworkModule(torch.nn.Module): + def __init__(self, dim, state_dict=None): + super().__init__() + + self.linear1 = torch.nn.Linear(dim, dim * 2) + self.linear2 = torch.nn.Linear(dim * 2, dim) + + if state_dict is not None: + self.load_state_dict(state_dict, strict=True) + else: + + self.linear1.weight.data.normal_(mean=0.0, std=0.01) + self.linear1.bias.data.zero_() + self.linear2.weight.data.normal_(mean=0.0, std=0.01) + self.linear2.bias.data.zero_() + + self.to(devices.device) + + def forward(self, x): + return x + (self.linear2(self.linear1(x))) + + +class Hypernetwork: + filename = None + name = None + + def __init__(self, name=None): + self.filename = None + self.name = name + self.layers = {} + self.step = 0 + self.sd_checkpoint = None + self.sd_checkpoint_name = None + + for size in [320, 640, 768, 1280]: + self.layers[size] = (HypernetworkModule(size), HypernetworkModule(size)) + + def weights(self): + res = [] + + for k, layers in self.layers.items(): + for layer in layers: + layer.train() + res += [layer.linear1.weight, layer.linear1.bias, layer.linear2.weight, layer.linear2.bias] + + return res + + def save(self, filename): + state_dict = {} + + for k, v in self.layers.items(): + state_dict[k] = (v[0].state_dict(), v[1].state_dict()) + + state_dict['step'] = self.step + state_dict['name'] = self.name + state_dict['sd_checkpoint'] = self.sd_checkpoint + state_dict['sd_checkpoint_name'] = self.sd_checkpoint_name + + torch.save(state_dict, filename) + + def load(self, filename): + self.filename = filename + if self.name is None: + self.name = os.path.splitext(os.path.basename(filename))[0] + + state_dict = torch.load(filename, map_location='cpu') + + for size, sd in state_dict.items(): + if type(size) == int: + self.layers[size] = (HypernetworkModule(size, sd[0]), HypernetworkModule(size, sd[1])) + + self.name = state_dict.get('name', self.name) + self.step = state_dict.get('step', 0) + self.sd_checkpoint = state_dict.get('sd_checkpoint', None) + self.sd_checkpoint_name = state_dict.get('sd_checkpoint_name', None) + + +def list_hypernetworks(path): + res = {} + for filename in glob.iglob(os.path.join(path, '**/*.pt'), recursive=True): + name = os.path.splitext(os.path.basename(filename))[0] + res[name] = filename + return res + + +def load_hypernetwork(filename): + path = shared.hypernetworks.get(filename, None) + if path is not None: + print(f"Loading hypernetwork {filename}") + try: + shared.loaded_hypernetwork = Hypernetwork() + shared.loaded_hypernetwork.load(path) + + except Exception: + print(f"Error loading hypernetwork {path}", file=sys.stderr) + print(traceback.format_exc(), file=sys.stderr) + else: + if shared.loaded_hypernetwork is not None: + print(f"Unloading hypernetwork") + + shared.loaded_hypernetwork = None + + +def apply_hypernetwork(hypernetwork, context, layer=None): + hypernetwork_layers = (hypernetwork.layers if hypernetwork is not None else {}).get(context.shape[2], None) + + if hypernetwork_layers is None: + return context, context + + if layer is not None: + layer.hyper_k = hypernetwork_layers[0] + layer.hyper_v = hypernetwork_layers[1] + + context_k = hypernetwork_layers[0](context) + context_v = hypernetwork_layers[1](context) + return context_k, context_v + + +def attention_CrossAttention_forward(self, x, context=None, mask=None): + h = self.heads + + q = self.to_q(x) + context = default(context, x) + + context_k, context_v = apply_hypernetwork(shared.loaded_hypernetwork, context, self) + k = self.to_k(context_k) + v = self.to_v(context_v) + + q, k, v = map(lambda t: rearrange(t, 'b n (h d) -> (b h) n d', h=h), (q, k, v)) + + sim = einsum('b i d, b j d -> b i j', q, k) * self.scale + + if mask is not None: + mask = rearrange(mask, 'b ... -> b (...)') + max_neg_value = -torch.finfo(sim.dtype).max + mask = repeat(mask, 'b j -> (b h) () j', h=h) + sim.masked_fill_(~mask, max_neg_value) + + # attention, what we cannot get enough of + attn = sim.softmax(dim=-1) + + out = einsum('b i j, b j d -> b i d', attn, v) + out = rearrange(out, '(b h) n d -> b n (h d)', h=h) + return self.to_out(out) + + +def train_hypernetwork(hypernetwork_name, learn_rate, data_root, log_directory, steps, create_image_every, save_hypernetwork_every, template_file, preview_image_prompt): + assert hypernetwork_name, 'embedding not selected' + + path = shared.hypernetworks.get(hypernetwork_name, None) + shared.loaded_hypernetwork = Hypernetwork() + shared.loaded_hypernetwork.load(path) + + shared.state.textinfo = "Initializing hypernetwork training..." + shared.state.job_count = steps + + filename = os.path.join(shared.cmd_opts.hypernetwork_dir, f'{hypernetwork_name}.pt') + + log_directory = os.path.join(log_directory, datetime.datetime.now().strftime("%Y-%m-%d"), hypernetwork_name) + + if save_hypernetwork_every > 0: + hypernetwork_dir = os.path.join(log_directory, "hypernetworks") + os.makedirs(hypernetwork_dir, exist_ok=True) + else: + hypernetwork_dir = None + + if create_image_every > 0: + images_dir = os.path.join(log_directory, "images") + os.makedirs(images_dir, exist_ok=True) + else: + images_dir = None + + cond_model = shared.sd_model.cond_stage_model + + shared.state.textinfo = f"Preparing dataset from {html.escape(data_root)}..." + with torch.autocast("cuda"): + ds = modules.textual_inversion.dataset.PersonalizedBase(data_root=data_root, width=512, height=512, repeats=1, placeholder_token=hypernetwork_name, model=shared.sd_model, device=devices.device, template_file=template_file) + + hypernetwork = shared.loaded_hypernetwork + weights = hypernetwork.weights() + for weight in weights: + weight.requires_grad = True + + optimizer = torch.optim.AdamW(weights, lr=learn_rate) + + losses = torch.zeros((32,)) + + last_saved_file = "" + last_saved_image = "" + + ititial_step = hypernetwork.step or 0 + if ititial_step > steps: + return hypernetwork, filename + + pbar = tqdm.tqdm(enumerate(ds), total=steps - ititial_step) + for i, (x, text) in pbar: + hypernetwork.step = i + ititial_step + + if hypernetwork.step > steps: + break + + if shared.state.interrupted: + break + + with torch.autocast("cuda"): + c = cond_model([text]) + + x = x.to(devices.device) + loss = shared.sd_model(x.unsqueeze(0), c)[0] + del x + + losses[hypernetwork.step % losses.shape[0]] = loss.item() + + optimizer.zero_grad() + loss.backward() + optimizer.step() + + pbar.set_description(f"loss: {losses.mean():.7f}") + + if hypernetwork.step > 0 and hypernetwork_dir is not None and hypernetwork.step % save_hypernetwork_every == 0: + last_saved_file = os.path.join(hypernetwork_dir, f'{hypernetwork_name}-{hypernetwork.step}.pt') + hypernetwork.save(last_saved_file) + + if hypernetwork.step > 0 and images_dir is not None and hypernetwork.step % create_image_every == 0: + last_saved_image = os.path.join(images_dir, f'{hypernetwork_name}-{hypernetwork.step}.png') + + preview_text = text if preview_image_prompt == "" else preview_image_prompt + + p = processing.StableDiffusionProcessingTxt2Img( + sd_model=shared.sd_model, + prompt=preview_text, + steps=20, + do_not_save_grid=True, + do_not_save_samples=True, + ) + + processed = processing.process_images(p) + image = processed.images[0] + + shared.state.current_image = image + image.save(last_saved_image) + + last_saved_image += f", prompt: {preview_text}" + + shared.state.job_no = hypernetwork.step + + shared.state.textinfo = f""" +

+Loss: {losses.mean():.7f}
+Step: {hypernetwork.step}
+Last prompt: {html.escape(text)}
+Last saved embedding: {html.escape(last_saved_file)}
+Last saved image: {html.escape(last_saved_image)}
+

+""" + + checkpoint = sd_models.select_checkpoint() + + hypernetwork.sd_checkpoint = checkpoint.hash + hypernetwork.sd_checkpoint_name = checkpoint.model_name + hypernetwork.save(filename) + + return hypernetwork, filename + + diff --git a/modules/hypernetworks/ui.py b/modules/hypernetworks/ui.py new file mode 100644 index 00000000..811bc31e --- /dev/null +++ b/modules/hypernetworks/ui.py @@ -0,0 +1,43 @@ +import html +import os + +import gradio as gr + +import modules.textual_inversion.textual_inversion +import modules.textual_inversion.preprocess +from modules import sd_hijack, shared +from modules.hypernetworks import hypernetwork + + +def create_hypernetwork(name): + fn = os.path.join(shared.cmd_opts.hypernetwork_dir, f"{name}.pt") + assert not os.path.exists(fn), f"file {fn} already exists" + + hypernet = modules.hypernetwork.hypernetwork.Hypernetwork(name=name) + hypernet.save(fn) + + shared.reload_hypernetworks() + + return gr.Dropdown.update(choices=sorted([x for x in shared.hypernetworks.keys()])), f"Created: {fn}", "" + + +def train_hypernetwork(*args): + + initial_hypernetwork = shared.loaded_hypernetwork + + try: + sd_hijack.undo_optimizations() + + hypernetwork, filename = modules.hypernetwork.hypernetwork.train_hypernetwork(*args) + + res = f""" +Training {'interrupted' if shared.state.interrupted else 'finished'} at {hypernetwork.step} steps. +Hypernetwork saved to {html.escape(filename)} +""" + return res, "" + except Exception: + raise + finally: + shared.loaded_hypernetwork = initial_hypernetwork + sd_hijack.apply_optimizations() + diff --git a/modules/sd_hijack.py b/modules/sd_hijack.py index f873049a..f07ec041 100644 --- a/modules/sd_hijack.py +++ b/modules/sd_hijack.py @@ -37,7 +37,7 @@ def apply_optimizations(): def undo_optimizations(): - from modules.hypernetwork import hypernetwork + from modules.hypernetworks import hypernetwork ldm.modules.attention.CrossAttention.forward = hypernetwork.attention_CrossAttention_forward ldm.modules.diffusionmodules.model.nonlinearity = diffusionmodules_model_nonlinearity diff --git a/modules/sd_hijack_optimizations.py b/modules/sd_hijack_optimizations.py index 27e571fc..3349b9c3 100644 --- a/modules/sd_hijack_optimizations.py +++ b/modules/sd_hijack_optimizations.py @@ -9,7 +9,7 @@ from ldm.util import default from einops import rearrange from modules import shared -from modules.hypernetwork import hypernetwork +from modules.hypernetworks import hypernetwork if shared.cmd_opts.xformers or shared.cmd_opts.force_enable_xformers: diff --git a/modules/shared.py b/modules/shared.py index 375e3afb..1dc2ccf2 100644 --- a/modules/shared.py +++ b/modules/shared.py @@ -14,7 +14,7 @@ import modules.sd_models import modules.styles import modules.devices as devices from modules import sd_samplers -from modules.hypernetwork import hypernetwork +from modules.hypernetworks import hypernetwork from modules.paths import models_path, script_path, sd_path sd_model_file = os.path.join(script_path, 'model.ckpt') diff --git a/modules/ui.py b/modules/ui.py index f57f32db..42e5d866 100644 --- a/modules/ui.py +++ b/modules/ui.py @@ -39,7 +39,7 @@ import modules.generation_parameters_copypaste from modules import prompt_parser from modules.images import save_image import modules.textual_inversion.ui -import modules.hypernetwork.ui +import modules.hypernetworks.ui # this is a fix for Windows users. Without it, javascript files will be served with text/html content-type and the browser will not show any UI mimetypes.init() -- cgit v1.2.1 From b0583be0884cd17dafb408fd79b52b2a0a972563 Mon Sep 17 00:00:00 2001 From: AUTOMATIC <16777216c@gmail.com> Date: Tue, 11 Oct 2022 15:54:34 +0300 Subject: more renames --- modules/hypernetworks/ui.py | 4 ++-- modules/ui.py | 4 ++-- 2 files changed, 4 insertions(+), 4 deletions(-) (limited to 'modules') diff --git a/modules/hypernetworks/ui.py b/modules/hypernetworks/ui.py index 811bc31e..e7540f41 100644 --- a/modules/hypernetworks/ui.py +++ b/modules/hypernetworks/ui.py @@ -13,7 +13,7 @@ def create_hypernetwork(name): fn = os.path.join(shared.cmd_opts.hypernetwork_dir, f"{name}.pt") assert not os.path.exists(fn), f"file {fn} already exists" - hypernet = modules.hypernetwork.hypernetwork.Hypernetwork(name=name) + hypernet = modules.hypernetworks.hypernetwork.Hypernetwork(name=name) hypernet.save(fn) shared.reload_hypernetworks() @@ -28,7 +28,7 @@ def train_hypernetwork(*args): try: sd_hijack.undo_optimizations() - hypernetwork, filename = modules.hypernetwork.hypernetwork.train_hypernetwork(*args) + hypernetwork, filename = modules.hypernetworks.hypernetwork.train_hypernetwork(*args) res = f""" Training {'interrupted' if shared.state.interrupted else 'finished'} at {hypernetwork.step} steps. diff --git a/modules/ui.py b/modules/ui.py index 42e5d866..ee333c3b 100644 --- a/modules/ui.py +++ b/modules/ui.py @@ -1111,7 +1111,7 @@ def create_ui(wrap_gradio_gpu_call): ) create_hypernetwork.click( - fn=modules.hypernetwork.ui.create_hypernetwork, + fn=modules.hypernetworks.ui.create_hypernetwork, inputs=[ new_hypernetwork_name, ], @@ -1164,7 +1164,7 @@ def create_ui(wrap_gradio_gpu_call): ) train_hypernetwork.click( - fn=wrap_gradio_gpu_call(modules.hypernetwork.ui.train_hypernetwork, extra_outputs=[gr.update()]), + fn=wrap_gradio_gpu_call(modules.hypernetworks.ui.train_hypernetwork, extra_outputs=[gr.update()]), _js="start_training_textual_inversion", inputs=[ train_hypernetwork_name, -- cgit v1.2.1 From d01a2d01560b31937df1f3433d210c18f97d32fa Mon Sep 17 00:00:00 2001 From: papuSpartan Date: Tue, 11 Oct 2022 08:03:31 -0500 Subject: move list refresh to webui.py and add stdout indicating it's doing so --- modules/ui.py | 3 --- 1 file changed, 3 deletions(-) (limited to 'modules') diff --git a/modules/ui.py b/modules/ui.py index 06ff118f..ae9317a3 100644 --- a/modules/ui.py +++ b/modules/ui.py @@ -39,7 +39,6 @@ import modules.generation_parameters_copypaste from modules import prompt_parser from modules.images import save_image import modules.textual_inversion.ui -from modules.sd_models import list_models # this is a fix for Windows users. Without it, javascript files will be served with text/html content-type and the browser will not show any UI mimetypes.init() @@ -1291,8 +1290,6 @@ Requested path was: {f} shared.state.interrupt() settings_interface.gradio_ref.do_restart = True - # refresh models so that new models/.ckpt's show up on reload - list_models() restart_gradio.click( fn=request_restart, -- cgit v1.2.1 From 66b7d7584f0b44ce1316425808c27ca7df38293c Mon Sep 17 00:00:00 2001 From: AUTOMATIC <16777216c@gmail.com> Date: Tue, 11 Oct 2022 17:03:00 +0300 Subject: become even stricter with pickles no pickle shall pass thank you again, RyotaK --- modules/safe.py | 17 +++++++++++++++++ 1 file changed, 17 insertions(+) (limited to 'modules') diff --git a/modules/safe.py b/modules/safe.py index 05917463..20be16a5 100644 --- a/modules/safe.py +++ b/modules/safe.py @@ -10,6 +10,7 @@ import torch import numpy import _codecs import zipfile +import re # PyTorch 1.13 and later have _TypedStorage renamed to TypedStorage @@ -54,11 +55,27 @@ class RestrictedUnpickler(pickle.Unpickler): raise pickle.UnpicklingError(f"global '{module}/{name}' is forbidden") +allowed_zip_names = ["archive/data.pkl", "archive/version"] +allowed_zip_names_re = re.compile(r"^archive/data/\d+$") + + +def check_zip_filenames(filename, names): + for name in names: + if name in allowed_zip_names: + continue + if allowed_zip_names_re.match(name): + continue + + raise Exception(f"bad file inside {filename}: {name}") + + def check_pt(filename): try: # new pytorch format is a zip file with zipfile.ZipFile(filename) as z: + check_zip_filenames(filename, z.namelist()) + with z.open('archive/data.pkl') as file: unpickler = RestrictedUnpickler(file) unpickler.load() -- cgit v1.2.1 From c0484f1b986ce7acb0e3596f6089a191279f5442 Mon Sep 17 00:00:00 2001 From: brkirch Date: Mon, 10 Oct 2022 22:48:54 -0400 Subject: Add cross-attention optimization from InvokeAI * Add cross-attention optimization from InvokeAI (~30% speed improvement on MPS) * Add command line option for it * Make it default when CUDA is unavailable --- modules/sd_hijack.py | 5 ++- modules/sd_hijack_optimizations.py | 79 ++++++++++++++++++++++++++++++++++++++ modules/shared.py | 5 ++- 3 files changed, 86 insertions(+), 3 deletions(-) (limited to 'modules') diff --git a/modules/sd_hijack.py b/modules/sd_hijack.py index f07ec041..5a1b167f 100644 --- a/modules/sd_hijack.py +++ b/modules/sd_hijack.py @@ -30,8 +30,11 @@ def apply_optimizations(): elif cmd_opts.opt_split_attention_v1: print("Applying v1 cross attention optimization.") ldm.modules.attention.CrossAttention.forward = sd_hijack_optimizations.split_cross_attention_forward_v1 + elif not cmd_opts.disable_opt_split_attention and (cmd_opts.opt_split_attention_invokeai or not torch.cuda.is_available()): + print("Applying cross attention optimization (InvokeAI).") + ldm.modules.attention.CrossAttention.forward = sd_hijack_optimizations.split_cross_attention_forward_invokeAI elif not cmd_opts.disable_opt_split_attention and (cmd_opts.opt_split_attention or torch.cuda.is_available()): - print("Applying cross attention optimization.") + print("Applying cross attention optimization (Doggettx).") ldm.modules.attention.CrossAttention.forward = sd_hijack_optimizations.split_cross_attention_forward ldm.modules.diffusionmodules.model.AttnBlock.forward = sd_hijack_optimizations.cross_attention_attnblock_forward diff --git a/modules/sd_hijack_optimizations.py b/modules/sd_hijack_optimizations.py index 3349b9c3..870226c5 100644 --- a/modules/sd_hijack_optimizations.py +++ b/modules/sd_hijack_optimizations.py @@ -1,6 +1,7 @@ import math import sys import traceback +import psutil import torch from torch import einsum @@ -116,6 +117,84 @@ def split_cross_attention_forward(self, x, context=None, mask=None): return self.to_out(r2) +# -- From https://github.com/invoke-ai/InvokeAI/blob/main/ldm/modules/attention.py (with hypernetworks support added) -- + +mem_total_gb = psutil.virtual_memory().total // (1 << 30) + +def einsum_op_compvis(q, k, v): + s = einsum('b i d, b j d -> b i j', q, k) + s = s.softmax(dim=-1, dtype=s.dtype) + return einsum('b i j, b j d -> b i d', s, v) + +def einsum_op_slice_0(q, k, v, slice_size): + r = torch.zeros(q.shape[0], q.shape[1], v.shape[2], device=q.device, dtype=q.dtype) + for i in range(0, q.shape[0], slice_size): + end = i + slice_size + r[i:end] = einsum_op_compvis(q[i:end], k[i:end], v[i:end]) + return r + +def einsum_op_slice_1(q, k, v, slice_size): + r = torch.zeros(q.shape[0], q.shape[1], v.shape[2], device=q.device, dtype=q.dtype) + for i in range(0, q.shape[1], slice_size): + end = i + slice_size + r[:, i:end] = einsum_op_compvis(q[:, i:end], k, v) + return r + +def einsum_op_mps_v1(q, k, v): + if q.shape[1] <= 4096: # (512x512) max q.shape[1]: 4096 + return einsum_op_compvis(q, k, v) + else: + slice_size = math.floor(2**30 / (q.shape[0] * q.shape[1])) + return einsum_op_slice_1(q, k, v, slice_size) + +def einsum_op_mps_v2(q, k, v): + if mem_total_gb > 8 and q.shape[1] <= 4096: + return einsum_op_compvis(q, k, v) + else: + return einsum_op_slice_0(q, k, v, 1) + +def einsum_op_tensor_mem(q, k, v, max_tensor_mb): + size_mb = q.shape[0] * q.shape[1] * k.shape[1] * q.element_size() // (1 << 20) + if size_mb <= max_tensor_mb: + return einsum_op_compvis(q, k, v) + div = 1 << int((size_mb - 1) / max_tensor_mb).bit_length() + if div <= q.shape[0]: + return einsum_op_slice_0(q, k, v, q.shape[0] // div) + return einsum_op_slice_1(q, k, v, max(q.shape[1] // div, 1)) + +def einsum_op(q, k, v): + if q.device.type == 'mps': + if mem_total_gb >= 32: + return einsum_op_mps_v1(q, k, v) + return einsum_op_mps_v2(q, k, v) + + # Smaller slices are faster due to L2/L3/SLC caches. + # Tested on i7 with 8MB L3 cache. + return einsum_op_tensor_mem(q, k, v, 32) + +def split_cross_attention_forward_invokeAI(self, x, context=None, mask=None): + h = self.heads + + q = self.to_q(x) + context = default(context, x) + + hypernetwork = shared.loaded_hypernetwork + hypernetwork_layers = (hypernetwork.layers if hypernetwork is not None else {}).get(context.shape[2], None) + + if hypernetwork_layers is not None: + k = self.to_k(hypernetwork_layers[0](context)) * self.scale + v = self.to_v(hypernetwork_layers[1](context)) + else: + k = self.to_k(context) * self.scale + v = self.to_v(context) + del context, x + + q, k, v = map(lambda t: rearrange(t, 'b n (h d) -> (b h) n d', h=h), (q, k, v)) + r = einsum_op(q, k, v) + return self.to_out(rearrange(r, '(b h) n d -> b n (h d)', h=h)) + +# -- End of code from https://github.com/invoke-ai/InvokeAI/blob/main/ldm/modules/attention.py -- + def xformers_attention_forward(self, x, context=None, mask=None): h = self.heads q_in = self.to_q(x) diff --git a/modules/shared.py b/modules/shared.py index 1dc2ccf2..20b45f23 100644 --- a/modules/shared.py +++ b/modules/shared.py @@ -50,9 +50,10 @@ parser.add_argument("--ldsr-models-path", type=str, help="Path to directory with parser.add_argument("--xformers", action='store_true', help="enable xformers for cross attention layers") parser.add_argument("--force-enable-xformers", action='store_true', help="enable xformers for cross attention layers regardless of whether the checking code thinks you can run it; do not make bug reports if this fails to work") parser.add_argument("--deepdanbooru", action='store_true', help="enable deepdanbooru interrogator") -parser.add_argument("--opt-split-attention", action='store_true', help="force-enables cross-attention layer optimization. By default, it's on for torch.cuda and off for other torch devices.") -parser.add_argument("--disable-opt-split-attention", action='store_true', help="force-disables cross-attention layer optimization") +parser.add_argument("--opt-split-attention", action='store_true', help="force-enables Doggettx's cross-attention layer optimization. By default, it's on for torch cuda.") +parser.add_argument("--opt-split-attention-invokeai", action='store_true', help="force-enables InvokeAI's cross-attention layer optimization. By default, it's on when cuda is unavailable.") parser.add_argument("--opt-split-attention-v1", action='store_true', help="enable older version of split attention optimization that does not consume all the VRAM it can find") +parser.add_argument("--disable-opt-split-attention", action='store_true', help="force-disables cross-attention layer optimization") parser.add_argument("--use-cpu", nargs='+',choices=['SD', 'GFPGAN', 'BSRGAN', 'ESRGAN', 'SCUNet', 'CodeFormer'], help="use CPU as torch device for specified modules", default=[]) parser.add_argument("--listen", action='store_true', help="launch gradio with 0.0.0.0 as server name, allowing to respond to network requests") parser.add_argument("--port", type=int, help="launch gradio with given server port, you need root/admin rights for ports < 1024, defaults to 7860 if available", default=None) -- cgit v1.2.1 From 98fd5cde72d5bda1620ab78416c7828fdc3dc10b Mon Sep 17 00:00:00 2001 From: brkirch Date: Mon, 10 Oct 2022 23:55:48 -0400 Subject: Add check for psutil --- modules/sd_hijack.py | 10 ++++++++-- modules/sd_hijack_optimizations.py | 19 +++++++++++++++---- 2 files changed, 23 insertions(+), 6 deletions(-) (limited to 'modules') diff --git a/modules/sd_hijack.py b/modules/sd_hijack.py index 5a1b167f..ac70f876 100644 --- a/modules/sd_hijack.py +++ b/modules/sd_hijack.py @@ -10,6 +10,7 @@ from torch.nn.functional import silu import modules.textual_inversion.textual_inversion from modules import prompt_parser, devices, sd_hijack_optimizations, shared from modules.shared import opts, device, cmd_opts +from modules.sd_hijack_optimizations import invokeAI_mps_available import ldm.modules.attention import ldm.modules.diffusionmodules.model @@ -31,8 +32,13 @@ def apply_optimizations(): print("Applying v1 cross attention optimization.") ldm.modules.attention.CrossAttention.forward = sd_hijack_optimizations.split_cross_attention_forward_v1 elif not cmd_opts.disable_opt_split_attention and (cmd_opts.opt_split_attention_invokeai or not torch.cuda.is_available()): - print("Applying cross attention optimization (InvokeAI).") - ldm.modules.attention.CrossAttention.forward = sd_hijack_optimizations.split_cross_attention_forward_invokeAI + if not invokeAI_mps_available and shared.device.type == 'mps': + print("The InvokeAI cross attention optimization for MPS requires the psutil package which is not installed.") + print("Applying v1 cross attention optimization.") + ldm.modules.attention.CrossAttention.forward = sd_hijack_optimizations.split_cross_attention_forward_v1 + else: + print("Applying cross attention optimization (InvokeAI).") + ldm.modules.attention.CrossAttention.forward = sd_hijack_optimizations.split_cross_attention_forward_invokeAI elif not cmd_opts.disable_opt_split_attention and (cmd_opts.opt_split_attention or torch.cuda.is_available()): print("Applying cross attention optimization (Doggettx).") ldm.modules.attention.CrossAttention.forward = sd_hijack_optimizations.split_cross_attention_forward diff --git a/modules/sd_hijack_optimizations.py b/modules/sd_hijack_optimizations.py index 870226c5..2a4ac7e0 100644 --- a/modules/sd_hijack_optimizations.py +++ b/modules/sd_hijack_optimizations.py @@ -1,7 +1,7 @@ import math import sys import traceback -import psutil +import importlib import torch from torch import einsum @@ -117,9 +117,20 @@ def split_cross_attention_forward(self, x, context=None, mask=None): return self.to_out(r2) -# -- From https://github.com/invoke-ai/InvokeAI/blob/main/ldm/modules/attention.py (with hypernetworks support added) -- -mem_total_gb = psutil.virtual_memory().total // (1 << 30) +def check_for_psutil(): + try: + spec = importlib.util.find_spec('psutil') + return spec is not None + except ModuleNotFoundError: + return False + +invokeAI_mps_available = check_for_psutil() + +# -- Taken from https://github.com/invoke-ai/InvokeAI -- +if invokeAI_mps_available: + import psutil + mem_total_gb = psutil.virtual_memory().total // (1 << 30) def einsum_op_compvis(q, k, v): s = einsum('b i d, b j d -> b i j', q, k) @@ -193,7 +204,7 @@ def split_cross_attention_forward_invokeAI(self, x, context=None, mask=None): r = einsum_op(q, k, v) return self.to_out(rearrange(r, '(b h) n d -> b n (h d)', h=h)) -# -- End of code from https://github.com/invoke-ai/InvokeAI/blob/main/ldm/modules/attention.py -- +# -- End of code from https://github.com/invoke-ai/InvokeAI -- def xformers_attention_forward(self, x, context=None, mask=None): h = self.heads -- cgit v1.2.1 From 574c8e554a5371eca2cbf344764cb241c6ec4efc Mon Sep 17 00:00:00 2001 From: brkirch Date: Tue, 11 Oct 2022 03:32:11 -0400 Subject: Add InvokeAI and lstein to credits, add back CUDA support --- modules/sd_hijack_optimizations.py | 13 +++++++++++++ 1 file changed, 13 insertions(+) (limited to 'modules') diff --git a/modules/sd_hijack_optimizations.py b/modules/sd_hijack_optimizations.py index 2a4ac7e0..f006427f 100644 --- a/modules/sd_hijack_optimizations.py +++ b/modules/sd_hijack_optimizations.py @@ -173,7 +173,20 @@ def einsum_op_tensor_mem(q, k, v, max_tensor_mb): return einsum_op_slice_0(q, k, v, q.shape[0] // div) return einsum_op_slice_1(q, k, v, max(q.shape[1] // div, 1)) +def einsum_op_cuda(q, k, v): + stats = torch.cuda.memory_stats(q.device) + mem_active = stats['active_bytes.all.current'] + mem_reserved = stats['reserved_bytes.all.current'] + mem_free_cuda, _ = torch.cuda.mem_get_info(q.device) + mem_free_torch = mem_reserved - mem_active + mem_free_total = mem_free_cuda + mem_free_torch + # Divide factor of safety as there's copying and fragmentation + return self.einsum_op_tensor_mem(q, k, v, mem_free_total / 3.3 / (1 << 20)) + def einsum_op(q, k, v): + if q.device.type == 'cuda': + return einsum_op_cuda(q, k, v) + if q.device.type == 'mps': if mem_total_gb >= 32: return einsum_op_mps_v1(q, k, v) -- cgit v1.2.1 From 861db783c7acfcb93cf0b5191db3d50f9a9bc531 Mon Sep 17 00:00:00 2001 From: brkirch Date: Tue, 11 Oct 2022 05:13:17 -0400 Subject: Use apply_hypernetwork function --- modules/sd_hijack_optimizations.py | 14 ++++---------- 1 file changed, 4 insertions(+), 10 deletions(-) (limited to 'modules') diff --git a/modules/sd_hijack_optimizations.py b/modules/sd_hijack_optimizations.py index f006427f..79405525 100644 --- a/modules/sd_hijack_optimizations.py +++ b/modules/sd_hijack_optimizations.py @@ -202,16 +202,10 @@ def split_cross_attention_forward_invokeAI(self, x, context=None, mask=None): q = self.to_q(x) context = default(context, x) - hypernetwork = shared.loaded_hypernetwork - hypernetwork_layers = (hypernetwork.layers if hypernetwork is not None else {}).get(context.shape[2], None) - - if hypernetwork_layers is not None: - k = self.to_k(hypernetwork_layers[0](context)) * self.scale - v = self.to_v(hypernetwork_layers[1](context)) - else: - k = self.to_k(context) * self.scale - v = self.to_v(context) - del context, x + context_k, context_v = hypernetwork.apply_hypernetwork(shared.loaded_hypernetwork, context) + k = self.to_k(context_k) * self.scale + v = self.to_v(context_v) + del context, context_k, context_v, x q, k, v = map(lambda t: rearrange(t, 'b n (h d) -> (b h) n d', h=h), (q, k, v)) r = einsum_op(q, k, v) -- cgit v1.2.1 From d682444ecc99319fbd2b142a12727501e2884ba7 Mon Sep 17 00:00:00 2001 From: AUTOMATIC <16777216c@gmail.com> Date: Tue, 11 Oct 2022 18:04:47 +0300 Subject: add option to select hypernetwork modules when creating --- modules/hypernetworks/hypernetwork.py | 4 ++-- modules/hypernetworks/ui.py | 4 ++-- modules/ui.py | 2 ++ 3 files changed, 6 insertions(+), 4 deletions(-) (limited to 'modules') diff --git a/modules/hypernetworks/hypernetwork.py b/modules/hypernetworks/hypernetwork.py index aa701bda..b081f14e 100644 --- a/modules/hypernetworks/hypernetwork.py +++ b/modules/hypernetworks/hypernetwork.py @@ -42,7 +42,7 @@ class Hypernetwork: filename = None name = None - def __init__(self, name=None): + def __init__(self, name=None, enable_sizes=None): self.filename = None self.name = name self.layers = {} @@ -50,7 +50,7 @@ class Hypernetwork: self.sd_checkpoint = None self.sd_checkpoint_name = None - for size in [320, 640, 768, 1280]: + for size in enable_sizes or [320, 640, 768, 1280]: self.layers[size] = (HypernetworkModule(size), HypernetworkModule(size)) def weights(self): diff --git a/modules/hypernetworks/ui.py b/modules/hypernetworks/ui.py index e7540f41..cdddcce1 100644 --- a/modules/hypernetworks/ui.py +++ b/modules/hypernetworks/ui.py @@ -9,11 +9,11 @@ from modules import sd_hijack, shared from modules.hypernetworks import hypernetwork -def create_hypernetwork(name): +def create_hypernetwork(name, enable_sizes): fn = os.path.join(shared.cmd_opts.hypernetwork_dir, f"{name}.pt") assert not os.path.exists(fn), f"file {fn} already exists" - hypernet = modules.hypernetworks.hypernetwork.Hypernetwork(name=name) + hypernet = modules.hypernetworks.hypernetwork.Hypernetwork(name=name, enable_sizes=[int(x) for x in enable_sizes]) hypernet.save(fn) shared.reload_hypernetworks() diff --git a/modules/ui.py b/modules/ui.py index f2d16b12..14b87b92 100644 --- a/modules/ui.py +++ b/modules/ui.py @@ -1037,6 +1037,7 @@ def create_ui(wrap_gradio_gpu_call): gr.HTML(value="

