From ca45ff1ae6fdd5c2dcd754fde95dd29f49bd414b Mon Sep 17 00:00:00 2001
From: ljleb <set>
Date: Mon, 24 Jul 2023 13:52:24 -0400
Subject: add postprocess_batch_list callback

---
 modules/processing.py | 24 +++++++++++++++++++++++-
 1 file changed, 23 insertions(+), 1 deletion(-)

(limited to 'modules/processing.py')

diff --git a/modules/processing.py b/modules/processing.py
index a74a5302..c16404f4 100644
--- a/modules/processing.py
+++ b/modules/processing.py
@@ -717,7 +717,25 @@ def process_images_inner(p: StableDiffusionProcessing) -> Processed:
         p.all_subseeds = [int(subseed) + x for x in range(len(p.all_prompts))]
 
     def infotext(iteration=0, position_in_batch=0, use_main_prompt=False):
-        return create_infotext(p, p.all_prompts, p.all_seeds, p.all_subseeds, comments, iteration, position_in_batch, use_main_prompt)
+        all_prompts = p.all_prompts[:]
+        all_seeds = p.all_seeds[:]
+        all_subseeds = p.all_subseeds[:]
+
+        # apply changes to generation data
+        all_prompts[n * p.batch_size:(n + 1) * p.batch_size] = p.prompts
+        all_seeds[n * p.batch_size:(n + 1) * p.batch_size] = p.seeds
+        all_subseeds[n * p.batch_size:(n + 1) * p.batch_size] = p.subseeds
+
+        # update p.all_negative_prompts in case extensions changed the size of the batch
+        # create_infotext below uses it
+        old_negative_prompts = p.all_negative_prompts[n * p.batch_size:(n + 1) * p.batch_size]
+        p.all_negative_prompts[n * p.batch_size:(n + 1) * p.batch_size] = p.negative_prompts
+
+        try:
+            return create_infotext(p, all_prompts, all_seeds, all_subseeds, comments, iteration, position_in_batch, use_main_prompt)
+        finally:
+            # restore p.all_negative_prompts in case extensions changed the size of the batch
+            p.all_negative_prompts[n * p.batch_size:n * p.batch_size + len(p.negative_prompts)] = old_negative_prompts
 
     if os.path.exists(cmd_opts.embeddings_dir) and not p.do_not_reload_embeddings:
         model_hijack.embedding_db.load_textual_inversion_embeddings()
@@ -806,6 +824,10 @@ def process_images_inner(p: StableDiffusionProcessing) -> Processed:
             if p.scripts is not None:
                 p.scripts.postprocess_batch(p, x_samples_ddim, batch_number=n)
 
+                postprocess_batch_list_args = scripts.PostprocessBatchListArgs(list(x_samples_ddim))
+                p.scripts.postprocess_batch_list(p, postprocess_batch_list_args, batch_number=n)
+                x_samples_ddim = postprocess_batch_list_args.images
+
             for i, x_sample in enumerate(x_samples_ddim):
                 p.batch_index = i
 
-- 
cgit v1.2.1


From 6b68b590321fcac2ad6d71c5aee1ac02687328d7 Mon Sep 17 00:00:00 2001
From: ljleb <set>
Date: Mon, 24 Jul 2023 15:38:52 -0400
Subject: use local vars

---
 modules/processing.py | 12 ++++++------
 1 file changed, 6 insertions(+), 6 deletions(-)

(limited to 'modules/processing.py')

diff --git a/modules/processing.py b/modules/processing.py
index c16404f4..7043477f 100644
--- a/modules/processing.py
+++ b/modules/processing.py
@@ -722,20 +722,20 @@ def process_images_inner(p: StableDiffusionProcessing) -> Processed:
         all_subseeds = p.all_subseeds[:]
 
         # apply changes to generation data
-        all_prompts[n * p.batch_size:(n + 1) * p.batch_size] = p.prompts
-        all_seeds[n * p.batch_size:(n + 1) * p.batch_size] = p.seeds
-        all_subseeds[n * p.batch_size:(n + 1) * p.batch_size] = p.subseeds
+        all_prompts[iteration * p.batch_size:(iteration + 1) * p.batch_size] = p.prompts
+        all_seeds[iteration * p.batch_size:(iteration + 1) * p.batch_size] = p.seeds
+        all_subseeds[iteration * p.batch_size:(iteration + 1) * p.batch_size] = p.subseeds
 
         # update p.all_negative_prompts in case extensions changed the size of the batch
         # create_infotext below uses it
-        old_negative_prompts = p.all_negative_prompts[n * p.batch_size:(n + 1) * p.batch_size]
-        p.all_negative_prompts[n * p.batch_size:(n + 1) * p.batch_size] = p.negative_prompts
+        old_negative_prompts = p.all_negative_prompts[iteration * p.batch_size:(iteration + 1) * p.batch_size]
+        p.all_negative_prompts[iteration * p.batch_size:(iteration + 1) * p.batch_size] = p.negative_prompts
 
         try:
             return create_infotext(p, all_prompts, all_seeds, all_subseeds, comments, iteration, position_in_batch, use_main_prompt)
         finally:
             # restore p.all_negative_prompts in case extensions changed the size of the batch
-            p.all_negative_prompts[n * p.batch_size:n * p.batch_size + len(p.negative_prompts)] = old_negative_prompts
+            p.all_negative_prompts[iteration * p.batch_size:iteration * p.batch_size + len(p.negative_prompts)] = old_negative_prompts
 
     if os.path.exists(cmd_opts.embeddings_dir) and not p.do_not_reload_embeddings:
         model_hijack.embedding_db.load_textual_inversion_embeddings()
-- 
cgit v1.2.1


From 5b06607476d1ef2c9d16fe8b21c786b2ca13b95c Mon Sep 17 00:00:00 2001
From: ljleb <set>
Date: Mon, 24 Jul 2023 15:43:06 -0400
Subject: simplify

---
 modules/processing.py | 8 +++++---
 1 file changed, 5 insertions(+), 3 deletions(-)

(limited to 'modules/processing.py')

diff --git a/modules/processing.py b/modules/processing.py
index 7043477f..6dc178e1 100644
--- a/modules/processing.py
+++ b/modules/processing.py
@@ -718,24 +718,26 @@ def process_images_inner(p: StableDiffusionProcessing) -> Processed:
 
     def infotext(iteration=0, position_in_batch=0, use_main_prompt=False):
         all_prompts = p.all_prompts[:]
+        all_negative_prompts = p.all_negative_prompts[:]
         all_seeds = p.all_seeds[:]
         all_subseeds = p.all_subseeds[:]
 
         # apply changes to generation data
         all_prompts[iteration * p.batch_size:(iteration + 1) * p.batch_size] = p.prompts
+        all_negative_prompts[iteration * p.batch_size:(iteration + 1) * p.batch_size] = p.negative_prompts
         all_seeds[iteration * p.batch_size:(iteration + 1) * p.batch_size] = p.seeds
         all_subseeds[iteration * p.batch_size:(iteration + 1) * p.batch_size] = p.subseeds
 
         # update p.all_negative_prompts in case extensions changed the size of the batch
         # create_infotext below uses it
-        old_negative_prompts = p.all_negative_prompts[iteration * p.batch_size:(iteration + 1) * p.batch_size]
-        p.all_negative_prompts[iteration * p.batch_size:(iteration + 1) * p.batch_size] = p.negative_prompts
+        old_negative_prompts = p.all_negative_prompts
+        p.all_negative_prompts = all_negative_prompts
 
         try:
             return create_infotext(p, all_prompts, all_seeds, all_subseeds, comments, iteration, position_in_batch, use_main_prompt)
         finally:
             # restore p.all_negative_prompts in case extensions changed the size of the batch
-            p.all_negative_prompts[iteration * p.batch_size:iteration * p.batch_size + len(p.negative_prompts)] = old_negative_prompts
+            p.all_negative_prompts = old_negative_prompts
 
     if os.path.exists(cmd_opts.embeddings_dir) and not p.do_not_reload_embeddings:
         model_hijack.embedding_db.load_textual_inversion_embeddings()
-- 
cgit v1.2.1


From ae36e0899fe912cd701fc4bae5c9d0ce9a5b3e41 Mon Sep 17 00:00:00 2001
From: AUTOMATIC1111 <16777216c@gmail.com>
Date: Wed, 26 Jul 2023 06:36:06 +0300
Subject: alternative solution for infotext issue

---
 modules/processing.py | 62 ++++++++++++++++++++++-----------------------------
 1 file changed, 27 insertions(+), 35 deletions(-)