Create a new hypernetwork

") new_hypernetwork_name = gr.Textbox(label="Name") + new_hypernetwork_sizes = gr.CheckboxGroup(label="Modules", value=["768", "320", "640", "1280"], choices=["768", "320", "640", "1280"]) with gr.Row(): with gr.Column(scale=3): @@ -1114,6 +1115,7 @@ def create_ui(wrap_gradio_gpu_call): fn=modules.hypernetworks.ui.create_hypernetwork, inputs=[ new_hypernetwork_name, + new_hypernetwork_sizes, ], outputs=[ train_hypernetwork_name, -- cgit v1.2.1 From ff4ef13dd591ec52f196f344f47537695df95364 Mon Sep 17 00:00:00 2001 From: JC_Array Date: Tue, 11 Oct 2022 10:24:27 -0500 Subject: removed unneeded print --- modules/deepbooru.py | 1 - 1 file changed, 1 deletion(-) (limited to 'modules') diff --git a/modules/deepbooru.py b/modules/deepbooru.py index e31e92c0..89dcac3c 100644 --- a/modules/deepbooru.py +++ b/modules/deepbooru.py @@ -119,7 +119,6 @@ def get_deepbooru_tags_from_model(model, tags, pil_image, threshold, alpha_sort) # sort tags result_tags_out = [] sort_ndx = 0 - print(alpha_sort) if alpha_sort: sort_ndx = 1 -- cgit v1.2.1 From 6d09b8d1df3a96e1380bb1650f5961781630af96 Mon Sep 17 00:00:00 2001 From: AUTOMATIC <16777216c@gmail.com> Date: Tue, 11 Oct 2022 18:33:57 +0300 Subject: produce error when training with medvram/lowvram enabled --- modules/hypernetworks/ui.py | 2 ++ modules/textual_inversion/ui.py | 3 +++ 2 files changed, 5 insertions(+) (limited to 'modules') diff --git a/modules/hypernetworks/ui.py b/modules/hypernetworks/ui.py index cdddcce1..3541a388 100644 --- a/modules/hypernetworks/ui.py +++ b/modules/hypernetworks/ui.py @@ -25,6 +25,8 @@ def train_hypernetwork(*args): initial_hypernetwork = shared.loaded_hypernetwork + assert not shared.cmd_opts.lowvram and not shared.cmd_opts.medvram, 'Training models with lowvram or medvram is not possible' + try: sd_hijack.undo_optimizations() diff --git a/modules/textual_inversion/ui.py b/modules/textual_inversion/ui.py index c57de1f9..70f47343 100644 --- a/modules/textual_inversion/ui.py +++ b/modules/textual_inversion/ui.py @@ -22,6 +22,9 @@ def preprocess(*args): def train_embedding(*args): + + assert not shared.cmd_opts.lowvram and not shared.cmd_opts.medvram, 'Training models with lowvram or medvram is not possible' + try: sd_hijack.undo_optimizations() -- cgit v1.2.1 From d4ea5f4d8631f778d11efcde397e4a5b8801d43b Mon Sep 17 00:00:00 2001 From: AUTOMATIC <16777216c@gmail.com> Date: Tue, 11 Oct 2022 19:03:08 +0300 Subject: add an option to unload models during hypernetwork training to save VRAM --- modules/hypernetworks/hypernetwork.py | 25 +++++++++++++++------- modules/hypernetworks/ui.py | 4 +++- modules/shared.py | 4 ++++ modules/textual_inversion/dataset.py | 29 ++++++++++++++++++-------- modules/textual_inversion/textual_inversion.py | 2 +- 5 files changed, 46 insertions(+), 18 deletions(-) (limited to 'modules') diff --git a/modules/hypernetworks/hypernetwork.py b/modules/hypernetworks/hypernetwork.py index b081f14e..4700e1ec 100644 --- a/modules/hypernetworks/hypernetwork.py +++ b/modules/hypernetworks/hypernetwork.py @@ -175,6 +175,7 @@ def train_hypernetwork(hypernetwork_name, learn_rate, data_root, log_directory, filename = os.path.join(shared.cmd_opts.hypernetwork_dir, f'{hypernetwork_name}.pt') log_directory = os.path.join(log_directory, datetime.datetime.now().strftime("%Y-%m-%d"), hypernetwork_name) + unload = shared.opts.unload_models_when_training if save_hypernetwork_every > 0: hypernetwork_dir = os.path.join(log_directory, "hypernetworks") @@ -188,11 +189,13 @@ def train_hypernetwork(hypernetwork_name, learn_rate, data_root, log_directory, else: images_dir = None - cond_model = shared.sd_model.cond_stage_model - shared.state.textinfo = f"Preparing dataset from {html.escape(data_root)}..." with torch.autocast("cuda"): - ds = modules.textual_inversion.dataset.PersonalizedBase(data_root=data_root, width=512, height=512, repeats=1, placeholder_token=hypernetwork_name, model=shared.sd_model, device=devices.device, template_file=template_file) + ds = modules.textual_inversion.dataset.PersonalizedBase(data_root=data_root, width=512, height=512, repeats=1, placeholder_token=hypernetwork_name, model=shared.sd_model, device=devices.device, template_file=template_file, include_cond=True) + + if unload: + shared.sd_model.cond_stage_model.to(devices.cpu) + shared.sd_model.first_stage_model.to(devices.cpu) hypernetwork = shared.loaded_hypernetwork weights = hypernetwork.weights() @@ -211,7 +214,7 @@ def train_hypernetwork(hypernetwork_name, learn_rate, data_root, log_directory, return hypernetwork, filename pbar = tqdm.tqdm(enumerate(ds), total=steps - ititial_step) - for i, (x, text) in pbar: + for i, (x, text, cond) in pbar: hypernetwork.step = i + ititial_step if hypernetwork.step > steps: @@ -221,11 +224,11 @@ def train_hypernetwork(hypernetwork_name, learn_rate, data_root, log_directory, break with torch.autocast("cuda"): - c = cond_model([text]) - + cond = cond.to(devices.device) x = x.to(devices.device) - loss = shared.sd_model(x.unsqueeze(0), c)[0] + loss = shared.sd_model(x.unsqueeze(0), cond)[0] del x + del cond losses[hypernetwork.step % losses.shape[0]] = loss.item() @@ -244,6 +247,10 @@ def train_hypernetwork(hypernetwork_name, learn_rate, data_root, log_directory, preview_text = text if preview_image_prompt == "" else preview_image_prompt + optimizer.zero_grad() + shared.sd_model.cond_stage_model.to(devices.device) + shared.sd_model.first_stage_model.to(devices.device) + p = processing.StableDiffusionProcessingTxt2Img( sd_model=shared.sd_model, prompt=preview_text, @@ -255,6 +262,10 @@ def train_hypernetwork(hypernetwork_name, learn_rate, data_root, log_directory, processed = processing.process_images(p) image = processed.images[0] + if unload: + shared.sd_model.cond_stage_model.to(devices.cpu) + shared.sd_model.first_stage_model.to(devices.cpu) + shared.state.current_image = image image.save(last_saved_image) diff --git a/modules/hypernetworks/ui.py b/modules/hypernetworks/ui.py index 3541a388..c67facbb 100644 --- a/modules/hypernetworks/ui.py +++ b/modules/hypernetworks/ui.py @@ -5,7 +5,7 @@ import gradio as gr import modules.textual_inversion.textual_inversion import modules.textual_inversion.preprocess -from modules import sd_hijack, shared +from modules import sd_hijack, shared, devices from modules.hypernetworks import hypernetwork @@ -41,5 +41,7 @@ Hypernetwork saved to {html.escape(filename)} raise finally: shared.loaded_hypernetwork = initial_hypernetwork + shared.sd_model.cond_stage_model.to(devices.device) + shared.sd_model.first_stage_model.to(devices.device) sd_hijack.apply_optimizations() diff --git a/modules/shared.py b/modules/shared.py index 20b45f23..c1092ff7 100644 --- a/modules/shared.py +++ b/modules/shared.py @@ -228,6 +228,10 @@ options_templates.update(options_section(('system', "System"), { "multiple_tqdm": OptionInfo(True, "Add a second progress bar to the console that shows progress for an entire job."), })) +options_templates.update(options_section(('training', "Training"), { + "unload_models_when_training": OptionInfo(False, "Unload VAE and CLIP form VRAM when training"), +})) + options_templates.update(options_section(('sd', "Stable Diffusion"), { "sd_model_checkpoint": OptionInfo(None, "Stable Diffusion checkpoint", gr.Dropdown, lambda: {"choices": modules.sd_models.checkpoint_tiles()}, show_on_main_page=True), "sd_hypernetwork": OptionInfo("None", "Stable Diffusion finetune hypernetwork", gr.Dropdown, lambda: {"choices": ["None"] + [x for x in hypernetworks.keys()]}), diff --git a/modules/textual_inversion/dataset.py b/modules/textual_inversion/dataset.py index 4d006366..f61f40d3 100644 --- a/modules/textual_inversion/dataset.py +++ b/modules/textual_inversion/dataset.py @@ -8,14 +8,14 @@ from torchvision import transforms import random import tqdm -from modules import devices +from modules import devices, shared import re re_tag = re.compile(r"[a-zA-Z][_\w\d()]+") class PersonalizedBase(Dataset): - def __init__(self, data_root, width, height, repeats, flip_p=0.5, placeholder_token="*", model=None, device=None, template_file=None): + def __init__(self, data_root, width, height, repeats, flip_p=0.5, placeholder_token="*", model=None, device=None, template_file=None, include_cond=False): self.placeholder_token = placeholder_token @@ -32,6 +32,8 @@ class PersonalizedBase(Dataset): assert data_root, 'dataset directory not specified' + cond_model = shared.sd_model.cond_stage_model + self.image_paths = [os.path.join(data_root, file_path) for file_path in os.listdir(data_root)] print("Preparing dataset...") for path in tqdm.tqdm(self.image_paths): @@ -53,7 +55,13 @@ class PersonalizedBase(Dataset): init_latent = model.get_first_stage_encoding(model.encode_first_stage(torchdata.unsqueeze(dim=0))).squeeze() init_latent = init_latent.to(devices.cpu) - self.dataset.append((init_latent, filename_tokens)) + if include_cond: + text = self.create_text(filename_tokens) + cond = cond_model([text]).to(devices.cpu) + else: + cond = None + + self.dataset.append((init_latent, filename_tokens, cond)) self.length = len(self.dataset) * repeats @@ -64,6 +72,12 @@ class PersonalizedBase(Dataset): def shuffle(self): self.indexes = self.initial_indexes[torch.randperm(self.initial_indexes.shape[0])] + def create_text(self, filename_tokens): + text = random.choice(self.lines) + text = text.replace("[name]", self.placeholder_token) + text = text.replace("[filewords]", ' '.join(filename_tokens)) + return text + def __len__(self): return self.length @@ -72,10 +86,7 @@ class PersonalizedBase(Dataset): self.shuffle() index = self.indexes[i % len(self.indexes)] - x, filename_tokens = self.dataset[index] - - text = random.choice(self.lines) - text = text.replace("[name]", self.placeholder_token) - text = text.replace("[filewords]", ' '.join(filename_tokens)) + x, filename_tokens, cond = self.dataset[index] - return x, text + text = self.create_text(filename_tokens) + return x, text, cond diff --git a/modules/textual_inversion/textual_inversion.py b/modules/textual_inversion/textual_inversion.py index bb05cdc6..35f4bd9e 100644 --- a/modules/textual_inversion/textual_inversion.py +++ b/modules/textual_inversion/textual_inversion.py @@ -201,7 +201,7 @@ def train_embedding(embedding_name, learn_rate, data_root, log_directory, traini return embedding, filename pbar = tqdm.tqdm(enumerate(ds), total=steps-ititial_step) - for i, (x, text) in pbar: + for i, (x, text, _) in pbar: embedding.step = i + ititial_step if embedding.step > steps: -- cgit v1.2.1 From 6a9ea5b41cf92cd9e980349bb5034439f4e7a58b Mon Sep 17 00:00:00 2001 From: AUTOMATIC <16777216c@gmail.com> Date: Tue, 11 Oct 2022 19:22:30 +0300 Subject: prevent extra modules from being saved/loaded with hypernet --- modules/hypernetworks/hypernetwork.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) (limited to 'modules') diff --git a/modules/hypernetworks/hypernetwork.py b/modules/hypernetworks/hypernetwork.py index 4700e1ec..5608e799 100644 --- a/modules/hypernetworks/hypernetwork.py +++ b/modules/hypernetworks/hypernetwork.py @@ -50,7 +50,7 @@ class Hypernetwork: self.sd_checkpoint = None self.sd_checkpoint_name = None - for size in enable_sizes or [320, 640, 768, 1280]: + for size in enable_sizes or []: self.layers[size] = (HypernetworkModule(size), HypernetworkModule(size)) def weights(self): -- cgit v1.2.1 From c080f52ceae73b893155eff7de577aaf1a982a2f Mon Sep 17 00:00:00 2001 From: DepFA <35278260+dfaker@users.noreply.github.com> Date: Tue, 11 Oct 2022 19:37:58 +0100 Subject: move embedding logic to separate file --- modules/textual_inversion/image_embedding.py | 234 +++++++++++++++++++++++++++ 1 file changed, 234 insertions(+) create mode 100644 modules/textual_inversion/image_embedding.py (limited to 'modules') diff --git a/modules/textual_inversion/image_embedding.py b/modules/textual_inversion/image_embedding.py new file mode 100644 index 00000000..6ad39602 --- /dev/null +++ b/modules/textual_inversion/image_embedding.py @@ -0,0 +1,234 @@ +import base64 +import json +import numpy as np +import zlib +from PIL import Image,PngImagePlugin,ImageDraw,ImageFont +from fonts.ttf import Roboto +import torch + +class EmbeddingEncoder(json.JSONEncoder): + def default(self, obj): + if isinstance(obj, torch.Tensor): + return {'TORCHTENSOR':obj.cpu().detach().numpy().tolist()} + return json.JSONEncoder.default(self, obj) + +class EmbeddingDecoder(json.JSONDecoder): + def __init__(self, *args, **kwargs): + json.JSONDecoder.__init__(self, object_hook=self.object_hook, *args, **kwargs) + def object_hook(self, d): + if 'TORCHTENSOR' in d: + return torch.from_numpy(np.array(d['TORCHTENSOR'])) + return d + +def embedding_to_b64(data): + d = json.dumps(data,cls=EmbeddingEncoder) + return base64.b64encode(d.encode()) + +def embedding_from_b64(data): + d = base64.b64decode(data) + return json.loads(d,cls=EmbeddingDecoder) + +def lcg(m=2**32, a=1664525, c=1013904223, seed=0): + while True: + seed = (a * seed + c) % m + yield seed%255 + +def xor_block(block): + g = lcg() + randblock = np.array([next(g) for _ in range(np.product(block.shape))]).astype(np.uint8).reshape(block.shape) + return np.bitwise_xor(block.astype(np.uint8),randblock & 0x0F) + +def style_block(block,sequence): + im = Image.new('RGB',(block.shape[1],block.shape[0])) + draw = ImageDraw.Draw(im) + i=0 + for x in range(-6,im.size[0],8): + for yi,y in enumerate(range(-6,im.size[1],8)): + offset=0 + if yi%2==0: + offset=4 + shade = sequence[i%len(sequence)] + i+=1 + draw.ellipse((x+offset, y, x+6+offset, y+6), fill =(shade,shade,shade) ) + + fg = np.array(im).astype(np.uint8) & 0xF0 + + return block ^ fg + +def insert_image_data_embed(image,data): + d = 3 + data_compressed = zlib.compress( json.dumps(data,cls=EmbeddingEncoder).encode(),level=9) + data_np_ = np.frombuffer(data_compressed,np.uint8).copy() + data_np_high = data_np_ >> 4 + data_np_low = data_np_ & 0x0F + + h = image.size[1] + next_size = data_np_low.shape[0] + (h-(data_np_low.shape[0]%h)) + next_size = next_size + ((h*d)-(next_size%(h*d))) + + data_np_low.resize(next_size) + data_np_low = data_np_low.reshape((h,-1,d)) + + data_np_high.resize(next_size) + data_np_high = data_np_high.reshape((h,-1,d)) + + edge_style = list(data['string_to_param'].values())[0].cpu().detach().numpy().tolist()[0][:1024] + edge_style = (np.abs(edge_style)/np.max(np.abs(edge_style))*255).astype(np.uint8) + + data_np_low = style_block(data_np_low,sequence=edge_style) + data_np_low = xor_block(data_np_low) + data_np_high = style_block(data_np_high,sequence=edge_style[::-1]) + data_np_high = xor_block(data_np_high) + + im_low = Image.fromarray(data_np_low,mode='RGB') + im_high = Image.fromarray(data_np_high,mode='RGB') + + background = Image.new('RGB',(image.size[0]+im_low.size[0]+im_high.size[0]+2,image.size[1]),(0,0,0)) + background.paste(im_low,(0,0)) + background.paste(image,(im_low.size[0]+1,0)) + background.paste(im_high,(im_low.size[0]+1+image.size[0]+1,0)) + + return background + +def crop_black(img,tol=0): + mask = (img>tol).all(2) + mask0,mask1 = mask.any(0),mask.any(1) + col_start,col_end = mask0.argmax(),mask.shape[1]-mask0[::-1].argmax() + row_start,row_end = mask1.argmax(),mask.shape[0]-mask1[::-1].argmax() + return img[row_start:row_end,col_start:col_end] + +def extract_image_data_embed(image): + d=3 + outarr = crop_black(np.array(image.convert('RGB').getdata()).reshape(image.size[1],image.size[0],d ).astype(np.uint8) ) & 0x0F + black_cols = np.where( np.sum(outarr, axis=(0,2))==0) + if black_cols[0].shape[0] < 2: + print('No Image data blocks found.') + return None + + data_block_lower = outarr[:,:black_cols[0].min(),:].astype(np.uint8) + data_block_upper = outarr[:,black_cols[0].max()+1:,:].astype(np.uint8) + + data_block_lower = xor_block(data_block_lower) + data_block_upper = xor_block(data_block_upper) + + data_block = (data_block_upper << 4) | (data_block_lower) + data_block = data_block.flatten().tobytes() + + data = zlib.decompress(data_block) + return json.loads(data,cls=EmbeddingDecoder) + +def addCaptionLines(lines,image,initialx,textfont): + draw = ImageDraw.Draw(image) + hstart =initialx + for fill,line in lines: + fontsize = 32 + font = ImageFont.truetype(textfont, fontsize) + _,_,w, h = draw.textbbox((0,0),line,font=font) + fontsize = min( int(fontsize * ((image.size[0]-35)/w) ), 28) + font = ImageFont.truetype(textfont, fontsize) + _,_,w,h = draw.textbbox((0,0),line,font=font) + draw.text(((image.size[0]-w)/2,hstart), line, font=font, fill=fill) + hstart += h + return hstart + +def caption_image(image,prelines,postlines,background=(51, 51, 51),font=None): + if font is None: + try: + font = ImageFont.truetype(opts.font or Roboto, fontsize) + font = opts.font or Roboto + except Exception: + font = Roboto + + sample_image = image + background = Image.new("RGBA", (sample_image.size[0],sample_image.size[1]+1024), background) + hoffset = addCaptionLines(prelines,background,5,font)+16 + background.paste(sample_image,(0,hoffset)) + hoffset = hoffset+sample_image.size[1]+8 + hoffset = addCaptionLines(postlines,background,hoffset,font) + background = background.crop((0,0,sample_image.size[0],hoffset+8)) + return background + +def caption_image_overlay(srcimage,title,footerLeft,footerMid,footerRight,textfont=None): + from math import cos + + image = srcimage.copy() + + if textfont is None: + try: + textfont = ImageFont.truetype(opts.font or Roboto, fontsize) + textfont = opts.font or Roboto + except Exception: + textfont = Roboto + + factor = 1.5 + gradient = Image.new('RGBA', (1,image.size[1]), color=(0,0,0,0)) + for y in range(image.size[1]): + mag = 1-cos(y/image.size[1]*factor) + mag = max(mag,1-cos((image.size[1]-y)/image.size[1]*factor*1.1)) + gradient.putpixel((0, y), (0,0,0,int(mag*255))) + image = Image.alpha_composite(image.convert('RGBA'), gradient.resize(image.size)) + + draw = ImageDraw.Draw(image) + fontsize = 32 + font = ImageFont.truetype(textfont, fontsize) + padding = 10 + + _,_,w, h = draw.textbbox((0,0),title,font=font) + fontsize = min( int(fontsize * (((image.size[0]*0.75)-(padding*4))/w) ), 72) + font = ImageFont.truetype(textfont, fontsize) + _,_,w,h = draw.textbbox((0,0),title,font=font) + draw.text((padding,padding), title, anchor='lt', font=font, fill=(255,255,255,230)) + + _,_,w, h = draw.textbbox((0,0),footerLeft,font=font) + fontsize_left = min( int(fontsize * (((image.size[0]/3)-(padding))/w) ), 72) + _,_,w, h = draw.textbbox((0,0),footerMid,font=font) + fontsize_mid = min( int(fontsize * (((image.size[0]/3)-(padding))/w) ), 72) + _,_,w, h = draw.textbbox((0,0),footerRight,font=font) + fontsize_right = min( int(fontsize * (((image.size[0]/3)-(padding))/w) ), 72) + + font = ImageFont.truetype(textfont, min(fontsize_left,fontsize_mid,fontsize_right)) + + draw.text((padding,image.size[1]-padding), footerLeft, anchor='ls', font=font, fill=(255,255,255,230)) + draw.text((image.size[0]/2,image.size[1]-padding), footerMid, anchor='ms', font=font, fill=(255,255,255,230)) + draw.text((image.size[0]-padding,image.size[1]-padding), footerRight, anchor='rs', font=font, fill=(255,255,255,230)) + + return image + +if __name__ == '__main__': + + image = Image.new('RGBA',(512,512),(255,255,200,255)) + caption_image(image,[((255,255,255),'line a'),((255,255,255),'line b')], + [((255,255,255),'line c'),((255,255,255),'line d')]) + + image = Image.new('RGBA',(512,512),(255,255,200,255)) + cap_image = caption_image_overlay(image, 'title', 'footerLeft', 'footerMid', 'footerRight') + + test_embed = {'string_to_param':{'*':torch.from_numpy(np.random.random((2, 4096)))}} + + embedded_image = insert_image_data_embed(cap_image, test_embed) + + retrived_embed = extract_image_data_embed(embedded_image) + + assert str(retrived_embed) == str(test_embed) + + embedded_image2 = insert_image_data_embed(cap_image, retrived_embed) + + assert embedded_image == embedded_image2 + + g = lcg() + shared_random = np.array([next(g) for _ in range(100)]).astype(np.uint8).tolist() + + reference_random = [253, 242, 127, 44, 157, 27, 239, 133, 38, 79, 167, 4, 177, + 95, 130, 79, 78, 14, 52, 215, 220, 194, 126, 28, 240, 179, + 160, 153, 149, 50, 105, 14, 21, 218, 199, 18, 54, 198, 193, + 38, 128, 19, 53, 195, 124, 75, 205, 12, 6, 145, 0, 28, + 30, 148, 8, 45, 218, 171, 55, 249, 97, 166, 12, 35, 0, + 41, 221, 122, 215, 170, 31, 113, 186, 97, 119, 31, 23, 185, + 66, 140, 30, 41, 37, 63, 137, 109, 216, 55, 159, 145, 82, + 204, 86, 73, 222, 44, 198, 118, 240, 97] + + assert shared_random == reference_random + + hunna_kay_random_sum = sum(np.array([next(g) for _ in range(100000)]).astype(np.uint8).tolist()) + + assert 12731374 == hunna_kay_random_sum \ No newline at end of file -- cgit v1.2.1 From e5fbf5c755b7c306696546405385d5d2314e555b Mon Sep 17 00:00:00 2001 From: DepFA <35278260+dfaker@users.noreply.github.com> Date: Tue, 11 Oct 2022 19:46:33 +0100 Subject: remove embedding related image functions from images --- modules/images.py | 77 ------------------------------------------------------- 1 file changed, 77 deletions(-) (limited to 'modules') diff --git a/modules/images.py b/modules/images.py index e62eec8e..c0a90676 100644 --- a/modules/images.py +++ b/modules/images.py @@ -463,80 +463,3 @@ def save_image(image, path, basename, seed=None, prompt=None, extension='png', i txt_fullfn = None return fullfn, txt_fullfn - -def addCaptionLines(lines,image,initialx,textfont): - draw = ImageDraw.Draw(image) - hstart =initialx - for fill,line in lines: - fontSize = 32 - font = ImageFont.truetype(textfont, fontSize) - _,_,w, h = draw.textbbox((0,0),line,font=font) - fontSize = min( int(fontSize * ((image.size[0]-35)/w) ), 28) - font = ImageFont.truetype(textfont, fontSize) - _,_,w,h = draw.textbbox((0,0),line,font=font) - draw.text(((image.size[0]-w)/2,hstart), line, font=font, fill=fill) - hstart += h - return hstart - -def captionImge(image,prelines,postlines,background=(51, 51, 51),font=None): - if font is None: - try: - font = ImageFont.truetype(opts.font or Roboto, fontsize) - font = opts.font or Roboto - except Exception: - font = Roboto - - sampleImage = image - background = Image.new("RGBA", (sampleImage.size[0],sampleImage.size[1]+1024), background) - hoffset = addCaptionLines(prelines,background,5,font)+16 - background.paste(sampleImage,(0,hoffset)) - hoffset = hoffset+sampleImage.size[1]+8 - hoffset = addCaptionLines(postlines,background,hoffset,font) - background = background.crop((0,0,sampleImage.size[0],hoffset+8)) - return background - -def captionImageOverlay(srcimage,title,footerLeft,footerMid,footerRight,textfont=None): - from math import cos - - image = srcimage.copy() - - if textfont is None: - try: - textfont = ImageFont.truetype(opts.font or Roboto, fontsize) - textfont = opts.font or Roboto - except Exception: - textfont = Roboto - - factor = 1.5 - gradient = Image.new('RGBA', (1,image.size[1]), color=(0,0,0,0)) - for y in range(image.size[1]): - mag = 1-cos(y/image.size[1]*factor) - mag = max(mag,1-cos((image.size[1]-y)/image.size[1]*factor*1.1)) - gradient.putpixel((0, y), (0,0,0,int(mag*255))) - image = Image.alpha_composite(image.convert('RGBA'), gradient.resize(image.size)) - - draw = ImageDraw.Draw(image) - fontSize = 32 - font = ImageFont.truetype(textfont, fontSize) - padding = 10 - - _,_,w, h = draw.textbbox((0,0),title,font=font) - fontSize = min( int(fontSize * (((image.size[0]*0.75)-(padding*4))/w) ), 72) - font = ImageFont.truetype(textfont, fontSize) - _,_,w,h = draw.textbbox((0,0),title,font=font) - draw.text((padding,padding), title, anchor='lt', font=font, fill=(255,255,255,230)) - - _,_,w, h = draw.textbbox((0,0),footerLeft,font=font) - fontSizeleft = min( int(fontSize * (((image.size[0]/3)-(padding))/w) ), 72) - _,_,w, h = draw.textbbox((0,0),footerMid,font=font) - fontSizemid = min( int(fontSize * (((image.size[0]/3)-(padding))/w) ), 72) - _,_,w, h = draw.textbbox((0,0),footerRight,font=font) - fontSizeright = min( int(fontSize * (((image.size[0]/3)-(padding))/w) ), 72) - - font = ImageFont.truetype(textfont, min(fontSizeleft,fontSizemid,fontSizeright)) - - draw.text((padding,image.size[1]-padding), footerLeft, anchor='ls', font=font, fill=(255,255,255,230)) - draw.text((image.size[0]/2,image.size[1]-padding), footerMid, anchor='ms', font=font, fill=(255,255,255,230)) - draw.text((image.size[0]-padding,image.size[1]-padding), footerRight, anchor='rs', font=font, fill=(255,255,255,230)) - - return image -- cgit v1.2.1 From 61788c0538415fa9ca1dd1b306519c116b18bd2c Mon Sep 17 00:00:00 2001 From: DepFA <35278260+dfaker@users.noreply.github.com> Date: Tue, 11 Oct 2022 19:50:50 +0100 Subject: shift embedding logic out of textual_inversion --- modules/textual_inversion/textual_inversion.py | 125 ++----------------------- 1 file changed, 6 insertions(+), 119 deletions(-) (limited to 'modules') diff --git a/modules/textual_inversion/textual_inversion.py b/modules/textual_inversion/textual_inversion.py index 8c66aeb5..22b4ae7f 100644 --- a/modules/textual_inversion/textual_inversion.py +++ b/modules/textual_inversion/textual_inversion.py @@ -7,124 +7,11 @@ import tqdm import html import datetime -from PIL import Image,PngImagePlugin,ImageDraw -from ..images import captionImageOverlay -import numpy as np -import base64 -import json -import zlib +from PIL import Image,PngImagePlugin from modules import shared, devices, sd_hijack, processing, sd_models import modules.textual_inversion.dataset -class EmbeddingEncoder(json.JSONEncoder): - def default(self, obj): - if isinstance(obj, torch.Tensor): - return {'TORCHTENSOR':obj.cpu().detach().numpy().tolist()} - return json.JSONEncoder.default(self, obj) - -class EmbeddingDecoder(json.JSONDecoder): - def __init__(self, *args, **kwargs): - json.JSONDecoder.__init__(self, object_hook=self.object_hook, *args, **kwargs) - def object_hook(self, d): - if 'TORCHTENSOR' in d: - return torch.from_numpy(np.array(d['TORCHTENSOR'])) - return d - -def embeddingToB64(data): - d = json.dumps(data,cls=EmbeddingEncoder) - return base64.b64encode(d.encode()) - -def embeddingFromB64(data): - d = base64.b64decode(data) - return json.loads(d,cls=EmbeddingDecoder) - -def lcg(m=2**32, a=1664525, c=1013904223, seed=0): - while True: - seed = (a * seed + c) % m - yield seed - -def xorBlock(block): - g = lcg() - randblock = np.array([next(g) for _ in range(np.product(block.shape))]).astype(np.uint8).reshape(block.shape) - return np.bitwise_xor(block.astype(np.uint8),randblock & 0x0F) - -def styleBlock(block,sequence): - im = Image.new('RGB',(block.shape[1],block.shape[0])) - draw = ImageDraw.Draw(im) - i=0 - for x in range(-6,im.size[0],8): - for yi,y in enumerate(range(-6,im.size[1],8)): - offset=0 - if yi%2==0: - offset=4 - shade = sequence[i%len(sequence)] - i+=1 - draw.ellipse((x+offset, y, x+6+offset, y+6), fill =(shade,shade,shade) ) - - fg = np.array(im).astype(np.uint8) & 0xF0 - return block ^ fg - -def insertImageDataEmbed(image,data): - d = 3 - data_compressed = zlib.compress( json.dumps(data,cls=EmbeddingEncoder).encode(),level=9) - dnp = np.frombuffer(data_compressed,np.uint8).copy() - dnphigh = dnp >> 4 - dnplow = dnp & 0x0F - - h = image.size[1] - next_size = dnplow.shape[0] + (h-(dnplow.shape[0]%h)) - next_size = next_size + ((h*d)-(next_size%(h*d))) - - dnplow.resize(next_size) - dnplow = dnplow.reshape((h,-1,d)) - - dnphigh.resize(next_size) - dnphigh = dnphigh.reshape((h,-1,d)) - - edgeStyleWeights = list(data['string_to_param'].values())[0].cpu().detach().numpy().tolist()[0][:1024] - edgeStyleWeights = (np.abs(edgeStyleWeights)/np.max(np.abs(edgeStyleWeights))*255).astype(np.uint8) - - dnplow = styleBlock(dnplow,sequence=edgeStyleWeights) - dnplow = xorBlock(dnplow) - dnphigh = styleBlock(dnphigh,sequence=edgeStyleWeights[::-1]) - dnphigh = xorBlock(dnphigh) - - imlow = Image.fromarray(dnplow,mode='RGB') - imhigh = Image.fromarray(dnphigh,mode='RGB') - - background = Image.new('RGB',(image.size[0]+imlow.size[0]+imhigh.size[0]+2,image.size[1]),(0,0,0)) - background.paste(imlow,(0,0)) - background.paste(image,(imlow.size[0]+1,0)) - background.paste(imhigh,(imlow.size[0]+1+image.size[0]+1,0)) - - return background - -def crop_black(img,tol=0): - mask = (img>tol).all(2) - mask0,mask1 = mask.any(0),mask.any(1) - col_start,col_end = mask0.argmax(),mask.shape[1]-mask0[::-1].argmax() - row_start,row_end = mask1.argmax(),mask.shape[0]-mask1[::-1].argmax() - return img[row_start:row_end,col_start:col_end] - -def extractImageDataEmbed(image): - d=3 - outarr = crop_black(np.array(image.convert('RGB').getdata()).reshape(image.size[1],image.size[0],d ).astype(np.uint8) ) & 0x0F - blackCols = np.where( np.sum(outarr, axis=(0,2))==0) - if blackCols[0].shape[0] < 2: - print('No Image data blocks found.') - return None - - dataBlocklower = outarr[:,:blackCols[0].min(),:].astype(np.uint8) - dataBlockupper = outarr[:,blackCols[0].max()+1:,:].astype(np.uint8) - - dataBlocklower = xorBlock(dataBlocklower) - dataBlockupper = xorBlock(dataBlockupper) - - dataBlock = (dataBlockupper << 4) | (dataBlocklower) - dataBlock = dataBlock.flatten().tobytes() - data = zlib.decompress(dataBlock) - return json.loads(data,cls=EmbeddingDecoder) class Embedding: def __init__(self, vec, name, step=None): @@ -199,10 +86,10 @@ class EmbeddingDatabase: if filename.upper().endswith('.PNG'): embed_image = Image.open(path) if 'sd-ti-embedding' in embed_image.text: - data = embeddingFromB64(embed_image.text['sd-ti-embedding']) + data = embedding_from_b64(embed_image.text['sd-ti-embedding']) name = data.get('name',name) else: - data = extractImageDataEmbed(embed_image) + data = extract_image_data_embed(embed_image) name = data.get('name',name) else: data = torch.load(path, map_location="cpu") @@ -393,7 +280,7 @@ def train_embedding(embedding_name, learn_rate, data_root, log_directory, traini info = PngImagePlugin.PngInfo() data = torch.load(last_saved_file) - info.add_text("sd-ti-embedding", embeddingToB64(data)) + info.add_text("sd-ti-embedding", embedding_to_b64(data)) title = "<{}>".format(data.get('name','???')) checkpoint = sd_models.select_checkpoint() @@ -401,8 +288,8 @@ def train_embedding(embedding_name, learn_rate, data_root, log_directory, traini footer_mid = '[{}]'.format(checkpoint.hash) footer_right = '{}'.format(embedding.step) - captioned_image = captionImageOverlay(image,title,footer_left,footer_mid,footer_right) - captioned_image = insertImageDataEmbed(captioned_image,data) + captioned_image = caption_image_overlay(image,title,footer_left,footer_mid,footer_right) + captioned_image = insert_image_data_embed(captioned_image,data) captioned_image.save(last_saved_image_chunks, "PNG", pnginfo=info) -- cgit v1.2.1 From db71290d2659d3b58ff9b57a82e4721a9eab9229 Mon Sep 17 00:00:00 2001 From: DepFA <35278260+dfaker@users.noreply.github.com> Date: Tue, 11 Oct 2022 19:55:54 +0100 Subject: remove old caption method --- modules/textual_inversion/image_embedding.py | 39 ++-------------------------- 1 file changed, 2 insertions(+), 37 deletions(-) (limited to 'modules') diff --git a/modules/textual_inversion/image_embedding.py b/modules/textual_inversion/image_embedding.py index 6ad39602..c67028a5 100644 --- a/modules/textual_inversion/image_embedding.py +++ b/modules/textual_inversion/image_embedding.py @@ -117,37 +117,6 @@ def extract_image_data_embed(image): data = zlib.decompress(data_block) return json.loads(data,cls=EmbeddingDecoder) -def addCaptionLines(lines,image,initialx,textfont): - draw = ImageDraw.Draw(image) - hstart =initialx - for fill,line in lines: - fontsize = 32 - font = ImageFont.truetype(textfont, fontsize) - _,_,w, h = draw.textbbox((0,0),line,font=font) - fontsize = min( int(fontsize * ((image.size[0]-35)/w) ), 28) - font = ImageFont.truetype(textfont, fontsize) - _,_,w,h = draw.textbbox((0,0),line,font=font) - draw.text(((image.size[0]-w)/2,hstart), line, font=font, fill=fill) - hstart += h - return hstart - -def caption_image(image,prelines,postlines,background=(51, 51, 51),font=None): - if font is None: - try: - font = ImageFont.truetype(opts.font or Roboto, fontsize) - font = opts.font or Roboto - except Exception: - font = Roboto - - sample_image = image - background = Image.new("RGBA", (sample_image.size[0],sample_image.size[1]+1024), background) - hoffset = addCaptionLines(prelines,background,5,font)+16 - background.paste(sample_image,(0,hoffset)) - hoffset = hoffset+sample_image.size[1]+8 - hoffset = addCaptionLines(postlines,background,hoffset,font) - background = background.crop((0,0,sample_image.size[0],hoffset+8)) - return background - def caption_image_overlay(srcimage,title,footerLeft,footerMid,footerRight,textfont=None): from math import cos @@ -195,11 +164,7 @@ def caption_image_overlay(srcimage,title,footerLeft,footerMid,footerRight,textfo return image if __name__ == '__main__': - - image = Image.new('RGBA',(512,512),(255,255,200,255)) - caption_image(image,[((255,255,255),'line a'),((255,255,255),'line b')], - [((255,255,255),'line c'),((255,255,255),'line d')]) - + image = Image.new('RGBA',(512,512),(255,255,200,255)) cap_image = caption_image_overlay(image, 'title', 'footerLeft', 'footerMid', 'footerRight') @@ -231,4 +196,4 @@ if __name__ == '__main__': hunna_kay_random_sum = sum(np.array([next(g) for _ in range(100000)]).astype(np.uint8).tolist()) - assert 12731374 == hunna_kay_random_sum \ No newline at end of file + assert 12731374 == hunna_kay_random_sum -- cgit v1.2.1 From d6fcc6b87bc00fcdecea276fe5b7c7945f7a8b14 Mon Sep 17 00:00:00 2001 From: AUTOMATIC <16777216c@gmail.com> Date: Tue, 11 Oct 2022 22:03:05 +0300 Subject: apply lr schedule to hypernets --- modules/hypernetworks/hypernetwork.py | 19 ++++++++--- modules/textual_inversion/learn_schedule.py | 34 ++++++++++++++++++++ modules/textual_inversion/textual_inversion.py | 44 +++----------------------- modules/ui.py | 2 +- 4 files changed, 54 insertions(+), 45 deletions(-) create mode 100644 modules/textual_inversion/learn_schedule.py (limited to 'modules') diff --git a/modules/hypernetworks/hypernetwork.py b/modules/hypernetworks/hypernetwork.py index 5608e799..470659df 100644 --- a/modules/hypernetworks/hypernetwork.py +++ b/modules/hypernetworks/hypernetwork.py @@ -14,6 +14,7 @@ import torch from torch import einsum from einops import rearrange, repeat import modules.textual_inversion.dataset +from modules.textual_inversion.learn_schedule import LearnSchedule class HypernetworkModule(torch.nn.Module): @@ -202,8 +203,6 @@ def train_hypernetwork(hypernetwork_name, learn_rate, data_root, log_directory, for weight in weights: weight.requires_grad = True - optimizer = torch.optim.AdamW(weights, lr=learn_rate) - losses = torch.zeros((32,)) last_saved_file = "" @@ -213,12 +212,24 @@ def train_hypernetwork(hypernetwork_name, learn_rate, data_root, log_directory, if ititial_step > steps: return hypernetwork, filename + schedules = iter(LearnSchedule(learn_rate, steps, ititial_step)) + (learn_rate, end_step) = next(schedules) + print(f'Training at rate of {learn_rate} until step {end_step}') + + optimizer = torch.optim.AdamW(weights, lr=learn_rate) + pbar = tqdm.tqdm(enumerate(ds), total=steps - ititial_step) for i, (x, text, cond) in pbar: hypernetwork.step = i + ititial_step - if hypernetwork.step > steps: - break + if hypernetwork.step > end_step: + try: + (learn_rate, end_step) = next(schedules) + except Exception: + break + tqdm.tqdm.write(f'Training at rate of {learn_rate} until step {end_step}') + for pg in optimizer.param_groups: + pg['lr'] = learn_rate if shared.state.interrupted: break diff --git a/modules/textual_inversion/learn_schedule.py b/modules/textual_inversion/learn_schedule.py new file mode 100644 index 00000000..db720271 --- /dev/null +++ b/modules/textual_inversion/learn_schedule.py @@ -0,0 +1,34 @@ + +class LearnSchedule: + def __init__(self, learn_rate, max_steps, cur_step=0): + pairs = learn_rate.split(',') + self.rates = [] + self.it = 0 + self.maxit = 0 + for i, pair in enumerate(pairs): + tmp = pair.split(':') + if len(tmp) == 2: + step = int(tmp[1]) + if step > cur_step: + self.rates.append((float(tmp[0]), min(step, max_steps))) + self.maxit += 1 + if step > max_steps: + return + elif step == -1: + self.rates.append((float(tmp[0]), max_steps)) + self.maxit += 1 + return + else: + self.rates.append((float(tmp[0]), max_steps)) + self.maxit += 1 + return + + def __iter__(self): + return self + + def __next__(self): + if self.it < self.maxit: + self.it += 1 + return self.rates[self.it - 1] + else: + raise StopIteration diff --git a/modules/textual_inversion/textual_inversion.py b/modules/textual_inversion/textual_inversion.py index 47a27faf..7717837d 100644 --- a/modules/textual_inversion/textual_inversion.py +++ b/modules/textual_inversion/textual_inversion.py @@ -10,6 +10,7 @@ import datetime from modules import shared, devices, sd_hijack, processing, sd_models import modules.textual_inversion.dataset +from modules.textual_inversion.learn_schedule import LearnSchedule class Embedding: @@ -198,11 +199,8 @@ def train_embedding(embedding_name, learn_rate, data_root, log_directory, traini if ititial_step > steps: return embedding, filename - tr_img_len = len([os.path.join(data_root, file_path) for file_path in os.listdir(data_root)]) - epoch_len = (tr_img_len * num_repeats) + tr_img_len - - scheduleIter = iter(LearnSchedule(learn_rate, steps, ititial_step)) - (learn_rate, end_step) = next(scheduleIter) + schedules = iter(LearnSchedule(learn_rate, steps, ititial_step)) + (learn_rate, end_step) = next(schedules) print(f'Training at rate of {learn_rate} until step {end_step}') optimizer = torch.optim.AdamW([embedding.vec], lr=learn_rate) @@ -213,7 +211,7 @@ def train_embedding(embedding_name, learn_rate, data_root, log_directory, traini if embedding.step > end_step: try: - (learn_rate, end_step) = next(scheduleIter) + (learn_rate, end_step) = next(schedules) except: break tqdm.tqdm.write(f'Training at rate of {learn_rate} until step {end_step}') @@ -288,37 +286,3 @@ Last saved image: {html.escape(last_saved_image)}
embedding.save(filename) return embedding, filename - -class LearnSchedule: - def __init__(self, learn_rate, max_steps, cur_step=0): - pairs = learn_rate.split(',') - self.rates = [] - self.it = 0 - self.maxit = 0 - for i, pair in enumerate(pairs): - tmp = pair.split(':') - if len(tmp) == 2: - step = int(tmp[1]) - if step > cur_step: - self.rates.append((float(tmp[0]), min(step, max_steps))) - self.maxit += 1 - if step > max_steps: - return - elif step == -1: - self.rates.append((float(tmp[0]), max_steps)) - self.maxit += 1 - return - else: - self.rates.append((float(tmp[0]), max_steps)) - self.maxit += 1 - return - - def __iter__(self): - return self - - def __next__(self): - if self.it < self.maxit: - self.it += 1 - return self.rates[self.it - 1] - else: - raise StopIteration diff --git a/modules/ui.py b/modules/ui.py index 2b688e32..1204eef7 100644 --- a/modules/ui.py +++ b/modules/ui.py @@ -1070,7 +1070,7 @@ def create_ui(wrap_gradio_gpu_call): gr.HTML(value="

Train an embedding; must specify a directory with a set of 1:1 ratio images

") train_embedding_name = gr.Dropdown(label='Embedding', choices=sorted(sd_hijack.model_hijack.embedding_db.word_embeddings.keys())) train_hypernetwork_name = gr.Dropdown(label='Hypernetwork', choices=[x for x in shared.hypernetworks.keys()]) - learn_rate = gr.Textbox(label='Learning rate', placeholder="Learning rate", value = "5.0e-03") + learn_rate = gr.Textbox(label='Learning rate', placeholder="Learning rate", value="0.005") dataset_directory = gr.Textbox(label='Dataset directory', placeholder="Path to directory with input images") log_directory = gr.Textbox(label='Log directory', placeholder="Path to directory where to write outputs", value="textual_inversion") template_file = gr.Textbox(label='Prompt template file', value=os.path.join(script_path, "textual_inversion_templates", "style_filewords.txt")) -- cgit v1.2.1 From aa75d5cfe8c84768b0f5d16f977ddba298677379 Mon Sep 17 00:00:00 2001 From: DepFA <35278260+dfaker@users.noreply.github.com> Date: Tue, 11 Oct 2022 20:06:13 +0100 Subject: correct conflict resolution typo --- modules/textual_inversion/textual_inversion.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) (limited to 'modules') diff --git a/modules/textual_inversion/textual_inversion.py b/modules/textual_inversion/textual_inversion.py index 22b4ae7f..789383ce 100644 --- a/modules/textual_inversion/textual_inversion.py +++ b/modules/textual_inversion/textual_inversion.py @@ -169,7 +169,7 @@ def create_embedding(name, num_vectors_per_token, init_text='*'): -def train_embedding(embedding_name, learn_rate, data_root, log_directory, training_width, training_height, steps, num_repeats, create_image_every, save_embedding_every, template_file, save_image_with_stored_embedding, preview_image_prompt) +def train_embedding(embedding_name, learn_rate, data_root, log_directory, training_width, training_height, steps, num_repeats, create_image_every, save_embedding_every, template_file, save_image_with_stored_embedding, preview_image_prompt): assert embedding_name, 'embedding not selected' shared.state.textinfo = "Initializing textual inversion training..." -- cgit v1.2.1 From 91d7ee0d097a7ea203d261b570cd2b834837d9e2 Mon Sep 17 00:00:00 2001 From: DepFA <35278260+dfaker@users.noreply.github.com> Date: Tue, 11 Oct 2022 20:09:10 +0100 Subject: update imports --- modules/textual_inversion/textual_inversion.py | 3 +++ 1 file changed, 3 insertions(+) (limited to 'modules') diff --git a/modules/textual_inversion/textual_inversion.py b/modules/textual_inversion/textual_inversion.py index 789383ce..ff0a62b3 100644 --- a/modules/textual_inversion/textual_inversion.py +++ b/modules/textual_inversion/textual_inversion.py @@ -12,6 +12,9 @@ from PIL import Image,PngImagePlugin from modules import shared, devices, sd_hijack, processing, sd_models import modules.textual_inversion.dataset +from modules.textual_inversion.image_embedding import( embedding_to_b64,embedding_from_b64, + insert_image_data_embed,extract_image_data_embed, + caption_image_overlay ) class Embedding: def __init__(self, vec, name, step=None): -- cgit v1.2.1 From 5f3317376bb7952bc5145f05f16c1bbd466efc85 Mon Sep 17 00:00:00 2001 From: DepFA <35278260+dfaker@users.noreply.github.com> Date: Tue, 11 Oct 2022 20:09:49 +0100 Subject: spacing --- modules/textual_inversion/textual_inversion.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) (limited to 'modules') diff --git a/modules/textual_inversion/textual_inversion.py b/modules/textual_inversion/textual_inversion.py index ff0a62b3..485ef46c 100644 --- a/modules/textual_inversion/textual_inversion.py +++ b/modules/textual_inversion/textual_inversion.py @@ -12,7 +12,7 @@ from PIL import Image,PngImagePlugin from modules import shared, devices, sd_hijack, processing, sd_models import modules.textual_inversion.dataset -from modules.textual_inversion.image_embedding import( embedding_to_b64,embedding_from_b64, +from modules.textual_inversion.image_embedding import (embedding_to_b64,embedding_from_b64, insert_image_data_embed,extract_image_data_embed, caption_image_overlay ) -- cgit v1.2.1 From 7e6a6e00ad6f3b7ef43c8120db9ecac6e8d6bea5 Mon Sep 17 00:00:00 2001 From: DepFA <35278260+dfaker@users.noreply.github.com> Date: Tue, 11 Oct 2022 20:20:46 +0100 Subject: Add files via upload --- modules/textual_inversion/test_embedding.png | Bin 0 -> 489220 bytes 1 file changed, 0 insertions(+), 0 deletions(-) create mode 100644 modules/textual_inversion/test_embedding.png (limited to 'modules') diff --git a/modules/textual_inversion/test_embedding.png b/modules/textual_inversion/test_embedding.png new file mode 100644 index 00000000..07e2d9af Binary files /dev/null and b/modules/textual_inversion/test_embedding.png differ -- cgit v1.2.1 From 66ec505975aaa305a217fc27281ce368cbaef281 Mon Sep 17 00:00:00 2001 From: DepFA <35278260+dfaker@users.noreply.github.com> Date: Tue, 11 Oct 2022 20:21:30 +0100 Subject: add file based test --- modules/textual_inversion/image_embedding.py | 8 ++++++++ 1 file changed, 8 insertions(+) (limited to 'modules') diff --git a/modules/textual_inversion/image_embedding.py b/modules/textual_inversion/image_embedding.py index c67028a5..1224fb42 100644 --- a/modules/textual_inversion/image_embedding.py +++ b/modules/textual_inversion/image_embedding.py @@ -164,6 +164,14 @@ def caption_image_overlay(srcimage,title,footerLeft,footerMid,footerRight,textfo return image if __name__ == '__main__': + + testEmbed = Image.open('test_embedding.png') + + data = extract_image_data_embed(testEmbed) + assert data is not None + + data = embedding_from_b64(testEmbed.text['sd-ti-embedding']) + assert data is not None image = Image.new('RGBA',(512,512),(255,255,200,255)) cap_image = caption_image_overlay(image, 'title', 'footerLeft', 'footerMid', 'footerRight') -- cgit v1.2.1 From 6be32b31d181e42c639dad3451229aa7b9cfd1cf Mon Sep 17 00:00:00 2001 From: AUTOMATIC <16777216c@gmail.com> Date: Tue, 11 Oct 2022 23:07:09 +0300 Subject: reports that training with medvram is possible. --- modules/hypernetworks/ui.py | 2 +- modules/textual_inversion/ui.py | 2 +- 2 files changed, 2 insertions(+), 2 deletions(-) (limited to 'modules') diff --git a/modules/hypernetworks/ui.py b/modules/hypernetworks/ui.py index c67facbb..dfa599af 100644 --- a/modules/hypernetworks/ui.py +++ b/modules/hypernetworks/ui.py @@ -25,7 +25,7 @@ def train_hypernetwork(*args): initial_hypernetwork = shared.loaded_hypernetwork - assert not shared.cmd_opts.lowvram and not shared.cmd_opts.medvram, 'Training models with lowvram or medvram is not possible' + assert not shared.cmd_opts.lowvram, 'Training models with lowvram is not possible' try: sd_hijack.undo_optimizations() diff --git a/modules/textual_inversion/ui.py b/modules/textual_inversion/ui.py index 70f47343..36881e7a 100644 --- a/modules/textual_inversion/ui.py +++ b/modules/textual_inversion/ui.py @@ -23,7 +23,7 @@ def preprocess(*args): def train_embedding(*args): - assert not shared.cmd_opts.lowvram and not shared.cmd_opts.medvram, 'Training models with lowvram or medvram is not possible' + assert not shared.cmd_opts.lowvram, 'Training models with lowvram not possible' try: sd_hijack.undo_optimizations() -- cgit v1.2.1 From f53f703aebc801c4204182d52bb1e0bef9808e1f Mon Sep 17 00:00:00 2001 From: JC_Array Date: Tue, 11 Oct 2022 18:12:12 -0500 Subject: resolved conflicts, moved settings under interrogate section, settings only show if deepbooru flag is enabled --- modules/deepbooru.py | 2 +- modules/shared.py | 19 +++++++++---------- modules/textual_inversion/preprocess.py | 2 +- modules/ui.py | 2 +- 4 files changed, 12 insertions(+), 13 deletions(-) (limited to 'modules') diff --git a/modules/deepbooru.py b/modules/deepbooru.py index 89dcac3c..29529949 100644 --- a/modules/deepbooru.py +++ b/modules/deepbooru.py @@ -8,7 +8,7 @@ def get_deepbooru_tags(pil_image): This method is for running only one image at a time for simple use. Used to the img2img interrogate. """ from modules import shared # prevents circular reference - create_deepbooru_process(shared.opts.deepbooru_threshold, shared.opts.deepbooru_sort_alpha) + create_deepbooru_process(shared.opts.interrogate_deepbooru_score_threshold, shared.opts.deepbooru_sort_alpha) shared.deepbooru_process_return["value"] = -1 shared.deepbooru_process_queue.put(pil_image) while shared.deepbooru_process_return["value"] == -1: diff --git a/modules/shared.py b/modules/shared.py index 817203f8..5456c477 100644 --- a/modules/shared.py +++ b/modules/shared.py @@ -248,15 +248,20 @@ options_templates.update(options_section(('sd', "Stable Diffusion"), { "random_artist_categories": OptionInfo([], "Allowed categories for random artists selection when using the Roll button", gr.CheckboxGroup, {"choices": artist_db.categories()}), })) -options_templates.update(options_section(('interrogate', "Interrogate Options"), { +interrogate_option_dictionary = { "interrogate_keep_models_in_memory": OptionInfo(False, "Interrogate: keep models in VRAM"), "interrogate_use_builtin_artists": OptionInfo(True, "Interrogate: use artists from artists.csv"), "interrogate_clip_num_beams": OptionInfo(1, "Interrogate: num_beams for BLIP", gr.Slider, {"minimum": 1, "maximum": 16, "step": 1}), "interrogate_clip_min_length": OptionInfo(24, "Interrogate: minimum description length (excluding artists, etc..)", gr.Slider, {"minimum": 1, "maximum": 128, "step": 1}), "interrogate_clip_max_length": OptionInfo(48, "Interrogate: maximum description length", gr.Slider, {"minimum": 1, "maximum": 256, "step": 1}), - "interrogate_clip_dict_limit": OptionInfo(1500, "Interrogate: maximum number of lines in text file (0 = No limit)"), - "interrogate_deepbooru_score_threshold": OptionInfo(0.5, "Interrogate: deepbooru score threshold", gr.Slider, {"minimum": 0, "maximum": 1, "step": 0.01}), -})) + "interrogate_clip_dict_limit": OptionInfo(1500, "Interrogate: maximum number of lines in text file (0 = No limit)") +} + +if cmd_opts.deepdanbooru: + interrogate_option_dictionary["interrogate_deepbooru_score_threshold"] = OptionInfo(0.5, "Interrogate: deepbooru score threshold", gr.Slider, {"minimum": 0, "maximum": 1, "step": 0.01}) + interrogate_option_dictionary["deepbooru_sort_alpha"] = OptionInfo(True, "Interrogate: deepbooru sort alphabetically", gr.Checkbox) + +options_templates.update(options_section(('interrogate', "Interrogate Options"), interrogate_option_dictionary)) options_templates.update(options_section(('ui', "User interface"), { "show_progressbar": OptionInfo(True, "Show progressbar"), @@ -282,12 +287,6 @@ options_templates.update(options_section(('sampler-params', "Sampler parameters" 'eta_noise_seed_delta': OptionInfo(0, "Eta noise seed delta", gr.Number, {"precision": 0}), })) -if cmd_opts.deepdanbooru: - options_templates.update(options_section(('deepbooru-params', "DeepBooru parameters"), { - "deepbooru_sort_alpha": OptionInfo(True, "Sort Alphabetical", gr.Checkbox), - 'deepbooru_threshold': OptionInfo(0.5, "Threshold", gr.Slider, {"minimum": 0.0, "maximum": 1.0, "step": 0.01}), - })) - class Options: data = None diff --git a/modules/textual_inversion/preprocess.py b/modules/textual_inversion/preprocess.py index a96388d6..113cecf1 100644 --- a/modules/textual_inversion/preprocess.py +++ b/modules/textual_inversion/preprocess.py @@ -29,7 +29,7 @@ def preprocess(process_src, process_dst, process_width, process_height, process_ shared.interrogator.load() if process_caption_deepbooru: - deepbooru.create_deepbooru_process(opts.deepbooru_threshold, opts.deepbooru_sort_alpha) + deepbooru.create_deepbooru_process(opts.interrogate_deepbooru_score_threshold, opts.deepbooru_sort_alpha) def save_pic_with_caption(image, index): if process_caption: diff --git a/modules/ui.py b/modules/ui.py index 2891fc8c..fa45edca 100644 --- a/modules/ui.py +++ b/modules/ui.py @@ -317,7 +317,7 @@ def interrogate(image): def interrogate_deepbooru(image): - prompt = get_deepbooru_tags(image, opts.interrogate_deepbooru_score_threshold) + prompt = get_deepbooru_tags(image) return gr_show(True) if prompt is None else prompt -- cgit v1.2.1 From 65b973ac4e547a325f30a05f852b161421af2041 Mon Sep 17 00:00:00 2001 From: supersteve3d <39339941+supersteve3d@users.noreply.github.com> Date: Wed, 12 Oct 2022 08:21:52 +0800 Subject: Update shared.py Correct typo to "Unload VAE and CLIP from VRAM when training" in settings tab. --- modules/shared.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) (limited to 'modules') diff --git a/modules/shared.py b/modules/shared.py index c1092ff7..46bc740c 100644 --- a/modules/shared.py +++ b/modules/shared.py @@ -229,7 +229,7 @@ options_templates.update(options_section(('system', "System"), { })) options_templates.update(options_section(('training', "Training"), { - "unload_models_when_training": OptionInfo(False, "Unload VAE and CLIP form VRAM when training"), + "unload_models_when_training": OptionInfo(False, "Unload VAE and CLIP from VRAM when training"), })) options_templates.update(options_section(('sd', "Stable Diffusion"), { -- cgit v1.2.1 From d717eb079cd6b7fa7a4f97c0a10d400bdec753fb Mon Sep 17 00:00:00 2001 From: Greg Fuller Date: Tue, 11 Oct 2022 18:02:41 -0700 Subject: Interrogate: add option to include ranks in output Since the UI also allows users to specify ranks, it can be useful to show people what ranks are being returned by interrogate This can also give much better results when feeding the interrogate results back into either img2img or txt2img, especially when trying to generate a specific character or scene for which you have a similar concept image Testing Steps: Launch Webui with command line arg: --deepdanbooru Navigate to img2img tab, use interrogate DeepBooru, verify tags appears as before. Use "Interrogate CLIP", verify prompt appears as before Navigate to Settings tab, enable new option, click "apply settings" Navigate to img2img, Interrogate DeepBooru again, verify that weights appear and are properly formatted. Note that "Interrogate CLIP" prompt is still unchanged In my testing, this change has no effect to "Interrogate CLIP", as it seems to generate a sentence-structured caption, and not a set of tags. (reproduce changes from https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/2149/commits/6ed4faac46c45ca7353f228aca9b436bbaba7bc7) --- modules/deepbooru.py | 14 +++++++++----- modules/interrogate.py | 7 +++++-- modules/shared.py | 1 + modules/ui.py | 5 ++--- 4 files changed, 17 insertions(+), 10 deletions(-) (limited to 'modules') diff --git a/modules/deepbooru.py b/modules/deepbooru.py index 7e3c0618..32d741e2 100644 --- a/modules/deepbooru.py +++ b/modules/deepbooru.py @@ -3,7 +3,7 @@ from concurrent.futures import ProcessPoolExecutor from multiprocessing import get_context -def _load_tf_and_return_tags(pil_image, threshold): +def _load_tf_and_return_tags(pil_image, threshold, include_ranks): import deepdanbooru as dd import tensorflow as tf import numpy as np @@ -52,12 +52,16 @@ def _load_tf_and_return_tags(pil_image, threshold): if result_dict[tag] >= threshold: if tag.startswith("rating:"): continue - result_tags_out.append(tag) + tag_formatted = tag.replace('_', ' ').replace(':', ' ') + if include_ranks: + result_tags_out.append(f'({tag_formatted}:{result_dict[tag]})') + else: + result_tags_out.append(tag_formatted) result_tags_print.append(f'{result_dict[tag]} {tag}') print('\n'.join(sorted(result_tags_print, reverse=True))) - return ', '.join(result_tags_out).replace('_', ' ').replace(':', ' ') + return ', '.join(result_tags_out) def subprocess_init_no_cuda(): @@ -65,9 +69,9 @@ def subprocess_init_no_cuda(): os.environ["CUDA_VISIBLE_DEVICES"] = "-1" -def get_deepbooru_tags(pil_image, threshold=0.5): +def get_deepbooru_tags(pil_image, threshold=0.5, include_ranks=False): context = get_context('spawn') with ProcessPoolExecutor(initializer=subprocess_init_no_cuda, mp_context=context) as executor: - f = executor.submit(_load_tf_and_return_tags, pil_image, threshold, ) + f = executor.submit(_load_tf_and_return_tags, pil_image, threshold, include_ranks) ret = f.result() # will rethrow any exceptions return ret \ No newline at end of file diff --git a/modules/interrogate.py b/modules/interrogate.py index 635e266e..af858cc0 100644 --- a/modules/interrogate.py +++ b/modules/interrogate.py @@ -123,7 +123,7 @@ class InterrogateModels: return caption[0] - def interrogate(self, pil_image): + def interrogate(self, pil_image, include_ranks=False): res = None try: @@ -156,7 +156,10 @@ class InterrogateModels: for name, topn, items in self.categories: matches = self.rank(image_features, items, top_count=topn) for match, score in matches: - res += ", " + match + if include_ranks: + res += ", " + match + else: + res += f", ({match}:{score})" except Exception: print(f"Error interrogating", file=sys.stderr) diff --git a/modules/shared.py b/modules/shared.py index c1092ff7..3e0bfd72 100644 --- a/modules/shared.py +++ b/modules/shared.py @@ -251,6 +251,7 @@ options_templates.update(options_section(('sd', "Stable Diffusion"), { options_templates.update(options_section(('interrogate', "Interrogate Options"), { "interrogate_keep_models_in_memory": OptionInfo(False, "Interrogate: keep models in VRAM"), "interrogate_use_builtin_artists": OptionInfo(True, "Interrogate: use artists from artists.csv"), + "interrogate_return_ranks": OptionInfo(False, "Interrogate: include ranks of model tags matches in results (Has no effect on caption-based interrogators)."), "interrogate_clip_num_beams": OptionInfo(1, "Interrogate: num_beams for BLIP", gr.Slider, {"minimum": 1, "maximum": 16, "step": 1}), "interrogate_clip_min_length": OptionInfo(24, "Interrogate: minimum description length (excluding artists, etc..)", gr.Slider, {"minimum": 1, "maximum": 128, "step": 1}), "interrogate_clip_max_length": OptionInfo(48, "Interrogate: maximum description length", gr.Slider, {"minimum": 1, "maximum": 256, "step": 1}), diff --git a/modules/ui.py b/modules/ui.py index 1204eef7..f4dbe247 100644 --- a/modules/ui.py +++ b/modules/ui.py @@ -311,13 +311,12 @@ def apply_styles(prompt, prompt_neg, style1_name, style2_name): def interrogate(image): - prompt = shared.interrogator.interrogate(image) - + prompt = shared.interrogator.interrogate(image, include_ranks=opts.interrogate_return_ranks) return gr_show(True) if prompt is None else prompt def interrogate_deepbooru(image): - prompt = get_deepbooru_tags(image, opts.interrogate_deepbooru_score_threshold) + prompt = get_deepbooru_tags(image, opts.interrogate_deepbooru_score_threshold, opts.interrogate_return_ranks) return gr_show(True) if prompt is None else prompt -- cgit v1.2.1 From 6ac2ec2b78bc5fabd09cb866dd9a71061d669269 Mon Sep 17 00:00:00 2001 From: AUTOMATIC <16777216c@gmail.com> Date: Wed, 12 Oct 2022 07:01:20 +0300 Subject: create dir for hypernetworks --- modules/shared.py | 1 + 1 file changed, 1 insertion(+) (limited to 'modules') diff --git a/modules/shared.py b/modules/shared.py index c1092ff7..e65e77f8 100644 --- a/modules/shared.py +++ b/modules/shared.py @@ -86,6 +86,7 @@ parallel_processing_allowed = not cmd_opts.lowvram and not cmd_opts.medvram xformers_available = False config_filename = cmd_opts.ui_settings_file +os.makedirs(cmd_opts.hypernetwork_dir, exist_ok=True) hypernetworks = hypernetwork.list_hypernetworks(cmd_opts.hypernetwork_dir) loaded_hypernetwork = None -- cgit v1.2.1 From fec2221eeaafb50afd26ba3e109bf6f928011e69 Mon Sep 17 00:00:00 2001 From: Greg Fuller Date: Tue, 11 Oct 2022 19:29:38 -0700 Subject: Truncate error text to fix service lockup / stall What: * Update wrap_gradio_call to add a limit to the maximum amount of text output Why: * wrap_gradio_call currently prints out a list of the arguments provided to the failing function. * if that function is save_image, this causes the entire image to be printed to stderr * If the image is large, this can cause the service to lock up while attempting to print all the text * It is easy to generate large images using the x/y plot script * it is easy to encounter image save exceptions, including if the output directory does not exist / cannot be written to, or if the file is too big * The huge amount of log spam is confusing and not particularly helpful --- modules/ui.py | 9 ++++++++- 1 file changed, 8 insertions(+), 1 deletion(-) (limited to 'modules') diff --git a/modules/ui.py b/modules/ui.py index 1204eef7..33a49d3b 100644 --- a/modules/ui.py +++ b/modules/ui.py @@ -181,8 +181,15 @@ def wrap_gradio_call(func, extra_outputs=None): try: res = list(func(*args, **kwargs)) except Exception as e: + # When printing out our debug argument list, do not print out more than a MB of text + max_debug_str_len = 131072 # (1024*1024)/8 + print("Error completing request", file=sys.stderr) - print("Arguments:", args, kwargs, file=sys.stderr) + argStr = f"Arguments: {str(args)} {str(kwargs)}" + print(argStr[:max_debug_str_len], file=sys.stderr) + if len(argStr) > max_debug_str_len: + print(f"(Argument list truncated at {max_debug_str_len}/{len(argStr)} characters)", file=sys.stderr) + print(traceback.format_exc(), file=sys.stderr) shared.state.job = "" -- cgit v1.2.1 From 336bd8703c7b4d71f2f096f303599925a30b8167 Mon Sep 17 00:00:00 2001 From: AUTOMATIC <16777216c@gmail.com> Date: Wed, 12 Oct 2022 09:00:07 +0300 Subject: just add the deepdanbooru settings unconditionally --- modules/shared.py | 13 ++++--------- 1 file changed, 4 insertions(+), 9 deletions(-) (limited to 'modules') diff --git a/modules/shared.py b/modules/shared.py index f150e024..42e99741 100644 --- a/modules/shared.py +++ b/modules/shared.py @@ -249,20 +249,15 @@ options_templates.update(options_section(('sd', "Stable Diffusion"), { "random_artist_categories": OptionInfo([], "Allowed categories for random artists selection when using the Roll button", gr.CheckboxGroup, {"choices": artist_db.categories()}), })) -interrogate_option_dictionary = { +options_templates.update(options_section(('interrogate', "Interrogate Options"), { "interrogate_keep_models_in_memory": OptionInfo(False, "Interrogate: keep models in VRAM"), "interrogate_use_builtin_artists": OptionInfo(True, "Interrogate: use artists from artists.csv"), "interrogate_clip_num_beams": OptionInfo(1, "Interrogate: num_beams for BLIP", gr.Slider, {"minimum": 1, "maximum": 16, "step": 1}), "interrogate_clip_min_length": OptionInfo(24, "Interrogate: minimum description length (excluding artists, etc..)", gr.Slider, {"minimum": 1, "maximum": 128, "step": 1}), "interrogate_clip_max_length": OptionInfo(48, "Interrogate: maximum description length", gr.Slider, {"minimum": 1, "maximum": 256, "step": 1}), - "interrogate_clip_dict_limit": OptionInfo(1500, "Interrogate: maximum number of lines in text file (0 = No limit)") -} - -if cmd_opts.deepdanbooru: - interrogate_option_dictionary["interrogate_deepbooru_score_threshold"] = OptionInfo(0.5, "Interrogate: deepbooru score threshold", gr.Slider, {"minimum": 0, "maximum": 1, "step": 0.01}) - interrogate_option_dictionary["deepbooru_sort_alpha"] = OptionInfo(True, "Interrogate: deepbooru sort alphabetically", gr.Checkbox) - -options_templates.update(options_section(('interrogate', "Interrogate Options"), interrogate_option_dictionary)) + "interrogate_deepbooru_score_threshold": OptionInfo(0.5, "Interrogate: deepbooru score threshold", gr.Slider, {"minimum": 0, "maximum": 1, "step": 0.01}), + "deepbooru_sort_alpha": OptionInfo(True, "Interrogate: deepbooru sort alphabetically"), +})) options_templates.update(options_section(('ui', "User interface"), { "show_progressbar": OptionInfo(True, "Show progressbar"), -- cgit v1.2.1 From 57e03cdd244eee4e33ccab7554b3594563a3d0cd Mon Sep 17 00:00:00 2001 From: brkirch Date: Wed, 12 Oct 2022 00:54:24 -0400 Subject: Ensure the directory exists before saving to it The directory for the images saved with the Save button may still not exist, so it needs to be created prior to opening the log.csv file. --- modules/ui.py | 2 ++ 1 file changed, 2 insertions(+) (limited to 'modules') diff --git a/modules/ui.py b/modules/ui.py index 00bf09ae..cd67b84b 100644 --- a/modules/ui.py +++ b/modules/ui.py @@ -131,6 +131,8 @@ def save_files(js_data, images, do_make_zip, index): images = [images[index]] start_index = index + os.makedirs(opts.outdir_save, exist_ok=True) + with open(os.path.join(opts.outdir_save, "log.csv"), "a", encoding="utf8", newline='') as file: at_start = file.tell() == 0 writer = csv.writer(file) -- cgit v1.2.1 From 2d006ce16cd95d587533656c3ac4991495e96f23 Mon Sep 17 00:00:00 2001 From: Milly Date: Mon, 10 Oct 2022 00:56:36 +0900 Subject: xy_grid: Find hypernetwork by closest name --- modules/hypernetworks/hypernetwork.py | 11 +++++++++++ 1 file changed, 11 insertions(+) (limited to 'modules') diff --git a/modules/hypernetworks/hypernetwork.py b/modules/hypernetworks/hypernetwork.py index 470659df..8f2192e2 100644 --- a/modules/hypernetworks/hypernetwork.py +++ b/modules/hypernetworks/hypernetwork.py @@ -120,6 +120,17 @@ def load_hypernetwork(filename): shared.loaded_hypernetwork = None +def find_closest_hypernetwork_name(search: str): + if not search: + return None + search = search.lower() + applicable = [name for name in shared.hypernetworks if search in name.lower()] + if not applicable: + return None + applicable = sorted(applicable, key=lambda name: len(name)) + return applicable[0] + + def apply_hypernetwork(hypernetwork, context, layer=None): hypernetwork_layers = (hypernetwork.layers if hypernetwork is not None else {}).get(context.shape[2], None) -- cgit v1.2.1 From ee015a1af66a94a75c914659fa0d321e702a0a87 Mon Sep 17 00:00:00 2001 From: AUTOMATIC <16777216c@gmail.com> Date: Wed, 12 Oct 2022 11:05:57 +0300 Subject: change textual inversion tab to train remake train interface to use tabs --- modules/hypernetworks/hypernetwork.py | 2 +- modules/ui.py | 22 +++++++++------------- 2 files changed, 10 insertions(+), 14 deletions(-) (limited to 'modules') diff --git a/modules/hypernetworks/hypernetwork.py b/modules/hypernetworks/hypernetwork.py index 8f2192e2..8314450a 100644 --- a/modules/hypernetworks/hypernetwork.py +++ b/modules/hypernetworks/hypernetwork.py @@ -175,7 +175,7 @@ def attention_CrossAttention_forward(self, x, context=None, mask=None): def train_hypernetwork(hypernetwork_name, learn_rate, data_root, log_directory, steps, create_image_every, save_hypernetwork_every, template_file, preview_image_prompt): - assert hypernetwork_name, 'embedding not selected' + assert hypernetwork_name, 'hypernetwork not selected' path = shared.hypernetworks.get(hypernetwork_name, None) shared.loaded_hypernetwork = Hypernetwork() diff --git a/modules/ui.py b/modules/ui.py index 4bfdd275..86a2da6c 100644 --- a/modules/ui.py +++ b/modules/ui.py @@ -1035,14 +1035,14 @@ def create_ui(wrap_gradio_gpu_call): sd_hijack.model_hijack.embedding_db.load_textual_inversion_embeddings() - with gr.Blocks() as textual_inversion_interface: + with gr.Blocks() as train_interface: with gr.Row().style(equal_height=False): - with gr.Column(): - with gr.Group(): - gr.HTML(value="

See wiki for detailed explanation.

") + gr.HTML(value="

See wiki for detailed explanation.

") - gr.HTML(value="

Create a new embedding

") + with gr.Row().style(equal_height=False): + with gr.Tabs(elem_id="train_tabs"): + with gr.Tab(label="Create embedding"): new_embedding_name = gr.Textbox(label="Name") initialization_text = gr.Textbox(label="Initialization text", value="*") nvpt = gr.Slider(label="Number of vectors per token", minimum=1, maximum=75, step=1, value=1) @@ -1054,9 +1054,7 @@ def create_ui(wrap_gradio_gpu_call): with gr.Column(): create_embedding = gr.Button(value="Create embedding", variant='primary') - with gr.Group(): - gr.HTML(value="

Create a new hypernetwork

") - + with gr.Tab(label="Create hypernetwork"): new_hypernetwork_name = gr.Textbox(label="Name") new_hypernetwork_sizes = gr.CheckboxGroup(label="Modules", value=["768", "320", "640", "1280"], choices=["768", "320", "640", "1280"]) @@ -1067,9 +1065,7 @@ def create_ui(wrap_gradio_gpu_call): with gr.Column(): create_hypernetwork = gr.Button(value="Create hypernetwork", variant='primary') - with gr.Group(): - gr.HTML(value="

Preprocess images

") - + with gr.Tab(label="Preprocess images"): process_src = gr.Textbox(label='Source directory') process_dst = gr.Textbox(label='Destination directory') process_width = gr.Slider(minimum=64, maximum=2048, step=64, label="Width", value=512) @@ -1091,7 +1087,7 @@ def create_ui(wrap_gradio_gpu_call): with gr.Column(): run_preprocess = gr.Button(value="Preprocess", variant='primary') - with gr.Group(): + with gr.Tab(label="Train"): gr.HTML(value="

Train an embedding; must specify a directory with a set of 1:1 ratio images

") train_embedding_name = gr.Dropdown(label='Embedding', choices=sorted(sd_hijack.model_hijack.embedding_db.word_embeddings.keys())) train_hypernetwork_name = gr.Dropdown(label='Hypernetwork', choices=[x for x in shared.hypernetworks.keys()]) @@ -1388,7 +1384,7 @@ Requested path was: {f} (extras_interface, "Extras", "extras"), (pnginfo_interface, "PNG Info", "pnginfo"), (modelmerger_interface, "Checkpoint Merger", "modelmerger"), - (textual_inversion_interface, "Textual inversion", "ti"), + (train_interface, "Train", "ti"), (settings_interface, "Settings", "settings"), ] -- cgit v1.2.1 From 80f3cf2bb2ce3f00d801cae2c3a8c20a8d4167d8 Mon Sep 17 00:00:00 2001 From: hentailord85ez <112723046+hentailord85ez@users.noreply.github.com> Date: Tue, 11 Oct 2022 19:48:53 +0100 Subject: Account when lines are mismatched --- modules/sd_hijack.py | 12 +++++++++++- 1 file changed, 11 insertions(+), 1 deletion(-) (limited to 'modules') diff --git a/modules/sd_hijack.py b/modules/sd_hijack.py index ac70f876..2753d4fa 100644 --- a/modules/sd_hijack.py +++ b/modules/sd_hijack.py @@ -321,7 +321,17 @@ class FrozenCLIPEmbedderWithCustomWords(torch.nn.Module): fixes.append(fix[1]) self.hijack.fixes.append(fixes) - z1 = self.process_tokens([x[:75] for x in remade_batch_tokens], [x[:75] for x in batch_multipliers]) + tokens = [] + multipliers = [] + for i in range(len(remade_batch_tokens)): + if len(remade_batch_tokens[i]) > 0: + tokens.append(remade_batch_tokens[i][:75]) + multipliers.append(batch_multipliers[i][:75]) + else: + tokens.append([self.wrapped.tokenizer.eos_token_id] * 75) + multipliers.append([1.0] * 75) + + z1 = self.process_tokens(tokens, multipliers) z = z1 if z is None else torch.cat((z, z1), axis=-2) remade_batch_tokens = rem_tokens -- cgit v1.2.1 From 429442f4a6aab7301efb89d27bef524fe827e81a Mon Sep 17 00:00:00 2001 From: AUTOMATIC <16777216c@gmail.com> Date: Wed, 12 Oct 2022 13:38:03 +0300 Subject: fix iterator bug for #2295 --- modules/sd_hijack.py | 8 ++++---- 1 file changed, 4 insertions(+), 4 deletions(-) (limited to 'modules') diff --git a/modules/sd_hijack.py b/modules/sd_hijack.py index 2753d4fa..c81722a0 100644 --- a/modules/sd_hijack.py +++ b/modules/sd_hijack.py @@ -323,10 +323,10 @@ class FrozenCLIPEmbedderWithCustomWords(torch.nn.Module): tokens = [] multipliers = [] - for i in range(len(remade_batch_tokens)): - if len(remade_batch_tokens[i]) > 0: - tokens.append(remade_batch_tokens[i][:75]) - multipliers.append(batch_multipliers[i][:75]) + for j in range(len(remade_batch_tokens)): + if len(remade_batch_tokens[j]) > 0: + tokens.append(remade_batch_tokens[j][:75]) + multipliers.append(batch_multipliers[j][:75]) else: tokens.append([self.wrapped.tokenizer.eos_token_id] * 75) multipliers.append([1.0] * 75) -- cgit v1.2.1 From 50be33e953be93c40814262c6dbce36e66004528 Mon Sep 17 00:00:00 2001 From: DepFA <35278260+dfaker@users.noreply.github.com> Date: Wed, 12 Oct 2022 13:13:25 +0100 Subject: formatting --- modules/textual_inversion/image_embedding.py | 170 ++++++++++++++------------- 1 file changed, 91 insertions(+), 79 deletions(-) (limited to 'modules') diff --git a/modules/textual_inversion/image_embedding.py b/modules/textual_inversion/image_embedding.py index 1224fb42..898ce3b3 100644 --- a/modules/textual_inversion/image_embedding.py +++ b/modules/textual_inversion/image_embedding.py @@ -2,122 +2,134 @@ import base64 import json import numpy as np import zlib -from PIL import Image,PngImagePlugin,ImageDraw,ImageFont +from PIL import Image, PngImagePlugin, ImageDraw, ImageFont from fonts.ttf import Roboto import torch + class EmbeddingEncoder(json.JSONEncoder): def default(self, obj): if isinstance(obj, torch.Tensor): - return {'TORCHTENSOR':obj.cpu().detach().numpy().tolist()} + return {'TORCHTENSOR': obj.cpu().detach().numpy().tolist()} return json.JSONEncoder.default(self, obj) + class EmbeddingDecoder(json.JSONDecoder): def __init__(self, *args, **kwargs): json.JSONDecoder.__init__(self, object_hook=self.object_hook, *args, **kwargs) + def object_hook(self, d): if 'TORCHTENSOR' in d: return torch.from_numpy(np.array(d['TORCHTENSOR'])) return d + def embedding_to_b64(data): - d = json.dumps(data,cls=EmbeddingEncoder) + d = json.dumps(data, cls=EmbeddingEncoder) return base64.b64encode(d.encode()) + def embedding_from_b64(data): d = base64.b64decode(data) - return json.loads(d,cls=EmbeddingDecoder) + return json.loads(d, cls=EmbeddingDecoder) + def lcg(m=2**32, a=1664525, c=1013904223, seed=0): while True: seed = (a * seed + c) % m - yield seed%255 + yield seed % 255 + def xor_block(block): g = lcg() randblock = np.array([next(g) for _ in range(np.product(block.shape))]).astype(np.uint8).reshape(block.shape) - return np.bitwise_xor(block.astype(np.uint8),randblock & 0x0F) + return np.bitwise_xor(block.astype(np.uint8), randblock & 0x0F) -def style_block(block,sequence): - im = Image.new('RGB',(block.shape[1],block.shape[0])) + +def style_block(block, sequence): + im = Image.new('RGB', (block.shape[1], block.shape[0])) draw = ImageDraw.Draw(im) - i=0 - for x in range(-6,im.size[0],8): - for yi,y in enumerate(range(-6,im.size[1],8)): - offset=0 - if yi%2==0: - offset=4 - shade = sequence[i%len(sequence)] - i+=1 - draw.ellipse((x+offset, y, x+6+offset, y+6), fill =(shade,shade,shade) ) + i = 0 + for x in range(-6, im.size[0], 8): + for yi, y in enumerate(range(-6, im.size[1], 8)): + offset = 0 + if yi % 2 == 0: + offset = 4 + shade = sequence[i % len(sequence)] + i += 1 + draw.ellipse((x+offset, y, x+6+offset, y+6), fill=(shade, shade, shade)) fg = np.array(im).astype(np.uint8) & 0xF0 return block ^ fg -def insert_image_data_embed(image,data): + +def insert_image_data_embed(image, data): d = 3 - data_compressed = zlib.compress( json.dumps(data,cls=EmbeddingEncoder).encode(),level=9) - data_np_ = np.frombuffer(data_compressed,np.uint8).copy() + data_compressed = zlib.compress(json.dumps(data, cls=EmbeddingEncoder).encode(), level=9) + data_np_ = np.frombuffer(data_compressed, np.uint8).copy() data_np_high = data_np_ >> 4 - data_np_low = data_np_ & 0x0F - + data_np_low = data_np_ & 0x0F + h = image.size[1] - next_size = data_np_low.shape[0] + (h-(data_np_low.shape[0]%h)) - next_size = next_size + ((h*d)-(next_size%(h*d))) + next_size = data_np_low.shape[0] + (h-(data_np_low.shape[0] % h)) + next_size = next_size + ((h*d)-(next_size % (h*d))) data_np_low.resize(next_size) - data_np_low = data_np_low.reshape((h,-1,d)) + data_np_low = data_np_low.reshape((h, -1, d)) data_np_high.resize(next_size) - data_np_high = data_np_high.reshape((h,-1,d)) + data_np_high = data_np_high.reshape((h, -1, d)) edge_style = list(data['string_to_param'].values())[0].cpu().detach().numpy().tolist()[0][:1024] edge_style = (np.abs(edge_style)/np.max(np.abs(edge_style))*255).astype(np.uint8) - data_np_low = style_block(data_np_low,sequence=edge_style) - data_np_low = xor_block(data_np_low) - data_np_high = style_block(data_np_high,sequence=edge_style[::-1]) - data_np_high = xor_block(data_np_high) + data_np_low = style_block(data_np_low, sequence=edge_style) + data_np_low = xor_block(data_np_low) + data_np_high = style_block(data_np_high, sequence=edge_style[::-1]) + data_np_high = xor_block(data_np_high) - im_low = Image.fromarray(data_np_low,mode='RGB') - im_high = Image.fromarray(data_np_high,mode='RGB') + im_low = Image.fromarray(data_np_low, mode='RGB') + im_high = Image.fromarray(data_np_high, mode='RGB') - background = Image.new('RGB',(image.size[0]+im_low.size[0]+im_high.size[0]+2,image.size[1]),(0,0,0)) - background.paste(im_low,(0,0)) - background.paste(image,(im_low.size[0]+1,0)) - background.paste(im_high,(im_low.size[0]+1+image.size[0]+1,0)) + background = Image.new('RGB', (image.size[0]+im_low.size[0]+im_high.size[0]+2, image.size[1]), (0, 0, 0)) + background.paste(im_low, (0, 0)) + background.paste(image, (im_low.size[0]+1, 0)) + background.paste(im_high, (im_low.size[0]+1+image.size[0]+1, 0)) return background -def crop_black(img,tol=0): - mask = (img>tol).all(2) - mask0,mask1 = mask.any(0),mask.any(1) - col_start,col_end = mask0.argmax(),mask.shape[1]-mask0[::-1].argmax() - row_start,row_end = mask1.argmax(),mask.shape[0]-mask1[::-1].argmax() - return img[row_start:row_end,col_start:col_end] + +def crop_black(img, tol=0): + mask = (img > tol).all(2) + mask0, mask1 = mask.any(0), mask.any(1) + col_start, col_end = mask0.argmax(), mask.shape[1]-mask0[::-1].argmax() + row_start, row_end = mask1.argmax(), mask.shape[0]-mask1[::-1].argmax() + return img[row_start:row_end, col_start:col_end] + def extract_image_data_embed(image): - d=3 - outarr = crop_black(np.array(image.convert('RGB').getdata()).reshape(image.size[1],image.size[0],d ).astype(np.uint8) ) & 0x0F - black_cols = np.where( np.sum(outarr, axis=(0,2))==0) + d = 3 + outarr = crop_black(np.array(image.convert('RGB').getdata()).reshape(image.size[1], image.size[0], d).astype(np.uint8)) & 0x0F + black_cols = np.where(np.sum(outarr, axis=(0, 2)) == 0) if black_cols[0].shape[0] < 2: print('No Image data blocks found.') return None - data_block_lower = outarr[:,:black_cols[0].min(),:].astype(np.uint8) - data_block_upper = outarr[:,black_cols[0].max()+1:,:].astype(np.uint8) + data_block_lower = outarr[:, :black_cols[0].min(), :].astype(np.uint8) + data_block_upper = outarr[:, black_cols[0].max()+1:, :].astype(np.uint8) data_block_lower = xor_block(data_block_lower) data_block_upper = xor_block(data_block_upper) - + data_block = (data_block_upper << 4) | (data_block_lower) data_block = data_block.flatten().tobytes() data = zlib.decompress(data_block) - return json.loads(data,cls=EmbeddingDecoder) + return json.loads(data, cls=EmbeddingDecoder) + -def caption_image_overlay(srcimage,title,footerLeft,footerMid,footerRight,textfont=None): +def caption_image_overlay(srcimage, title, footerLeft, footerMid, footerRight, textfont=None): from math import cos image = srcimage.copy() @@ -130,11 +142,11 @@ def caption_image_overlay(srcimage,title,footerLeft,footerMid,footerRight,textfo textfont = Roboto factor = 1.5 - gradient = Image.new('RGBA', (1,image.size[1]), color=(0,0,0,0)) + gradient = Image.new('RGBA', (1, image.size[1]), color=(0, 0, 0, 0)) for y in range(image.size[1]): mag = 1-cos(y/image.size[1]*factor) - mag = max(mag,1-cos((image.size[1]-y)/image.size[1]*factor*1.1)) - gradient.putpixel((0, y), (0,0,0,int(mag*255))) + mag = max(mag, 1-cos((image.size[1]-y)/image.size[1]*factor*1.1)) + gradient.putpixel((0, y), (0, 0, 0, int(mag*255))) image = Image.alpha_composite(image.convert('RGBA'), gradient.resize(image.size)) draw = ImageDraw.Draw(image) @@ -142,41 +154,41 @@ def caption_image_overlay(srcimage,title,footerLeft,footerMid,footerRight,textfo font = ImageFont.truetype(textfont, fontsize) padding = 10 - _,_,w, h = draw.textbbox((0,0),title,font=font) - fontsize = min( int(fontsize * (((image.size[0]*0.75)-(padding*4))/w) ), 72) + _, _, w, h = draw.textbbox((0, 0), title, font=font) + fontsize = min(int(fontsize * (((image.size[0]*0.75)-(padding*4))/w)), 72) font = ImageFont.truetype(textfont, fontsize) - _,_,w,h = draw.textbbox((0,0),title,font=font) - draw.text((padding,padding), title, anchor='lt', font=font, fill=(255,255,255,230)) + _, _, w, h = draw.textbbox((0, 0), title, font=font) + draw.text((padding, padding), title, anchor='lt', font=font, fill=(255, 255, 255, 230)) - _,_,w, h = draw.textbbox((0,0),footerLeft,font=font) - fontsize_left = min( int(fontsize * (((image.size[0]/3)-(padding))/w) ), 72) - _,_,w, h = draw.textbbox((0,0),footerMid,font=font) - fontsize_mid = min( int(fontsize * (((image.size[0]/3)-(padding))/w) ), 72) - _,_,w, h = draw.textbbox((0,0),footerRight,font=font) - fontsize_right = min( int(fontsize * (((image.size[0]/3)-(padding))/w) ), 72) + _, _, w, h = draw.textbbox((0, 0), footerLeft, font=font) + fontsize_left = min(int(fontsize * (((image.size[0]/3)-(padding))/w)), 72) + _, _, w, h = draw.textbbox((0, 0), footerMid, font=font) + fontsize_mid = min(int(fontsize * (((image.size[0]/3)-(padding))/w)), 72) + _, _, w, h = draw.textbbox((0, 0), footerRight, font=font) + fontsize_right = min(int(fontsize * (((image.size[0]/3)-(padding))/w)), 72) - font = ImageFont.truetype(textfont, min(fontsize_left,fontsize_mid,fontsize_right)) + font = ImageFont.truetype(textfont, min(fontsize_left, fontsize_mid, fontsize_right)) - draw.text((padding,image.size[1]-padding), footerLeft, anchor='ls', font=font, fill=(255,255,255,230)) - draw.text((image.size[0]/2,image.size[1]-padding), footerMid, anchor='ms', font=font, fill=(255,255,255,230)) - draw.text((image.size[0]-padding,image.size[1]-padding), footerRight, anchor='rs', font=font, fill=(255,255,255,230)) + draw.text((padding, image.size[1]-padding), footerLeft, anchor='ls', font=font, fill=(255, 255, 255, 230)) + draw.text((image.size[0]/2, image.size[1]-padding), footerMid, anchor='ms', font=font, fill=(255, 255, 255, 230)) + draw.text((image.size[0]-padding, image.size[1]-padding), footerRight, anchor='rs', font=font, fill=(255, 255, 255, 230)) return image + if __name__ == '__main__': testEmbed = Image.open('test_embedding.png') - data = extract_image_data_embed(testEmbed) assert data is not None data = embedding_from_b64(testEmbed.text['sd-ti-embedding']) assert data is not None - - image = Image.new('RGBA',(512,512),(255,255,200,255)) + + image = Image.new('RGBA', (512, 512), (255, 255, 200, 255)) cap_image = caption_image_overlay(image, 'title', 'footerLeft', 'footerMid', 'footerRight') - test_embed = {'string_to_param':{'*':torch.from_numpy(np.random.random((2, 4096)))}} + test_embed = {'string_to_param': {'*': torch.from_numpy(np.random.random((2, 4096)))}} embedded_image = insert_image_data_embed(cap_image, test_embed) @@ -191,16 +203,16 @@ if __name__ == '__main__': g = lcg() shared_random = np.array([next(g) for _ in range(100)]).astype(np.uint8).tolist() - reference_random = [253, 242, 127, 44, 157, 27, 239, 133, 38, 79, 167, 4, 177, - 95, 130, 79, 78, 14, 52, 215, 220, 194, 126, 28, 240, 179, - 160, 153, 149, 50, 105, 14, 21, 218, 199, 18, 54, 198, 193, - 38, 128, 19, 53, 195, 124, 75, 205, 12, 6, 145, 0, 28, - 30, 148, 8, 45, 218, 171, 55, 249, 97, 166, 12, 35, 0, - 41, 221, 122, 215, 170, 31, 113, 186, 97, 119, 31, 23, 185, - 66, 140, 30, 41, 37, 63, 137, 109, 216, 55, 159, 145, 82, + reference_random = [253, 242, 127, 44, 157, 27, 239, 133, 38, 79, 167, 4, 177, + 95, 130, 79, 78, 14, 52, 215, 220, 194, 126, 28, 240, 179, + 160, 153, 149, 50, 105, 14, 21, 218, 199, 18, 54, 198, 193, + 38, 128, 19, 53, 195, 124, 75, 205, 12, 6, 145, 0, 28, + 30, 148, 8, 45, 218, 171, 55, 249, 97, 166, 12, 35, 0, + 41, 221, 122, 215, 170, 31, 113, 186, 97, 119, 31, 23, 185, + 66, 140, 30, 41, 37, 63, 137, 109, 216, 55, 159, 145, 82, 204, 86, 73, 222, 44, 198, 118, 240, 97] - assert shared_random == reference_random + assert shared_random == reference_random hunna_kay_random_sum = sum(np.array([next(g) for _ in range(100000)]).astype(np.uint8).tolist()) -- cgit v1.2.1 From 10a2de644f8ea4cfade88e85d768da3480f4c9f0 Mon Sep 17 00:00:00 2001 From: DepFA <35278260+dfaker@users.noreply.github.com> Date: Wed, 12 Oct 2022 13:15:35 +0100 Subject: formatting --- modules/textual_inversion/textual_inversion.py | 22 +++++++++++----------- 1 file changed, 11 insertions(+), 11 deletions(-) (limited to 'modules') diff --git a/modules/textual_inversion/textual_inversion.py b/modules/textual_inversion/textual_inversion.py index 485ef46c..b072d745 100644 --- a/modules/textual_inversion/textual_inversion.py +++ b/modules/textual_inversion/textual_inversion.py @@ -7,14 +7,14 @@ import tqdm import html import datetime -from PIL import Image,PngImagePlugin +from PIL import Image, PngImagePlugin from modules import shared, devices, sd_hijack, processing, sd_models import modules.textual_inversion.dataset -from modules.textual_inversion.image_embedding import (embedding_to_b64,embedding_from_b64, - insert_image_data_embed,extract_image_data_embed, - caption_image_overlay ) +from modules.textual_inversion.image_embedding import (embedding_to_b64, embedding_from_b64, + insert_image_data_embed, extract_image_data_embed, + caption_image_overlay) class Embedding: def __init__(self, vec, name, step=None): @@ -90,10 +90,10 @@ class EmbeddingDatabase: embed_image = Image.open(path) if 'sd-ti-embedding' in embed_image.text: data = embedding_from_b64(embed_image.text['sd-ti-embedding']) - name = data.get('name',name) + name = data.get('name', name) else: data = extract_image_data_embed(embed_image) - name = data.get('name',name) + name = data.get('name', name) else: data = torch.load(path, map_location="cpu") @@ -278,24 +278,24 @@ def train_embedding(embedding_name, learn_rate, data_root, log_directory, traini shared.state.current_image = image if save_image_with_stored_embedding and os.path.exists(last_saved_file): - + last_saved_image_chunks = os.path.join(images_embeds_dir, f'{embedding_name}-{embedding.step}.png') info = PngImagePlugin.PngInfo() data = torch.load(last_saved_file) info.add_text("sd-ti-embedding", embedding_to_b64(data)) - title = "<{}>".format(data.get('name','???')) + title = "<{}>".format(data.get('name', '???')) checkpoint = sd_models.select_checkpoint() footer_left = checkpoint.model_name footer_mid = '[{}]'.format(checkpoint.hash) footer_right = '{}'.format(embedding.step) - captioned_image = caption_image_overlay(image,title,footer_left,footer_mid,footer_right) - captioned_image = insert_image_data_embed(captioned_image,data) + captioned_image = caption_image_overlay(image, title, footer_left, footer_mid, footer_right) + captioned_image = insert_image_data_embed(captioned_image, data) captioned_image.save(last_saved_image_chunks, "PNG", pnginfo=info) - + image.save(last_saved_image) last_saved_image += f", prompt: {preview_text}" -- cgit v1.2.1 From e05573e1adc1cde1e3bd7eb651a1ab27c446b3d5 Mon Sep 17 00:00:00 2001 From: yfszzx Date: Wed, 12 Oct 2022 20:47:55 +0800 Subject: images history improvement --- modules/images_history.py | 67 ++++++++++++++++++++++++++++++----------------- 1 file changed, 43 insertions(+), 24 deletions(-) (limited to 'modules') diff --git a/modules/images_history.py b/modules/images_history.py index 23f55b30..77f692fe 100644 --- a/modules/images_history.py +++ b/modules/images_history.py @@ -1,15 +1,29 @@ import os -def get_recent_images(dir_name, page_index, step, image_index): - #print(image_index) +import shutil +def get_recent_images(dir_name, page_index, step, image_index, tabname): + print(f"renew page {page_index}") page_index = int(page_index) f_list = os.listdir(dir_name) file_list = [] for file in f_list: if file[-4:] == ".txt": continue - file_list.append(file) + #subdirectories + if file[-10:].rfind(".") < 0: + sub_dir = os.path.join(dir_name, file) + if os.path.isfile(sub_dir): + continue + sub_file_list = os.listdir(sub_dir) + for sub_file in sub_file_list: + if sub_file[-4:] == ".txt": + continue + if os.path.isfile(os.path.join(sub_dir, sub_file) ): + file_list.append(os.path.join(file, sub_file)) + continue + file_list.append(file) + file_list = sorted(file_list, key=lambda file: -os.path.getctime(os.path.join(dir_name, file))) - num = 48 + num = 48 if tabname != "extras" else 12 max_page_index = len(file_list) // num + 1 page_index = max_page_index if page_index == -1 else page_index + step page_index = 1 if page_index < 1 else page_index @@ -26,26 +40,28 @@ def get_recent_images(dir_name, page_index, step, image_index): hide_image = os.path.join(dir_name, current_file) return [os.path.join(dir_name, file) for file in file_list], page_index, file_list, current_file, hide_image def first_page_click(dir_name, page_index, image_index, tabname): - return get_recent_images(dir_name, 1, 0, image_index) + return get_recent_images(dir_name, 1, 0, image_index, tabname) def end_page_click(dir_name, page_index, image_index, tabname): - return get_recent_images(dir_name, -1, 0, image_index) + return get_recent_images(dir_name, -1, 0, image_index, tabname) def prev_page_click(dir_name, page_index, image_index, tabname): - return get_recent_images(dir_name, page_index, -1, image_index) + return get_recent_images(dir_name, page_index, -1, image_index, tabname) def next_page_click(dir_name, page_index, image_index, tabname): - return get_recent_images(dir_name, page_index, 1, image_index) + return get_recent_images(dir_name, page_index, 1, image_index, tabname) def page_index_change(dir_name, page_index, image_index, tabname): - return get_recent_images(dir_name, page_index, 0, image_index) + return get_recent_images(dir_name, page_index, 0, image_index, tabname) def show_image_info(num, image_path, filenames): - #print("set img",num) + print(f"select image {num}") file = filenames[int(num)] return file, num, os.path.join(image_path, file) def delete_image(tabname, dir_name, name, page_index, filenames, image_index): - #print("filename", name) path = os.path.join(dir_name, name) - if os.path.exists(path): + if os.path.exists(path): print(f"Delete file {path}") - os.remove(path) + os.remove(path) + txt_file = os.path.splitext(path)[0] + ".txt" + if os.path.exists(txt_file): + os.remove(txt_file) new_file_list = [] for f in filenames: if f == name: @@ -64,25 +80,26 @@ def show_images_history(gr, opts, tabname, run_pnginfo, switch_dict): elif tabname == "extras": dir_name = opts.outdir_extras_samples with gr.Row(): - renew_page = gr.Button('Renew', elem_id=tabname + "_images_history_renew_page") - first_page = gr.Button('First', elem_id=tabname + "_images_history_first_page") - prev_page = gr.Button('Prev') + renew_page = gr.Button('Renew Page', elem_id=tabname + "_images_history_renew_page") + first_page = gr.Button('First Page') + prev_page = gr.Button('Prev Page') page_index = gr.Number(value=1, label="Page Index") - next_page = gr.Button('Next', elem_id=tabname + "_images_history_next_page") - end_page = gr.Button('End') + next_page = gr.Button('Next Page') + end_page = gr.Button('End Page') with gr.Row(elem_id=tabname + "_images_history"): with gr.Row(): - with gr.Column(): - history_gallery = gr.Gallery(show_label=False).style(grid=6) + with gr.Column(scale=2): + history_gallery = gr.Gallery(show_label=False, elem_id=tabname + "_images_history_gallery").style(grid=6) + delete = gr.Button('Delete', elem_id=tabname + "_images_history_del_button") with gr.Column(): with gr.Row(): - delete = gr.Button('Delete') + #pnginfo = gr.Button('PNG info') pnginfo_send_to_txt2img = gr.Button('Send to txt2img') pnginfo_send_to_img2img = gr.Button('Send to img2img') with gr.Row(): with gr.Column(): - img_file_info = gr.Textbox(label="Generate Info") - img_file_name = gr.Textbox(label="File Name") + img_file_info = gr.Textbox(label="Generate Info", interactive=False) + img_file_name = gr.Textbox(label="File Name", interactive=False) with gr.Row(): # hiden items img_path = gr.Textbox(dir_name, visible=False) @@ -90,7 +107,7 @@ def show_images_history(gr, opts, tabname, run_pnginfo, switch_dict): image_index = gr.Textbox(value=-1, visible=False) set_index = gr.Button('set_index', elem_id=tabname + "_images_history_set_index", visible=False) filenames = gr.State() - hide_image = gr.Image(visible=False, type="pil") + hide_image = gr.Image(type="pil", visible=False) info1 = gr.Textbox(visible=False) info2 = gr.Textbox(visible=False) @@ -111,6 +128,8 @@ def show_images_history(gr, opts, tabname, run_pnginfo, switch_dict): set_index.click(show_image_info, _js="images_history_get_current_img", inputs=[tabname_box, img_path, filenames], outputs=[img_file_name, image_index, hide_image]) delete.click(delete_image,_js="images_history_delete", inputs=[tabname_box, img_path, img_file_name, page_index, filenames, image_index], outputs=[page_index, filenames]) hide_image.change(fn=run_pnginfo, inputs=[hide_image], outputs=[info1, img_file_info, info2]) + hide_image.change(fn=None, _js="images_history_enable_del_buttons", inputs=None, outputs=None) + #pnginfo.click(fn=run_pnginfo, inputs=[hide_image], outputs=[info1, img_file_info, info2]) switch_dict["fn"](pnginfo_send_to_txt2img, switch_dict["t2i"], img_file_info, 'switch_to_txt2img') switch_dict["fn"](pnginfo_send_to_img2img, switch_dict["i2i"], img_file_info, 'switch_to_img2img_img2img') -- cgit v1.2.1 From a1a94b8b5f342f467aecc53b21b80ed0227ee76a Mon Sep 17 00:00:00 2001 From: yfszzx Date: Thu, 13 Oct 2022 00:19:34 +0800 Subject: images history improvement --- modules/images_history.py | 7 ++++--- modules/ui.py | 40 +++++++++++++++++++--------------------- 2 files changed, 23 insertions(+), 24 deletions(-) (limited to 'modules') diff --git a/modules/images_history.py b/modules/images_history.py index 2bc4b7ee..1bca0ad9 100644 --- a/modules/images_history.py +++ b/modules/images_history.py @@ -61,7 +61,7 @@ def delete_image(tabname, dir_name, name, page_index, filenames, image_index): os.remove(path) txt_file = os.path.splitext(path)[0] + ".txt" if os.path.exists(txt_file): - os.remove(txt_file) + os.remove(txt_file) new_file_list = [] for f in filenames: if f == name: @@ -88,7 +88,7 @@ def show_images_history(gr, opts, tabname, run_pnginfo, switch_dict): end_page = gr.Button('End Page') with gr.Row(elem_id=tabname + "_images_history"): with gr.Row(): - with gr.Column(scale=2): + with gr.Column(scale=2): history_gallery = gr.Gallery(show_label=False, elem_id=tabname + "_images_history_gallery").style(grid=6) delete = gr.Button('Delete', elem_id=tabname + "_images_history_del_button") with gr.Column(): @@ -126,9 +126,10 @@ def show_images_history(gr, opts, tabname, run_pnginfo, switch_dict): #other funcitons set_index.click(show_image_info, _js="images_history_get_current_img", inputs=[tabname_box, img_path, filenames], outputs=[img_file_name, image_index, hide_image]) + img_file_name.change(fn=None, _js="images_history_enable_del_buttons", inputs=None, outputs=None) delete.click(delete_image,_js="images_history_delete", inputs=[tabname_box, img_path, img_file_name, page_index, filenames, image_index], outputs=[page_index, filenames]) hide_image.change(fn=run_pnginfo, inputs=[hide_image], outputs=[info1, img_file_info, info2]) - hide_image.change(fn=None, _js="images_history_enable_del_buttons", inputs=None, outputs=None) + #pnginfo.click(fn=run_pnginfo, inputs=[hide_image], outputs=[info1, img_file_info, info2]) switch_dict["fn"](pnginfo_send_to_txt2img, switch_dict["t2i"], img_file_info, 'switch_to_txt2img') switch_dict["fn"](pnginfo_send_to_img2img, switch_dict["i2i"], img_file_info, 'switch_to_img2img_img2img') diff --git a/modules/ui.py b/modules/ui.py index 94297ba6..8cd12b51 100644 --- a/modules/ui.py +++ b/modules/ui.py @@ -39,7 +39,7 @@ import modules.generation_parameters_copypaste from modules import prompt_parser from modules.images import save_image import modules.textual_inversion.ui -import modules.hypernetwork.ui +import modules.hypernetworks.ui import modules.images_history as img_his # this is a fix for Windows users. Without it, javascript files will be served with text/html content-type and the browser will not show any UI @@ -554,6 +554,7 @@ def create_ui(wrap_gradio_gpu_call): custom_inputs = modules.scripts.scripts_txt2img.setup_ui(is_img2img=False) with gr.Column(variant='panel'): + with gr.Group(): txt2img_preview = gr.Image(elem_id='txt2img_preview', visible=False) txt2img_gallery = gr.Gallery(label='Output', show_label=False, elem_id='txt2img_gallery').style(grid=4) @@ -573,9 +574,9 @@ def create_ui(wrap_gradio_gpu_call): with gr.Row(): download_files = gr.File(None, file_count="multiple", interactive=False, show_label=False, visible=False) - with gr.Group(): - html_info = gr.HTML() - generation_info = gr.Textbox(visible=False) + with gr.Group(): + html_info = gr.HTML() + generation_info = gr.Textbox(visible=False) connect_reuse_seed(seed, reuse_seed, generation_info, dummy_component, is_subseed=False) connect_reuse_seed(subseed, reuse_subseed, generation_info, dummy_component, is_subseed=True) @@ -669,7 +670,6 @@ def create_ui(wrap_gradio_gpu_call): ] modules.generation_parameters_copypaste.connect_paste(paste, txt2img_paste_fields, txt2img_prompt) token_button.click(fn=update_token_counter, inputs=[txt2img_prompt, steps], outputs=[token_counter]) - with gr.Blocks(analytics_enabled=False) as img2img_interface: img2img_prompt, roll, img2img_prompt_style, img2img_negative_prompt, img2img_prompt_style2, submit, img2img_interrogate, img2img_deepbooru, img2img_prompt_style_apply, img2img_save_style, paste, token_counter, token_button = create_toprow(is_img2img=True) @@ -762,10 +762,10 @@ def create_ui(wrap_gradio_gpu_call): with gr.Row(): download_files = gr.File(None, file_count="multiple", interactive=False, show_label=False, visible=False) - with gr.Group(): - html_info = gr.HTML() - generation_info = gr.Textbox(visible=False) - + with gr.Group(): + html_info = gr.HTML() + generation_info = gr.Textbox(visible=False) + connect_reuse_seed(seed, reuse_seed, generation_info, dummy_component, is_subseed=False) connect_reuse_seed(subseed, reuse_subseed, generation_info, dummy_component, is_subseed=True) @@ -1016,6 +1016,13 @@ def create_ui(wrap_gradio_gpu_call): inputs=[image], outputs=[html, generation_info, html2], ) + #images history + images_history_switch_dict = { + "fn":modules.generation_parameters_copypaste.connect_paste, + "t2i":txt2img_paste_fields, + "i2i":img2img_paste_fields + } + images_history = img_his.create_history_tabs(gr, opts, wrap_gradio_call(modules.extras.run_pnginfo), images_history_switch_dict) with gr.Blocks() as modelmerger_interface: with gr.Row().style(equal_height=False): @@ -1285,16 +1292,7 @@ Requested path was: {f} opts.save(shared.config_filename) - return f'{changed} settings changed.', opts.dumpjson() - - #images history - images_history_switch_dict = { - "fn":modules.generation_parameters_copypaste.connect_paste, - "t2i":txt2img_paste_fields, - "i2i":img2img_paste_fields - } - images_history = img_his.create_history_tabs(gr, opts, wrap_gradio_call(modules.extras.run_pnginfo), images_history_switch_dict) - + return f'{changed} settings changed.', opts.dumpjson() def run_settings_single(value, key): if not opts.same_type(value, opts.data_labels[key].default): @@ -1393,11 +1391,10 @@ Requested path was: {f} (img2img_interface, "img2img", "img2img"), (extras_interface, "Extras", "extras"), (pnginfo_interface, "PNG Info", "pnginfo"), - (modelmerger_interface, "Checkpoint Merger", "modelmerger"), (images_history, "History", "images_history"), + (modelmerger_interface, "Checkpoint Merger", "modelmerger"), (train_interface, "Train", "ti"), (settings_interface, "Settings", "settings"), - ] with open(os.path.join(script_path, "style.css"), "r", encoding="utf8") as file: @@ -1616,3 +1613,4 @@ if 'gradio_routes_templates_response' not in globals(): gradio_routes_templates_response = gradio.routes.templates.TemplateResponse gradio.routes.templates.TemplateResponse = template_response + -- cgit v1.2.1 From a2aa2a68bc7868320b502a78765be597e507ce45 Mon Sep 17 00:00:00 2001 From: yfszzx Date: Thu, 13 Oct 2022 00:21:16 +0800 Subject: images history improvement --- modules/images_history.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) (limited to 'modules') diff --git a/modules/images_history.py b/modules/images_history.py index 1bca0ad9..6408973c 100644 --- a/modules/images_history.py +++ b/modules/images_history.py @@ -1,7 +1,7 @@ import os import shutil def get_recent_images(dir_name, page_index, step, image_index, tabname): - print(f"renew page {page_index}") + #print(f"renew page {page_index}") page_index = int(page_index) f_list = os.listdir(dir_name) file_list = [] @@ -51,7 +51,7 @@ def page_index_change(dir_name, page_index, image_index, tabname): return get_recent_images(dir_name, page_index, 0, image_index, tabname) def show_image_info(num, image_path, filenames): - print(f"select image {num}") + #print(f"select image {num}") file = filenames[int(num)] return file, num, os.path.join(image_path, file) def delete_image(tabname, dir_name, name, page_index, filenames, image_index): -- cgit v1.2.1 From c3c8eef9fd5a0c8b26319e32ca4a19b56204e6df Mon Sep 17 00:00:00 2001 From: AUTOMATIC <16777216c@gmail.com> Date: Wed, 12 Oct 2022 20:49:47 +0300 Subject: train: change filename processing to be more simple and configurable train: make it possible to make text files with prompts train: rework scheduler so that there's less repeating code in textual inversion and hypernets train: move epochs setting to options --- modules/hypernetworks/hypernetwork.py | 40 +++++++++------------- modules/shared.py | 3 ++ modules/textual_inversion/dataset.py | 47 +++++++++++++++++++------- modules/textual_inversion/learn_schedule.py | 37 +++++++++++++++++++- modules/textual_inversion/textual_inversion.py | 35 +++++++------------ modules/ui.py | 2 -- 6 files changed, 102 insertions(+), 62 deletions(-) (limited to 'modules') diff --git a/modules/hypernetworks/hypernetwork.py b/modules/hypernetworks/hypernetwork.py index 8314450a..b6c06d49 100644 --- a/modules/hypernetworks/hypernetwork.py +++ b/modules/hypernetworks/hypernetwork.py @@ -14,7 +14,7 @@ import torch from torch import einsum from einops import rearrange, repeat import modules.textual_inversion.dataset -from modules.textual_inversion.learn_schedule import LearnSchedule +from modules.textual_inversion.learn_schedule import LearnRateScheduler class HypernetworkModule(torch.nn.Module): @@ -223,31 +223,23 @@ def train_hypernetwork(hypernetwork_name, learn_rate, data_root, log_directory, if ititial_step > steps: return hypernetwork, filename - schedules = iter(LearnSchedule(learn_rate, steps, ititial_step)) - (learn_rate, end_step) = next(schedules) - print(f'Training at rate of {learn_rate} until step {end_step}') - - optimizer = torch.optim.AdamW(weights, lr=learn_rate) + scheduler = LearnRateScheduler(learn_rate, steps, ititial_step) + optimizer = torch.optim.AdamW(weights, lr=scheduler.learn_rate) pbar = tqdm.tqdm(enumerate(ds), total=steps - ititial_step) - for i, (x, text, cond) in pbar: + for i, entry in pbar: hypernetwork.step = i + ititial_step - if hypernetwork.step > end_step: - try: - (learn_rate, end_step) = next(schedules) - except Exception: - break - tqdm.tqdm.write(f'Training at rate of {learn_rate} until step {end_step}') - for pg in optimizer.param_groups: - pg['lr'] = learn_rate + scheduler.apply(optimizer, hypernetwork.step) + if scheduler.finished: + break if shared.state.interrupted: break with torch.autocast("cuda"): - cond = cond.to(devices.device) - x = x.to(devices.device) + cond = entry.cond.to(devices.device) + x = entry.latent.to(devices.device) loss = shared.sd_model(x.unsqueeze(0), cond)[0] del x del cond @@ -267,7 +259,7 @@ def train_hypernetwork(hypernetwork_name, learn_rate, data_root, log_directory, if hypernetwork.step > 0 and images_dir is not None and hypernetwork.step % create_image_every == 0: last_saved_image = os.path.join(images_dir, f'{hypernetwork_name}-{hypernetwork.step}.png') - preview_text = text if preview_image_prompt == "" else preview_image_prompt + preview_text = entry.cond_text if preview_image_prompt == "" else preview_image_prompt optimizer.zero_grad() shared.sd_model.cond_stage_model.to(devices.device) @@ -282,16 +274,16 @@ def train_hypernetwork(hypernetwork_name, learn_rate, data_root, log_directory, ) processed = processing.process_images(p) - image = processed.images[0] + image = processed.images[0] if len(processed.images)>0 else None if unload: shared.sd_model.cond_stage_model.to(devices.cpu) shared.sd_model.first_stage_model.to(devices.cpu) - shared.state.current_image = image - image.save(last_saved_image) - - last_saved_image += f", prompt: {preview_text}" + if image is not None: + shared.state.current_image = image + image.save(last_saved_image) + last_saved_image += f", prompt: {preview_text}" shared.state.job_no = hypernetwork.step @@ -299,7 +291,7 @@ def train_hypernetwork(hypernetwork_name, learn_rate, data_root, log_directory,