(limited to 'modules/processing.py')

diff --git a/modules/processing.py b/modules/processing.py
index 6dc178e1..146e409a 100644
--- a/modules/processing.py
+++ b/modules/processing.py
@@ -600,8 +600,12 @@ def program_version():
     return res
 
 
-def create_infotext(p, all_prompts, all_seeds, all_subseeds, comments=None, iteration=0, position_in_batch=0, use_main_prompt=False):
-    index = position_in_batch + iteration * p.batch_size
+def create_infotext(p, all_prompts, all_seeds, all_subseeds, comments=None, iteration=0, position_in_batch=0, use_main_prompt=False, index=None, all_negative_prompts=None):
+    if index is None:
+        index = position_in_batch + iteration * p.batch_size
+
+    if all_negative_prompts is None:
+        all_negative_prompts = p.all_negative_prompts
 
     clip_skip = getattr(p, 'clip_skip', opts.CLIP_stop_at_last_layers)
     enable_hr = getattr(p, 'enable_hr', False)
@@ -642,7 +646,7 @@ def create_infotext(p, all_prompts, all_seeds, all_subseeds, comments=None, iter
     generation_params_text = ", ".join([k if k == v else f'{k}: {generation_parameters_copypaste.quote(v)}' for k, v in generation_params.items() if v is not None])
 
     prompt_text = p.prompt if use_main_prompt else all_prompts[index]
-    negative_prompt_text = f"\nNegative prompt: {p.all_negative_prompts[index]}" if p.all_negative_prompts[index] else ""
+    negative_prompt_text = f"\nNegative prompt: {all_negative_prompts[index]}" if all_negative_prompts[index] else ""
 
     return f"{prompt_text}{negative_prompt_text}\n{generation_params_text}".strip()
 
@@ -716,29 +720,6 @@ def process_images_inner(p: StableDiffusionProcessing) -> Processed:
     else:
         p.all_subseeds = [int(subseed) + x for x in range(len(p.all_prompts))]
 
-    def infotext(iteration=0, position_in_batch=0, use_main_prompt=False):
-        all_prompts = p.all_prompts[:]
-        all_negative_prompts = p.all_negative_prompts[:]
-        all_seeds = p.all_seeds[:]
-        all_subseeds = p.all_subseeds[:]
-
-        # apply changes to generation data
-        all_prompts[iteration * p.batch_size:(iteration + 1) * p.batch_size] = p.prompts
-        all_negative_prompts[iteration * p.batch_size:(iteration + 1) * p.batch_size] = p.negative_prompts
-        all_seeds[iteration * p.batch_size:(iteration + 1) * p.batch_size] = p.seeds
-        all_subseeds[iteration * p.batch_size:(iteration + 1) * p.batch_size] = p.subseeds
-
-        # update p.all_negative_prompts in case extensions changed the size of the batch
-        # create_infotext below uses it
-        old_negative_prompts = p.all_negative_prompts
-        p.all_negative_prompts = all_negative_prompts
-
-        try:
-            return create_infotext(p, all_prompts, all_seeds, all_subseeds, comments, iteration, position_in_batch, use_main_prompt)
-        finally:
-            # restore p.all_negative_prompts in case extensions changed the size of the batch
-            p.all_negative_prompts = old_negative_prompts
-
     if os.path.exists(cmd_opts.embeddings_dir) and not p.do_not_reload_embeddings:
         model_hijack.embedding_db.load_textual_inversion_embeddings()
 
@@ -826,9 +807,20 @@ def process_images_inner(p: StableDiffusionProcessing) -> Processed:
             if p.scripts is not None:
                 p.scripts.postprocess_batch(p, x_samples_ddim, batch_number=n)
 