Loss: {losses.mean():.7f}
Step: {hypernetwork.step}
-Last prompt: {html.escape(text)}
+Last prompt: {html.escape(entry.cond_text)}
Last saved embedding: {html.escape(last_saved_file)}
Last saved image: {html.escape(last_saved_image)}

diff --git a/modules/shared.py b/modules/shared.py index 42e99741..e64e69fc 100644 --- a/modules/shared.py +++ b/modules/shared.py @@ -231,6 +231,9 @@ options_templates.update(options_section(('system', "System"), { options_templates.update(options_section(('training', "Training"), { "unload_models_when_training": OptionInfo(False, "Unload VAE and CLIP from VRAM when training"), + "dataset_filename_word_regex": OptionInfo("", "Filename word regex"), + "dataset_filename_join_string": OptionInfo(" ", "Filename join string"), + "training_image_repeats_per_epoch": OptionInfo(100, "Number of repeats for a single input image per epoch; used only for displaying epoch number", gr.Number, {"precision": 0}), })) options_templates.update(options_section(('sd', "Stable Diffusion"), { diff --git a/modules/textual_inversion/dataset.py b/modules/textual_inversion/dataset.py index f61f40d3..67e90afe 100644 --- a/modules/textual_inversion/dataset.py +++ b/modules/textual_inversion/dataset.py @@ -11,11 +11,21 @@ import tqdm from modules import devices, shared import re -re_tag = re.compile(r"[a-zA-Z][_\w\d()]+") +re_numbers_at_start = re.compile(r"^[-\d]+\s*") + + +class DatasetEntry: + def __init__(self, filename=None, latent=None, filename_text=None): + self.filename = filename + self.latent = latent + self.filename_text = filename_text + self.cond = None + self.cond_text = None class PersonalizedBase(Dataset): def __init__(self, data_root, width, height, repeats, flip_p=0.5, placeholder_token="*", model=None, device=None, template_file=None, include_cond=False): + re_word = re.compile(shared.opts.dataset_filename_word_regex) if len(shared.opts.dataset_filename_word_regex)>0 else None self.placeholder_token = placeholder_token @@ -42,9 +52,18 @@ class PersonalizedBase(Dataset): except Exception: continue + text_filename = os.path.splitext(path)[0] + ".txt" filename = os.path.basename(path) - filename_tokens = os.path.splitext(filename)[0] - filename_tokens = re_tag.findall(filename_tokens) + + if os.path.exists(text_filename): + with open(text_filename, "r", encoding="utf8") as file: + filename_text = file.read() + else: + filename_text = os.path.splitext(filename)[0] + filename_text = re.sub(re_numbers_at_start, '', filename_text) + if re_word: + tokens = re_word.findall(filename_text) + filename_text = (shared.opts.dataset_filename_join_string or "").join(tokens) npimage = np.array(image).astype(np.uint8) npimage = (npimage / 127.5 - 1.0).astype(np.float32) @@ -55,13 +74,13 @@ class PersonalizedBase(Dataset): init_latent = model.get_first_stage_encoding(model.encode_first_stage(torchdata.unsqueeze(dim=0))).squeeze() init_latent = init_latent.to(devices.cpu) + entry = DatasetEntry(filename=path, filename_text=filename_text, latent=init_latent) + if include_cond: - text = self.create_text(filename_tokens) - cond = cond_model([text]).to(devices.cpu) - else: - cond = None + entry.cond_text = self.create_text(filename_text) + entry.cond = cond_model([entry.cond_text]).to(devices.cpu) - self.dataset.append((init_latent, filename_tokens, cond)) + self.dataset.append(entry) self.length = len(self.dataset) * repeats @@ -72,10 +91,10 @@ class PersonalizedBase(Dataset): def shuffle(self): self.indexes = self.initial_indexes[torch.randperm(self.initial_indexes.shape[0])] - def create_text(self, filename_tokens): + def create_text(self, filename_text): text = random.choice(self.lines) text = text.replace("[name]", self.placeholder_token) - text = text.replace("[filewords]", ' '.join(filename_tokens)) + text = text.replace("[filewords]", filename_text) return text def __len__(self): @@ -86,7 +105,9 @@ class PersonalizedBase(Dataset): self.shuffle() index = self.indexes[i % len(self.indexes)] - x, filename_tokens, cond = self.dataset[index] + entry = self.dataset[index] + + if entry.cond is None: + entry.cond_text = self.create_text(entry.filename_text) - text = self.create_text(filename_tokens) - return x, text, cond + return entry diff --git a/modules/textual_inversion/learn_schedule.py b/modules/textual_inversion/learn_schedule.py index db720271..2062726a 100644 --- a/modules/textual_inversion/learn_schedule.py +++ b/modules/textual_inversion/learn_schedule.py @@ -1,6 +1,12 @@ +import tqdm -class LearnSchedule: + +class LearnScheduleIterator: def __init__(self, learn_rate, max_steps, cur_step=0): + """ + specify learn_rate as "0.001:100, 0.00001:1000, 1e-5:10000" to have lr of 0.001 until step 100, 0.00001 until 1000, 1e-5:10000 until 10000 + """ + pairs = learn_rate.split(',') self.rates = [] self.it = 0 @@ -32,3 +38,32 @@ class LearnSchedule: return self.rates[self.it - 1] else: raise StopIteration + + +class LearnRateScheduler: + def __init__(self, learn_rate, max_steps, cur_step=0, verbose=True): + self.schedules = LearnScheduleIterator(learn_rate, max_steps, cur_step) + (self.learn_rate, self.end_step) = next(self.schedules) + self.verbose = verbose + + if self.verbose: + print(f'Training at rate of {self.learn_rate} until step {self.end_step}') + + self.finished = False + + def apply(self, optimizer, step_number): + if step_number <= self.end_step: + return + + try: + (self.learn_rate, self.end_step) = next(self.schedules) + except Exception: + self.finished = True + return + + if self.verbose: + tqdm.tqdm.write(f'Training at rate of {self.learn_rate} until step {self.end_step}') + + for pg in optimizer.param_groups: + pg['lr'] = self.learn_rate + diff --git a/modules/textual_inversion/textual_inversion.py b/modules/textual_inversion/textual_inversion.py index c5153e4a..fa0e33a2 100644 --- a/modules/textual_inversion/textual_inversion.py +++ b/modules/textual_inversion/textual_inversion.py @@ -11,7 +11,7 @@ from PIL import Image, PngImagePlugin from modules import shared, devices, sd_hijack, processing, sd_models import modules.textual_inversion.dataset -from modules.textual_inversion.learn_schedule import LearnSchedule +from modules.textual_inversion.learn_schedule import LearnRateScheduler from modules.textual_inversion.image_embedding import (embedding_to_b64, embedding_from_b64, insert_image_data_embed, extract_image_data_embed, @@ -172,8 +172,7 @@ def create_embedding(name, num_vectors_per_token, init_text='*'): return fn - -def train_embedding(embedding_name, learn_rate, data_root, log_directory, training_width, training_height, steps, num_repeats, create_image_every, save_embedding_every, template_file, save_image_with_stored_embedding, preview_image_prompt): +def train_embedding(embedding_name, learn_rate, data_root, log_directory, training_width, training_height, steps, create_image_every, save_embedding_every, template_file, save_image_with_stored_embedding, preview_image_prompt): assert embedding_name, 'embedding not selected' shared.state.textinfo = "Initializing textual inversion training..." @@ -205,7 +204,7 @@ def train_embedding(embedding_name, learn_rate, data_root, log_directory, traini shared.state.textinfo = f"Preparing dataset from {html.escape(data_root)}..." with torch.autocast("cuda"): - ds = modules.textual_inversion.dataset.PersonalizedBase(data_root=data_root, width=training_width, height=training_height, repeats=num_repeats, placeholder_token=embedding_name, model=shared.sd_model, device=devices.device, template_file=template_file) + ds = modules.textual_inversion.dataset.PersonalizedBase(data_root=data_root, width=training_width, height=training_height, repeats=shared.opts.training_image_repeats_per_epoch, placeholder_token=embedding_name, model=shared.sd_model, device=devices.device, template_file=template_file) hijack = sd_hijack.model_hijack @@ -221,32 +220,24 @@ def train_embedding(embedding_name, learn_rate, data_root, log_directory, traini if ititial_step > steps: return embedding, filename - schedules = iter(LearnSchedule(learn_rate, steps, ititial_step)) - (learn_rate, end_step) = next(schedules) - print(f'Training at rate of {learn_rate} until step {end_step}') - - optimizer = torch.optim.AdamW([embedding.vec], lr=learn_rate) + scheduler = LearnRateScheduler(learn_rate, steps, ititial_step) + optimizer = torch.optim.AdamW([embedding.vec], lr=scheduler.learn_rate) pbar = tqdm.tqdm(enumerate(ds), total=steps-ititial_step) - for i, (x, text, _) in pbar: + for i, entry in pbar: embedding.step = i + ititial_step - if embedding.step > end_step: - try: - (learn_rate, end_step) = next(schedules) - except: - break - tqdm.tqdm.write(f'Training at rate of {learn_rate} until step {end_step}') - for pg in optimizer.param_groups: - pg['lr'] = learn_rate + scheduler.apply(optimizer, embedding.step) + if scheduler.finished: + break if shared.state.interrupted: break with torch.autocast("cuda"): - c = cond_model([text]) + c = cond_model([entry.cond_text]) - x = x.to(devices.device) + x = entry.latent.to(devices.device) loss = shared.sd_model(x.unsqueeze(0), c)[0] del x @@ -268,7 +259,7 @@ def train_embedding(embedding_name, learn_rate, data_root, log_directory, traini if embedding.step > 0 and images_dir is not None and embedding.step % create_image_every == 0: last_saved_image = os.path.join(images_dir, f'{embedding_name}-{embedding.step}.png') - preview_text = text if preview_image_prompt == "" else preview_image_prompt + preview_text = entry.cond_text if preview_image_prompt == "" else preview_image_prompt p = processing.StableDiffusionProcessingTxt2Img( sd_model=shared.sd_model, @@ -314,7 +305,7 @@ def train_embedding(embedding_name, learn_rate, data_root, log_directory, traini

Loss: {losses.mean():.7f}
Step: {embedding.step}
-Last prompt: {html.escape(text)}
+Last prompt: {html.escape(entry.cond_text)}
Last saved embedding: {html.escape(last_saved_file)}
Last saved image: {html.escape(last_saved_image)}

diff --git a/modules/ui.py b/modules/ui.py index 2b332267..c42535c8 100644 --- a/modules/ui.py +++ b/modules/ui.py @@ -1098,7 +1098,6 @@ def create_ui(wrap_gradio_gpu_call): training_width = gr.Slider(minimum=64, maximum=2048, step=64, label="Width", value=512) training_height = gr.Slider(minimum=64, maximum=2048, step=64, label="Height", value=512) steps = gr.Number(label='Max steps', value=100000, precision=0) - num_repeats = gr.Number(label='Number of repeats for a single input image per epoch', value=100, precision=0) create_image_every = gr.Number(label='Save an image to log directory every N steps, 0 to disable', value=500, precision=0) save_embedding_every = gr.Number(label='Save a copy of embedding to log directory every N steps, 0 to disable', value=500, precision=0) save_image_with_stored_embedding = gr.Checkbox(label='Save images with embedding in PNG chunks', value=True) @@ -1176,7 +1175,6 @@ def create_ui(wrap_gradio_gpu_call): training_width, training_height, steps, - num_repeats, create_image_every, save_embedding_every, template_file, -- cgit v1.2.1 From 698d303b04e293635bfb49c525409f3bcf671dce Mon Sep 17 00:00:00 2001 From: AUTOMATIC <16777216c@gmail.com> Date: Wed, 12 Oct 2022 21:55:43 +0300 Subject: deepbooru: added option to use spaces or underscores deepbooru: added option to quote (\) in tags deepbooru/BLIP: write caption to file instead of image filename deepbooru/BLIP: now possible to use both for captions deepbooru: process is stopped even if an exception occurs --- modules/deepbooru.py | 65 ++++++++++++++++++----- modules/shared.py | 2 + modules/textual_inversion/preprocess.py | 92 ++++++++++++++------------------- modules/ui.py | 7 +-- 4 files changed, 95 insertions(+), 71 deletions(-) (limited to 'modules') diff --git a/modules/deepbooru.py b/modules/deepbooru.py index 29529949..419e6a9c 100644 --- a/modules/deepbooru.py +++ b/modules/deepbooru.py @@ -2,33 +2,44 @@ import os.path from concurrent.futures import ProcessPoolExecutor import multiprocessing import time +import re + +re_special = re.compile(r'([\\()])') def get_deepbooru_tags(pil_image): """ This method is for running only one image at a time for simple use. Used to the img2img interrogate. """ from modules import shared # prevents circular reference - create_deepbooru_process(shared.opts.interrogate_deepbooru_score_threshold, shared.opts.deepbooru_sort_alpha) - shared.deepbooru_process_return["value"] = -1 - shared.deepbooru_process_queue.put(pil_image) - while shared.deepbooru_process_return["value"] == -1: - time.sleep(0.2) - tags = shared.deepbooru_process_return["value"] - release_process() - return tags + try: + create_deepbooru_process(shared.opts.interrogate_deepbooru_score_threshold, create_deepbooru_opts()) + return get_tags_from_process(pil_image) + finally: + release_process() + + +def create_deepbooru_opts(): + from modules import shared -def deepbooru_process(queue, deepbooru_process_return, threshold, alpha_sort): + return { + "use_spaces": shared.opts.deepbooru_use_spaces, + "use_escape": shared.opts.deepbooru_escape, + "alpha_sort": shared.opts.deepbooru_sort_alpha, + } + + +def deepbooru_process(queue, deepbooru_process_return, threshold, deepbooru_opts): model, tags = get_deepbooru_tags_model() while True: # while process is running, keep monitoring queue for new image pil_image = queue.get() if pil_image == "QUIT": break else: - deepbooru_process_return["value"] = get_deepbooru_tags_from_model(model, tags, pil_image, threshold, alpha_sort) + deepbooru_process_return["value"] = get_deepbooru_tags_from_model(model, tags, pil_image, threshold, deepbooru_opts) -def create_deepbooru_process(threshold, alpha_sort): +def create_deepbooru_process(threshold, deepbooru_opts): """ Creates deepbooru process. A queue is created to send images into the process. This enables multiple images to be processed in a row without reloading the model or creating a new process. To return the data, a shared @@ -41,10 +52,23 @@ def create_deepbooru_process(threshold, alpha_sort): shared.deepbooru_process_queue = shared.deepbooru_process_manager.Queue() shared.deepbooru_process_return = shared.deepbooru_process_manager.dict() shared.deepbooru_process_return["value"] = -1 - shared.deepbooru_process = multiprocessing.Process(target=deepbooru_process, args=(shared.deepbooru_process_queue, shared.deepbooru_process_return, threshold, alpha_sort)) + shared.deepbooru_process = multiprocessing.Process(target=deepbooru_process, args=(shared.deepbooru_process_queue, shared.deepbooru_process_return, threshold, deepbooru_opts)) shared.deepbooru_process.start() +def get_tags_from_process(image): + from modules import shared + + shared.deepbooru_process_return["value"] = -1 + shared.deepbooru_process_queue.put(image) + while shared.deepbooru_process_return["value"] == -1: + time.sleep(0.2) + caption = shared.deepbooru_process_return["value"] + shared.deepbooru_process_return["value"] = -1 + + return caption + + def release_process(): """ Stops the deepbooru process to return used memory @@ -81,10 +105,15 @@ def get_deepbooru_tags_model(): return model, tags -def get_deepbooru_tags_from_model(model, tags, pil_image, threshold, alpha_sort): +def get_deepbooru_tags_from_model(model, tags, pil_image, threshold, deepbooru_opts): import deepdanbooru as dd import tensorflow as tf import numpy as np + + alpha_sort = deepbooru_opts['alpha_sort'] + use_spaces = deepbooru_opts['use_spaces'] + use_escape = deepbooru_opts['use_escape'] + width = model.input_shape[2] height = model.input_shape[1] image = np.array(pil_image) @@ -129,4 +158,12 @@ def get_deepbooru_tags_from_model(model, tags, pil_image, threshold, alpha_sort) print('\n'.join(sorted(result_tags_print, reverse=True))) - return ', '.join(result_tags_out).replace('_', ' ').replace(':', ' ') + tags_text = ', '.join(result_tags_out) + + if use_spaces: + tags_text = tags_text.replace('_', ' ') + + if use_escape: + tags_text = re.sub(re_special, r'\\\1', tags_text) + + return tags_text.replace(':', ' ') diff --git a/modules/shared.py b/modules/shared.py index e64e69fc..78b73aae 100644 --- a/modules/shared.py +++ b/modules/shared.py @@ -260,6 +260,8 @@ options_templates.update(options_section(('interrogate', "Interrogate Options"), "interrogate_clip_max_length": OptionInfo(48, "Interrogate: maximum description length", gr.Slider, {"minimum": 1, "maximum": 256, "step": 1}), "interrogate_deepbooru_score_threshold": OptionInfo(0.5, "Interrogate: deepbooru score threshold", gr.Slider, {"minimum": 0, "maximum": 1, "step": 0.01}), "deepbooru_sort_alpha": OptionInfo(True, "Interrogate: deepbooru sort alphabetically"), + "deepbooru_use_spaces": OptionInfo(False, "use spaces for tags in deepbooru"), + "deepbooru_escape": OptionInfo(True, "escape (\\) brackets in deepbooru (so they are used as literal brackets and not for emphasis)"), })) options_templates.update(options_section(('ui', "User interface"), { diff --git a/modules/textual_inversion/preprocess.py b/modules/textual_inversion/preprocess.py index 113cecf1..3047bede 100644 --- a/modules/textual_inversion/preprocess.py +++ b/modules/textual_inversion/preprocess.py @@ -10,7 +10,28 @@ from modules.shared import opts, cmd_opts if cmd_opts.deepdanbooru: import modules.deepbooru as deepbooru + def preprocess(process_src, process_dst, process_width, process_height, process_flip, process_split, process_caption, process_caption_deepbooru=False): + try: + if process_caption: + shared.interrogator.load() + + if process_caption_deepbooru: + deepbooru.create_deepbooru_process(opts.interrogate_deepbooru_score_threshold, deepbooru.create_deepbooru_opts()) + + preprocess_work(process_src, process_dst, process_width, process_height, process_flip, process_split, process_caption, process_caption_deepbooru) + + finally: + + if process_caption: + shared.interrogator.send_blip_to_ram() + + if process_caption_deepbooru: + deepbooru.release_process() + + + +def preprocess_work(process_src, process_dst, process_width, process_height, process_flip, process_split, process_caption, process_caption_deepbooru=False): width = process_width height = process_height src = os.path.abspath(process_src) @@ -25,30 +46,28 @@ def preprocess(process_src, process_dst, process_width, process_height, process_ shared.state.textinfo = "Preprocessing..." shared.state.job_count = len(files) - if process_caption: - shared.interrogator.load() - - if process_caption_deepbooru: - deepbooru.create_deepbooru_process(opts.interrogate_deepbooru_score_threshold, opts.deepbooru_sort_alpha) - def save_pic_with_caption(image, index): + caption = "" + if process_caption: - caption = "-" + shared.interrogator.generate_caption(image) - caption = sanitize_caption(os.path.join(dst, f"{index:05}-{subindex[0]}"), caption, ".png") - elif process_caption_deepbooru: - shared.deepbooru_process_return["value"] = -1 - shared.deepbooru_process_queue.put(image) - while shared.deepbooru_process_return["value"] == -1: - time.sleep(0.2) - caption = "-" + shared.deepbooru_process_return["value"] - caption = sanitize_caption(os.path.join(dst, f"{index:05}-{subindex[0]}"), caption, ".png") - shared.deepbooru_process_return["value"] = -1 - else: - caption = filename - caption = os.path.splitext(caption)[0] - caption = os.path.basename(caption) + caption += shared.interrogator.generate_caption(image) + + if process_caption_deepbooru: + if len(caption) > 0: + caption += ", " + caption += deepbooru.get_tags_from_process(image) + + filename_part = filename + filename_part = os.path.splitext(filename_part)[0] + filename_part = os.path.basename(filename_part) + + basename = f"{index:05}-{subindex[0]}-{filename_part}" + image.save(os.path.join(dst, f"{basename}.png")) + + if len(caption) > 0: + with open(os.path.join(dst, f"{basename}.txt"), "w", encoding="utf8") as file: + file.write(caption) - image.save(os.path.join(dst, f"{index:05}-{subindex[0]}{caption}.png")) subindex[0] += 1 def save_pic(image, index): @@ -93,34 +112,3 @@ def preprocess(process_src, process_dst, process_width, process_height, process_ save_pic(img, index) shared.state.nextjob() - - if process_caption: - shared.interrogator.send_blip_to_ram() - - if process_caption_deepbooru: - deepbooru.release_process() - - -def sanitize_caption(base_path, original_caption, suffix): - operating_system = platform.system().lower() - if (operating_system == "windows"): - invalid_path_characters = "\\/:*?\"<>|" - max_path_length = 259 - else: - invalid_path_characters = "/" #linux/macos - max_path_length = 1023 - caption = original_caption - for invalid_character in invalid_path_characters: - caption = caption.replace(invalid_character, "") - fixed_path_length = len(base_path) + len(suffix) - if fixed_path_length + len(caption) <= max_path_length: - return caption - caption_tokens = caption.split() - new_caption = "" - for token in caption_tokens: - last_caption = new_caption - new_caption = new_caption + token + " " - if (len(new_caption) + fixed_path_length - 1 > max_path_length): - break - print(f"\nPath will be too long. Truncated caption: {original_caption}\nto: {last_caption}", file=sys.stderr) - return last_caption.strip() diff --git a/modules/ui.py b/modules/ui.py index c42535c8..e07ee0e1 100644 --- a/modules/ui.py +++ b/modules/ui.py @@ -1074,11 +1074,8 @@ def create_ui(wrap_gradio_gpu_call): with gr.Row(): process_flip = gr.Checkbox(label='Create flipped copies') process_split = gr.Checkbox(label='Split oversized images into two') - process_caption = gr.Checkbox(label='Use BLIP caption as filename') - if cmd_opts.deepdanbooru: - process_caption_deepbooru = gr.Checkbox(label='Use deepbooru caption as filename') - else: - process_caption_deepbooru = gr.Checkbox(label='Use deepbooru caption as filename', visible=False) + process_caption = gr.Checkbox(label='Use BLIP for caption') + process_caption_deepbooru = gr.Checkbox(label='Use deepbooru for caption', visible=True if cmd_opts.deepdanbooru else False) with gr.Row(): with gr.Column(scale=3): -- cgit v1.2.1 From efefa4862c6c75115d3da9f768348630cc32bdea Mon Sep 17 00:00:00 2001 From: Greg Fuller Date: Wed, 12 Oct 2022 13:03:00 -0700 Subject: [1/?] [wip] Reintroduce opts.interrogate_return_ranks looks functionally correct, needs testing Needs particular testing care around whether the colon usage (:) will break anything in whatever new use cases were introduced by https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/2143 --- modules/deepbooru.py | 25 ++++++++++++++----------- 1 file changed, 14 insertions(+), 11 deletions(-) (limited to 'modules') diff --git a/modules/deepbooru.py b/modules/deepbooru.py index 419e6a9c..2cbf2cab 100644 --- a/modules/deepbooru.py +++ b/modules/deepbooru.py @@ -26,6 +26,7 @@ def create_deepbooru_opts(): "use_spaces": shared.opts.deepbooru_use_spaces, "use_escape": shared.opts.deepbooru_escape, "alpha_sort": shared.opts.deepbooru_sort_alpha, + "include_ranks": shared.opts.interrogate_return_ranks, } @@ -113,6 +114,7 @@ def get_deepbooru_tags_from_model(model, tags, pil_image, threshold, deepbooru_o alpha_sort = deepbooru_opts['alpha_sort'] use_spaces = deepbooru_opts['use_spaces'] use_escape = deepbooru_opts['use_escape'] + include_ranks = deepbooru_opts['include_ranks'] width = model.input_shape[2] height = model.input_shape[1] @@ -151,19 +153,20 @@ def get_deepbooru_tags_from_model(model, tags, pil_image, threshold, deepbooru_o if alpha_sort: sort_ndx = 1 - # sort by reverse by likelihood and normal for alpha + # sort by reverse by likelihood and normal for alpha, and format tag text as requested unsorted_tags_in_theshold.sort(key=lambda y: y[sort_ndx], reverse=(not alpha_sort)) for weight, tag in unsorted_tags_in_theshold: - result_tags_out.append(tag) + # note: tag_outformat will still have a colon if include_ranks is True + tag_outformat = tag.replace(':', ' ') + if use_spaces: + tag_outformat = tag_outformat.replace('_', ' ') + if use_escape: + tag_outformat = re.sub(re_special, r'\\\1', tag_outformat) + if include_ranks: + use_escape += f":{weight:.3f}" - print('\n'.join(sorted(result_tags_print, reverse=True))) - - tags_text = ', '.join(result_tags_out) + result_tags_out.append(tag_outformat) - if use_spaces: - tags_text = tags_text.replace('_', ' ') - - if use_escape: - tags_text = re.sub(re_special, r'\\\1', tags_text) + print('\n'.join(sorted(result_tags_print, reverse=True))) - return tags_text.replace(':', ' ') + return ', '.join(result_tags_out) -- cgit v1.2.1 From f776254b12361b5bae16f6629bcdcb47b450c48d Mon Sep 17 00:00:00 2001 From: Greg Fuller Date: Wed, 12 Oct 2022 13:08:06 -0700 Subject: [2/?] [wip] ignore OPT_INCLUDE_RANKS for training filenames --- modules/deepbooru.py | 3 ++- modules/textual_inversion/preprocess.py | 4 +++- 2 files changed, 5 insertions(+), 2 deletions(-) (limited to 'modules') diff --git a/modules/deepbooru.py b/modules/deepbooru.py index 2cbf2cab..fcc05819 100644 --- a/modules/deepbooru.py +++ b/modules/deepbooru.py @@ -19,6 +19,7 @@ def get_deepbooru_tags(pil_image): release_process() +OPT_INCLUDE_RANKS = "include_ranks" def create_deepbooru_opts(): from modules import shared @@ -26,7 +27,7 @@ def create_deepbooru_opts(): "use_spaces": shared.opts.deepbooru_use_spaces, "use_escape": shared.opts.deepbooru_escape, "alpha_sort": shared.opts.deepbooru_sort_alpha, - "include_ranks": shared.opts.interrogate_return_ranks, + OPT_INCLUDE_RANKS: shared.opts.interrogate_return_ranks, } diff --git a/modules/textual_inversion/preprocess.py b/modules/textual_inversion/preprocess.py index 3047bede..886cf0c3 100644 --- a/modules/textual_inversion/preprocess.py +++ b/modules/textual_inversion/preprocess.py @@ -17,7 +17,9 @@ def preprocess(process_src, process_dst, process_width, process_height, process_ shared.interrogator.load() if process_caption_deepbooru: - deepbooru.create_deepbooru_process(opts.interrogate_deepbooru_score_threshold, deepbooru.create_deepbooru_opts()) + db_opts = deepbooru.create_deepbooru_opts() + db_opts[deepbooru.OPT_INCLUDE_RANKS] = False + deepbooru.create_deepbooru_process(opts.interrogate_deepbooru_score_threshold, db_opts) preprocess_work(process_src, process_dst, process_width, process_height, process_flip, process_split, process_caption, process_caption_deepbooru) -- cgit v1.2.1 From 514456101b142b47acf87f6de95bad1a23d73be7 Mon Sep 17 00:00:00 2001 From: Greg Fuller Date: Wed, 12 Oct 2022 13:14:13 -0700 Subject: [3/?] [wip] fix incorrect variable reference still needs testing --- modules/deepbooru.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) (limited to 'modules') diff --git a/modules/deepbooru.py b/modules/deepbooru.py index fcc05819..c2004696 100644 --- a/modules/deepbooru.py +++ b/modules/deepbooru.py @@ -164,7 +164,7 @@ def get_deepbooru_tags_from_model(model, tags, pil_image, threshold, deepbooru_o if use_escape: tag_outformat = re.sub(re_special, r'\\\1', tag_outformat) if include_ranks: - use_escape += f":{weight:.3f}" + tag_outformat += f":{weight:.3f}" result_tags_out.append(tag_outformat) -- cgit v1.2.1 From 1cfc2a18981ee56bdb69a2de7b463a11ad05e329 Mon Sep 17 00:00:00 2001 From: Melan Date: Wed, 12 Oct 2022 23:36:29 +0200 Subject: Save a csv containing the loss while training --- modules/hypernetworks/hypernetwork.py | 17 ++++++++++++++++- modules/textual_inversion/textual_inversion.py | 17 ++++++++++++++++- modules/ui.py | 3 +++ 3 files changed, 35 insertions(+), 2 deletions(-) (limited to 'modules') diff --git a/modules/hypernetworks/hypernetwork.py b/modules/hypernetworks/hypernetwork.py index b6c06d49..6522078f 100644 --- a/modules/hypernetworks/hypernetwork.py +++ b/modules/hypernetworks/hypernetwork.py @@ -5,6 +5,7 @@ import os import sys import traceback import tqdm +import csv import torch @@ -174,7 +175,7 @@ def attention_CrossAttention_forward(self, x, context=None, mask=None): return self.to_out(out) -def train_hypernetwork(hypernetwork_name, learn_rate, data_root, log_directory, steps, create_image_every, save_hypernetwork_every, template_file, preview_image_prompt): +def train_hypernetwork(hypernetwork_name, learn_rate, data_root, log_directory, steps, create_image_every, save_hypernetwork_every, write_csv_every, template_file, preview_image_prompt): assert hypernetwork_name, 'hypernetwork not selected' path = shared.hypernetworks.get(hypernetwork_name, None) @@ -256,6 +257,20 @@ def train_hypernetwork(hypernetwork_name, learn_rate, data_root, log_directory, last_saved_file = os.path.join(hypernetwork_dir, f'{hypernetwork_name}-{hypernetwork.step}.pt') hypernetwork.save(last_saved_file) + print(f"{write_csv_every} > {hypernetwork.step % write_csv_every == 0}, {write_csv_every}") + if write_csv_every > 0 and hypernetwork_dir is not None and hypernetwork.step % write_csv_every == 0: + write_csv_header = False if os.path.exists(os.path.join(hypernetwork_dir, "hypernetwork_loss.csv")) else True + + with open(os.path.join(hypernetwork_dir, "hypernetwork_loss.csv"), "a+") as fout: + + csv_writer = csv.DictWriter(fout, fieldnames=["step", "loss"]) + + if write_csv_header: + csv_writer.writeheader() + + csv_writer.writerow({"step": hypernetwork.step, + "loss": f"{losses.mean():.7f}"}) + if hypernetwork.step > 0 and images_dir is not None and hypernetwork.step % create_image_every == 0: last_saved_image = os.path.join(images_dir, f'{hypernetwork_name}-{hypernetwork.step}.png') diff --git a/modules/textual_inversion/textual_inversion.py b/modules/textual_inversion/textual_inversion.py index fa0e33a2..25038a89 100644 --- a/modules/textual_inversion/textual_inversion.py +++ b/modules/textual_inversion/textual_inversion.py @@ -6,6 +6,7 @@ import torch import tqdm import html import datetime +import csv from PIL import Image, PngImagePlugin @@ -172,7 +173,7 @@ def create_embedding(name, num_vectors_per_token, init_text='*'): return fn -def train_embedding(embedding_name, learn_rate, data_root, log_directory, training_width, training_height, steps, create_image_every, save_embedding_every, template_file, save_image_with_stored_embedding, preview_image_prompt): +def train_embedding(embedding_name, learn_rate, data_root, log_directory, training_width, training_height, steps, create_image_every, save_embedding_every, write_csv_every, template_file, save_image_with_stored_embedding, preview_image_prompt): assert embedding_name, 'embedding not selected' shared.state.textinfo = "Initializing textual inversion training..." @@ -256,6 +257,20 @@ def train_embedding(embedding_name, learn_rate, data_root, log_directory, traini last_saved_file = os.path.join(embedding_dir, f'{embedding_name}-{embedding.step}.pt') embedding.save(last_saved_file) + if write_csv_every > 0 and log_directory is not None and embedding.step % write_csv_every == 0: + write_csv_header = False if os.path.exists(os.path.join(log_directory, "textual_inversion_loss.csv")) else True + + with open(os.path.join(log_directory, "textual_inversion_loss.csv"), "a+") as fout: + + csv_writer = csv.DictWriter(fout, fieldnames=["epoch", "epoch_step", "loss"]) + + if write_csv_header: + csv_writer.writeheader() + + csv_writer.writerow({"epoch": epoch_num + 1, + "epoch_step": epoch_step - 1, + "loss": f"{losses.mean():.7f}"}) + if embedding.step > 0 and images_dir is not None and embedding.step % create_image_every == 0: last_saved_image = os.path.join(images_dir, f'{embedding_name}-{embedding.step}.png') diff --git a/modules/ui.py b/modules/ui.py index e07ee0e1..1195c2f1 100644 --- a/modules/ui.py +++ b/modules/ui.py @@ -1096,6 +1096,7 @@ def create_ui(wrap_gradio_gpu_call): training_height = gr.Slider(minimum=64, maximum=2048, step=64, label="Height", value=512) steps = gr.Number(label='Max steps', value=100000, precision=0) create_image_every = gr.Number(label='Save an image to log directory every N steps, 0 to disable', value=500, precision=0) + write_csv_every = gr.Number(label='Save an csv containing the loss to log directory every N steps, 0 to disable', value=500, precision=0) save_embedding_every = gr.Number(label='Save a copy of embedding to log directory every N steps, 0 to disable', value=500, precision=0) save_image_with_stored_embedding = gr.Checkbox(label='Save images with embedding in PNG chunks', value=True) preview_image_prompt = gr.Textbox(label='Preview prompt', value="") @@ -1174,6 +1175,7 @@ def create_ui(wrap_gradio_gpu_call): steps, create_image_every, save_embedding_every, + write_csv_every, template_file, save_image_with_stored_embedding, preview_image_prompt, @@ -1195,6 +1197,7 @@ def create_ui(wrap_gradio_gpu_call): steps, create_image_every, save_embedding_every, + write_csv_every, template_file, preview_image_prompt, ], -- cgit v1.2.1 From 54e0051bdd7dea7348825c09600ec61ea0771cb8 Mon Sep 17 00:00:00 2001 From: d8ahazard Date: Wed, 12 Oct 2022 18:17:26 -0500 Subject: Add drag/drop param loading. Drop an image or generational text onto the prompt bar, it loads the info for parsing. --- modules/images.py | 20 ++++++++++++++++++++ modules/ui.py | 30 +++++++++++++++++++++++++++++- 2 files changed, 49 insertions(+), 1 deletion(-) (limited to 'modules') diff --git a/modules/images.py b/modules/images.py index c0a90676..f1155b7f 100644 --- a/modules/images.py +++ b/modules/images.py @@ -463,3 +463,23 @@ def save_image(image, path, basename, seed=None, prompt=None, extension='png', i txt_fullfn = None return fullfn, txt_fullfn + + +def image_data(image_path): + file, ext = os.path.splitext(image_path.name) + data = {} + if "png" in ext: + image = Image.open(image_path.name, "r") + print(f"Image data requested for {image_path.name} {image.format} of {type(image)}") + try: + data = image.text["parameters"] + except Exception as e: + print(f"Exception: {e}") + pass + print(f"Image data: {data}") + if "txt" in ext: + myfile = open(image_path.name, 'r') + data = myfile.read() + myfile.close() + + return data, None diff --git a/modules/ui.py b/modules/ui.py index 2b332267..dd793c39 100644 --- a/modules/ui.py +++ b/modules/ui.py @@ -431,7 +431,6 @@ def create_toprow(is_img2img): with gr.Column(scale=80): with gr.Row(): prompt = gr.Textbox(label="Prompt", elem_id=f"{id_part}_prompt", show_label=False, placeholder="Prompt", lines=2) - with gr.Column(scale=1, elem_id="roll_col"): roll = gr.Button(value=art_symbol, elem_id="roll", visible=len(shared.artist_db.artists) > 0) paste = gr.Button(value=paste_symbol, elem_id="paste") @@ -513,6 +512,7 @@ def create_ui(wrap_gradio_gpu_call): with gr.Blocks(analytics_enabled=False) as txt2img_interface: txt2img_prompt, roll, txt2img_prompt_style, txt2img_negative_prompt, txt2img_prompt_style2, submit, _, _, txt2img_prompt_style_apply, txt2img_save_style, paste, token_counter, token_button = create_toprow(is_img2img=False) dummy_component = gr.Label(visible=False) + txt_prompt_img = gr.File(label="", elem_id="txt2img_prompt_image", file_count="single", type="file", visible=False) with gr.Row(elem_id='txt2img_progress_row'): with gr.Column(scale=1): @@ -614,6 +614,18 @@ def create_ui(wrap_gradio_gpu_call): txt2img_prompt.submit(**txt2img_args) submit.click(**txt2img_args) + txt_prompt_img.change( + fn=modules.images.image_data, + # _js = "get_extras_tab_index", + inputs=[ + txt_prompt_img + ], + outputs=[ + txt2img_prompt, + txt_prompt_img + ] + ) + enable_hr.change( fn=lambda x: gr_show(x), inputs=[enable_hr], @@ -674,6 +686,9 @@ def create_ui(wrap_gradio_gpu_call): img2img_prompt, roll, img2img_prompt_style, img2img_negative_prompt, img2img_prompt_style2, submit, img2img_interrogate, img2img_deepbooru, img2img_prompt_style_apply, img2img_save_style, paste, token_counter, token_button = create_toprow(is_img2img=True) with gr.Row(elem_id='img2img_progress_row'): + img2img_prompt_img = gr.File(label="", elem_id="txt_prompt_image", file_count="single", type="file", + visible=False) + with gr.Column(scale=1): pass @@ -768,6 +783,18 @@ def create_ui(wrap_gradio_gpu_call): connect_reuse_seed(seed, reuse_seed, generation_info, dummy_component, is_subseed=False) connect_reuse_seed(subseed, reuse_subseed, generation_info, dummy_component, is_subseed=True) + img2img_prompt_img.change( + fn=modules.images.image_data, + # _js = "get_extras_tab_index", + inputs=[ + txt_prompt_img + ], + outputs=[ + img2img_prompt, + img2img_prompt_img + ] + ) + mask_mode.change( lambda mode, img: { init_img_with_mask: gr_show(mode == 0), @@ -956,6 +983,7 @@ def create_ui(wrap_gradio_gpu_call): button_id = "hidden_element" if shared.cmd_opts.hide_ui_dir_config else '' open_extras_folder = gr.Button('Open output directory', elem_id=button_id) + submit.click( fn=wrap_gradio_gpu_call(modules.extras.run_extras), _js="get_extras_tab_index", -- cgit v1.2.1 From 716a9e034f1aff434083363b218bd6043a774fc2 Mon Sep 17 00:00:00 2001 From: yfszzx Date: Thu, 13 Oct 2022 12:19:50 +0800 Subject: images history delete a number of images consecutively next --- modules/images_history.py | 44 ++++++++++++++++++++++++-------------------- 1 file changed, 24 insertions(+), 20 deletions(-) (limited to 'modules') diff --git a/modules/images_history.py b/modules/images_history.py index 6408973c..f812ea4e 100644 --- a/modules/images_history.py +++ b/modules/images_history.py @@ -54,23 +54,26 @@ def show_image_info(num, image_path, filenames): #print(f"select image {num}") file = filenames[int(num)] return file, num, os.path.join(image_path, file) -def delete_image(tabname, dir_name, name, page_index, filenames, image_index): - path = os.path.join(dir_name, name) - if os.path.exists(path): - print(f"Delete file {path}") - os.remove(path) - txt_file = os.path.splitext(path)[0] + ".txt" - if os.path.exists(txt_file): - os.remove(txt_file) - new_file_list = [] - for f in filenames: - if f == name: - continue - new_file_list.append(f) - else: - print(f"Not exists file {path}") - new_file_list = filenames - return page_index, new_file_list +def delete_image(delete_num, tabname, dir_name, name, page_index, filenames, image_index): + delete_num = int(delete_num) + index = list(filenames).index(name) + i = 0 + new_file_list = [] + for name in filenames: + if i >= index and i < index + delete_num: + path = os.path.join(dir_name, name) + if os.path.exists(path): + print(f"Delete file {path}") + os.remove(path) + txt_file = os.path.splitext(path)[0] + ".txt" + if os.path.exists(txt_file): + os.remove(txt_file) + else: + print(f"Not exists file {path}") + else: + new_file_list.append(name) + i += 1 + return page_index, new_file_list, 1 def show_images_history(gr, opts, tabname, run_pnginfo, switch_dict): if tabname == "txt2img": @@ -90,10 +93,11 @@ def show_images_history(gr, opts, tabname, run_pnginfo, switch_dict): with gr.Row(): with gr.Column(scale=2): history_gallery = gr.Gallery(show_label=False, elem_id=tabname + "_images_history_gallery").style(grid=6) - delete = gr.Button('Delete', elem_id=tabname + "_images_history_del_button") + with gr.Row(): + delete = gr.Button('Delete', elem_id=tabname + "_images_history_del_button") + delete_num = gr.Number(value=1, interactive=True, label="number of images to delete consecutively next") with gr.Column(): with gr.Row(): - #pnginfo = gr.Button('PNG info') pnginfo_send_to_txt2img = gr.Button('Send to txt2img') pnginfo_send_to_img2img = gr.Button('Send to img2img') with gr.Row(): @@ -127,7 +131,7 @@ def show_images_history(gr, opts, tabname, run_pnginfo, switch_dict): #other funcitons set_index.click(show_image_info, _js="images_history_get_current_img", inputs=[tabname_box, img_path, filenames], outputs=[img_file_name, image_index, hide_image]) img_file_name.change(fn=None, _js="images_history_enable_del_buttons", inputs=None, outputs=None) - delete.click(delete_image,_js="images_history_delete", inputs=[tabname_box, img_path, img_file_name, page_index, filenames, image_index], outputs=[page_index, filenames]) + delete.click(delete_image,_js="images_history_delete", inputs=[delete_num, tabname_box, img_path, img_file_name, page_index, filenames, image_index], outputs=[page_index, filenames, delete_num]) hide_image.change(fn=run_pnginfo, inputs=[hide_image], outputs=[info1, img_file_info, info2]) #pnginfo.click(fn=run_pnginfo, inputs=[hide_image], outputs=[info1, img_file_info, info2]) -- cgit v1.2.1 From 78592d404acba7db3baf8d78bdc19266906e684a Mon Sep 17 00:00:00 2001 From: AUTOMATIC <16777216c@gmail.com> Date: Thu, 13 Oct 2022 07:40:03 +0300 Subject: remove interrogate option I accidentally deleted --- modules/shared.py | 1 + 1 file changed, 1 insertion(+) (limited to 'modules') diff --git a/modules/shared.py b/modules/shared.py index 78b73aae..9bda45c1 100644 --- a/modules/shared.py +++ b/modules/shared.py @@ -258,6 +258,7 @@ options_templates.update(options_section(('interrogate', "Interrogate Options"), "interrogate_clip_num_beams": OptionInfo(1, "Interrogate: num_beams for BLIP", gr.Slider, {"minimum": 1, "maximum": 16, "step": 1}), "interrogate_clip_min_length": OptionInfo(24, "Interrogate: minimum description length (excluding artists, etc..)", gr.Slider, {"minimum": 1, "maximum": 128, "step": 1}), "interrogate_clip_max_length": OptionInfo(48, "Interrogate: maximum description length", gr.Slider, {"minimum": 1, "maximum": 256, "step": 1}), + "interrogate_clip_dict_limit": OptionInfo(1500, "CLIP: maximum number of lines in text file (0 = No limit)"), "interrogate_deepbooru_score_threshold": OptionInfo(0.5, "Interrogate: deepbooru score threshold", gr.Slider, {"minimum": 0, "maximum": 1, "step": 0.01}), "deepbooru_sort_alpha": OptionInfo(True, "Interrogate: deepbooru sort alphabetically"), "deepbooru_use_spaces": OptionInfo(False, "use spaces for tags in deepbooru"), -- cgit v1.2.1 From 04c0e643f2eec68d93a76db171b4d70595808702 Mon Sep 17 00:00:00 2001 From: Greg Fuller Date: Wed, 12 Oct 2022 22:13:53 -0700 Subject: Merge branch 'master' of https://github.com/HunterVacui/stable-diffusion-webui --- modules/deepbooru.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) (limited to 'modules') diff --git a/modules/deepbooru.py b/modules/deepbooru.py index c2004696..f34f3788 100644 --- a/modules/deepbooru.py +++ b/modules/deepbooru.py @@ -164,7 +164,7 @@ def get_deepbooru_tags_from_model(model, tags, pil_image, threshold, deepbooru_o if use_escape: tag_outformat = re.sub(re_special, r'\\\1', tag_outformat) if include_ranks: - tag_outformat += f":{weight:.3f}" + tag_outformat = f"({tag_outformat}:{weight:.3f})" result_tags_out.append(tag_outformat) -- cgit v1.2.1 From e72adc999b3531370eafb9d316924ac497feb445 Mon Sep 17 00:00:00 2001 From: Trung Ngo Date: Sat, 8 Oct 2022 22:57:19 -0500 Subject: Restore last generation params --- modules/generation_parameters_copypaste.py | 8 ++++++++ modules/processing.py | 4 ++++ 2 files changed, 12 insertions(+) (limited to 'modules') diff --git a/modules/generation_parameters_copypaste.py b/modules/generation_parameters_copypaste.py index ac1ba7f4..3e75aecc 100644 --- a/modules/generation_parameters_copypaste.py +++ b/modules/generation_parameters_copypaste.py @@ -1,5 +1,7 @@ +import os import re import gradio as gr +from modules.shared import script_path re_param_code = r"\s*([\w ]+):\s*([^,]+)(?:,|$)" re_param = re.compile(re_param_code) @@ -61,6 +63,12 @@ Steps: 20, Sampler: Euler a, CFG scale: 7, Seed: 965400086, Size: 512x512, Model def connect_paste(button, paste_fields, input_comp, js=None): def paste_func(prompt): + if not prompt: + filename = os.path.join(script_path, "params.txt") + if os.path.exists(filename): + with open(filename, "r", encoding="utf8") as file: + prompt = file.read() + params = parse_generation_parameters(prompt) res = [] diff --git a/modules/processing.py b/modules/processing.py index 698b3069..d5172f00 100644 --- a/modules/processing.py +++ b/modules/processing.py @@ -324,6 +324,10 @@ def process_images(p: StableDiffusionProcessing) -> Processed: else: assert p.prompt is not None + with open(os.path.join(shared.script_path, "params.txt"), "w", encoding="utf8") as file: + processed = Processed(p, [], p.seed, "") + file.write(processed.infotext(p, 0)) + devices.torch_gc() seed = get_fixed_seed(p.seed) -- cgit v1.2.1 From fde7fefa2ea23747f1107e3e46bf60c08a1134f1 Mon Sep 17 00:00:00 2001 From: AUTOMATIC <16777216c@gmail.com> Date: Thu, 13 Oct 2022 12:26:34 +0300 Subject: update #2336 to prevent reading params.txt when --hide-ui-dir-config option is enabled (for servers, since this will let some users access others' params) --- modules/generation_parameters_copypaste.py | 3 ++- 1 file changed, 2 insertions(+), 1 deletion(-) (limited to 'modules') diff --git a/modules/generation_parameters_copypaste.py b/modules/generation_parameters_copypaste.py index 3e75aecc..c27826b6 100644 --- a/modules/generation_parameters_copypaste.py +++ b/modules/generation_parameters_copypaste.py @@ -2,6 +2,7 @@ import os import re import gradio as gr from modules.shared import script_path +from modules import shared re_param_code = r"\s*([\w ]+):\s*([^,]+)(?:,|$)" re_param = re.compile(re_param_code) @@ -63,7 +64,7 @@ Steps: 20, Sampler: Euler a, CFG scale: 7, Seed: 965400086, Size: 512x512, Model def connect_paste(button, paste_fields, input_comp, js=None): def paste_func(prompt): - if not prompt: + if not prompt and not shared.cmd_opts.hide_ui_dir_config: filename = os.path.join(script_path, "params.txt") if os.path.exists(filename): with open(filename, "r", encoding="utf8") as file: -- cgit v1.2.1 From aeacbac218c47f61f1d0d3f3b429c9038b8faf0f Mon Sep 17 00:00:00 2001 From: Greg Fuller Date: Tue, 11 Oct 2022 19:46:33 -0700 Subject: Fix save error --- modules/ui.py | 5 ++++- 1 file changed, 4 insertions(+), 1 deletion(-) (limited to 'modules') diff --git a/modules/ui.py b/modules/ui.py index e07ee0e1..4fa405a9 100644 --- a/modules/ui.py +++ b/modules/ui.py @@ -148,7 +148,10 @@ def save_files(js_data, images, do_make_zip, index): is_grid = image_index < p.index_of_first_image i = 0 if is_grid else (image_index - p.index_of_first_image) - fullfn, txt_fullfn = save_image(image, path, "", seed=p.all_seeds[i], prompt=p.all_prompts[i], extension=extension, info=p.infotexts[image_index], grid=is_grid, p=p, save_to_dirs=save_to_dirs) + seed = p.all_seeds[i] if len(p.all_seeds) > 1 else p.seed + prompt = p.all_prompts[i] if len(p.all_prompts) > 1 else p.prompt + info = p.infotexts[image_index] if len(p.infotexts) > 1 else p.infotexts[0] + fullfn, txt_fullfn = save_image(image, path, "", seed=seed, prompt=prompt, extension=extension, info=info, grid=is_grid, p=p, save_to_dirs=save_to_dirs) filename = os.path.relpath(fullfn, path) filenames.append(filename) -- cgit v1.2.1 From 8711c2fe0135d5c160a57db41cb79ed1942ce7fa Mon Sep 17 00:00:00 2001 From: Greg Fuller Date: Wed, 12 Oct 2022 16:12:12 -0700 Subject: Fix metadata contents --- modules/ui.py | 5 +---- 1 file changed, 1 insertion(+), 4 deletions(-) (limited to 'modules') diff --git a/modules/ui.py b/modules/ui.py index 4fa405a9..e07ee0e1 100644 --- a/modules/ui.py +++ b/modules/ui.py @@ -148,10 +148,7 @@ def save_files(js_data, images, do_make_zip, index): is_grid = image_index < p.index_of_first_image i = 0 if is_grid else (image_index - p.index_of_first_image) - seed = p.all_seeds[i] if len(p.all_seeds) > 1 else p.seed - prompt = p.all_prompts[i] if len(p.all_prompts) > 1 else p.prompt - info = p.infotexts[image_index] if len(p.infotexts) > 1 else p.infotexts[0] - fullfn, txt_fullfn = save_image(image, path, "", seed=seed, prompt=prompt, extension=extension, info=info, grid=is_grid, p=p, save_to_dirs=save_to_dirs) + fullfn, txt_fullfn = save_image(image, path, "", seed=p.all_seeds[i], prompt=p.all_prompts[i], extension=extension, info=p.infotexts[image_index], grid=is_grid, p=p, save_to_dirs=save_to_dirs) filename = os.path.relpath(fullfn, path) filenames.append(filename) -- cgit v1.2.1 From a3f02e4690844715a510b7bc857a0971dd05c4d8 Mon Sep 17 00:00:00 2001 From: Greg Fuller Date: Wed, 12 Oct 2022 16:48:53 -0700 Subject: fix prompt in log.csv --- modules/ui.py | 8 ++++++-- 1 file changed, 6 insertions(+), 2 deletions(-) (limited to 'modules') diff --git a/modules/ui.py b/modules/ui.py index e07ee0e1..edb4dab1 100644 --- a/modules/ui.py +++ b/modules/ui.py @@ -139,6 +139,8 @@ def save_files(js_data, images, do_make_zip, index): if at_start: writer.writerow(["prompt", "seed", "width", "height", "sampler", "cfgs", "steps", "filename", "negative_prompt"]) + log_prompt=data["prompt"] + log_seed=data["seed"] for image_index, filedata in enumerate(images, start_index): if filedata.startswith("data:image/png;base64,"): filedata = filedata[len("data:image/png;base64,"):] @@ -148,7 +150,9 @@ def save_files(js_data, images, do_make_zip, index): is_grid = image_index < p.index_of_first_image i = 0 if is_grid else (image_index - p.index_of_first_image) - fullfn, txt_fullfn = save_image(image, path, "", seed=p.all_seeds[i], prompt=p.all_prompts[i], extension=extension, info=p.infotexts[image_index], grid=is_grid, p=p, save_to_dirs=save_to_dirs) + log_seed=p.all_seeds[i] + log_prompt=p.all_prompts[i] + fullfn, txt_fullfn = save_image(image, path, "", seed=log_seed, prompt=log_prompt, extension=extension, info=p.infotexts[image_index], grid=is_grid, p=p, save_to_dirs=save_to_dirs) filename = os.path.relpath(fullfn, path) filenames.append(filename) @@ -157,7 +161,7 @@ def save_files(js_data, images, do_make_zip, index): filenames.append(os.path.basename(txt_fullfn)) fullfns.append(txt_fullfn) - writer.writerow([data["prompt"], data["seed"], data["width"], data["height"], data["sampler"], data["cfg_scale"], data["steps"], filenames[0], data["negative_prompt"]]) + writer.writerow([log_prompt, log_seed, data["width"], data["height"], data["sampler"], data["cfg_scale"], data["steps"], filenames[0], data["negative_prompt"]]) # Make Zip if do_make_zip: -- cgit v1.2.1 From fed7f0e281a42ea962bbe422e018468bafa6f1e6 Mon Sep 17 00:00:00 2001 From: Greg Fuller Date: Wed, 12 Oct 2022 23:09:30 -0700 Subject: Revert "fix prompt in log.csv" This reverts commit e4b5d1696429ab78dae9779420ce6ec4cd9c5f67. --- modules/ui.py | 8 ++------ 1 file changed, 2 insertions(+), 6 deletions(-) (limited to 'modules') diff --git a/modules/ui.py b/modules/ui.py index edb4dab1..e07ee0e1 100644 --- a/modules/ui.py +++ b/modules/ui.py @@ -139,8 +139,6 @@ def save_files(js_data, images, do_make_zip, index): if at_start: writer.writerow(["prompt", "seed", "width", "height", "sampler", "cfgs", "steps", "filename", "negative_prompt"]) - log_prompt=data["prompt"] - log_seed=data["seed"] for image_index, filedata in enumerate(images, start_index): if filedata.startswith("data:image/png;base64,"): filedata = filedata[len("data:image/png;base64,"):] @@ -150,9 +148,7 @@ def save_files(js_data, images, do_make_zip, index): is_grid = image_index < p.index_of_first_image i = 0 if is_grid else (image_index - p.index_of_first_image) - log_seed=p.all_seeds[i] - log_prompt=p.all_prompts[i] - fullfn, txt_fullfn = save_image(image, path, "", seed=log_seed, prompt=log_prompt, extension=extension, info=p.infotexts[image_index], grid=is_grid, p=p, save_to_dirs=save_to_dirs) + fullfn, txt_fullfn = save_image(image, path, "", seed=p.all_seeds[i], prompt=p.all_prompts[i], extension=extension, info=p.infotexts[image_index], grid=is_grid, p=p, save_to_dirs=save_to_dirs) filename = os.path.relpath(fullfn, path) filenames.append(filename) @@ -161,7 +157,7 @@ def save_files(js_data, images, do_make_zip, index): filenames.append(os.path.basename(txt_fullfn)) fullfns.append(txt_fullfn) - writer.writerow([log_prompt, log_seed, data["width"], data["height"], data["sampler"], data["cfg_scale"], data["steps"], filenames[0], data["negative_prompt"]]) + writer.writerow([data["prompt"], data["seed"], data["width"], data["height"], data["sampler"], data["cfg_scale"], data["steps"], filenames[0], data["negative_prompt"]]) # Make Zip if do_make_zip: -- cgit v1.2.1 From 8636b50aea83f9c743f005722d9f3f8ee9303e00 Mon Sep 17 00:00:00 2001 From: Melan Date: Thu, 13 Oct 2022 12:37:58 +0200 Subject: Add learn_rate to csv and removed a left-over debug statement --- modules/hypernetworks/hypernetwork.py | 6 +++--- modules/textual_inversion/textual_inversion.py | 5 +++-- 2 files changed, 6 insertions(+), 5 deletions(-) (limited to 'modules') diff --git a/modules/hypernetworks/hypernetwork.py b/modules/hypernetworks/hypernetwork.py index 6522078f..2751a8c8 100644 --- a/modules/hypernetworks/hypernetwork.py +++ b/modules/hypernetworks/hypernetwork.py @@ -257,19 +257,19 @@ def train_hypernetwork(hypernetwork_name, learn_rate, data_root, log_directory, last_saved_file = os.path.join(hypernetwork_dir, f'{hypernetwork_name}-{hypernetwork.step}.pt') hypernetwork.save(last_saved_file) - print(f"{write_csv_every} > {hypernetwork.step % write_csv_every == 0}, {write_csv_every}") if write_csv_every > 0 and hypernetwork_dir is not None and hypernetwork.step % write_csv_every == 0: write_csv_header = False if os.path.exists(os.path.join(hypernetwork_dir, "hypernetwork_loss.csv")) else True with open(os.path.join(hypernetwork_dir, "hypernetwork_loss.csv"), "a+") as fout: - csv_writer = csv.DictWriter(fout, fieldnames=["step", "loss"]) + csv_writer = csv.DictWriter(fout, fieldnames=["step", "loss", "learn_rate"]) if write_csv_header: csv_writer.writeheader() csv_writer.writerow({"step": hypernetwork.step, - "loss": f"{losses.mean():.7f}"}) + "loss": f"{losses.mean():.7f}", + "learn_rate": scheduler.learn_rate}) if hypernetwork.step > 0 and images_dir is not None and hypernetwork.step % create_image_every == 0: last_saved_image = os.path.join(images_dir, f'{hypernetwork_name}-{hypernetwork.step}.png') diff --git a/modules/textual_inversion/textual_inversion.py b/modules/textual_inversion/textual_inversion.py index 25038a89..b83df079 100644 --- a/modules/textual_inversion/textual_inversion.py +++ b/modules/textual_inversion/textual_inversion.py @@ -262,14 +262,15 @@ def train_embedding(embedding_name, learn_rate, data_root, log_directory, traini with open(os.path.join(log_directory, "textual_inversion_loss.csv"), "a+") as fout: - csv_writer = csv.DictWriter(fout, fieldnames=["epoch", "epoch_step", "loss"]) + csv_writer = csv.DictWriter(fout, fieldnames=["epoch", "epoch_step", "loss", "learn_rate"]) if write_csv_header: csv_writer.writeheader() csv_writer.writerow({"epoch": epoch_num + 1, "epoch_step": epoch_step - 1, - "loss": f"{losses.mean():.7f}"}) + "loss": f"{losses.mean():.7f}", + "learn_rate": scheduler.learn_rate}) if embedding.step > 0 and images_dir is not None and embedding.step % create_image_every == 0: last_saved_image = os.path.join(images_dir, f'{embedding_name}-{embedding.step}.png') -- cgit v1.2.1 From bb7baf6b9cb6b4b9fa09b6f07ef997db32fe6e58 Mon Sep 17 00:00:00 2001 From: AUTOMATIC <16777216c@gmail.com> Date: Thu, 13 Oct 2022 16:07:18 +0300 Subject: add option to change what's shown in quicksettings bar --- modules/shared.py | 4 ++-- modules/ui.py | 16 +++++++++------- 2 files changed, 11 insertions(+), 9 deletions(-) (limited to 'modules') diff --git a/modules/shared.py b/modules/shared.py index 5f6101a4..4d3ed625 100644 --- a/modules/shared.py +++ b/modules/shared.py @@ -152,7 +152,6 @@ class OptionInfo: self.component_args = component_args self.onchange = onchange self.section = None - self.show_on_main_page = show_on_main_page def options_section(section_identifier, options_dict): @@ -237,7 +236,7 @@ options_templates.update(options_section(('training', "Training"), { })) options_templates.update(options_section(('sd', "Stable Diffusion"), { - "sd_model_checkpoint": OptionInfo(None, "Stable Diffusion checkpoint", gr.Dropdown, lambda: {"choices": modules.sd_models.checkpoint_tiles()}, show_on_main_page=True), + "sd_model_checkpoint": OptionInfo(None, "Stable Diffusion checkpoint", gr.Dropdown, lambda: {"choices": modules.sd_models.checkpoint_tiles()}), "sd_hypernetwork": OptionInfo("None", "Stable Diffusion finetune hypernetwork", gr.Dropdown, lambda: {"choices": ["None"] + [x for x in hypernetworks.keys()]}), "img2img_color_correction": OptionInfo(False, "Apply color correction to img2img results to match original colors."), "save_images_before_color_correction": OptionInfo(False, "Save a copy of image before applying color correction to img2img results"), @@ -250,6 +249,7 @@ options_templates.update(options_section(('sd', "Stable Diffusion"), { "filter_nsfw": OptionInfo(False, "Filter NSFW content"), 'CLIP_stop_at_last_layers': OptionInfo(1, "Stop At last layers of CLIP model", gr.Slider, {"minimum": 1, "maximum": 12, "step": 1}), "random_artist_categories": OptionInfo([], "Allowed categories for random artists selection when using the Roll button", gr.CheckboxGroup, {"choices": artist_db.categories()}), + 'quicksettings': OptionInfo("sd_model_checkpoint", "Quicksettings list"), })) options_templates.update(options_section(('interrogate', "Interrogate Options"), { diff --git a/modules/ui.py b/modules/ui.py index e07ee0e1..a0529860 100644 --- a/modules/ui.py +++ b/modules/ui.py @@ -1305,6 +1305,9 @@ Requested path was: {f} settings_cols = 3 items_per_col = int(len(opts.data_labels) * 0.9 / settings_cols) + quicksettings_names = [x.strip() for x in opts.quicksettings.split(",")] + quicksettings_names = set(x for x in quicksettings_names if x != 'quicksettings') + quicksettings_list = [] cols_displayed = 0 @@ -1329,7 +1332,7 @@ Requested path was: {f} gr.HTML(elem_id="settings_header_text_{}".format(item.section[0]), value='

{}

'.format(item.section[1])) - if item.show_on_main_page: + if k in quicksettings_names: quicksettings_list.append((i, k, item)) components.append(dummy_component) else: @@ -1338,7 +1341,11 @@ Requested path was: {f} components.append(component) items_displayed += 1 - request_notifications = gr.Button(value='Request browser notifications', elem_id="request_notifications") + with gr.Row(): + request_notifications = gr.Button(value='Request browser notifications', elem_id="request_notifications") + reload_script_bodies = gr.Button(value='Reload custom script bodies (No ui updates, No restart)', variant='secondary') + restart_gradio = gr.Button(value='Restart Gradio and Refresh components (Custom Scripts, ui.py, js and css only)', variant='primary') + request_notifications.click( fn=lambda: None, inputs=[], @@ -1346,10 +1353,6 @@ Requested path was: {f} _js='function(){}' ) - with gr.Row(): - reload_script_bodies = gr.Button(value='Reload custom script bodies (No ui updates, No restart)', variant='secondary') - restart_gradio = gr.Button(value='Restart Gradio and Refresh components (Custom Scripts, ui.py, js and css only)', variant='primary') - def reload_scripts(): modules.scripts.reload_script_body_only() @@ -1364,7 +1367,6 @@ Requested path was: {f} shared.state.interrupt() settings_interface.gradio_ref.do_restart = True - restart_gradio.click( fn=request_restart, inputs=[], -- cgit v1.2.1 From a10b0e11fc22cc67b6a3664f2ddd17425d8433a8 Mon Sep 17 00:00:00 2001 From: AUTOMATIC <16777216c@gmail.com> Date: Thu, 13 Oct 2022 19:22:41 +0300 Subject: options to refresh list of models and hypernetworks --- modules/shared.py | 9 +++++---- modules/ui.py | 33 +++++++++++++++++++++++++++++---- 2 files changed, 34 insertions(+), 8 deletions(-) (limited to 'modules') diff --git a/modules/shared.py b/modules/shared.py index 4d3ed625..d8e3a286 100644 --- a/modules/shared.py +++ b/modules/shared.py @@ -13,7 +13,7 @@ import modules.memmon import modules.sd_models import modules.styles import modules.devices as devices -from modules import sd_samplers +from modules import sd_samplers, sd_models from modules.hypernetworks import hypernetwork from modules.paths import models_path, script_path, sd_path @@ -145,13 +145,14 @@ def realesrgan_models_names(): class OptionInfo: - def __init__(self, default=None, label="", component=None, component_args=None, onchange=None, show_on_main_page=False): + def __init__(self, default=None, label="", component=None, component_args=None, onchange=None, show_on_main_page=False, refresh=None): self.default = default self.label = label self.component = component self.component_args = component_args self.onchange = onchange self.section = None + self.refresh = refresh def options_section(section_identifier, options_dict): @@ -236,8 +237,8 @@ options_templates.update(options_section(('training', "Training"), { })) options_templates.update(options_section(('sd', "Stable Diffusion"), { - "sd_model_checkpoint": OptionInfo(None, "Stable Diffusion checkpoint", gr.Dropdown, lambda: {"choices": modules.sd_models.checkpoint_tiles()}), - "sd_hypernetwork": OptionInfo("None", "Stable Diffusion finetune hypernetwork", gr.Dropdown, lambda: {"choices": ["None"] + [x for x in hypernetworks.keys()]}), + "sd_model_checkpoint": OptionInfo(None, "Stable Diffusion checkpoint", gr.Dropdown, lambda: {"choices": modules.sd_models.checkpoint_tiles()}, refresh=sd_models.list_models), + "sd_hypernetwork": OptionInfo("None", "Stable Diffusion finetune hypernetwork", gr.Dropdown, lambda: {"choices": ["None"] + [x for x in hypernetworks.keys()]}, refresh=reload_hypernetworks), "img2img_color_correction": OptionInfo(False, "Apply color correction to img2img results to match original colors."), "save_images_before_color_correction": OptionInfo(False, "Save a copy of image before applying color correction to img2img results"), "img2img_fix_steps": OptionInfo(False, "With img2img, do exactly the amount of steps the slider specifies (normally you'd do less with less denoising)."), diff --git a/modules/ui.py b/modules/ui.py index a0529860..0a58f6be 100644 --- a/modules/ui.py +++ b/modules/ui.py @@ -78,6 +78,8 @@ reuse_symbol = '\u267b\ufe0f' # ♻️ art_symbol = '\U0001f3a8' # 🎨 paste_symbol = '\u2199\ufe0f' # ↙ folder_symbol = '\U0001f4c2' # 📂 +refresh_symbol = '\U0001f504' # 🔄 + def plaintext_to_html(text): text = "

" + "
\n".join([f"{html.escape(x)}" for x in text.split('\n')]) + "

" @@ -1210,8 +1212,7 @@ def create_ui(wrap_gradio_gpu_call): outputs=[], ) - - def create_setting_component(key): + def create_setting_component(key, is_quicksettings=False): def fun(): return opts.data[key] if key in opts.data else opts.data_labels[key].default @@ -1231,7 +1232,31 @@ def create_ui(wrap_gradio_gpu_call): else: raise Exception(f'bad options item type: {str(t)} for key {key}') - return comp(label=info.label, value=fun, **(args or {})) + if info.refresh is not None: + if is_quicksettings: + res = comp(label=info.label, value=fun, **(args or {})) + refresh_button = gr.Button(value=refresh_symbol, elem_id="refresh_"+key) + else: + with gr.Row(variant="compact"): + res = comp(label=info.label, value=fun, **(args or {})) + refresh_button = gr.Button(value=refresh_symbol, elem_id="refresh_" + key) + + def refresh(): + info.refresh() + refreshed_args = info.component_args() if callable(info.component_args) else info.component_args + res.choices = refreshed_args["choices"] + return gr.update(**(refreshed_args or {})) + + refresh_button.click( + fn=refresh, + inputs=[], + outputs=[res], + ) + else: + res = comp(label=info.label, value=fun, **(args or {})) + + + return res components = [] component_dict = {} @@ -1401,7 +1426,7 @@ Requested path was: {f} with gr.Blocks(css=css, analytics_enabled=False, title="Stable Diffusion") as demo: with gr.Row(elem_id="quicksettings"): for i, k, item in quicksettings_list: - component = create_setting_component(k) + component = create_setting_component(k, is_quicksettings=True) component_dict[k] = component settings_interface.gradio_ref = demo -- cgit v1.2.1 From 354ef0da3b1f0fa5c113d04b6c79e3908c848d23 Mon Sep 17 00:00:00 2001 From: AUTOMATIC <16777216c@gmail.com> Date: Thu, 13 Oct 2022 20:12:37 +0300 Subject: add hypernetwork multipliers --- modules/hypernetworks/hypernetwork.py | 8 +++++++- modules/shared.py | 5 ++++- modules/ui.py | 5 ++++- 3 files changed, 15 insertions(+), 3 deletions(-) (limited to 'modules') diff --git a/modules/hypernetworks/hypernetwork.py b/modules/hypernetworks/hypernetwork.py index b6c06d49..f1248bb7 100644 --- a/modules/hypernetworks/hypernetwork.py +++ b/modules/hypernetworks/hypernetwork.py @@ -18,6 +18,8 @@ from modules.textual_inversion.learn_schedule import LearnRateScheduler class HypernetworkModule(torch.nn.Module): + multiplier = 1.0 + def __init__(self, dim, state_dict=None): super().__init__() @@ -36,7 +38,11 @@ class HypernetworkModule(torch.nn.Module): self.to(devices.device) def forward(self, x): - return x + (self.linear2(self.linear1(x))) + return x + (self.linear2(self.linear1(x))) * self.multiplier + + +def apply_strength(value=None): + HypernetworkModule.multiplier = value if value is not None else shared.opts.sd_hypernetwork_strength class Hypernetwork: diff --git a/modules/shared.py b/modules/shared.py index d8e3a286..5901e605 100644 --- a/modules/shared.py +++ b/modules/shared.py @@ -238,7 +238,8 @@ options_templates.update(options_section(('training', "Training"), { options_templates.update(options_section(('sd', "Stable Diffusion"), { "sd_model_checkpoint": OptionInfo(None, "Stable Diffusion checkpoint", gr.Dropdown, lambda: {"choices": modules.sd_models.checkpoint_tiles()}, refresh=sd_models.list_models), - "sd_hypernetwork": OptionInfo("None", "Stable Diffusion finetune hypernetwork", gr.Dropdown, lambda: {"choices": ["None"] + [x for x in hypernetworks.keys()]}, refresh=reload_hypernetworks), + "sd_hypernetwork": OptionInfo("None", "Hypernetwork", gr.Dropdown, lambda: {"choices": ["None"] + [x for x in hypernetworks.keys()]}, refresh=reload_hypernetworks), + "sd_hypernetwork_strength": OptionInfo(1.0, "Hypernetwork strength", gr.Slider, {"minimum": 0.0, "maximum": 1.0, "step": 0.001}), "img2img_color_correction": OptionInfo(False, "Apply color correction to img2img results to match original colors."), "save_images_before_color_correction": OptionInfo(False, "Save a copy of image before applying color correction to img2img results"), "img2img_fix_steps": OptionInfo(False, "With img2img, do exactly the amount of steps the slider specifies (normally you'd do less with less denoising)."), @@ -348,6 +349,8 @@ class Options: item = self.data_labels.get(key) item.onchange = func + func() + def dumpjson(self): d = {k: self.data.get(k, self.data_labels.get(k).default) for k in self.data_labels.keys()} return json.dumps(d) diff --git a/modules/ui.py b/modules/ui.py index 0a58f6be..673014f2 100644 --- a/modules/ui.py +++ b/modules/ui.py @@ -1244,7 +1244,10 @@ def create_ui(wrap_gradio_gpu_call): def refresh(): info.refresh() refreshed_args = info.component_args() if callable(info.component_args) else info.component_args - res.choices = refreshed_args["choices"] + + for k, v in refreshed_args.items(): + setattr(res, k, v) + return gr.update(**(refreshed_args or {})) refresh_button.click( -- cgit v1.2.1 From 08b3f7aef15f74f4d2254b1274dd66fcc7940348 Mon Sep 17 00:00:00 2001 From: AUTOMATIC <16777216c@gmail.com> Date: Thu, 13 Oct 2022 20:42:27 +0300 Subject: emergency fix for broken send to buttons --- modules/ui.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) (limited to 'modules') diff --git a/modules/ui.py b/modules/ui.py index 673014f2..7446439d 100644 --- a/modules/ui.py +++ b/modules/ui.py @@ -1434,7 +1434,7 @@ Requested path was: {f} settings_interface.gradio_ref = demo - with gr.Tabs() as tabs: + with gr.Tabs(elem_id="tabs") as tabs: for interface, label, ifid in interfaces: with gr.TabItem(label, id=ifid, elem_id='tab_' + ifid): interface.render() -- cgit v1.2.1 From a1489f94283c07824a7a58353c03dc89541bbe49 Mon Sep 17 00:00:00 2001 From: yfszzx Date: Fri, 14 Oct 2022 07:13:38 +0800 Subject: images history fix all known bug --- modules/images_history.py | 51 +++++++++++++++++++++++++---------------------- 1 file changed, 27 insertions(+), 24 deletions(-) (limited to 'modules') diff --git a/modules/images_history.py b/modules/images_history.py index f812ea4e..cdfcffed 100644 --- a/modules/images_history.py +++ b/modules/images_history.py @@ -38,7 +38,7 @@ def get_recent_images(dir_name, page_index, step, image_index, tabname): else: current_file = file_list[int(image_index)] hide_image = os.path.join(dir_name, current_file) - return [os.path.join(dir_name, file) for file in file_list], page_index, file_list, current_file, hide_image + return [os.path.join(dir_name, file) for file in file_list], page_index, file_list, current_file, hide_image, "" def first_page_click(dir_name, page_index, image_index, tabname): return get_recent_images(dir_name, 1, 0, image_index, tabname) def end_page_click(dir_name, page_index, image_index, tabname): @@ -55,25 +55,28 @@ def show_image_info(num, image_path, filenames): file = filenames[int(num)] return file, num, os.path.join(image_path, file) def delete_image(delete_num, tabname, dir_name, name, page_index, filenames, image_index): - delete_num = int(delete_num) - index = list(filenames).index(name) - i = 0 - new_file_list = [] - for name in filenames: - if i >= index and i < index + delete_num: - path = os.path.join(dir_name, name) - if os.path.exists(path): - print(f"Delete file {path}") - os.remove(path) - txt_file = os.path.splitext(path)[0] + ".txt" - if os.path.exists(txt_file): - os.remove(txt_file) + if name == "": + return filenames, delete_num + else: + delete_num = int(delete_num) + index = list(filenames).index(name) + i = 0 + new_file_list = [] + for name in filenames: + if i >= index and i < index + delete_num: + path = os.path.join(dir_name, name) + if os.path.exists(path): + print(f"Delete file {path}") + os.remove(path) + txt_file = os.path.splitext(path)[0] + ".txt" + if os.path.exists(txt_file): + os.remove(txt_file) + else: + print(f"Not exists file {path}") else: - print(f"Not exists file {path}") - else: - new_file_list.append(name) - i += 1 - return page_index, new_file_list, 1 + new_file_list.append(name) + i += 1 + return new_file_list, 1 def show_images_history(gr, opts, tabname, run_pnginfo, switch_dict): if tabname == "txt2img": @@ -93,9 +96,9 @@ def show_images_history(gr, opts, tabname, run_pnginfo, switch_dict): with gr.Row(): with gr.Column(scale=2): history_gallery = gr.Gallery(show_label=False, elem_id=tabname + "_images_history_gallery").style(grid=6) - with gr.Row(): - delete = gr.Button('Delete', elem_id=tabname + "_images_history_del_button") - delete_num = gr.Number(value=1, interactive=True, label="number of images to delete consecutively next") + with gr.Row(): + delete_num = gr.Number(value=1, interactive=True, label="number of images to delete consecutively next") + delete = gr.Button('Delete', elem_id=tabname + "_images_history_del_button") with gr.Column(): with gr.Row(): pnginfo_send_to_txt2img = gr.Button('Send to txt2img') @@ -118,7 +121,7 @@ def show_images_history(gr, opts, tabname, run_pnginfo, switch_dict): # turn pages gallery_inputs = [img_path, page_index, image_index, tabname_box] - gallery_outputs = [history_gallery, page_index, filenames, img_file_name, hide_image] + gallery_outputs = [history_gallery, page_index, filenames, img_file_name, hide_image, img_file_name] first_page.click(first_page_click, _js="images_history_turnpage", inputs=gallery_inputs, outputs=gallery_outputs) next_page.click(next_page_click, _js="images_history_turnpage", inputs=gallery_inputs, outputs=gallery_outputs) @@ -131,7 +134,7 @@ def show_images_history(gr, opts, tabname, run_pnginfo, switch_dict): #other funcitons set_index.click(show_image_info, _js="images_history_get_current_img", inputs=[tabname_box, img_path, filenames], outputs=[img_file_name, image_index, hide_image]) img_file_name.change(fn=None, _js="images_history_enable_del_buttons", inputs=None, outputs=None) - delete.click(delete_image,_js="images_history_delete", inputs=[delete_num, tabname_box, img_path, img_file_name, page_index, filenames, image_index], outputs=[page_index, filenames, delete_num]) + delete.click(delete_image,_js="images_history_delete", inputs=[delete_num, tabname_box, img_path, img_file_name, page_index, filenames, image_index], outputs=[filenames, delete_num]) hide_image.change(fn=run_pnginfo, inputs=[hide_image], outputs=[info1, img_file_info, info2]) #pnginfo.click(fn=run_pnginfo, inputs=[hide_image], outputs=[info1, img_file_info, info2]) -- cgit v1.2.1 From 4a37c7eedeab579efec03e8dae3f3f9fd4a37b02 Mon Sep 17 00:00:00 2001 From: yfszzx Date: Fri, 14 Oct 2022 11:48:28 +0800 Subject: fix deep nesting directories problem --- modules/images_history.py | 76 ++++++++++++++++++++++++++--------------------- 1 file changed, 42 insertions(+), 34 deletions(-) (limited to 'modules') diff --git a/modules/images_history.py b/modules/images_history.py index cdfcffed..723f5301 100644 --- a/modules/images_history.py +++ b/modules/images_history.py @@ -1,44 +1,47 @@ import os import shutil -def get_recent_images(dir_name, page_index, step, image_index, tabname): - #print(f"renew page {page_index}") - page_index = int(page_index) - f_list = os.listdir(dir_name) - file_list = [] +def traverse_all_files(output_dir, image_list, curr_dir=None): + curr_path = output_dir if curr_dir is None else os.path.join(output_dir, curr_dir) + try: + f_list = os.listdir(curr_path) + except: + if curr_dir[-10:].rfind(".") > 0 and curr_dir[-4:] != ".txt": + image_list.append(curr_dir) + return image_list for file in f_list: + file = file if curr_dir is None else os.path.join(curr_dir, file) + file_path = os.path.join(curr_path, file) if file[-4:] == ".txt": - continue - #subdirectories - if file[-10:].rfind(".") < 0: - sub_dir = os.path.join(dir_name, file) - if os.path.isfile(sub_dir): - continue - sub_file_list = os.listdir(sub_dir) - for sub_file in sub_file_list: - if sub_file[-4:] == ".txt": - continue - if os.path.isfile(os.path.join(sub_dir, sub_file) ): - file_list.append(os.path.join(file, sub_file)) - continue - file_list.append(file) + pass + elif os.path.isfile(file_path) and file[-10:].rfind(".") > 0: + image_list.append(file) + else: + image_list = traverse_all_files(output_dir, image_list, file) + return image_list - file_list = sorted(file_list, key=lambda file: -os.path.getctime(os.path.join(dir_name, file))) + +def get_recent_images(dir_name, page_index, step, image_index, tabname): + page_index = int(page_index) + f_list = os.listdir(dir_name) + image_list = [] + image_list = traverse_all_files(dir_name, image_list) + image_list = sorted(image_list, key=lambda file: -os.path.getctime(os.path.join(dir_name, file))) num = 48 if tabname != "extras" else 12 - max_page_index = len(file_list) // num + 1 + max_page_index = len(image_list) // num + 1 page_index = max_page_index if page_index == -1 else page_index + step page_index = 1 if page_index < 1 else page_index page_index = max_page_index if page_index > max_page_index else page_index idx_frm = (page_index - 1) * num - file_list = file_list[idx_frm:idx_frm + num] - #print(f"Loading history page {page_index}") + image_list = image_list[idx_frm:idx_frm + num] image_index = int(image_index) - if image_index < 0 or image_index > len(file_list) - 1: + if image_index < 0 or image_index > len(image_list) - 1: current_file = None - hide_image = None + hidden = None else: - current_file = file_list[int(image_index)] - hide_image = os.path.join(dir_name, current_file) - return [os.path.join(dir_name, file) for file in file_list], page_index, file_list, current_file, hide_image, "" + current_file = image_list[int(image_index)] + hidden = os.path.join(dir_name, current_file) + return [os.path.join(dir_name, file) for file in image_list], page_index, image_list, current_file, hidden, "" + def first_page_click(dir_name, page_index, image_index, tabname): return get_recent_images(dir_name, 1, 0, image_index, tabname) def end_page_click(dir_name, page_index, image_index, tabname): @@ -85,6 +88,10 @@ def show_images_history(gr, opts, tabname, run_pnginfo, switch_dict): dir_name = opts.outdir_img2img_samples elif tabname == "extras": dir_name = opts.outdir_extras_samples + d = dir_name.split("/") + dir_name = d[0] + for p in d[1:]: + dir_name = os.path.join(dir_name, p) with gr.Row(): renew_page = gr.Button('Renew Page', elem_id=tabname + "_images_history_renew_page") first_page = gr.Button('First Page') @@ -109,19 +116,20 @@ def show_images_history(gr, opts, tabname, run_pnginfo, switch_dict): img_file_name = gr.Textbox(label="File Name", interactive=False) with gr.Row(): # hiden items - img_path = gr.Textbox(dir_name, visible=False) + + img_path = gr.Textbox(dir_name.rstrip("/") , visible=False) tabname_box = gr.Textbox(tabname, visible=False) image_index = gr.Textbox(value=-1, visible=False) set_index = gr.Button('set_index', elem_id=tabname + "_images_history_set_index", visible=False) filenames = gr.State() - hide_image = gr.Image(type="pil", visible=False) + hidden = gr.Image(type="pil", visible=False) info1 = gr.Textbox(visible=False) info2 = gr.Textbox(visible=False) # turn pages gallery_inputs = [img_path, page_index, image_index, tabname_box] - gallery_outputs = [history_gallery, page_index, filenames, img_file_name, hide_image, img_file_name] + gallery_outputs = [history_gallery, page_index, filenames, img_file_name, hidden, img_file_name] first_page.click(first_page_click, _js="images_history_turnpage", inputs=gallery_inputs, outputs=gallery_outputs) next_page.click(next_page_click, _js="images_history_turnpage", inputs=gallery_inputs, outputs=gallery_outputs) @@ -132,12 +140,12 @@ def show_images_history(gr, opts, tabname, run_pnginfo, switch_dict): #page_index.change(page_index_change, inputs=[tabname_box, img_path, page_index], outputs=[history_gallery, page_index]) #other funcitons - set_index.click(show_image_info, _js="images_history_get_current_img", inputs=[tabname_box, img_path, filenames], outputs=[img_file_name, image_index, hide_image]) + set_index.click(show_image_info, _js="images_history_get_current_img", inputs=[tabname_box, img_path, filenames], outputs=[img_file_name, image_index, hidden]) img_file_name.change(fn=None, _js="images_history_enable_del_buttons", inputs=None, outputs=None) delete.click(delete_image,_js="images_history_delete", inputs=[delete_num, tabname_box, img_path, img_file_name, page_index, filenames, image_index], outputs=[filenames, delete_num]) - hide_image.change(fn=run_pnginfo, inputs=[hide_image], outputs=[info1, img_file_info, info2]) + hidden.change(fn=run_pnginfo, inputs=[hidden], outputs=[info1, img_file_info, info2]) - #pnginfo.click(fn=run_pnginfo, inputs=[hide_image], outputs=[info1, img_file_info, info2]) + #pnginfo.click(fn=run_pnginfo, inputs=[hidden], outputs=[info1, img_file_info, info2]) switch_dict["fn"](pnginfo_send_to_txt2img, switch_dict["t2i"], img_file_info, 'switch_to_txt2img') switch_dict["fn"](pnginfo_send_to_img2img, switch_dict["i2i"], img_file_info, 'switch_to_img2img_img2img') -- cgit v1.2.1 From fdecb636855748e03efc40c846a0043800aadfcc Mon Sep 17 00:00:00 2001 From: AUTOMATIC <16777216c@gmail.com> Date: Fri, 14 Oct 2022 09:05:06 +0300 Subject: add an ability to merge three checkpoints --- modules/extras.py | 29 +++++++++++++++++++++-------- modules/ui.py | 11 +++++++---- 2 files changed, 28 insertions(+), 12 deletions(-) (limited to 'modules') diff --git a/modules/extras.py b/modules/extras.py index b24d7de3..532d869f 100644 --- a/modules/extras.py +++ b/modules/extras.py @@ -159,48 +159,61 @@ def run_pnginfo(image): return '', geninfo, info -def run_modelmerger(primary_model_name, secondary_model_name, interp_method, interp_amount, save_as_half, custom_name): +def run_modelmerger(primary_model_name, secondary_model_name, teritary_model_name, interp_method, interp_amount, save_as_half, custom_name): # Linear interpolation (https://en.wikipedia.org/wiki/Linear_interpolation) - def weighted_sum(theta0, theta1, alpha): + def weighted_sum(theta0, theta1, theta2, alpha): return ((1 - alpha) * theta0) + (alpha * theta1) # Smoothstep (https://en.wikipedia.org/wiki/Smoothstep) - def sigmoid(theta0, theta1, alpha): + def sigmoid(theta0, theta1, theta2, alpha): alpha = alpha * alpha * (3 - (2 * alpha)) return theta0 + ((theta1 - theta0) * alpha) # Inverse Smoothstep (https://en.wikipedia.org/wiki/Smoothstep) - def inv_sigmoid(theta0, theta1, alpha): + def inv_sigmoid(theta0, theta1, theta2, alpha): import math alpha = 0.5 - math.sin(math.asin(1.0 - 2.0 * alpha) / 3.0) return theta0 + ((theta1 - theta0) * alpha) + def add_difference(theta0, theta1, theta2, alpha): + return theta0 + (theta1 - theta2) * (1.0 - alpha) + primary_model_info = sd_models.checkpoints_list[primary_model_name] secondary_model_info = sd_models.checkpoints_list[secondary_model_name] + teritary_model_info = sd_models.checkpoints_list.get(teritary_model_name, None) print(f"Loading {primary_model_info.filename}...") primary_model = torch.load(primary_model_info.filename, map_location='cpu') + theta_0 = sd_models.get_state_dict_from_checkpoint(primary_model) print(f"Loading {secondary_model_info.filename}...") secondary_model = torch.load(secondary_model_info.filename, map_location='cpu') - - theta_0 = sd_models.get_state_dict_from_checkpoint(primary_model) theta_1 = sd_models.get_state_dict_from_checkpoint(secondary_model) + if teritary_model_info is not None: + print(f"Loading {teritary_model_info.filename}...") + teritary_model = torch.load(teritary_model_info.filename, map_location='cpu') + theta_2 = sd_models.get_state_dict_from_checkpoint(teritary_model) + else: + theta_2 = None + theta_funcs = { "Weighted Sum": weighted_sum, "Sigmoid": sigmoid, "Inverse Sigmoid": inv_sigmoid, + "Add difference": add_difference, } theta_func = theta_funcs[interp_method] print(f"Merging...") + for key in tqdm.tqdm(theta_0.keys()): if 'model' in key and key in theta_1: - theta_0[key] = theta_func(theta_0[key], theta_1[key], (float(1.0) - interp_amount)) # Need to reverse the interp_amount to match the desired mix ration in the merged checkpoint + theta_0[key] = theta_func(theta_0[key], theta_1[key], theta_2[key] if theta_2 else None, (float(1.0) - interp_amount)) # Need to reverse the interp_amount to match the desired mix ration in the merged checkpoint if save_as_half: theta_0[key] = theta_0[key].half() + # I believe this part should be discarded, but I'll leave it for now until I am sure for key in theta_1.keys(): if 'model' in key and key not in theta_0: theta_0[key] = theta_1[key] @@ -219,4 +232,4 @@ def run_modelmerger(primary_model_name, secondary_model_name, interp_method, int sd_models.list_models() print(f"Checkpoint saved.") - return ["Checkpoint saved to " + output_modelname] + [gr.Dropdown.update(choices=sd_models.checkpoint_tiles()) for _ in range(3)] + return ["Checkpoint saved to " + output_modelname] + [gr.Dropdown.update(choices=sd_models.checkpoint_tiles()) for _ in range(4)] diff --git a/modules/ui.py b/modules/ui.py index 7446439d..220fb80b 100644 --- a/modules/ui.py +++ b/modules/ui.py @@ -1024,11 +1024,12 @@ def create_ui(wrap_gradio_gpu_call): gr.HTML(value="

A merger of the two checkpoints will be generated in your checkpoint directory.

") with gr.Row(): - primary_model_name = gr.Dropdown(modules.sd_models.checkpoint_tiles(), elem_id="modelmerger_primary_model_name", label="Primary Model Name") - secondary_model_name = gr.Dropdown(modules.sd_models.checkpoint_tiles(), elem_id="modelmerger_secondary_model_name", label="Secondary Model Name") + primary_model_name = gr.Dropdown(modules.sd_models.checkpoint_tiles(), elem_id="modelmerger_primary_model_name", label="Primary model (A)") + secondary_model_name = gr.Dropdown(modules.sd_models.checkpoint_tiles(), elem_id="modelmerger_secondary_model_name", label="Secondary model (B)") + tertiary_model_name = gr.Dropdown(modules.sd_models.checkpoint_tiles(), elem_id="modelmerger_tertiary_model_name", label="Tertiary model (C)") custom_name = gr.Textbox(label="Custom Name (Optional)") - interp_amount = gr.Slider(minimum=0.0, maximum=1.0, step=0.05, label='Interpolation Amount', value=0.3) - interp_method = gr.Radio(choices=["Weighted Sum", "Sigmoid", "Inverse Sigmoid"], value="Weighted Sum", label="Interpolation Method") + interp_amount = gr.Slider(minimum=0.0, maximum=1.0, step=0.05, label='Interpolation amount (1 - M)', value=0.3) + interp_method = gr.Radio(choices=["Weighted Sum", "Sigmoid", "Inverse Sigmoid", "Add difference"], value="Weighted Sum", label="Interpolation Method") save_as_half = gr.Checkbox(value=False, label="Save as float16") modelmerger_merge = gr.Button(elem_id="modelmerger_merge", label="Merge", variant='primary') @@ -1473,6 +1474,7 @@ Requested path was: {f} inputs=[ primary_model_name, secondary_model_name, + tertiary_model_name, interp_method, interp_amount, save_as_half, @@ -1482,6 +1484,7 @@ Requested path was: {f} submit_result, primary_model_name, secondary_model_name, + tertiary_model_name, component_dict['sd_model_checkpoint'], ] ) -- cgit v1.2.1 From fdef8253a43ca5135923092ca9b85e878d980869 Mon Sep 17 00:00:00 2001 From: brkirch Date: Fri, 14 Oct 2022 04:42:53 -0400 Subject: Add 'interrogate' and 'all' choices to --use-cpu * Add 'interrogate' and 'all' choices to --use-cpu * Change type for --use-cpu argument to str.lower, so that choices are case insensitive --- modules/devices.py | 2 +- modules/interrogate.py | 14 +++++++------- modules/shared.py | 6 +++--- 3 files changed, 11 insertions(+), 11 deletions(-) (limited to 'modules') diff --git a/modules/devices.py b/modules/devices.py index 03ef58f1..eb422583 100644 --- a/modules/devices.py +++ b/modules/devices.py @@ -34,7 +34,7 @@ def enable_tf32(): errors.run(enable_tf32, "Enabling TF32") -device = device_gfpgan = device_bsrgan = device_esrgan = device_scunet = device_codeformer = get_optimal_device() +device = device_interrogate = device_gfpgan = device_bsrgan = device_esrgan = device_scunet = device_codeformer = get_optimal_device() dtype = torch.float16 dtype_vae = torch.float16 diff --git a/modules/interrogate.py b/modules/interrogate.py index af858cc0..9263d65a 100644 --- a/modules/interrogate.py +++ b/modules/interrogate.py @@ -55,7 +55,7 @@ class InterrogateModels: model, preprocess = clip.load(clip_model_name) model.eval() - model = model.to(shared.device) + model = model.to(devices.device_interrogate) return model, preprocess @@ -65,14 +65,14 @@ class InterrogateModels: if not shared.cmd_opts.no_half: self.blip_model = self.blip_model.half() - self.blip_model = self.blip_model.to(shared.device) + self.blip_model = self.blip_model.to(devices.device_interrogate) if self.clip_model is None: self.clip_model, self.clip_preprocess = self.load_clip_model() if not shared.cmd_opts.no_half: self.clip_model = self.clip_model.half() - self.clip_model = self.clip_model.to(shared.device) + self.clip_model = self.clip_model.to(devices.device_interrogate) self.dtype = next(self.clip_model.parameters()).dtype @@ -99,11 +99,11 @@ class InterrogateModels: text_array = text_array[0:int(shared.opts.interrogate_clip_dict_limit)] top_count = min(top_count, len(text_array)) - text_tokens = clip.tokenize([text for text in text_array], truncate=True).to(shared.device) + text_tokens = clip.tokenize([text for text in text_array], truncate=True).to(devices.device_interrogate) text_features = self.clip_model.encode_text(text_tokens).type(self.dtype) text_features /= text_features.norm(dim=-1, keepdim=True) - similarity = torch.zeros((1, len(text_array))).to(shared.device) + similarity = torch.zeros((1, len(text_array))).to(devices.device_interrogate) for i in range(image_features.shape[0]): similarity += (100.0 * image_features[i].unsqueeze(0) @ text_features.T).softmax(dim=-1) similarity /= image_features.shape[0] @@ -116,7 +116,7 @@ class InterrogateModels: transforms.Resize((blip_image_eval_size, blip_image_eval_size), interpolation=InterpolationMode.BICUBIC), transforms.ToTensor(), transforms.Normalize((0.48145466, 0.4578275, 0.40821073), (0.26862954, 0.26130258, 0.27577711)) - ])(pil_image).unsqueeze(0).type(self.dtype).to(shared.device) + ])(pil_image).unsqueeze(0).type(self.dtype).to(devices.device_interrogate) with torch.no_grad(): caption = self.blip_model.generate(gpu_image, sample=False, num_beams=shared.opts.interrogate_clip_num_beams, min_length=shared.opts.interrogate_clip_min_length, max_length=shared.opts.interrogate_clip_max_length) @@ -140,7 +140,7 @@ class InterrogateModels: res = caption - clip_image = self.clip_preprocess(pil_image).unsqueeze(0).type(self.dtype).to(shared.device) + clip_image = self.clip_preprocess(pil_image).unsqueeze(0).type(self.dtype).to(devices.device_interrogate) precision_scope = torch.autocast if shared.cmd_opts.precision == "autocast" else contextlib.nullcontext with torch.no_grad(), precision_scope("cuda"): diff --git a/modules/shared.py b/modules/shared.py index 5901e605..b6a5c1a8 100644 --- a/modules/shared.py +++ b/modules/shared.py @@ -54,7 +54,7 @@ parser.add_argument("--opt-split-attention", action='store_true', help="force-en parser.add_argument("--opt-split-attention-invokeai", action='store_true', help="force-enables InvokeAI's cross-attention layer optimization. By default, it's on when cuda is unavailable.") parser.add_argument("--opt-split-attention-v1", action='store_true', help="enable older version of split attention optimization that does not consume all the VRAM it can find") parser.add_argument("--disable-opt-split-attention", action='store_true', help="force-disables cross-attention layer optimization") -parser.add_argument("--use-cpu", nargs='+',choices=['SD', 'GFPGAN', 'BSRGAN', 'ESRGAN', 'SCUNet', 'CodeFormer'], help="use CPU as torch device for specified modules", default=[]) +parser.add_argument("--use-cpu", nargs='+',choices=['all', 'sd', 'interrogate', 'gfpgan', 'bsrgan', 'esrgan', 'scunet', 'codeformer'], help="use CPU as torch device for specified modules", default=[], type=str.lower) parser.add_argument("--listen", action='store_true', help="launch gradio with 0.0.0.0 as server name, allowing to respond to network requests") parser.add_argument("--port", type=int, help="launch gradio with given server port, you need root/admin rights for ports < 1024, defaults to 7860 if available", default=None) parser.add_argument("--show-negative-prompt", action='store_true', help="does not do anything", default=False) @@ -76,8 +76,8 @@ parser.add_argument("--disable-safe-unpickle", action='store_true', help="disabl cmd_opts = parser.parse_args() -devices.device, devices.device_gfpgan, devices.device_bsrgan, devices.device_esrgan, devices.device_scunet, devices.device_codeformer = \ -(devices.cpu if x in cmd_opts.use_cpu else devices.get_optimal_device() for x in ['SD', 'GFPGAN', 'BSRGAN', 'ESRGAN', 'SCUNet', 'CodeFormer']) +devices.device, devices.device_interrogate, devices.device_gfpgan, devices.device_bsrgan, devices.device_esrgan, devices.device_scunet, devices.device_codeformer = \ +(devices.cpu if any(y in cmd_opts.use_cpu for y in [x, 'all']) else devices.get_optimal_device() for x in ['sd', 'interrogate', 'gfpgan', 'bsrgan', 'esrgan', 'scunet', 'codeformer']) device = devices.device -- cgit v1.2.1 From 9e5ca5077f43bb3ec1a0ec41b47964cb38d544a6 Mon Sep 17 00:00:00 2001 From: AUTOMATIC <16777216c@gmail.com> Date: Fri, 14 Oct 2022 16:37:32 +0300 Subject: extra message for unpicking fails --- modules/safe.py | 9 ++++++++- 1 file changed, 8 insertions(+), 1 deletion(-) (limited to 'modules') diff --git a/modules/safe.py b/modules/safe.py index 20be16a5..399165a1 100644 --- a/modules/safe.py +++ b/modules/safe.py @@ -96,11 +96,18 @@ def load(filename, *args, **kwargs): if not shared.cmd_opts.disable_safe_unpickle: check_pt(filename) + except pickle.UnpicklingError: + print(f"Error verifying pickled file from {filename}:", file=sys.stderr) + print(traceback.format_exc(), file=sys.stderr) + print(f"-----> !!!! The file is most likely corrupted !!!! <-----", file=sys.stderr) + print(f"You can skip this check with --disable-safe-unpickle commandline argument, but that is not going to help you.\n\n", file=sys.stderr) + return None + except Exception: print(f"Error verifying pickled file from {filename}:", file=sys.stderr) print(traceback.format_exc(), file=sys.stderr) print(f"\nThe file may be malicious, so the program is not going to read it.", file=sys.stderr) - print(f"You can skip this check with --disable-safe-unpickle commandline argument.", file=sys.stderr) + print(f"You can skip this check with --disable-safe-unpickle commandline argument.\n\n", file=sys.stderr) return None return unsafe_torch_load(filename, *args, **kwargs) -- cgit v1.2.1 From b2261b53ae4ad01b3713bc73ff62ab7b6f479e26 Mon Sep 17 00:00:00 2001 From: Buckzor Date: Thu, 13 Oct 2022 17:07:06 +0100 Subject: Added first_pass_width and height as adjustable inputs to "High Res Fix" --- modules/processing.py | 6 ++++-- modules/txt2img.py | 5 ++++- modules/ui.py | 6 ++++++ 3 files changed, 14 insertions(+), 3 deletions(-) (limited to 'modules') diff --git a/modules/processing.py b/modules/processing.py index d5172f00..abbfdf98 100644 --- a/modules/processing.py +++ b/modules/processing.py @@ -506,11 +506,13 @@ class StableDiffusionProcessingTxt2Img(StableDiffusionProcessing): firstphase_width_truncated = 0 firstphase_height_truncated = 0 - def __init__(self, enable_hr=False, scale_latent=True, denoising_strength=0.75, **kwargs): + def __init__(self, enable_hr=False, scale_latent=True, denoising_strength=0.75, first_pass_width=512, first_pass_height=512, **kwargs): super().__init__(**kwargs) self.enable_hr = enable_hr self.scale_latent = scale_latent self.denoising_strength = denoising_strength + self.first_pass_width = first_pass_width + self.first_pass_height = first_pass_height def init(self, all_prompts, all_seeds, all_subseeds): if self.enable_hr: @@ -519,7 +521,7 @@ class StableDiffusionProcessingTxt2Img(StableDiffusionProcessing): else: state.job_count = state.job_count * 2 - desired_pixel_count = 512 * 512 + desired_pixel_count = self.first_pass_width * self.first_pass_height actual_pixel_count = self.width * self.height scale = math.sqrt(desired_pixel_count / actual_pixel_count) diff --git a/modules/txt2img.py b/modules/txt2img.py index e985242b..85cbece4 100644 --- a/modules/txt2img.py +++ b/modules/txt2img.py @@ -6,7 +6,7 @@ import modules.processing as processing from modules.ui import plaintext_to_html -def txt2img(prompt: str, negative_prompt: str, prompt_style: str, prompt_style2: str, steps: int, sampler_index: int, restore_faces: bool, tiling: bool, n_iter: int, batch_size: int, cfg_scale: float, seed: int, subseed: int, subseed_strength: float, seed_resize_from_h: int, seed_resize_from_w: int, seed_enable_extras: bool, height: int, width: int, enable_hr: bool, scale_latent: bool, denoising_strength: float, *args): +def txt2img(prompt: str, negative_prompt: str, prompt_style: str, prompt_style2: str, steps: int, sampler_index: int, restore_faces: bool, tiling: bool, n_iter: int, batch_size: int, cfg_scale: float, seed: int, subseed: int, subseed_strength: float, seed_resize_from_h: int, seed_resize_from_w: int, seed_enable_extras: bool, height: int, width: int, enable_hr: bool, scale_latent: bool, denoising_strength: float, first_pass_width: int, first_pass_height: int, *args): p = StableDiffusionProcessingTxt2Img( sd_model=shared.sd_model, outpath_samples=opts.outdir_samples or opts.outdir_txt2img_samples, @@ -32,6 +32,9 @@ def txt2img(prompt: str, negative_prompt: str, prompt_style: str, prompt_style2: enable_hr=enable_hr, scale_latent=scale_latent if enable_hr else None, denoising_strength=denoising_strength if enable_hr else None, + first_pass_width=first_pass_width if enable_hr else None, + first_pass_height=first_pass_height if enable_hr else None, + ) if cmd_opts.enable_console_prompts: diff --git a/modules/ui.py b/modules/ui.py index 220fb80b..544419b2 100644 --- a/modules/ui.py +++ b/modules/ui.py @@ -540,6 +540,8 @@ def create_ui(wrap_gradio_gpu_call): enable_hr = gr.Checkbox(label='Highres. fix', value=False) with gr.Row(visible=False) as hr_options: + first_pass_width = gr.Slider(minimum=64, maximum=1024, step=64, label="First pass width", value=512) + first_pass_height = gr.Slider(minimum=64, maximum=1024, step=64, label="First pass height", value=512) scale_latent = gr.Checkbox(label='Scale latent', value=False) denoising_strength = gr.Slider(minimum=0.0, maximum=1.0, step=0.01, label='Denoising strength', value=0.7) @@ -604,6 +606,8 @@ def create_ui(wrap_gradio_gpu_call): enable_hr, scale_latent, denoising_strength, + first_pass_width, + first_pass_height, ] + custom_inputs, outputs=[ txt2img_gallery, @@ -668,6 +672,8 @@ def create_ui(wrap_gradio_gpu_call): (denoising_strength, "Denoising strength"), (enable_hr, lambda d: "Denoising strength" in d), (hr_options, lambda d: gr.Row.update(visible="Denoising strength" in d)), + (first_pass_width, "First pass width"), + (first_pass_height, "First pass height"), ] modules.generation_parameters_copypaste.connect_paste(paste, txt2img_paste_fields, txt2img_prompt) token_button.click(fn=update_token_counter, inputs=[txt2img_prompt, steps], outputs=[token_counter]) -- cgit v1.2.1 From 40d1c6e423b4dc52b3bdae43d9e2442960760ced Mon Sep 17 00:00:00 2001 From: Buckzor Date: Thu, 13 Oct 2022 20:04:22 +0100 Subject: Option between stretch and crop for Highres. fix --- modules/processing.py | 34 ++++++++++++++++++++++------------ modules/txt2img.py | 7 ++++--- modules/ui.py | 25 ++++++++++++++++--------- 3 files changed, 42 insertions(+), 24 deletions(-) (limited to 'modules') diff --git a/modules/processing.py b/modules/processing.py index abbfdf98..0246f5dd 100644 --- a/modules/processing.py +++ b/modules/processing.py @@ -506,13 +506,14 @@ class StableDiffusionProcessingTxt2Img(StableDiffusionProcessing): firstphase_width_truncated = 0 firstphase_height_truncated = 0 - def __init__(self, enable_hr=False, scale_latent=True, denoising_strength=0.75, first_pass_width=512, first_pass_height=512, **kwargs): + def __init__(self, enable_hr=False, scale_latent=True, denoising_strength=0.75, firstphase_width=512, firstphase_height=512, crop_scale=False, **kwargs): super().__init__(**kwargs) self.enable_hr = enable_hr self.scale_latent = scale_latent self.denoising_strength = denoising_strength - self.first_pass_width = first_pass_width - self.first_pass_height = first_pass_height + self.firstphase_width = firstphase_width + self.firstphase_height = firstphase_height + self.crop_scale = crop_scale def init(self, all_prompts, all_seeds, all_subseeds): if self.enable_hr: @@ -521,14 +522,14 @@ class StableDiffusionProcessingTxt2Img(StableDiffusionProcessing): else: state.job_count = state.job_count * 2 - desired_pixel_count = self.first_pass_width * self.first_pass_height - actual_pixel_count = self.width * self.height - scale = math.sqrt(desired_pixel_count / actual_pixel_count) + #desired_pixel_count = self.firstphase_width * self.firstphase_height + #actual_pixel_count = self.width * self.height + #scale = math.sqrt(desired_pixel_count / actual_pixel_count) - self.firstphase_width = math.ceil(scale * self.width / 64) * 64 - self.firstphase_height = math.ceil(scale * self.height / 64) * 64 - self.firstphase_width_truncated = int(scale * self.width) - self.firstphase_height_truncated = int(scale * self.height) + #self.firstphase_width = math.ceil(scale * self.width / 64) * 64 + #self.firstphase_height = math.ceil(scale * self.height / 64) * 64 + #self.firstphase_width_truncated = int(scale * self.width) + #self.firstphase_height_truncated = int(scale * self.height) def sample(self, conditioning, unconditional_conditioning, seeds, subseeds, subseed_strength): self.sampler = sd_samplers.create_sampler_with_index(sd_samplers.samplers, self.sampler_index, self.sd_model) @@ -541,8 +542,17 @@ class StableDiffusionProcessingTxt2Img(StableDiffusionProcessing): x = create_random_tensors([opt_C, self.firstphase_height // opt_f, self.firstphase_width // opt_f], seeds=seeds, subseeds=subseeds, subseed_strength=self.subseed_strength, seed_resize_from_h=self.seed_resize_from_h, seed_resize_from_w=self.seed_resize_from_w, p=self) samples = self.sampler.sample(self, x, conditioning, unconditional_conditioning) - truncate_x = (self.firstphase_width - self.firstphase_width_truncated) // opt_f - truncate_y = (self.firstphase_height - self.firstphase_height_truncated) // opt_f + truncate_x = 0 + truncate_y = 0 + + if self.crop_scale: + if self.width/self.firstphase_width > self.height/self.firstphase_height: + #Crop to landscape + truncate_y = (self.width - self.firstphase_width)//2 // opt_f + + elif self.width/self.firstphase_width < self.height/self.firstphase_height: + #Crop to portrait + truncate_x = (self.height - self.firstphase_height)//2 // opt_f samples = samples[:, :, truncate_y//2:samples.shape[2]-truncate_y//2, truncate_x//2:samples.shape[3]-truncate_x//2] diff --git a/modules/txt2img.py b/modules/txt2img.py index 85cbece4..447ec3d3 100644 --- a/modules/txt2img.py +++ b/modules/txt2img.py @@ -6,7 +6,7 @@ import modules.processing as processing from modules.ui import plaintext_to_html -def txt2img(prompt: str, negative_prompt: str, prompt_style: str, prompt_style2: str, steps: int, sampler_index: int, restore_faces: bool, tiling: bool, n_iter: int, batch_size: int, cfg_scale: float, seed: int, subseed: int, subseed_strength: float, seed_resize_from_h: int, seed_resize_from_w: int, seed_enable_extras: bool, height: int, width: int, enable_hr: bool, scale_latent: bool, denoising_strength: float, first_pass_width: int, first_pass_height: int, *args): +def txt2img(prompt: str, negative_prompt: str, prompt_style: str, prompt_style2: str, steps: int, sampler_index: int, restore_faces: bool, tiling: bool, n_iter: int, batch_size: int, cfg_scale: float, seed: int, subseed: int, subseed_strength: float, seed_resize_from_h: int, seed_resize_from_w: int, seed_enable_extras: bool, height: int, width: int, enable_hr: bool, scale_latent: bool, denoising_strength: float, firstphase_width: int, firstphase_height: int, crop_scale: bool, *args): p = StableDiffusionProcessingTxt2Img( sd_model=shared.sd_model, outpath_samples=opts.outdir_samples or opts.outdir_txt2img_samples, @@ -32,8 +32,9 @@ def txt2img(prompt: str, negative_prompt: str, prompt_style: str, prompt_style2: enable_hr=enable_hr, scale_latent=scale_latent if enable_hr else None, denoising_strength=denoising_strength if enable_hr else None, - first_pass_width=first_pass_width if enable_hr else None, - first_pass_height=first_pass_height if enable_hr else None, + firstphase_width=firstphase_width if enable_hr else None, + firstphase_height=firstphase_height if enable_hr else None, + crop_scale=crop_scale if enable_hr else None, ) diff --git a/modules/ui.py b/modules/ui.py index 544419b2..f2d81f68 100644 --- a/modules/ui.py +++ b/modules/ui.py @@ -540,12 +540,18 @@ def create_ui(wrap_gradio_gpu_call): enable_hr = gr.Checkbox(label='Highres. fix', value=False) with gr.Row(visible=False) as hr_options: - first_pass_width = gr.Slider(minimum=64, maximum=1024, step=64, label="First pass width", value=512) - first_pass_height = gr.Slider(minimum=64, maximum=1024, step=64, label="First pass height", value=512) - scale_latent = gr.Checkbox(label='Scale latent', value=False) - denoising_strength = gr.Slider(minimum=0.0, maximum=1.0, step=0.01, label='Denoising strength', value=0.7) + with gr.Column(scale=1.0): + firstphase_width = gr.Slider(minimum=64, maximum=1024, step=64, label="First pass width", value=512) + firstphase_height = gr.Slider(minimum=64, maximum=1024, step=64, label="First pass height", value=512) + + with gr.Column(scale=1.0): + with gr.Row(): + crop_scale = gr.Checkbox(label='Crop when scaling', value=False) + scale_latent = gr.Checkbox(label='Scale latent', value=False) + with gr.Row(): + denoising_strength = gr.Slider(minimum=0.0, maximum=1.0, step=0.01, label='Denoising strength', value=0.7) - with gr.Row(): + with gr.Row(equal_height=True): batch_count = gr.Slider(minimum=1, step=1, label='Batch count', value=1) batch_size = gr.Slider(minimum=1, maximum=8, step=1, label='Batch size', value=1) @@ -606,8 +612,9 @@ def create_ui(wrap_gradio_gpu_call): enable_hr, scale_latent, denoising_strength, - first_pass_width, - first_pass_height, + firstphase_width, + firstphase_height, + crop_scale, ] + custom_inputs, outputs=[ txt2img_gallery, @@ -672,8 +679,8 @@ def create_ui(wrap_gradio_gpu_call): (denoising_strength, "Denoising strength"), (enable_hr, lambda d: "Denoising strength" in d), (hr_options, lambda d: gr.Row.update(visible="Denoising strength" in d)), - (first_pass_width, "First pass width"), - (first_pass_height, "First pass height"), + (firstphase_width, "First pass width"), + (firstphase_height, "First pass height"), ] modules.generation_parameters_copypaste.connect_paste(paste, txt2img_paste_fields, txt2img_prompt) token_button.click(fn=update_token_counter, inputs=[txt2img_prompt, steps], outputs=[token_counter]) -- cgit v1.2.1 From b382de2d77c653c565840ce92d27aa668a1934d7 Mon Sep 17 00:00:00 2001 From: Buckzor Date: Thu, 13 Oct 2022 22:23:22 +0100 Subject: Fixed Scale ratio problem --- modules/processing.py | 25 +++++++++++-------------- 1 file changed, 11 insertions(+), 14 deletions(-) (limited to 'modules') diff --git a/modules/processing.py b/modules/processing.py index 0246f5dd..d9b0e0e7 100644 --- a/modules/processing.py +++ b/modules/processing.py @@ -522,15 +522,6 @@ class StableDiffusionProcessingTxt2Img(StableDiffusionProcessing): else: state.job_count = state.job_count * 2 - #desired_pixel_count = self.firstphase_width * self.firstphase_height - #actual_pixel_count = self.width * self.height - #scale = math.sqrt(desired_pixel_count / actual_pixel_count) - - #self.firstphase_width = math.ceil(scale * self.width / 64) * 64 - #self.firstphase_height = math.ceil(scale * self.height / 64) * 64 - #self.firstphase_width_truncated = int(scale * self.width) - #self.firstphase_height_truncated = int(scale * self.height) - def sample(self, conditioning, unconditional_conditioning, seeds, subseeds, subseed_strength): self.sampler = sd_samplers.create_sampler_with_index(sd_samplers.samplers, self.sampler_index, self.sd_model) @@ -544,17 +535,23 @@ class StableDiffusionProcessingTxt2Img(StableDiffusionProcessing): truncate_x = 0 truncate_y = 0 + width_ratio = self.width/self.firstphase_width + height_ratio = self.height/self.firstphase_height if self.crop_scale: - if self.width/self.firstphase_width > self.height/self.firstphase_height: + if width_ratio > height_ratio: #Crop to landscape - truncate_y = (self.width - self.firstphase_width)//2 // opt_f + truncate_y = int((self.width - self.firstphase_width) / width_ratio / height_ratio / opt_f) - elif self.width/self.firstphase_width < self.height/self.firstphase_height: + elif width_ratio < height_ratio: #Crop to portrait - truncate_x = (self.height - self.firstphase_height)//2 // opt_f + truncate_x = int((self.height - self.firstphase_height) / width_ratio / height_ratio / opt_f) + + samples = samples[:, :, truncate_y//2:samples.shape[2]-truncate_y//2, truncate_x//2:samples.shape[3]-truncate_x//2] + + - samples = samples[:, :, truncate_y//2:samples.shape[2]-truncate_y//2, truncate_x//2:samples.shape[3]-truncate_x//2] + if self.scale_latent: samples = torch.nn.functional.interpolate(samples, size=(self.height // opt_f, self.width // opt_f), mode="bilinear") -- cgit v1.2.1 From e644b5a80beb54b6df4caa63fb19d889dd4ceff6 Mon Sep 17 00:00:00 2001 From: AUTOMATIC <16777216c@gmail.com> Date: Fri, 14 Oct 2022 17:03:03 +0300 Subject: remove scale latent and no-crop options from hires fix support copy-pasting new parameters for hires fix --- modules/processing.py | 64 ++++++++++++++++++++++----------------------------- modules/txt2img.py | 9 +++----- modules/ui.py | 19 ++++----------- 3 files changed, 35 insertions(+), 57 deletions(-) (limited to 'modules') diff --git a/modules/processing.py b/modules/processing.py index d9b0e0e7..100a259f 100644 --- a/modules/processing.py +++ b/modules/processing.py @@ -506,14 +506,12 @@ class StableDiffusionProcessingTxt2Img(StableDiffusionProcessing): firstphase_width_truncated = 0 firstphase_height_truncated = 0 - def __init__(self, enable_hr=False, scale_latent=True, denoising_strength=0.75, firstphase_width=512, firstphase_height=512, crop_scale=False, **kwargs): + def __init__(self, enable_hr=False, denoising_strength=0.75, firstphase_width=512, firstphase_height=512, **kwargs): super().__init__(**kwargs) self.enable_hr = enable_hr - self.scale_latent = scale_latent self.denoising_strength = denoising_strength self.firstphase_width = firstphase_width self.firstphase_height = firstphase_height - self.crop_scale = crop_scale def init(self, all_prompts, all_seeds, all_subseeds): if self.enable_hr: @@ -530,6 +528,8 @@ class StableDiffusionProcessingTxt2Img(StableDiffusionProcessing): samples = self.sampler.sample(self, x, conditioning, unconditional_conditioning) return samples + self.extra_generation_params["First pass size"] = f"{self.firstphase_width}x{self.firstphase_height}" + x = create_random_tensors([opt_C, self.firstphase_height // opt_f, self.firstphase_width // opt_f], seeds=seeds, subseeds=subseeds, subseed_strength=self.subseed_strength, seed_resize_from_h=self.seed_resize_from_h, seed_resize_from_w=self.seed_resize_from_w, p=self) samples = self.sampler.sample(self, x, conditioning, unconditional_conditioning) @@ -538,46 +538,36 @@ class StableDiffusionProcessingTxt2Img(StableDiffusionProcessing): width_ratio = self.width/self.firstphase_width height_ratio = self.height/self.firstphase_height - if self.crop_scale: - if width_ratio > height_ratio: - #Crop to landscape - truncate_y = int((self.width - self.firstphase_width) / width_ratio / height_ratio / opt_f) + if width_ratio > height_ratio: + truncate_y = int((self.width - self.firstphase_width) / width_ratio / height_ratio / opt_f) - elif width_ratio < height_ratio: - #Crop to portrait - truncate_x = int((self.height - self.firstphase_height) / width_ratio / height_ratio / opt_f) + elif width_ratio < height_ratio: + truncate_x = int((self.height - self.firstphase_height) / width_ratio / height_ratio / opt_f) - samples = samples[:, :, truncate_y//2:samples.shape[2]-truncate_y//2, truncate_x//2:samples.shape[3]-truncate_x//2] - - + samples = samples[:, :, truncate_y//2:samples.shape[2]-truncate_y//2, truncate_x//2:samples.shape[3]-truncate_x//2] - + decoded_samples = decode_first_stage(self.sd_model, samples) - if self.scale_latent: - samples = torch.nn.functional.interpolate(samples, size=(self.height // opt_f, self.width // opt_f), mode="bilinear") + if opts.upscaler_for_img2img is None or opts.upscaler_for_img2img == "None": + decoded_samples = torch.nn.functional.interpolate(decoded_samples, size=(self.height, self.width), mode="bilinear") else: - decoded_samples = decode_first_stage(self.sd_model, samples) + lowres_samples = torch.clamp((decoded_samples + 1.0) / 2.0, min=0.0, max=1.0) - if opts.upscaler_for_img2img is None or opts.upscaler_for_img2img == "None": - decoded_samples = torch.nn.functional.interpolate(decoded_samples, size=(self.height, self.width), mode="bilinear") - else: - lowres_samples = torch.clamp((decoded_samples + 1.0) / 2.0, min=0.0, max=1.0) - - batch_images = [] - for i, x_sample in enumerate(lowres_samples): - x_sample = 255. * np.moveaxis(x_sample.cpu().numpy(), 0, 2) - x_sample = x_sample.astype(np.uint8) - image = Image.fromarray(x_sample) - image = images.resize_image(0, image, self.width, self.height) - image = np.array(image).astype(np.float32) / 255.0 - image = np.moveaxis(image, 2, 0) - batch_images.append(image) - - decoded_samples = torch.from_numpy(np.array(batch_images)) - decoded_samples = decoded_samples.to(shared.device) - decoded_samples = 2. * decoded_samples - 1. - - samples = self.sd_model.get_first_stage_encoding(self.sd_model.encode_first_stage(decoded_samples)) + batch_images = [] + for i, x_sample in enumerate(lowres_samples): + x_sample = 255. * np.moveaxis(x_sample.cpu().numpy(), 0, 2) + x_sample = x_sample.astype(np.uint8) + image = Image.fromarray(x_sample) + image = images.resize_image(0, image, self.width, self.height) + image = np.array(image).astype(np.float32) / 255.0 + image = np.moveaxis(image, 2, 0) + batch_images.append(image) + + decoded_samples = torch.from_numpy(np.array(batch_images)) + decoded_samples = decoded_samples.to(shared.device) + decoded_samples = 2. * decoded_samples - 1. + + samples = self.sd_model.get_first_stage_encoding(self.sd_model.encode_first_stage(decoded_samples)) shared.state.nextjob() diff --git a/modules/txt2img.py b/modules/txt2img.py index 447ec3d3..2381347f 100644 --- a/modules/txt2img.py +++ b/modules/txt2img.py @@ -6,7 +6,7 @@ import modules.processing as processing from modules.ui import plaintext_to_html -def txt2img(prompt: str, negative_prompt: str, prompt_style: str, prompt_style2: str, steps: int, sampler_index: int, restore_faces: bool, tiling: bool, n_iter: int, batch_size: int, cfg_scale: float, seed: int, subseed: int, subseed_strength: float, seed_resize_from_h: int, seed_resize_from_w: int, seed_enable_extras: bool, height: int, width: int, enable_hr: bool, scale_latent: bool, denoising_strength: float, firstphase_width: int, firstphase_height: int, crop_scale: bool, *args): +def txt2img(prompt: str, negative_prompt: str, prompt_style: str, prompt_style2: str, steps: int, sampler_index: int, restore_faces: bool, tiling: bool, n_iter: int, batch_size: int, cfg_scale: float, seed: int, subseed: int, subseed_strength: float, seed_resize_from_h: int, seed_resize_from_w: int, seed_enable_extras: bool, height: int, width: int, enable_hr: bool, denoising_strength: float, firstphase_width: int, firstphase_height: int, *args): p = StableDiffusionProcessingTxt2Img( sd_model=shared.sd_model, outpath_samples=opts.outdir_samples or opts.outdir_txt2img_samples, @@ -30,12 +30,9 @@ def txt2img(prompt: str, negative_prompt: str, prompt_style: str, prompt_style2: restore_faces=restore_faces, tiling=tiling, enable_hr=enable_hr, - scale_latent=scale_latent if enable_hr else None, denoising_strength=denoising_strength if enable_hr else None, - firstphase_width=firstphase_width if enable_hr else None, - firstphase_height=firstphase_height if enable_hr else None, - crop_scale=crop_scale if enable_hr else None, - + firstphase_width=firstphase_width if enable_hr else None, + firstphase_height=firstphase_height if enable_hr else None, ) if cmd_opts.enable_console_prompts: diff --git a/modules/ui.py b/modules/ui.py index f2d81f68..d66ddc14 100644 --- a/modules/ui.py +++ b/modules/ui.py @@ -540,16 +540,9 @@ def create_ui(wrap_gradio_gpu_call): enable_hr = gr.Checkbox(label='Highres. fix', value=False) with gr.Row(visible=False) as hr_options: - with gr.Column(scale=1.0): - firstphase_width = gr.Slider(minimum=64, maximum=1024, step=64, label="First pass width", value=512) - firstphase_height = gr.Slider(minimum=64, maximum=1024, step=64, label="First pass height", value=512) - - with gr.Column(scale=1.0): - with gr.Row(): - crop_scale = gr.Checkbox(label='Crop when scaling', value=False) - scale_latent = gr.Checkbox(label='Scale latent', value=False) - with gr.Row(): - denoising_strength = gr.Slider(minimum=0.0, maximum=1.0, step=0.01, label='Denoising strength', value=0.7) + firstphase_width = gr.Slider(minimum=64, maximum=1024, step=64, label="First pass width", value=512) + firstphase_height = gr.Slider(minimum=64, maximum=1024, step=64, label="First pass height", value=512) + denoising_strength = gr.Slider(minimum=0.0, maximum=1.0, step=0.01, label='Denoising strength', value=0.7) with gr.Row(equal_height=True): batch_count = gr.Slider(minimum=1, step=1, label='Batch count', value=1) @@ -610,11 +603,9 @@ def create_ui(wrap_gradio_gpu_call): height, width, enable_hr, - scale_latent, denoising_strength, firstphase_width, firstphase_height, - crop_scale, ] + custom_inputs, outputs=[ txt2img_gallery, @@ -679,8 +670,8 @@ def create_ui(wrap_gradio_gpu_call): (denoising_strength, "Denoising strength"), (enable_hr, lambda d: "Denoising strength" in d), (hr_options, lambda d: gr.Row.update(visible="Denoising strength" in d)), - (firstphase_width, "First pass width"), - (firstphase_height, "First pass height"), + (firstphase_width, "First pass size-1"), + (firstphase_height, "First pass size-2"), ] modules.generation_parameters_copypaste.connect_paste(paste, txt2img_paste_fields, txt2img_prompt) token_button.click(fn=update_token_counter, inputs=[txt2img_prompt, steps], outputs=[token_counter]) -- cgit v1.2.1 From 0aec19d7837d8564355fdb286541db7165852e41 Mon Sep 17 00:00:00 2001 From: AUTOMATIC <16777216c@gmail.com> Date: Fri, 14 Oct 2022 18:15:03 +0300 Subject: make pasting into img2img prompt work make image params request not use temp files --- modules/images.py | 36 ++++++++++++++++++------------------ modules/ui.py | 9 +++------ 2 files changed, 21 insertions(+), 24 deletions(-) (limited to 'modules') diff --git a/modules/images.py b/modules/images.py index f1155b7f..68cdbc93 100644 --- a/modules/images.py +++ b/modules/images.py @@ -1,4 +1,5 @@ import datetime +import io import math import os from collections import namedtuple @@ -465,21 +466,20 @@ def save_image(image, path, basename, seed=None, prompt=None, extension='png', i return fullfn, txt_fullfn -def image_data(image_path): - file, ext = os.path.splitext(image_path.name) - data = {} - if "png" in ext: - image = Image.open(image_path.name, "r") - print(f"Image data requested for {image_path.name} {image.format} of {type(image)}") - try: - data = image.text["parameters"] - except Exception as e: - print(f"Exception: {e}") - pass - print(f"Image data: {data}") - if "txt" in ext: - myfile = open(image_path.name, 'r') - data = myfile.read() - myfile.close() - - return data, None +def image_data(data): + try: + image = Image.open(io.BytesIO(data)) + textinfo = image.text["parameters"] + return textinfo, None + except Exception: + pass + + try: + text = data.decode('utf8') + assert len(text) < 10000 + return text, None + + except Exception: + pass + + return '', None diff --git a/modules/ui.py b/modules/ui.py index 0a3ee887..6266db49 100644 --- a/modules/ui.py +++ b/modules/ui.py @@ -514,7 +514,7 @@ def create_ui(wrap_gradio_gpu_call): with gr.Blocks(analytics_enabled=False) as txt2img_interface: txt2img_prompt, roll, txt2img_prompt_style, txt2img_negative_prompt, txt2img_prompt_style2, submit, _, _, txt2img_prompt_style_apply, txt2img_save_style, paste, token_counter, token_button = create_toprow(is_img2img=False) dummy_component = gr.Label(visible=False) - txt_prompt_img = gr.File(label="", elem_id="txt2img_prompt_image", file_count="single", type="file", visible=False) + txt_prompt_img = gr.File(label="", elem_id="txt2img_prompt_image", file_count="single", type="bytes", visible=False) with gr.Row(elem_id='txt2img_progress_row'): with gr.Column(scale=1): @@ -620,7 +620,6 @@ def create_ui(wrap_gradio_gpu_call): txt_prompt_img.change( fn=modules.images.image_data, - # _js = "get_extras_tab_index", inputs=[ txt_prompt_img ], @@ -692,8 +691,7 @@ def create_ui(wrap_gradio_gpu_call): img2img_prompt, roll, img2img_prompt_style, img2img_negative_prompt, img2img_prompt_style2, submit, img2img_interrogate, img2img_deepbooru, img2img_prompt_style_apply, img2img_save_style, paste, token_counter, token_button = create_toprow(is_img2img=True) with gr.Row(elem_id='img2img_progress_row'): - img2img_prompt_img = gr.File(label="", elem_id="txt_prompt_image", file_count="single", type="file", - visible=False) + img2img_prompt_img = gr.File(label="", elem_id="img2img_prompt_image", file_count="single", type="bytes", visible=False) with gr.Column(scale=1): pass @@ -791,9 +789,8 @@ def create_ui(wrap_gradio_gpu_call): img2img_prompt_img.change( fn=modules.images.image_data, - # _js = "get_extras_tab_index", inputs=[ - txt_prompt_img + img2img_prompt_img ], outputs=[ img2img_prompt, -- cgit v1.2.1 From 67f447ddcc8a17d11939c3801dca635dc22944c7 Mon Sep 17 00:00:00 2001 From: AUTOMATIC <16777216c@gmail.com> Date: Fri, 14 Oct 2022 19:30:28 +0300 Subject: possibility to load checkpoint, clip skip, and hypernet from infotext --- modules/ui.py | 52 +++++++++++++++++++++++++++++++++++++++++++++------- 1 file changed, 45 insertions(+), 7 deletions(-) (limited to 'modules') diff --git a/modules/ui.py b/modules/ui.py index 6266db49..a37a4e17 100644 --- a/modules/ui.py +++ b/modules/ui.py @@ -22,7 +22,7 @@ import gradio as gr import gradio.utils import gradio.routes -from modules import sd_hijack +from modules import sd_hijack, sd_models from modules.paths import script_path from modules.shared import opts, cmd_opts if cmd_opts.deepdanbooru: @@ -507,12 +507,38 @@ def setup_progressbar(progressbar, preview, id_part, textinfo=None): ) +def apply_setting(key, value): + if value is None: + return gr.update() + + if key == "sd_model_checkpoint": + ckpt_info = sd_models.get_closet_checkpoint_match(value) + + if ckpt_info is not None: + value = ckpt_info.title + else: + return gr.update() + + comp_args = opts.data_labels[key].component_args + if comp_args and isinstance(comp_args, dict) and comp_args.get('visible') is False: + return + + valtype = type(opts.data_labels[key].default) + oldval = opts.data[key] + opts.data[key] = valtype(value) if valtype != type(None) else value + if oldval != value and opts.data_labels[key].onchange is not None: + opts.data_labels[key].onchange() + + opts.save(shared.config_filename) + return value + + def create_ui(wrap_gradio_gpu_call): import modules.img2img import modules.txt2img with gr.Blocks(analytics_enabled=False) as txt2img_interface: - txt2img_prompt, roll, txt2img_prompt_style, txt2img_negative_prompt, txt2img_prompt_style2, submit, _, _, txt2img_prompt_style_apply, txt2img_save_style, paste, token_counter, token_button = create_toprow(is_img2img=False) + txt2img_prompt, roll, txt2img_prompt_style, txt2img_negative_prompt, txt2img_prompt_style2, submit, _, _, txt2img_prompt_style_apply, txt2img_save_style, txt2img_paste, token_counter, token_button = create_toprow(is_img2img=False) dummy_component = gr.Label(visible=False) txt_prompt_img = gr.File(label="", elem_id="txt2img_prompt_image", file_count="single", type="bytes", visible=False) @@ -684,11 +710,10 @@ def create_ui(wrap_gradio_gpu_call): (firstphase_width, "First pass size-1"), (firstphase_height, "First pass size-2"), ] - modules.generation_parameters_copypaste.connect_paste(paste, txt2img_paste_fields, txt2img_prompt) token_button.click(fn=update_token_counter, inputs=[txt2img_prompt, steps], outputs=[token_counter]) with gr.Blocks(analytics_enabled=False) as img2img_interface: - img2img_prompt, roll, img2img_prompt_style, img2img_negative_prompt, img2img_prompt_style2, submit, img2img_interrogate, img2img_deepbooru, img2img_prompt_style_apply, img2img_save_style, paste, token_counter, token_button = create_toprow(is_img2img=True) + img2img_prompt, roll, img2img_prompt_style, img2img_negative_prompt, img2img_prompt_style2, submit, img2img_interrogate, img2img_deepbooru, img2img_prompt_style_apply, img2img_save_style, img2img_paste, token_counter, token_button = create_toprow(is_img2img=True) with gr.Row(elem_id='img2img_progress_row'): img2img_prompt_img = gr.File(label="", elem_id="img2img_prompt_image", file_count="single", type="bytes", visible=False) @@ -938,7 +963,6 @@ def create_ui(wrap_gradio_gpu_call): (seed_resize_from_h, "Seed resize from-2"), (denoising_strength, "Denoising strength"), ] - modules.generation_parameters_copypaste.connect_paste(paste, img2img_paste_fields, img2img_prompt) token_button.click(fn=update_token_counter, inputs=[img2img_prompt, steps], outputs=[token_counter]) with gr.Blocks(analytics_enabled=False) as extras_interface: @@ -1580,8 +1604,22 @@ Requested path was: {f} outputs=[extras_image], ) - modules.generation_parameters_copypaste.connect_paste(pnginfo_send_to_txt2img, txt2img_paste_fields, generation_info, 'switch_to_txt2img') - modules.generation_parameters_copypaste.connect_paste(pnginfo_send_to_img2img, img2img_paste_fields, generation_info, 'switch_to_img2img_img2img') + settings_map = { + 'sd_hypernetwork': 'Hypernet', + 'CLIP_stop_at_last_layers': 'Clip skip', + 'sd_model_checkpoint': 'Model hash', + } + + settings_paste_fields = [ + (component_dict[k], lambda d, k=k, v=v: apply_setting(k, d.get(v, None))) + for k, v in settings_map.items() + ] + + modules.generation_parameters_copypaste.connect_paste(txt2img_paste, txt2img_paste_fields + settings_paste_fields, txt2img_prompt) + modules.generation_parameters_copypaste.connect_paste(img2img_paste, img2img_paste_fields + settings_paste_fields, img2img_prompt) + + modules.generation_parameters_copypaste.connect_paste(pnginfo_send_to_txt2img, txt2img_paste_fields + settings_paste_fields, generation_info, 'switch_to_txt2img') + modules.generation_parameters_copypaste.connect_paste(pnginfo_send_to_img2img, img2img_paste_fields + settings_paste_fields, generation_info, 'switch_to_img2img_img2img') ui_config_file = cmd_opts.ui_config_file ui_settings = {} -- cgit v1.2.1 From 2fb9891af3bb4c36a6de6b44937e927bda43c10d Mon Sep 17 00:00:00 2001 From: Gugubo <29143981+Gugubo@users.noreply.github.com> Date: Fri, 14 Oct 2022 14:19:39 +0200 Subject: Change grid row count autodetect to prevent empty spots Instead of just rounding (sometimes resulting in grids with "empty" spots), find a divisor. For example: 8 images will now result in a 4x2 grid instead of a 3x3 with one empty spot. --- modules/images.py | 5 +++-- 1 file changed, 3 insertions(+), 2 deletions(-) (limited to 'modules') diff --git a/modules/images.py b/modules/images.py index 68cdbc93..90eca37a 100644 --- a/modules/images.py +++ b/modules/images.py @@ -25,8 +25,9 @@ def image_grid(imgs, batch_size=1, rows=None): elif opts.n_rows == 0: rows = batch_size else: - rows = math.sqrt(len(imgs)) - rows = round(rows) + rows = math.floor(math.sqrt(len(imgs))) + while len(imgs) % rows != 0: + rows -= 1 cols = math.ceil(len(imgs) / rows) -- cgit v1.2.1 From 43f926aad1b77a4bb642c1d173adfae1f56cf42d Mon Sep 17 00:00:00 2001 From: Gugubo <29143981+Gugubo@users.noreply.github.com> Date: Fri, 14 Oct 2022 17:06:51 +0200 Subject: Add option to prevent empty spots in grid (1/2) --- modules/shared.py | 1 + 1 file changed, 1 insertion(+) (limited to 'modules') diff --git a/modules/shared.py b/modules/shared.py index b6a5c1a8..159f504f 100644 --- a/modules/shared.py +++ b/modules/shared.py @@ -175,6 +175,7 @@ options_templates.update(options_section(('saving-images', "Saving images/grids" "grid_format": OptionInfo('png', 'File format for grids'), "grid_extended_filename": OptionInfo(False, "Add extended info (seed, prompt) to filename when saving grid"), "grid_only_if_multiple": OptionInfo(True, "Do not save grids consisting of one picture"), + "grid_prevent_empty_spots": OptionInfo(False, "Prevent empty spots in grid (when set to autodetect)"), "n_rows": OptionInfo(-1, "Grid row count; use -1 for autodetect and 0 for it to be same as batch size", gr.Slider, {"minimum": -1, "maximum": 16, "step": 1}), "enable_pnginfo": OptionInfo(True, "Save text information about generation parameters as chunks to png files"), -- cgit v1.2.1 From 5f87dd1ee0960963e3f756c4ebe47652ff57f715 Mon Sep 17 00:00:00 2001 From: Gugubo <29143981+Gugubo@users.noreply.github.com> Date: Fri, 14 Oct 2022 17:07:24 +0200 Subject: Add option to prevent empty spots in grid (2/2) --- modules/images.py | 5 ++++- 1 file changed, 4 insertions(+), 1 deletion(-) (limited to 'modules') diff --git a/modules/images.py b/modules/images.py index 90eca37a..b9589563 100644 --- a/modules/images.py +++ b/modules/images.py @@ -24,10 +24,13 @@ def image_grid(imgs, batch_size=1, rows=None): rows = opts.n_rows elif opts.n_rows == 0: rows = batch_size - else: + elif opts.grid_prevent_empty_spots: rows = math.floor(math.sqrt(len(imgs))) while len(imgs) % rows != 0: rows -= 1 + else: + rows = math.sqrt(len(imgs)) + rows = round(rows) cols = math.ceil(len(imgs) / rows) -- cgit v1.2.1 From a8eeb2b7ad0c43ad60ac2ba8bd299b9cb265fdd3 Mon Sep 17 00:00:00 2001 From: Ljzd-PRO <63289359+Ljzd-PRO@users.noreply.github.com> Date: Thu, 13 Oct 2022 02:03:08 +0800 Subject: add `--lowram` parameter load models to VRM instead of RAM (for machines which have bigger VRM than RAM such as free Google Colab server) --- modules/shared.py | 1 + 1 file changed, 1 insertion(+) (limited to 'modules') diff --git a/modules/shared.py b/modules/shared.py index 159f504f..cd4a4714 100644 --- a/modules/shared.py +++ b/modules/shared.py @@ -34,6 +34,7 @@ parser.add_argument("--hypernetwork-dir", type=str, default=os.path.join(models_ parser.add_argument("--allow-code", action='store_true', help="allow custom script execution from webui") parser.add_argument("--medvram", action='store_true', help="enable stable diffusion model optimizations for sacrificing a little speed for low VRM usage") parser.add_argument("--lowvram", action='store_true', help="enable stable diffusion model optimizations for sacrificing a lot of speed for very low VRM usage") +parser.add_argument("--lowram", action='store_true', help="load models to VRM instead of RAM (for machines which have bigger VRM than RAM such as free Google Colab server)") parser.add_argument("--always-batch-cond-uncond", action='store_true', help="disables cond/uncond batching that is enabled to save memory with --medvram or --lowvram") parser.add_argument("--unload-gfpgan", action='store_true', help="does not do anything.") parser.add_argument("--precision", type=str, help="evaluate at this precision", choices=["full", "autocast"], default="autocast") -- cgit v1.2.1 From 4a216ded433ded315106e2989c5ff7dec1c49304 Mon Sep 17 00:00:00 2001 From: Ljzd-PRO <63289359+Ljzd-PRO@users.noreply.github.com> Date: Thu, 13 Oct 2022 02:07:49 +0800 Subject: load models to VRAM when using `--lowram` param load models to VRM instead of RAM (for machines which have bigger VRM than RAM such as free Google Colab server) --- modules/sd_models.py | 15 +++++++++++++-- 1 file changed, 13 insertions(+), 2 deletions(-) (limited to 'modules') diff --git a/modules/sd_models.py b/modules/sd_models.py index 0a55b4c3..78a198b9 100644 --- a/modules/sd_models.py +++ b/modules/sd_models.py @@ -134,7 +134,12 @@ def load_model_weights(model, checkpoint_info): print(f"Loading weights [{sd_model_hash}] from {checkpoint_file}") - pl_sd = torch.load(checkpoint_file, map_location="cpu") + if shared.cmd_opts.lowram: + print("Load to VRAM if GPU is available (low RAM)") + pl_sd = torch.load(checkpoint_file) + else: + pl_sd = torch.load(checkpoint_file, map_location="cpu") + if "global_step" in pl_sd: print(f"Global Step: {pl_sd['global_step']}") @@ -158,7 +163,13 @@ def load_model_weights(model, checkpoint_info): if os.path.exists(vae_file): print(f"Loading VAE weights from: {vae_file}") - vae_ckpt = torch.load(vae_file, map_location="cpu") + + if shared.cmd_opts.lowram: + print("Load to VRAM if GPU is available (low RAM)") + vae_ckpt = torch.load(vae_file) + else: + vae_ckpt = torch.load(vae_file, map_location="cpu") + vae_dict = {k: v for k, v in vae_ckpt["state_dict"].items() if k[0:4] != "loss"} model.first_stage_model.load_state_dict(vae_dict) -- cgit v1.2.1 From bb295f54785ac36dc6aa6f7103a3431464440fc3 Mon Sep 17 00:00:00 2001 From: AUTOMATIC <16777216c@gmail.com> Date: Fri, 14 Oct 2022 20:03:41 +0300 Subject: rework the code for lowram a bit --- modules/sd_models.py | 12 ++---------- modules/shared.py | 3 ++- 2 files changed, 4 insertions(+), 11 deletions(-) (limited to 'modules') diff --git a/modules/sd_models.py b/modules/sd_models.py index 78a198b9..3a01c93d 100644 --- a/modules/sd_models.py +++ b/modules/sd_models.py @@ -134,11 +134,7 @@ def load_model_weights(model, checkpoint_info): print(f"Loading weights [{sd_model_hash}] from {checkpoint_file}") - if shared.cmd_opts.lowram: - print("Load to VRAM if GPU is available (low RAM)") - pl_sd = torch.load(checkpoint_file) - else: - pl_sd = torch.load(checkpoint_file, map_location="cpu") + pl_sd = torch.load(checkpoint_file, map_location=shared.weight_load_location) if "global_step" in pl_sd: print(f"Global Step: {pl_sd['global_step']}") @@ -164,11 +160,7 @@ def load_model_weights(model, checkpoint_info): if os.path.exists(vae_file): print(f"Loading VAE weights from: {vae_file}") - if shared.cmd_opts.lowram: - print("Load to VRAM if GPU is available (low RAM)") - vae_ckpt = torch.load(vae_file) - else: - vae_ckpt = torch.load(vae_file, map_location="cpu") + vae_ckpt = torch.load(vae_file, map_location=shared.weight_load_location) vae_dict = {k: v for k, v in vae_ckpt["state_dict"].items() if k[0:4] != "loss"} diff --git a/modules/shared.py b/modules/shared.py index cd4a4714..695d29b6 100644 --- a/modules/shared.py +++ b/modules/shared.py @@ -34,7 +34,7 @@ parser.add_argument("--hypernetwork-dir", type=str, default=os.path.join(models_ parser.add_argument("--allow-code", action='store_true', help="allow custom script execution from webui") parser.add_argument("--medvram", action='store_true', help="enable stable diffusion model optimizations for sacrificing a little speed for low VRM usage") parser.add_argument("--lowvram", action='store_true', help="enable stable diffusion model optimizations for sacrificing a lot of speed for very low VRM usage") -parser.add_argument("--lowram", action='store_true', help="load models to VRM instead of RAM (for machines which have bigger VRM than RAM such as free Google Colab server)") +parser.add_argument("--lowram", action='store_true', help="load stable diffusion checkpoint weights to VRAM instead of RAM") parser.add_argument("--always-batch-cond-uncond", action='store_true', help="disables cond/uncond batching that is enabled to save memory with --medvram or --lowvram") parser.add_argument("--unload-gfpgan", action='store_true', help="does not do anything.") parser.add_argument("--precision", type=str, help="evaluate at this precision", choices=["full", "autocast"], default="autocast") @@ -81,6 +81,7 @@ devices.device, devices.device_interrogate, devices.device_gfpgan, devices.devic (devices.cpu if any(y in cmd_opts.use_cpu for y in [x, 'all']) else devices.get_optimal_device() for x in ['sd', 'interrogate', 'gfpgan', 'bsrgan', 'esrgan', 'scunet', 'codeformer']) device = devices.device +weight_load_location = None if cmd_opts.lowram else "cpu" batch_cond_uncond = cmd_opts.always_batch_cond_uncond or not (cmd_opts.lowvram or cmd_opts.medvram) parallel_processing_allowed = not cmd_opts.lowvram and not cmd_opts.medvram -- cgit v1.2.1 From c344ba3b325459abbf9b0df2c1b18f7bf99805b2 Mon Sep 17 00:00:00 2001 From: AUTOMATIC <16777216c@gmail.com> Date: Fri, 14 Oct 2022 20:31:49 +0300 Subject: add option to read generation params for learning previews from txt2img --- modules/hypernetworks/hypernetwork.py | 21 ++++++++++++++++----- modules/textual_inversion/textual_inversion.py | 25 ++++++++++++++++++------- modules/ui.py | 20 +++++++++++++++++--- 3 files changed, 51 insertions(+), 15 deletions(-) (limited to 'modules') diff --git a/modules/hypernetworks/hypernetwork.py b/modules/hypernetworks/hypernetwork.py index f1248bb7..e5cb1817 100644 --- a/modules/hypernetworks/hypernetwork.py +++ b/modules/hypernetworks/hypernetwork.py @@ -180,7 +180,7 @@ def attention_CrossAttention_forward(self, x, context=None, mask=None): return self.to_out(out) -def train_hypernetwork(hypernetwork_name, learn_rate, data_root, log_directory, steps, create_image_every, save_hypernetwork_every, template_file, preview_image_prompt): +def train_hypernetwork(hypernetwork_name, learn_rate, data_root, log_directory, steps, create_image_every, save_hypernetwork_every, template_file, preview_from_txt2img, preview_prompt, preview_negative_prompt, preview_steps, preview_sampler_index, preview_cfg_scale, preview_seed, preview_width, preview_height): assert hypernetwork_name, 'hypernetwork not selected' path = shared.hypernetworks.get(hypernetwork_name, None) @@ -265,20 +265,31 @@ def train_hypernetwork(hypernetwork_name, learn_rate, data_root, log_directory, if hypernetwork.step > 0 and images_dir is not None and hypernetwork.step % create_image_every == 0: last_saved_image = os.path.join(images_dir, f'{hypernetwork_name}-{hypernetwork.step}.png') - preview_text = entry.cond_text if preview_image_prompt == "" else preview_image_prompt - optimizer.zero_grad() shared.sd_model.cond_stage_model.to(devices.device) shared.sd_model.first_stage_model.to(devices.device) p = processing.StableDiffusionProcessingTxt2Img( sd_model=shared.sd_model, - prompt=preview_text, - steps=20, do_not_save_grid=True, do_not_save_samples=True, ) + if preview_from_txt2img: + p.prompt = preview_prompt + p.negative_prompt = preview_negative_prompt + p.steps = preview_steps + p.sampler_index = preview_sampler_index + p.cfg_scale = preview_cfg_scale + p.seed = preview_seed + p.width = preview_width + p.height = preview_height + else: + p.prompt = entry.cond_text + p.steps = 20 + + preview_text = p.prompt + processed = processing.process_images(p) image = processed.images[0] if len(processed.images)>0 else None diff --git a/modules/textual_inversion/textual_inversion.py b/modules/textual_inversion/textual_inversion.py index fa0e33a2..3d835358 100644 --- a/modules/textual_inversion/textual_inversion.py +++ b/modules/textual_inversion/textual_inversion.py @@ -172,7 +172,7 @@ def create_embedding(name, num_vectors_per_token, init_text='*'): return fn -def train_embedding(embedding_name, learn_rate, data_root, log_directory, training_width, training_height, steps, create_image_every, save_embedding_every, template_file, save_image_with_stored_embedding, preview_image_prompt): +def train_embedding(embedding_name, learn_rate, data_root, log_directory, training_width, training_height, steps, create_image_every, save_embedding_every, template_file, save_image_with_stored_embedding, preview_from_txt2img, preview_prompt, preview_negative_prompt, preview_steps, preview_sampler_index, preview_cfg_scale, preview_seed, preview_width, preview_height): assert embedding_name, 'embedding not selected' shared.state.textinfo = "Initializing textual inversion training..." @@ -259,18 +259,29 @@ def train_embedding(embedding_name, learn_rate, data_root, log_directory, traini if embedding.step > 0 and images_dir is not None and embedding.step % create_image_every == 0: last_saved_image = os.path.join(images_dir, f'{embedding_name}-{embedding.step}.png') - preview_text = entry.cond_text if preview_image_prompt == "" else preview_image_prompt - p = processing.StableDiffusionProcessingTxt2Img( sd_model=shared.sd_model, - prompt=preview_text, - steps=20, - height=training_height, - width=training_width, do_not_save_grid=True, do_not_save_samples=True, ) + if preview_from_txt2img: + p.prompt = preview_prompt + p.negative_prompt = preview_negative_prompt + p.steps = preview_steps + p.sampler_index = preview_sampler_index + p.cfg_scale = preview_cfg_scale + p.seed = preview_seed + p.width = preview_width + p.height = preview_height + else: + p.prompt = entry.cond_text + p.steps = 20 + p.width = training_width + p.height = training_height + + preview_text = p.prompt + processed = processing.process_images(p) image = processed.images[0] diff --git a/modules/ui.py b/modules/ui.py index 828bfeea..4a04c2cc 100644 --- a/modules/ui.py +++ b/modules/ui.py @@ -711,6 +711,18 @@ def create_ui(wrap_gradio_gpu_call): (firstphase_width, "First pass size-1"), (firstphase_height, "First pass size-2"), ] + + txt2img_preview_params = [ + txt2img_prompt, + txt2img_negative_prompt, + steps, + sampler_index, + cfg_scale, + seed, + width, + height, + ] + token_button.click(fn=update_token_counter, inputs=[txt2img_prompt, steps], outputs=[token_counter]) with gr.Blocks(analytics_enabled=False) as img2img_interface: @@ -1162,7 +1174,7 @@ def create_ui(wrap_gradio_gpu_call): create_image_every = gr.Number(label='Save an image to log directory every N steps, 0 to disable', value=500, precision=0) save_embedding_every = gr.Number(label='Save a copy of embedding to log directory every N steps, 0 to disable', value=500, precision=0) save_image_with_stored_embedding = gr.Checkbox(label='Save images with embedding in PNG chunks', value=True) - preview_image_prompt = gr.Textbox(label='Preview prompt', value="") + preview_from_txt2img = gr.Checkbox(label='Read parameters (prompt, etc...) from txt2img tab when making previews', value=False) with gr.Row(): interrupt_training = gr.Button(value="Interrupt") @@ -1240,7 +1252,8 @@ def create_ui(wrap_gradio_gpu_call): save_embedding_every, template_file, save_image_with_stored_embedding, - preview_image_prompt, + preview_from_txt2img, + *txt2img_preview_params, ], outputs=[ ti_output, @@ -1260,7 +1273,8 @@ def create_ui(wrap_gradio_gpu_call): create_image_every, save_embedding_every, template_file, - preview_image_prompt, + preview_from_txt2img, + *txt2img_preview_params, ], outputs=[ ti_output, -- cgit v1.2.1 From 2f0e089c7c8e1ad7d2ad658971c6fdec9622e3ab Mon Sep 17 00:00:00 2001 From: AUTOMATIC <16777216c@gmail.com> Date: Fri, 14 Oct 2022 21:20:28 +0300 Subject: should fix the issue with missing layers in chechpoint merger --- modules/extras.py | 7 ++++++- 1 file changed, 6 insertions(+), 1 deletion(-) (limited to 'modules') diff --git a/modules/extras.py b/modules/extras.py index 532d869f..2e7b3751 100644 --- a/modules/extras.py +++ b/modules/extras.py @@ -209,7 +209,12 @@ def run_modelmerger(primary_model_name, secondary_model_name, teritary_model_nam for key in tqdm.tqdm(theta_0.keys()): if 'model' in key and key in theta_1: - theta_0[key] = theta_func(theta_0[key], theta_1[key], theta_2[key] if theta_2 else None, (float(1.0) - interp_amount)) # Need to reverse the interp_amount to match the desired mix ration in the merged checkpoint + t2 = (theta_2 or {}).get(key) + if t2 is None: + t2 = torch.zeros_like(theta_0[key]) + + theta_0[key] = theta_func(theta_0[key], theta_1[key], t2, (float(1.0) - interp_amount)) # Need to reverse the interp_amount to match the desired mix ration in the merged checkpoint + if save_as_half: theta_0[key] = theta_0[key].half() -- cgit v1.2.1 From c250cb289c97fe303cef69064bf45899406f6a40 Mon Sep 17 00:00:00 2001 From: AUTOMATIC <16777216c@gmail.com> Date: Fri, 14 Oct 2022 22:01:49 +0300 Subject: change checkpoint merger to work in a more obvious way remove sigmoid and inverse sigmoid because they just did the same thing as weighed sum only with changed multiplier --- modules/extras.py | 24 +++++------------------- modules/ui.py | 4 ++-- 2 files changed, 7 insertions(+), 21 deletions(-) (limited to 'modules') diff --git a/modules/extras.py b/modules/extras.py index 2e7b3751..f2f5a7b0 100644 --- a/modules/extras.py +++ b/modules/extras.py @@ -159,24 +159,12 @@ def run_pnginfo(image): return '', geninfo, info -def run_modelmerger(primary_model_name, secondary_model_name, teritary_model_name, interp_method, interp_amount, save_as_half, custom_name): - # Linear interpolation (https://en.wikipedia.org/wiki/Linear_interpolation) +def run_modelmerger(primary_model_name, secondary_model_name, teritary_model_name, interp_method, multiplier, save_as_half, custom_name): def weighted_sum(theta0, theta1, theta2, alpha): return ((1 - alpha) * theta0) + (alpha * theta1) - # Smoothstep (https://en.wikipedia.org/wiki/Smoothstep) - def sigmoid(theta0, theta1, theta2, alpha): - alpha = alpha * alpha * (3 - (2 * alpha)) - return theta0 + ((theta1 - theta0) * alpha) - - # Inverse Smoothstep (https://en.wikipedia.org/wiki/Smoothstep) - def inv_sigmoid(theta0, theta1, theta2, alpha): - import math - alpha = 0.5 - math.sin(math.asin(1.0 - 2.0 * alpha) / 3.0) - return theta0 + ((theta1 - theta0) * alpha) - def add_difference(theta0, theta1, theta2, alpha): - return theta0 + (theta1 - theta2) * (1.0 - alpha) + return theta0 + (theta1 - theta2) * alpha primary_model_info = sd_models.checkpoints_list[primary_model_name] secondary_model_info = sd_models.checkpoints_list[secondary_model_name] @@ -198,9 +186,7 @@ def run_modelmerger(primary_model_name, secondary_model_name, teritary_model_nam theta_2 = None theta_funcs = { - "Weighted Sum": weighted_sum, - "Sigmoid": sigmoid, - "Inverse Sigmoid": inv_sigmoid, + "Weighted sum": weighted_sum, "Add difference": add_difference, } theta_func = theta_funcs[interp_method] @@ -213,7 +199,7 @@ def run_modelmerger(primary_model_name, secondary_model_name, teritary_model_nam if t2 is None: t2 = torch.zeros_like(theta_0[key]) - theta_0[key] = theta_func(theta_0[key], theta_1[key], t2, (float(1.0) - interp_amount)) # Need to reverse the interp_amount to match the desired mix ration in the merged checkpoint + theta_0[key] = theta_func(theta_0[key], theta_1[key], t2, multiplier) if save_as_half: theta_0[key] = theta_0[key].half() @@ -227,7 +213,7 @@ def run_modelmerger(primary_model_name, secondary_model_name, teritary_model_nam ckpt_dir = shared.cmd_opts.ckpt_dir or sd_models.model_path - filename = primary_model_info.model_name + '_' + str(round(interp_amount, 2)) + '-' + secondary_model_info.model_name + '_' + str(round((float(1.0) - interp_amount), 2)) + '-' + interp_method.replace(" ", "_") + '-merged.ckpt' + filename = primary_model_info.model_name + '_' + str(round(1-multiplier, 2)) + '-' + secondary_model_info.model_name + '_' + str(round(multiplier, 2)) + '-' + interp_method.replace(" ", "_") + '-merged.ckpt' filename = filename if custom_name == '' else (custom_name + '.ckpt') output_modelname = os.path.join(ckpt_dir, filename) diff --git a/modules/ui.py b/modules/ui.py index 4a04c2cc..a08ffc9b 100644 --- a/modules/ui.py +++ b/modules/ui.py @@ -1101,8 +1101,8 @@ def create_ui(wrap_gradio_gpu_call): secondary_model_name = gr.Dropdown(modules.sd_models.checkpoint_tiles(), elem_id="modelmerger_secondary_model_name", label="Secondary model (B)") tertiary_model_name = gr.Dropdown(modules.sd_models.checkpoint_tiles(), elem_id="modelmerger_tertiary_model_name", label="Tertiary model (C)") custom_name = gr.Textbox(label="Custom Name (Optional)") - interp_amount = gr.Slider(minimum=0.0, maximum=1.0, step=0.05, label='Interpolation amount (1 - M)', value=0.3) - interp_method = gr.Radio(choices=["Weighted Sum", "Sigmoid", "Inverse Sigmoid", "Add difference"], value="Weighted Sum", label="Interpolation Method") + interp_amount = gr.Slider(minimum=0.0, maximum=1.0, step=0.05, label='Multiplier (M) - set to 0 to get model A', value=0.3) + interp_method = gr.Radio(choices=["Weighted sum", "Add difference"], value="Weighted sum", label="Interpolation Method") save_as_half = gr.Checkbox(value=False, label="Save as float16") modelmerger_merge = gr.Button(elem_id="modelmerger_merge", label="Merge", variant='primary') -- cgit v1.2.1 From 03d62538aebeff51713619fe808c953bdb70193d Mon Sep 17 00:00:00 2001 From: AUTOMATIC <16777216c@gmail.com> Date: Fri, 14 Oct 2022 22:43:55 +0300 Subject: remove duplicate code for log loss, add step, make it read from options rather than gradio input --- modules/hypernetworks/hypernetwork.py | 20 ++++-------- modules/shared.py | 3 +- modules/textual_inversion/textual_inversion.py | 44 ++++++++++++++++++-------- modules/ui.py | 3 -- 4 files changed, 38 insertions(+), 32 deletions(-) (limited to 'modules') diff --git a/modules/hypernetworks/hypernetwork.py b/modules/hypernetworks/hypernetwork.py index edb8cba1..59c7ac6e 100644 --- a/modules/hypernetworks/hypernetwork.py +++ b/modules/hypernetworks/hypernetwork.py @@ -15,6 +15,7 @@ import torch from torch import einsum from einops import rearrange, repeat import modules.textual_inversion.dataset +from modules.textual_inversion import textual_inversion from modules.textual_inversion.learn_schedule import LearnRateScheduler @@ -210,7 +211,7 @@ def train_hypernetwork(hypernetwork_name, learn_rate, data_root, log_directory, shared.state.textinfo = f"Preparing dataset from {html.escape(data_root)}..." with torch.autocast("cuda"): - ds = modules.textual_inversion.dataset.PersonalizedBase(data_root=data_root, width=512, height=512, repeats=1, placeholder_token=hypernetwork_name, model=shared.sd_model, device=devices.device, template_file=template_file, include_cond=True) + ds = modules.textual_inversion.dataset.PersonalizedBase(data_root=data_root, width=512, height=512, repeats=shared.opts.training_image_repeats_per_epoch, placeholder_token=hypernetwork_name, model=shared.sd_model, device=devices.device, template_file=template_file, include_cond=True) if unload: shared.sd_model.cond_stage_model.to(devices.cpu) @@ -263,19 +264,10 @@ def train_hypernetwork(hypernetwork_name, learn_rate, data_root, log_directory, last_saved_file = os.path.join(hypernetwork_dir, f'{hypernetwork_name}-{hypernetwork.step}.pt') hypernetwork.save(last_saved_file) - if write_csv_every > 0 and hypernetwork_dir is not None and hypernetwork.step % write_csv_every == 0: - write_csv_header = False if os.path.exists(os.path.join(hypernetwork_dir, "hypernetwork_loss.csv")) else True - - with open(os.path.join(hypernetwork_dir, "hypernetwork_loss.csv"), "a+") as fout: - - csv_writer = csv.DictWriter(fout, fieldnames=["step", "loss", "learn_rate"]) - - if write_csv_header: - csv_writer.writeheader() - - csv_writer.writerow({"step": hypernetwork.step, - "loss": f"{losses.mean():.7f}", - "learn_rate": scheduler.learn_rate}) + textual_inversion.write_loss(log_directory, "hypernetwork_loss.csv", hypernetwork.step, len(ds), { + "loss": f"{losses.mean():.7f}", + "learn_rate": scheduler.learn_rate + }) if hypernetwork.step > 0 and images_dir is not None and hypernetwork.step % create_image_every == 0: last_saved_image = os.path.join(images_dir, f'{hypernetwork_name}-{hypernetwork.step}.png') diff --git a/modules/shared.py b/modules/shared.py index 695d29b6..d41a7ab3 100644 --- a/modules/shared.py +++ b/modules/shared.py @@ -236,7 +236,8 @@ options_templates.update(options_section(('training', "Training"), { "unload_models_when_training": OptionInfo(False, "Unload VAE and CLIP from VRAM when training"), "dataset_filename_word_regex": OptionInfo("", "Filename word regex"), "dataset_filename_join_string": OptionInfo(" ", "Filename join string"), - "training_image_repeats_per_epoch": OptionInfo(100, "Number of repeats for a single input image per epoch; used only for displaying epoch number", gr.Number, {"precision": 0}), + "training_image_repeats_per_epoch": OptionInfo(1, "Number of repeats for a single input image per epoch; used only for displaying epoch number", gr.Number, {"precision": 0}), + "training_write_csv_every": OptionInfo(500, "Save an csv containing the loss to log directory every N steps, 0 to disable"), })) options_templates.update(options_section(('sd', "Stable Diffusion"), { diff --git a/modules/textual_inversion/textual_inversion.py b/modules/textual_inversion/textual_inversion.py index 1f5ace6f..da0d77a0 100644 --- a/modules/textual_inversion/textual_inversion.py +++ b/modules/textual_inversion/textual_inversion.py @@ -173,6 +173,32 @@ def create_embedding(name, num_vectors_per_token, init_text='*'): return fn +def write_loss(log_directory, filename, step, epoch_len, values): + if shared.opts.training_write_csv_every == 0: + return + + if step % shared.opts.training_write_csv_every != 0: + return + + write_csv_header = False if os.path.exists(os.path.join(log_directory, filename)) else True + + with open(os.path.join(log_directory, filename), "a+", newline='') as fout: + csv_writer = csv.DictWriter(fout, fieldnames=["step", "epoch", "epoch_step", *(values.keys())]) + + if write_csv_header: + csv_writer.writeheader() + + epoch = step // epoch_len + epoch_step = step - epoch * epoch_len + + csv_writer.writerow({ + "step": step + 1, + "epoch": epoch + 1, + "epoch_step": epoch_step + 1, + **values, + }) + + def train_embedding(embedding_name, learn_rate, data_root, log_directory, training_width, training_height, steps, create_image_every, save_embedding_every, template_file, save_image_with_stored_embedding, preview_from_txt2img, preview_prompt, preview_negative_prompt, preview_steps, preview_sampler_index, preview_cfg_scale, preview_seed, preview_width, preview_height): assert embedding_name, 'embedding not selected' @@ -257,20 +283,10 @@ def train_embedding(embedding_name, learn_rate, data_root, log_directory, traini last_saved_file = os.path.join(embedding_dir, f'{embedding_name}-{embedding.step}.pt') embedding.save(last_saved_file) - if write_csv_every > 0 and log_directory is not None and embedding.step % write_csv_every == 0: - write_csv_header = False if os.path.exists(os.path.join(log_directory, "textual_inversion_loss.csv")) else True - - with open(os.path.join(log_directory, "textual_inversion_loss.csv"), "a+") as fout: - - csv_writer = csv.DictWriter(fout, fieldnames=["epoch", "epoch_step", "loss", "learn_rate"]) - - if write_csv_header: - csv_writer.writeheader() - - csv_writer.writerow({"epoch": epoch_num + 1, - "epoch_step": epoch_step - 1, - "loss": f"{losses.mean():.7f}", - "learn_rate": scheduler.learn_rate}) + write_loss(log_directory, "textual_inversion_loss.csv", embedding.step, len(ds), { + "loss": f"{losses.mean():.7f}", + "learn_rate": scheduler.learn_rate + }) if embedding.step > 0 and images_dir is not None and embedding.step % create_image_every == 0: last_saved_image = os.path.join(images_dir, f'{embedding_name}-{embedding.step}.png') diff --git a/modules/ui.py b/modules/ui.py index be4a43a7..a08ffc9b 100644 --- a/modules/ui.py +++ b/modules/ui.py @@ -1172,7 +1172,6 @@ def create_ui(wrap_gradio_gpu_call): training_height = gr.Slider(minimum=64, maximum=2048, step=64, label="Height", value=512) steps = gr.Number(label='Max steps', value=100000, precision=0) create_image_every = gr.Number(label='Save an image to log directory every N steps, 0 to disable', value=500, precision=0) - write_csv_every = gr.Number(label='Save an csv containing the loss to log directory every N steps, 0 to disable', value=500, precision=0) save_embedding_every = gr.Number(label='Save a copy of embedding to log directory every N steps, 0 to disable', value=500, precision=0) save_image_with_stored_embedding = gr.Checkbox(label='Save images with embedding in PNG chunks', value=True) preview_from_txt2img = gr.Checkbox(label='Read parameters (prompt, etc...) from txt2img tab when making previews', value=False) @@ -1251,7 +1250,6 @@ def create_ui(wrap_gradio_gpu_call): steps, create_image_every, save_embedding_every, - write_csv_every, template_file, save_image_with_stored_embedding, preview_from_txt2img, @@ -1274,7 +1272,6 @@ def create_ui(wrap_gradio_gpu_call): steps, create_image_every, save_embedding_every, - write_csv_every, template_file, preview_from_txt2img, *txt2img_preview_params, -- cgit v1.2.1 From e21f01f64504bc651da6e85216474bbd35ee010d Mon Sep 17 00:00:00 2001 From: Rae Fu Date: Thu, 13 Oct 2022 23:00:38 -0600 Subject: add checkpoint cache option to UI for faster model switching switching time reduced from ~1500ms to ~280ms --- modules/sd_models.py | 54 +++++++++++++++++++++++++++++++--------------------- modules/shared.py | 1 + 2 files changed, 33 insertions(+), 22 deletions(-) (limited to 'modules') diff --git a/modules/sd_models.py b/modules/sd_models.py index 0a55b4c3..f3660d8d 100644 --- a/modules/sd_models.py +++ b/modules/sd_models.py @@ -1,4 +1,4 @@ -import glob +import collections import os.path import sys from collections import namedtuple @@ -15,6 +15,7 @@ model_path = os.path.abspath(os.path.join(models_path, model_dir)) CheckpointInfo = namedtuple("CheckpointInfo", ['filename', 'title', 'hash', 'model_name', 'config']) checkpoints_list = {} +checkpoints_loaded = collections.OrderedDict() try: # this silences the annoying "Some weights of the model checkpoint were not used when initializing..." message at start. @@ -132,38 +133,46 @@ def load_model_weights(model, checkpoint_info): checkpoint_file = checkpoint_info.filename sd_model_hash = checkpoint_info.hash - print(f"Loading weights [{sd_model_hash}] from {checkpoint_file}") + if checkpoint_info not in checkpoints_loaded: + print(f"Loading weights [{sd_model_hash}] from {checkpoint_file}") - pl_sd = torch.load(checkpoint_file, map_location="cpu") - if "global_step" in pl_sd: - print(f"Global Step: {pl_sd['global_step']}") + pl_sd = torch.load(checkpoint_file, map_location="cpu") + if "global_step" in pl_sd: + print(f"Global Step: {pl_sd['global_step']}") - sd = get_state_dict_from_checkpoint(pl_sd) + sd = get_state_dict_from_checkpoint(pl_sd) + model.load_state_dict(sd, strict=False) - model.load_state_dict(sd, strict=False) + if shared.cmd_opts.opt_channelslast: + model.to(memory_format=torch.channels_last) - if shared.cmd_opts.opt_channelslast: - model.to(memory_format=torch.channels_last) + if not shared.cmd_opts.no_half: + model.half() - if not shared.cmd_opts.no_half: - model.half() + devices.dtype = torch.float32 if shared.cmd_opts.no_half else torch.float16 + devices.dtype_vae = torch.float32 if shared.cmd_opts.no_half or shared.cmd_opts.no_half_vae else torch.float16 - devices.dtype = torch.float32 if shared.cmd_opts.no_half else torch.float16 - devices.dtype_vae = torch.float32 if shared.cmd_opts.no_half or shared.cmd_opts.no_half_vae else torch.float16 + vae_file = os.path.splitext(checkpoint_file)[0] + ".vae.pt" - vae_file = os.path.splitext(checkpoint_file)[0] + ".vae.pt" + if not os.path.exists(vae_file) and shared.cmd_opts.vae_path is not None: + vae_file = shared.cmd_opts.vae_path - if not os.path.exists(vae_file) and shared.cmd_opts.vae_path is not None: - vae_file = shared.cmd_opts.vae_path + if os.path.exists(vae_file): + print(f"Loading VAE weights from: {vae_file}") + vae_ckpt = torch.load(vae_file, map_location="cpu") + vae_dict = {k: v for k, v in vae_ckpt["state_dict"].items() if k[0:4] != "loss"} - if os.path.exists(vae_file): - print(f"Loading VAE weights from: {vae_file}") - vae_ckpt = torch.load(vae_file, map_location="cpu") - vae_dict = {k: v for k, v in vae_ckpt["state_dict"].items() if k[0:4] != "loss"} + model.first_stage_model.load_state_dict(vae_dict) - model.first_stage_model.load_state_dict(vae_dict) + model.first_stage_model.to(devices.dtype_vae) - model.first_stage_model.to(devices.dtype_vae) + checkpoints_loaded[checkpoint_info] = model.state_dict().copy() + while len(checkpoints_loaded) > shared.opts.sd_checkpoint_cache: + checkpoints_loaded.popitem(last=False) # LRU + else: + print(f"Loading weights [{sd_model_hash}] from cache") + checkpoints_loaded.move_to_end(checkpoint_info) + model.load_state_dict(checkpoints_loaded[checkpoint_info]) model.sd_model_hash = sd_model_hash model.sd_model_checkpoint = checkpoint_file @@ -202,6 +211,7 @@ def reload_model_weights(sd_model, info=None): return if sd_model.sd_checkpoint_info.config != checkpoint_info.config: + checkpoints_loaded.clear() shared.sd_model = load_model() return shared.sd_model diff --git a/modules/shared.py b/modules/shared.py index 5901e605..b2090da1 100644 --- a/modules/shared.py +++ b/modules/shared.py @@ -238,6 +238,7 @@ options_templates.update(options_section(('training', "Training"), { options_templates.update(options_section(('sd', "Stable Diffusion"), { "sd_model_checkpoint": OptionInfo(None, "Stable Diffusion checkpoint", gr.Dropdown, lambda: {"choices": modules.sd_models.checkpoint_tiles()}, refresh=sd_models.list_models), + "sd_checkpoint_cache": OptionInfo(0, "Checkpoints to cache in RAM", gr.Slider, {"minimum": 0, "maximum": 10, "step": 1}), "sd_hypernetwork": OptionInfo("None", "Hypernetwork", gr.Dropdown, lambda: {"choices": ["None"] + [x for x in hypernetworks.keys()]}, refresh=reload_hypernetworks), "sd_hypernetwork_strength": OptionInfo(1.0, "Hypernetwork strength", gr.Slider, {"minimum": 0.0, "maximum": 1.0, "step": 0.001}), "img2img_color_correction": OptionInfo(False, "Apply color correction to img2img results to match original colors."), -- cgit v1.2.1 From cd58e44051f658f2efb544203a92837f43786372 Mon Sep 17 00:00:00 2001 From: AUTOMATIC <16777216c@gmail.com> Date: Fri, 14 Oct 2022 23:17:28 +0300 Subject: disabling history - i knew it was slow as fuck but i didn't realize it would also show galleries on launch --- modules/ui.py | 5 +++-- 1 file changed, 3 insertions(+), 2 deletions(-) (limited to 'modules') diff --git a/modules/ui.py b/modules/ui.py index a08ffc9b..6d193955 100644 --- a/modules/ui.py +++ b/modules/ui.py @@ -1089,7 +1089,8 @@ def create_ui(wrap_gradio_gpu_call): "t2i":txt2img_paste_fields, "i2i":img2img_paste_fields } - images_history = img_his.create_history_tabs(gr, opts, wrap_gradio_call(modules.extras.run_pnginfo), images_history_switch_dict) + + #images_history = img_his.create_history_tabs(gr, opts, wrap_gradio_call(modules.extras.run_pnginfo), images_history_switch_dict) with gr.Blocks() as modelmerger_interface: with gr.Row().style(equal_height=False): @@ -1486,7 +1487,7 @@ Requested path was: {f} (img2img_interface, "img2img", "img2img"), (extras_interface, "Extras", "extras"), (pnginfo_interface, "PNG Info", "pnginfo"), - (images_history, "History", "images_history"), + #(images_history, "History", "images_history"), (modelmerger_interface, "Checkpoint Merger", "modelmerger"), (train_interface, "Train", "ti"), (settings_interface, "Settings", "settings"), -- cgit v1.2.1 From 368f4cc4c73509c1968cd9defe068d8bf4ff7c4f Mon Sep 17 00:00:00 2001 From: AUTOMATIC <16777216c@gmail.com> Date: Fri, 14 Oct 2022 23:19:05 +0300 Subject: set firstpass w/h to 0 by default and rever to old behavior when any are 0 --- modules/processing.py | 49 ++++++++++++++++++++++++++++++------------------- modules/ui.py | 4 ++-- 2 files changed, 32 insertions(+), 21 deletions(-) (limited to 'modules') diff --git a/modules/processing.py b/modules/processing.py index 100a259f..a75b9f84 100644 --- a/modules/processing.py +++ b/modules/processing.py @@ -501,17 +501,15 @@ def process_images(p: StableDiffusionProcessing) -> Processed: class StableDiffusionProcessingTxt2Img(StableDiffusionProcessing): sampler = None - firstphase_width = 0 - firstphase_height = 0 - firstphase_width_truncated = 0 - firstphase_height_truncated = 0 - def __init__(self, enable_hr=False, denoising_strength=0.75, firstphase_width=512, firstphase_height=512, **kwargs): + def __init__(self, enable_hr=False, denoising_strength=0.75, firstphase_width=0, firstphase_height=0, **kwargs): super().__init__(**kwargs) self.enable_hr = enable_hr self.denoising_strength = denoising_strength self.firstphase_width = firstphase_width self.firstphase_height = firstphase_height + self.truncate_x = 0 + self.truncate_y = 0 def init(self, all_prompts, all_seeds, all_subseeds): if self.enable_hr: @@ -520,6 +518,32 @@ class StableDiffusionProcessingTxt2Img(StableDiffusionProcessing): else: state.job_count = state.job_count * 2 + if self.firstphase_width == 0 or self.firstphase_height == 0: + desired_pixel_count = 512 * 512 + actual_pixel_count = self.width * self.height + scale = math.sqrt(desired_pixel_count / actual_pixel_count) + self.firstphase_width = math.ceil(scale * self.width / 64) * 64 + self.firstphase_height = math.ceil(scale * self.height / 64) * 64 + firstphase_width_truncated = int(scale * self.width) + firstphase_height_truncated = int(scale * self.height) + + else: + self.extra_generation_params["First pass size"] = f"{self.firstphase_width}x{self.firstphase_height}" + + width_ratio = self.width / self.firstphase_width + height_ratio = self.height / self.firstphase_height + + if width_ratio > height_ratio: + firstphase_width_truncated = self.firstphase_width + firstphase_height_truncated = self.firstphase_width * self.height / self.width + else: + firstphase_width_truncated = self.firstphase_height * self.width / self.height + firstphase_height_truncated = self.firstphase_height + + self.truncate_x = int(self.firstphase_width - firstphase_width_truncated) // opt_f + self.truncate_y = int(self.firstphase_height - firstphase_height_truncated) // opt_f + + def sample(self, conditioning, unconditional_conditioning, seeds, subseeds, subseed_strength): self.sampler = sd_samplers.create_sampler_with_index(sd_samplers.samplers, self.sampler_index, self.sd_model) @@ -528,23 +552,10 @@ class StableDiffusionProcessingTxt2Img(StableDiffusionProcessing): samples = self.sampler.sample(self, x, conditioning, unconditional_conditioning) return samples - self.extra_generation_params["First pass size"] = f"{self.firstphase_width}x{self.firstphase_height}" - x = create_random_tensors([opt_C, self.firstphase_height // opt_f, self.firstphase_width // opt_f], seeds=seeds, subseeds=subseeds, subseed_strength=self.subseed_strength, seed_resize_from_h=self.seed_resize_from_h, seed_resize_from_w=self.seed_resize_from_w, p=self) samples = self.sampler.sample(self, x, conditioning, unconditional_conditioning) - truncate_x = 0 - truncate_y = 0 - width_ratio = self.width/self.firstphase_width - height_ratio = self.height/self.firstphase_height - - if width_ratio > height_ratio: - truncate_y = int((self.width - self.firstphase_width) / width_ratio / height_ratio / opt_f) - - elif width_ratio < height_ratio: - truncate_x = int((self.height - self.firstphase_height) / width_ratio / height_ratio / opt_f) - - samples = samples[:, :, truncate_y//2:samples.shape[2]-truncate_y//2, truncate_x//2:samples.shape[3]-truncate_x//2] + samples = samples[:, :, self.truncate_y//2:samples.shape[2]-self.truncate_y//2, self.truncate_x//2:samples.shape[3]-self.truncate_x//2] decoded_samples = decode_first_stage(self.sd_model, samples) diff --git a/modules/ui.py b/modules/ui.py index 6d193955..a1d18be9 100644 --- a/modules/ui.py +++ b/modules/ui.py @@ -567,8 +567,8 @@ def create_ui(wrap_gradio_gpu_call): enable_hr = gr.Checkbox(label='Highres. fix', value=False) with gr.Row(visible=False) as hr_options: - firstphase_width = gr.Slider(minimum=64, maximum=1024, step=64, label="First pass width", value=512) - firstphase_height = gr.Slider(minimum=64, maximum=1024, step=64, label="First pass height", value=512) + firstphase_width = gr.Slider(minimum=0, maximum=1024, step=64, label="First pass width", value=0) + firstphase_height = gr.Slider(minimum=0, maximum=1024, step=64, label="First pass height", value=0) denoising_strength = gr.Slider(minimum=0.0, maximum=1.0, step=0.01, label='Denoising strength', value=0.7) with gr.Row(equal_height=True): -- cgit v1.2.1 From 4d19f3b7d461fe0f63e7ccff936909b0ce0c6126 Mon Sep 17 00:00:00 2001 From: Melan Date: Fri, 14 Oct 2022 22:45:26 +0200 Subject: Raise an assertion error if no training images have been found. --- modules/textual_inversion/dataset.py | 3 ++- 1 file changed, 2 insertions(+), 1 deletion(-) (limited to 'modules') diff --git a/modules/textual_inversion/dataset.py b/modules/textual_inversion/dataset.py index 67e90afe..12e2f43b 100644 --- a/modules/textual_inversion/dataset.py +++ b/modules/textual_inversion/dataset.py @@ -81,7 +81,8 @@ class PersonalizedBase(Dataset): entry.cond = cond_model([entry.cond_text]).to(devices.cpu) self.dataset.append(entry) - + + assert len(self.dataset) > 1, "No images have been found in the dataset." self.length = len(self.dataset) * repeats self.initial_indexes = np.arange(self.length) % len(self.dataset) -- cgit v1.2.1 From 4dc426509918e90bf4557ecfd1f84031362360c0 Mon Sep 17 00:00:00 2001 From: AUTOMATIC <16777216c@gmail.com> Date: Sat, 15 Oct 2022 00:21:48 +0300 Subject: rename firstpass w/h to discard old user settings --- modules/ui.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) (limited to 'modules') diff --git a/modules/ui.py b/modules/ui.py index a1d18be9..c5d295ea 100644 --- a/modules/ui.py +++ b/modules/ui.py @@ -567,8 +567,8 @@ def create_ui(wrap_gradio_gpu_call): enable_hr = gr.Checkbox(label='Highres. fix', value=False) with gr.Row(visible=False) as hr_options: - firstphase_width = gr.Slider(minimum=0, maximum=1024, step=64, label="First pass width", value=0) - firstphase_height = gr.Slider(minimum=0, maximum=1024, step=64, label="First pass height", value=0) + firstphase_width = gr.Slider(minimum=0, maximum=1024, step=64, label="Firstpass width", value=0) + firstphase_height = gr.Slider(minimum=0, maximum=1024, step=64, label="Firstpass height", value=0) denoising_strength = gr.Slider(minimum=0.0, maximum=1.0, step=0.01, label='Denoising strength', value=0.7) with gr.Row(equal_height=True): -- cgit v1.2.1 From 4bbe5d62e042e78cfe1dc83492c2398a39a2455c Mon Sep 17 00:00:00 2001 From: AUTOMATIC <16777216c@gmail.com> Date: Sat, 15 Oct 2022 00:25:09 +0300 Subject: reformat lines in images_history.py --- modules/images_history.py | 182 +++++++++++++++++++++++++--------------------- 1 file changed, 98 insertions(+), 84 deletions(-) (limited to 'modules') diff --git a/modules/images_history.py b/modules/images_history.py index 723f5301..f5ef44fe 100644 --- a/modules/images_history.py +++ b/modules/images_history.py @@ -1,5 +1,7 @@ import os import shutil + + def traverse_all_files(output_dir, image_list, curr_dir=None): curr_path = output_dir if curr_dir is None else os.path.join(output_dir, curr_dir) try: @@ -16,10 +18,10 @@ def traverse_all_files(output_dir, image_list, curr_dir=None): elif os.path.isfile(file_path) and file[-10:].rfind(".") > 0: image_list.append(file) else: - image_list = traverse_all_files(output_dir, image_list, file) + image_list = traverse_all_files(output_dir, image_list, file) return image_list - + def get_recent_images(dir_name, page_index, step, image_index, tabname): page_index = int(page_index) f_list = os.listdir(dir_name) @@ -27,36 +29,48 @@ def get_recent_images(dir_name, page_index, step, image_index, tabname): image_list = traverse_all_files(dir_name, image_list) image_list = sorted(image_list, key=lambda file: -os.path.getctime(os.path.join(dir_name, file))) num = 48 if tabname != "extras" else 12 - max_page_index = len(image_list) // num + 1 + max_page_index = len(image_list) // num + 1 page_index = max_page_index if page_index == -1 else page_index + step - page_index = 1 if page_index < 1 else page_index + page_index = 1 if page_index < 1 else page_index page_index = max_page_index if page_index > max_page_index else page_index idx_frm = (page_index - 1) * num image_list = image_list[idx_frm:idx_frm + num] image_index = int(image_index) - if image_index < 0 or image_index > len(image_list) - 1: - current_file = None + if image_index < 0 or image_index > len(image_list) - 1: + current_file = None hidden = None else: - current_file = image_list[int(image_index)] + current_file = image_list[int(image_index)] hidden = os.path.join(dir_name, current_file) return [os.path.join(dir_name, file) for file in image_list], page_index, image_list, current_file, hidden, "" + def first_page_click(dir_name, page_index, image_index, tabname): return get_recent_images(dir_name, 1, 0, image_index, tabname) + + def end_page_click(dir_name, page_index, image_index, tabname): return get_recent_images(dir_name, -1, 0, image_index, tabname) + + def prev_page_click(dir_name, page_index, image_index, tabname): return get_recent_images(dir_name, page_index, -1, image_index, tabname) -def next_page_click(dir_name, page_index, image_index, tabname): + + +def next_page_click(dir_name, page_index, image_index, tabname): return get_recent_images(dir_name, page_index, 1, image_index, tabname) -def page_index_change(dir_name, page_index, image_index, tabname): + + +def page_index_change(dir_name, page_index, image_index, tabname): return get_recent_images(dir_name, page_index, 0, image_index, tabname) + def show_image_info(num, image_path, filenames): - #print(f"select image {num}") + # print(f"select image {num}") file = filenames[int(num)] return file, num, os.path.join(image_path, file) + + def delete_image(delete_num, tabname, dir_name, name, page_index, filenames, image_index): if name == "": return filenames, delete_num @@ -66,14 +80,14 @@ def delete_image(delete_num, tabname, dir_name, name, page_index, filenames, ima i = 0 new_file_list = [] for name in filenames: - if i >= index and i < index + delete_num: + if i >= index and i < index + delete_num: path = os.path.join(dir_name, name) - if os.path.exists(path): + if os.path.exists(path): print(f"Delete file {path}") os.remove(path) - txt_file = os.path.splitext(path)[0] + ".txt" + txt_file = os.path.splitext(path)[0] + ".txt" if os.path.exists(txt_file): - os.remove(txt_file) + os.remove(txt_file) else: print(f"Not exists file {path}") else: @@ -81,81 +95,81 @@ def delete_image(delete_num, tabname, dir_name, name, page_index, filenames, ima i += 1 return new_file_list, 1 + def show_images_history(gr, opts, tabname, run_pnginfo, switch_dict): - if tabname == "txt2img": - dir_name = opts.outdir_txt2img_samples - elif tabname == "img2img": - dir_name = opts.outdir_img2img_samples - elif tabname == "extras": - dir_name = opts.outdir_extras_samples - d = dir_name.split("/") - dir_name = d[0] - for p in d[1:]: - dir_name = os.path.join(dir_name, p) - with gr.Row(): - renew_page = gr.Button('Renew Page', elem_id=tabname + "_images_history_renew_page") - first_page = gr.Button('First Page') - prev_page = gr.Button('Prev Page') - page_index = gr.Number(value=1, label="Page Index") - next_page = gr.Button('Next Page') - end_page = gr.Button('End Page') - with gr.Row(elem_id=tabname + "_images_history"): - with gr.Row(): - with gr.Column(scale=2): - history_gallery = gr.Gallery(show_label=False, elem_id=tabname + "_images_history_gallery").style(grid=6) - with gr.Row(): - delete_num = gr.Number(value=1, interactive=True, label="number of images to delete consecutively next") - delete = gr.Button('Delete', elem_id=tabname + "_images_history_del_button") - with gr.Column(): - with gr.Row(): - pnginfo_send_to_txt2img = gr.Button('Send to txt2img') - pnginfo_send_to_img2img = gr.Button('Send to img2img') - with gr.Row(): - with gr.Column(): - img_file_info = gr.Textbox(label="Generate Info", interactive=False) - img_file_name = gr.Textbox(label="File Name", interactive=False) - with gr.Row(): - # hiden items - - img_path = gr.Textbox(dir_name.rstrip("/") , visible=False) - tabname_box = gr.Textbox(tabname, visible=False) - image_index = gr.Textbox(value=-1, visible=False) - set_index = gr.Button('set_index', elem_id=tabname + "_images_history_set_index", visible=False) - filenames = gr.State() - hidden = gr.Image(type="pil", visible=False) - info1 = gr.Textbox(visible=False) - info2 = gr.Textbox(visible=False) - - - # turn pages - gallery_inputs = [img_path, page_index, image_index, tabname_box] - gallery_outputs = [history_gallery, page_index, filenames, img_file_name, hidden, img_file_name] - - first_page.click(first_page_click, _js="images_history_turnpage", inputs=gallery_inputs, outputs=gallery_outputs) - next_page.click(next_page_click, _js="images_history_turnpage", inputs=gallery_inputs, outputs=gallery_outputs) - prev_page.click(prev_page_click, _js="images_history_turnpage", inputs=gallery_inputs, outputs=gallery_outputs) - end_page.click(end_page_click, _js="images_history_turnpage", inputs=gallery_inputs, outputs=gallery_outputs) - page_index.submit(page_index_change, _js="images_history_turnpage", inputs=gallery_inputs, outputs=gallery_outputs) - renew_page.click(page_index_change, _js="images_history_turnpage", inputs=gallery_inputs, outputs=gallery_outputs) - #page_index.change(page_index_change, inputs=[tabname_box, img_path, page_index], outputs=[history_gallery, page_index]) - - #other funcitons - set_index.click(show_image_info, _js="images_history_get_current_img", inputs=[tabname_box, img_path, filenames], outputs=[img_file_name, image_index, hidden]) - img_file_name.change(fn=None, _js="images_history_enable_del_buttons", inputs=None, outputs=None) - delete.click(delete_image,_js="images_history_delete", inputs=[delete_num, tabname_box, img_path, img_file_name, page_index, filenames, image_index], outputs=[filenames, delete_num]) - hidden.change(fn=run_pnginfo, inputs=[hidden], outputs=[info1, img_file_info, info2]) - - #pnginfo.click(fn=run_pnginfo, inputs=[hidden], outputs=[info1, img_file_info, info2]) - switch_dict["fn"](pnginfo_send_to_txt2img, switch_dict["t2i"], img_file_info, 'switch_to_txt2img') - switch_dict["fn"](pnginfo_send_to_img2img, switch_dict["i2i"], img_file_info, 'switch_to_img2img_img2img') - - + if tabname == "txt2img": + dir_name = opts.outdir_txt2img_samples + elif tabname == "img2img": + dir_name = opts.outdir_img2img_samples + elif tabname == "extras": + dir_name = opts.outdir_extras_samples + d = dir_name.split("/") + dir_name = d[0] + for p in d[1:]: + dir_name = os.path.join(dir_name, p) + with gr.Row(): + renew_page = gr.Button('Renew Page', elem_id=tabname + "_images_history_renew_page") + first_page = gr.Button('First Page') + prev_page = gr.Button('Prev Page') + page_index = gr.Number(value=1, label="Page Index") + next_page = gr.Button('Next Page') + end_page = gr.Button('End Page') + with gr.Row(elem_id=tabname + "_images_history"): + with gr.Row(): + with gr.Column(scale=2): + history_gallery = gr.Gallery(show_label=False, elem_id=tabname + "_images_history_gallery").style(grid=6) + with gr.Row(): + delete_num = gr.Number(value=1, interactive=True, label="number of images to delete consecutively next") + delete = gr.Button('Delete', elem_id=tabname + "_images_history_del_button") + with gr.Column(): + with gr.Row(): + pnginfo_send_to_txt2img = gr.Button('Send to txt2img') + pnginfo_send_to_img2img = gr.Button('Send to img2img') + with gr.Row(): + with gr.Column(): + img_file_info = gr.Textbox(label="Generate Info", interactive=False) + img_file_name = gr.Textbox(label="File Name", interactive=False) + with gr.Row(): + # hiden items + + img_path = gr.Textbox(dir_name.rstrip("/"), visible=False) + tabname_box = gr.Textbox(tabname, visible=False) + image_index = gr.Textbox(value=-1, visible=False) + set_index = gr.Button('set_index', elem_id=tabname + "_images_history_set_index", visible=False) + filenames = gr.State() + hidden = gr.Image(type="pil", visible=False) + info1 = gr.Textbox(visible=False) + info2 = gr.Textbox(visible=False) + + # turn pages + gallery_inputs = [img_path, page_index, image_index, tabname_box] + gallery_outputs = [history_gallery, page_index, filenames, img_file_name, hidden, img_file_name] + + first_page.click(first_page_click, _js="images_history_turnpage", inputs=gallery_inputs, outputs=gallery_outputs) + next_page.click(next_page_click, _js="images_history_turnpage", inputs=gallery_inputs, outputs=gallery_outputs) + prev_page.click(prev_page_click, _js="images_history_turnpage", inputs=gallery_inputs, outputs=gallery_outputs) + end_page.click(end_page_click, _js="images_history_turnpage", inputs=gallery_inputs, outputs=gallery_outputs) + page_index.submit(page_index_change, _js="images_history_turnpage", inputs=gallery_inputs, outputs=gallery_outputs) + renew_page.click(page_index_change, _js="images_history_turnpage", inputs=gallery_inputs, outputs=gallery_outputs) + # page_index.change(page_index_change, inputs=[tabname_box, img_path, page_index], outputs=[history_gallery, page_index]) + + # other funcitons + set_index.click(show_image_info, _js="images_history_get_current_img", inputs=[tabname_box, img_path, filenames], outputs=[img_file_name, image_index, hidden]) + img_file_name.change(fn=None, _js="images_history_enable_del_buttons", inputs=None, outputs=None) + delete.click(delete_image, _js="images_history_delete", inputs=[delete_num, tabname_box, img_path, img_file_name, page_index, filenames, image_index], outputs=[filenames, delete_num]) + hidden.change(fn=run_pnginfo, inputs=[hidden], outputs=[info1, img_file_info, info2]) + + # pnginfo.click(fn=run_pnginfo, inputs=[hidden], outputs=[info1, img_file_info, info2]) + switch_dict["fn"](pnginfo_send_to_txt2img, switch_dict["t2i"], img_file_info, 'switch_to_txt2img') + switch_dict["fn"](pnginfo_send_to_img2img, switch_dict["i2i"], img_file_info, 'switch_to_img2img_img2img') + + def create_history_tabs(gr, opts, run_pnginfo, switch_dict): with gr.Blocks(analytics_enabled=False) as images_history: with gr.Tabs() as tabs: with gr.Tab("txt2img history"): - with gr.Blocks(analytics_enabled=False) as images_history_txt2img: - show_images_history(gr, opts, "txt2img", run_pnginfo, switch_dict) + with gr.Blocks(analytics_enabled=False) as images_history_txt2img: + show_images_history(gr, opts, "txt2img", run_pnginfo, switch_dict) with gr.Tab("img2img history"): with gr.Blocks(analytics_enabled=False) as images_history_img2img: show_images_history(gr, opts, "img2img", run_pnginfo, switch_dict) -- cgit v1.2.1 From acedbe67d2b8a3af99ca3b9a2f809e7a2db285d1 Mon Sep 17 00:00:00 2001 From: AUTOMATIC <16777216c@gmail.com> Date: Sat, 15 Oct 2022 00:43:15 +0300 Subject: bring history tab back, make it behave; it's still slow but won't fuck anything up until you use it --- modules/ui.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) (limited to 'modules') diff --git a/modules/ui.py b/modules/ui.py index c5d295ea..1bc919c7 100644 --- a/modules/ui.py +++ b/modules/ui.py @@ -1090,7 +1090,7 @@ def create_ui(wrap_gradio_gpu_call): "i2i":img2img_paste_fields } - #images_history = img_his.create_history_tabs(gr, opts, wrap_gradio_call(modules.extras.run_pnginfo), images_history_switch_dict) + images_history = img_his.create_history_tabs(gr, opts, wrap_gradio_call(modules.extras.run_pnginfo), images_history_switch_dict) with gr.Blocks() as modelmerger_interface: with gr.Row().style(equal_height=False): @@ -1487,7 +1487,7 @@ Requested path was: {f} (img2img_interface, "img2img", "img2img"), (extras_interface, "Extras", "extras"), (pnginfo_interface, "PNG Info", "pnginfo"), - #(images_history, "History", "images_history"), + (images_history, "History", "images_history"), (modelmerger_interface, "Checkpoint Merger", "modelmerger"), (train_interface, "Train", "ti"), (settings_interface, "Settings", "settings"), -- cgit v1.2.1 From c7a86f7fe9c0b8967a87e8d709f507d2f44400d8 Mon Sep 17 00:00:00 2001 From: AUTOMATIC <16777216c@gmail.com> Date: Sat, 15 Oct 2022 09:24:59 +0300 Subject: add option to use batch size for training --- modules/hypernetworks/hypernetwork.py | 33 +++++++++++++++++++------- modules/textual_inversion/dataset.py | 31 ++++++++++++++---------- modules/textual_inversion/textual_inversion.py | 17 +++++++------ modules/ui.py | 3 +++ 4 files changed, 54 insertions(+), 30 deletions(-) (limited to 'modules') diff --git a/modules/hypernetworks/hypernetwork.py b/modules/hypernetworks/hypernetwork.py index 59c7ac6e..a2b3bc0a 100644 --- a/modules/hypernetworks/hypernetwork.py +++ b/modules/hypernetworks/hypernetwork.py @@ -182,7 +182,21 @@ def attention_CrossAttention_forward(self, x, context=None, mask=None): return self.to_out(out) -def train_hypernetwork(hypernetwork_name, learn_rate, data_root, log_directory, steps, create_image_every, save_hypernetwork_every, template_file, preview_from_txt2img, preview_prompt, preview_negative_prompt, preview_steps, preview_sampler_index, preview_cfg_scale, preview_seed, preview_width, preview_height): +def stack_conds(conds): + if len(conds) == 1: + return torch.stack(conds) + + # same as in reconstruct_multicond_batch + token_count = max([x.shape[0] for x in conds]) + for i in range(len(conds)): + if conds[i].shape[0] != token_count: + last_vector = conds[i][-1:] + last_vector_repeated = last_vector.repeat([token_count - conds[i].shape[0], 1]) + conds[i] = torch.vstack([conds[i], last_vector_repeated]) + + return torch.stack(conds) + +def train_hypernetwork(hypernetwork_name, learn_rate, batch_size, data_root, log_directory, steps, create_image_every, save_hypernetwork_every, template_file, preview_from_txt2img, preview_prompt, preview_negative_prompt, preview_steps, preview_sampler_index, preview_cfg_scale, preview_seed, preview_width, preview_height): assert hypernetwork_name, 'hypernetwork not selected' path = shared.hypernetworks.get(hypernetwork_name, None) @@ -211,7 +225,7 @@ def train_hypernetwork(hypernetwork_name, learn_rate, data_root, log_directory, shared.state.textinfo = f"Preparing dataset from {html.escape(data_root)}..." with torch.autocast("cuda"): - ds = modules.textual_inversion.dataset.PersonalizedBase(data_root=data_root, width=512, height=512, repeats=shared.opts.training_image_repeats_per_epoch, placeholder_token=hypernetwork_name, model=shared.sd_model, device=devices.device, template_file=template_file, include_cond=True) + ds = modules.textual_inversion.dataset.PersonalizedBase(data_root=data_root, width=512, height=512, repeats=shared.opts.training_image_repeats_per_epoch, placeholder_token=hypernetwork_name, model=shared.sd_model, device=devices.device, template_file=template_file, include_cond=True, batch_size=batch_size) if unload: shared.sd_model.cond_stage_model.to(devices.cpu) @@ -235,7 +249,7 @@ def train_hypernetwork(hypernetwork_name, learn_rate, data_root, log_directory, optimizer = torch.optim.AdamW(weights, lr=scheduler.learn_rate) pbar = tqdm.tqdm(enumerate(ds), total=steps - ititial_step) - for i, entry in pbar: + for i, entries in pbar: hypernetwork.step = i + ititial_step scheduler.apply(optimizer, hypernetwork.step) @@ -246,11 +260,12 @@ def train_hypernetwork(hypernetwork_name, learn_rate, data_root, log_directory, break with torch.autocast("cuda"): - cond = entry.cond.to(devices.device) - x = entry.latent.to(devices.device) - loss = shared.sd_model(x.unsqueeze(0), cond)[0] + c = stack_conds([entry.cond for entry in entries]).to(devices.device) +# c = torch.vstack([entry.cond for entry in entries]).to(devices.device) + x = torch.stack([entry.latent for entry in entries]).to(devices.device) + loss = shared.sd_model(x, c)[0] del x - del cond + del c losses[hypernetwork.step % losses.shape[0]] = loss.item() @@ -292,7 +307,7 @@ def train_hypernetwork(hypernetwork_name, learn_rate, data_root, log_directory, p.width = preview_width p.height = preview_height else: - p.prompt = entry.cond_text + p.prompt = entries[0].cond_text p.steps = 20 preview_text = p.prompt @@ -315,7 +330,7 @@ def train_hypernetwork(hypernetwork_name, learn_rate, data_root, log_directory,

Loss: {losses.mean():.7f}
Step: {hypernetwork.step}
-Last prompt: {html.escape(entry.cond_text)}
+Last prompt: {html.escape(entries[0].cond_text)}
Last saved embedding: {html.escape(last_saved_file)}
Last saved image: {html.escape(last_saved_image)}

diff --git a/modules/textual_inversion/dataset.py b/modules/textual_inversion/dataset.py index 67e90afe..bd99c0cb 100644 --- a/modules/textual_inversion/dataset.py +++ b/modules/textual_inversion/dataset.py @@ -24,11 +24,12 @@ class DatasetEntry: class PersonalizedBase(Dataset): - def __init__(self, data_root, width, height, repeats, flip_p=0.5, placeholder_token="*", model=None, device=None, template_file=None, include_cond=False): - re_word = re.compile(shared.opts.dataset_filename_word_regex) if len(shared.opts.dataset_filename_word_regex)>0 else None + def __init__(self, data_root, width, height, repeats, flip_p=0.5, placeholder_token="*", model=None, device=None, template_file=None, include_cond=False, batch_size=1): + re_word = re.compile(shared.opts.dataset_filename_word_regex) if len(shared.opts.dataset_filename_word_regex) > 0 else None self.placeholder_token = placeholder_token + self.batch_size = batch_size self.width = width self.height = height self.flip = transforms.RandomHorizontalFlip(p=flip_p) @@ -78,13 +79,13 @@ class PersonalizedBase(Dataset): if include_cond: entry.cond_text = self.create_text(filename_text) - entry.cond = cond_model([entry.cond_text]).to(devices.cpu) + entry.cond = cond_model([entry.cond_text]).to(devices.cpu).squeeze(0) self.dataset.append(entry) - self.length = len(self.dataset) * repeats + self.length = len(self.dataset) * repeats // batch_size - self.initial_indexes = np.arange(self.length) % len(self.dataset) + self.initial_indexes = np.arange(len(self.dataset)) self.indexes = None self.shuffle() @@ -101,13 +102,19 @@ class PersonalizedBase(Dataset): return self.length def __getitem__(self, i): - if i % len(self.dataset) == 0: - self.shuffle() + res = [] - index = self.indexes[i % len(self.indexes)] - entry = self.dataset[index] + for j in range(self.batch_size): + position = i * self.batch_size + j + if position % len(self.indexes) == 0: + self.shuffle() - if entry.cond is None: - entry.cond_text = self.create_text(entry.filename_text) + index = self.indexes[position % len(self.indexes)] + entry = self.dataset[index] - return entry + if entry.cond is None: + entry.cond_text = self.create_text(entry.filename_text) + + res.append(entry) + + return res diff --git a/modules/textual_inversion/textual_inversion.py b/modules/textual_inversion/textual_inversion.py index da0d77a0..e754747e 100644 --- a/modules/textual_inversion/textual_inversion.py +++ b/modules/textual_inversion/textual_inversion.py @@ -199,7 +199,7 @@ def write_loss(log_directory, filename, step, epoch_len, values): }) -def train_embedding(embedding_name, learn_rate, data_root, log_directory, training_width, training_height, steps, create_image_every, save_embedding_every, template_file, save_image_with_stored_embedding, preview_from_txt2img, preview_prompt, preview_negative_prompt, preview_steps, preview_sampler_index, preview_cfg_scale, preview_seed, preview_width, preview_height): +def train_embedding(embedding_name, learn_rate, batch_size, data_root, log_directory, training_width, training_height, steps, create_image_every, save_embedding_every, template_file, save_image_with_stored_embedding, preview_from_txt2img, preview_prompt, preview_negative_prompt, preview_steps, preview_sampler_index, preview_cfg_scale, preview_seed, preview_width, preview_height): assert embedding_name, 'embedding not selected' shared.state.textinfo = "Initializing textual inversion training..." @@ -231,7 +231,7 @@ def train_embedding(embedding_name, learn_rate, data_root, log_directory, traini shared.state.textinfo = f"Preparing dataset from {html.escape(data_root)}..." with torch.autocast("cuda"): - ds = modules.textual_inversion.dataset.PersonalizedBase(data_root=data_root, width=training_width, height=training_height, repeats=shared.opts.training_image_repeats_per_epoch, placeholder_token=embedding_name, model=shared.sd_model, device=devices.device, template_file=template_file) + ds = modules.textual_inversion.dataset.PersonalizedBase(data_root=data_root, width=training_width, height=training_height, repeats=shared.opts.training_image_repeats_per_epoch, placeholder_token=embedding_name, model=shared.sd_model, device=devices.device, template_file=template_file, batch_size=batch_size) hijack = sd_hijack.model_hijack @@ -251,7 +251,7 @@ def train_embedding(embedding_name, learn_rate, data_root, log_directory, traini optimizer = torch.optim.AdamW([embedding.vec], lr=scheduler.learn_rate) pbar = tqdm.tqdm(enumerate(ds), total=steps-ititial_step) - for i, entry in pbar: + for i, entries in pbar: embedding.step = i + ititial_step scheduler.apply(optimizer, embedding.step) @@ -262,10 +262,9 @@ def train_embedding(embedding_name, learn_rate, data_root, log_directory, traini break with torch.autocast("cuda"): - c = cond_model([entry.cond_text]) - - x = entry.latent.to(devices.device) - loss = shared.sd_model(x.unsqueeze(0), c)[0] + c = cond_model([entry.cond_text for entry in entries]) + x = torch.stack([entry.latent for entry in entries]).to(devices.device) + loss = shared.sd_model(x, c)[0] del x losses[embedding.step % losses.shape[0]] = loss.item() @@ -307,7 +306,7 @@ def train_embedding(embedding_name, learn_rate, data_root, log_directory, traini p.width = preview_width p.height = preview_height else: - p.prompt = entry.cond_text + p.prompt = entries[0].cond_text p.steps = 20 p.width = training_width p.height = training_height @@ -348,7 +347,7 @@ def train_embedding(embedding_name, learn_rate, data_root, log_directory, traini

Loss: {losses.mean():.7f}
Step: {embedding.step}
-Last prompt: {html.escape(entry.cond_text)}
+Last prompt: {html.escape(entries[0].cond_text)}
Last saved embedding: {html.escape(last_saved_file)}
Last saved image: {html.escape(last_saved_image)}

diff --git a/modules/ui.py b/modules/ui.py index 1bc919c7..45550ea8 100644 --- a/modules/ui.py +++ b/modules/ui.py @@ -1166,6 +1166,7 @@ def create_ui(wrap_gradio_gpu_call): train_embedding_name = gr.Dropdown(label='Embedding', choices=sorted(sd_hijack.model_hijack.embedding_db.word_embeddings.keys())) train_hypernetwork_name = gr.Dropdown(label='Hypernetwork', choices=[x for x in shared.hypernetworks.keys()]) learn_rate = gr.Textbox(label='Learning rate', placeholder="Learning rate", value="0.005") + batch_size = gr.Number(label='Batch size', value=1, precision=0) dataset_directory = gr.Textbox(label='Dataset directory', placeholder="Path to directory with input images") log_directory = gr.Textbox(label='Log directory', placeholder="Path to directory where to write outputs", value="textual_inversion") template_file = gr.Textbox(label='Prompt template file', value=os.path.join(script_path, "textual_inversion_templates", "style_filewords.txt")) @@ -1244,6 +1245,7 @@ def create_ui(wrap_gradio_gpu_call): inputs=[ train_embedding_name, learn_rate, + batch_size, dataset_directory, log_directory, training_width, @@ -1268,6 +1270,7 @@ def create_ui(wrap_gradio_gpu_call): inputs=[ train_hypernetwork_name, learn_rate, + batch_size, dataset_directory, log_directory, steps, -- cgit v1.2.1 From db27b987a97fc8b7894a9dd34bd7641536f9c424 Mon Sep 17 00:00:00 2001 From: aoirusann Date: Sat, 15 Oct 2022 11:48:13 +0800 Subject: Add hint for `ctrl/alt enter` And duplicate implementations are removed --- modules/ui.py | 10 ++++++++-- 1 file changed, 8 insertions(+), 2 deletions(-) (limited to 'modules') diff --git a/modules/ui.py b/modules/ui.py index 45550ea8..baf4c397 100644 --- a/modules/ui.py +++ b/modules/ui.py @@ -433,7 +433,10 @@ def create_toprow(is_img2img): with gr.Row(): with gr.Column(scale=80): with gr.Row(): - prompt = gr.Textbox(label="Prompt", elem_id=f"{id_part}_prompt", show_label=False, placeholder="Prompt", lines=2) + prompt = gr.Textbox(label="Prompt", elem_id=f"{id_part}_prompt", show_label=False, lines=2, + placeholder="Prompt (press Ctrl+Enter or Alt+Enter to generate)" + ) + with gr.Column(scale=1, elem_id="roll_col"): roll = gr.Button(value=art_symbol, elem_id="roll", visible=len(shared.artist_db.artists) > 0) paste = gr.Button(value=paste_symbol, elem_id="paste") @@ -446,7 +449,10 @@ def create_toprow(is_img2img): with gr.Row(): with gr.Column(scale=8): with gr.Row(): - negative_prompt = gr.Textbox(label="Negative prompt", elem_id="negative_prompt", show_label=False, placeholder="Negative prompt", lines=2) + negative_prompt = gr.Textbox(label="Negative prompt", elem_id=f"{id_part}_neg_prompt", show_label=False, lines=2, + placeholder="Negative prompt (press Ctrl+Enter or Alt+Enter to generate)" + ) + with gr.Column(scale=1, elem_id="roll_col"): sh = gr.Button(elem_id="sh", visible=True) -- cgit v1.2.1 From cd28465bf87d911965790513c37e6881e4231523 Mon Sep 17 00:00:00 2001 From: ddPn08 Date: Sat, 15 Oct 2022 10:56:02 +0900 Subject: do not force relative paths in image history --- modules/images_history.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) (limited to 'modules') diff --git a/modules/images_history.py b/modules/images_history.py index f5ef44fe..09c749fe 100644 --- a/modules/images_history.py +++ b/modules/images_history.py @@ -104,7 +104,7 @@ def show_images_history(gr, opts, tabname, run_pnginfo, switch_dict): elif tabname == "extras": dir_name = opts.outdir_extras_samples d = dir_name.split("/") - dir_name = d[0] + dir_name = "/" if dir_name.startswith("/") else d[0] for p in d[1:]: dir_name = os.path.join(dir_name, p) with gr.Row(): -- cgit v1.2.1 From 0da6c1809996f0f696d4047faf4b9c9939e26daa Mon Sep 17 00:00:00 2001 From: ddPn08 Date: Sat, 15 Oct 2022 11:22:05 +0900 Subject: use "outdir_samples" if specified --- modules/images_history.py | 4 +++- 1 file changed, 3 insertions(+), 1 deletion(-) (limited to 'modules') diff --git a/modules/images_history.py b/modules/images_history.py index 09c749fe..9260df8a 100644 --- a/modules/images_history.py +++ b/modules/images_history.py @@ -97,7 +97,9 @@ def delete_image(delete_num, tabname, dir_name, name, page_index, filenames, ima def show_images_history(gr, opts, tabname, run_pnginfo, switch_dict): - if tabname == "txt2img": + if opts.outdir_samples != "": + dir_name = opts.outdir_samples + elif tabname == "txt2img": dir_name = opts.outdir_txt2img_samples elif tabname == "img2img": dir_name = opts.outdir_img2img_samples -- cgit v1.2.1 From a13af34b902bebc5df9509228380206a01f1245b Mon Sep 17 00:00:00 2001 From: githublsx Date: Thu, 13 Oct 2022 20:05:07 -0700 Subject: Set to -1 when seed input is none --- modules/processing.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) (limited to 'modules') diff --git a/modules/processing.py b/modules/processing.py index a75b9f84..7e2a416d 100644 --- a/modules/processing.py +++ b/modules/processing.py @@ -140,7 +140,7 @@ class Processed: self.sampler_noise_scheduler_override = p.sampler_noise_scheduler_override self.prompt = self.prompt if type(self.prompt) != list else self.prompt[0] self.negative_prompt = self.negative_prompt if type(self.negative_prompt) != list else self.negative_prompt[0] - self.seed = int(self.seed if type(self.seed) != list else self.seed[0]) + self.seed = int(self.seed if type(self.seed) != list else self.seed[0]) if self.seed is not None else -1 self.subseed = int(self.subseed if type(self.subseed) != list else self.subseed[0]) if self.subseed is not None else -1 self.all_prompts = all_prompts or [self.prompt] -- cgit v1.2.1 From c24df4b486a48c60f48276f7760a9acb4a13e22d Mon Sep 17 00:00:00 2001 From: CookieHCl Date: Sat, 15 Oct 2022 03:26:36 +0900 Subject: Disable compiling deepbooru model This is only necessary when you have to train, and compiling model produces warning. --- modules/deepbooru.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) (limited to 'modules') diff --git a/modules/deepbooru.py b/modules/deepbooru.py index f34f3788..4ad334a1 100644 --- a/modules/deepbooru.py +++ b/modules/deepbooru.py @@ -102,7 +102,7 @@ def get_deepbooru_tags_model(): tags = dd.project.load_tags_from_project(model_path) model = dd.project.load_model_from_project( - model_path, compile_model=True + model_path, compile_model=False ) return model, tags -- cgit v1.2.1