-                postprocess_batch_list_args = scripts.PostprocessBatchListArgs(list(x_samples_ddim))
-                p.scripts.postprocess_batch_list(p, postprocess_batch_list_args, batch_number=n)
-                x_samples_ddim = postprocess_batch_list_args.images
+            batch_params = scripts.PostprocessBatchListArgs(
+                list(x_samples_ddim),
+                p.all_prompts[n * p.batch_size:(n + 1) * p.batch_size],
+                p.all_negative_prompts[n * p.batch_size:(n + 1) * p.batch_size],
+                p.seeds,
+                p.subseeds,
+            )
+
+            if p.scripts is not None:
+                p.scripts.postprocess_batch_list(p, batch_params, batch_number=n)
+                x_samples_ddim = batch_params.images
+
+            def infotext(index=0, use_main_prompt=False):
+                return create_infotext(p, batch_params.prompts, batch_params.seeds, batch_params.subseeds, use_main_prompt=use_main_prompt, index=index, all_negative_prompts=batch_params.negative_prompts)
 
             for i, x_sample in enumerate(x_samples_ddim):
                 p.batch_index = i
@@ -838,7 +830,7 @@ def process_images_inner(p: StableDiffusionProcessing) -> Processed:
 
                 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, "", p.seeds[i], p.prompts[i], opts.samples_format, info=infotext(n, i), p=p, suffix="-before-face-restoration")
+                        images.save_image(Image.fromarray(x_sample), p.outpath_samples, "", p.seeds[i], p.prompts[i], opts.samples_format, info=infotext(i), p=p, suffix="-before-face-restoration")
 
                     devices.torch_gc()
 
@@ -855,15 +847,15 @@ def process_images_inner(p: StableDiffusionProcessing) -> Processed:
                 if p.color_corrections is not None and i < len(p.color_corrections):
                     if opts.save and not p.do_not_save_samples and opts.save_images_before_color_correction:
                         image_without_cc = apply_overlay(image, p.paste_to, i, p.overlay_images)
-                        images.save_image(image_without_cc, p.outpath_samples, "", p.seeds[i], p.prompts[i], opts.samples_format, info=infotext(n, i), p=p, suffix="-before-color-correction")
+                        images.save_image(image_without_cc, p.outpath_samples, "", p.seeds[i], p.prompts[i], opts.samples_format, info=infotext(i), p=p, suffix="-before-color-correction")
                     image = apply_color_correction(p.color_corrections[i], image)
 
                 image = apply_overlay(image, p.paste_to, i, p.overlay_images)
 
                 if opts.samples_save and not p.do_not_save_samples:
-                    images.save_image(image, p.outpath_samples, "", p.seeds[i], p.prompts[i], opts.samples_format, info=infotext(n, i), p=p)
+                    images.save_image(image, p.outpath_samples, "", p.seeds[i], p.prompts[i], opts.samples_format, info=infotext(i), p=p)
 
-                text = infotext(n, i)
+                text = infotext(i)
                 infotexts.append(text)
                 if opts.enable_pnginfo:
                     image.info["parameters"] = text
@@ -874,10 +866,10 @@ def process_images_inner(p: StableDiffusionProcessing) -> Processed:
                     image_mask_composite = Image.composite(image.convert('RGBA').convert('RGBa'), Image.new('RGBa', image.size), images.resize_image(2, p.mask_for_overlay, image.width, image.height).convert('L')).convert('RGBA')
 
                     if opts.save_mask:
-                        images.save_image(image_mask, p.outpath_samples, "", p.seeds[i], p.prompts[i], opts.samples_format, info=infotext(n, i), p=p, suffix="-mask")
+                        images.save_image(image_mask, p.outpath_samples, "", p.seeds[i], p.prompts[i], opts.samples_format, info=infotext(i), p=p, suffix="-mask")
 
                     if opts.save_mask_composite:
-                        images.save_image(image_mask_composite, p.outpath_samples, "", p.seeds[i], p.prompts[i], opts.samples_format, info=infotext(n, i), p=p, suffix="-mask-composite")
+                        images.save_image(image_mask_composite, p.outpath_samples, "", p.seeds[i], p.prompts[i], opts.samples_format, info=infotext(i), p=p, suffix="-mask-composite")
 
                     if opts.return_mask:
                         output_images.append(image_mask)
-- 
cgit v1.2.1


From 7c22bbd3ad5a149e0cf29df887405188fb2d0471 Mon Sep 17 00:00:00 2001
From: AUTOMATIC1111 <16777216c@gmail.com>
Date: Wed, 26 Jul 2023 07:04:07 +0300
Subject: attempt 2

---
 modules/processing.py | 28 ++++++++++++++++------------
 1 file changed, 16 insertions(+), 12 deletions(-)

(limited to 'modules/processing.py')

diff --git a/modules/processing.py b/modules/processing.py
index 146e409a..e9108f11 100644
--- a/modules/processing.py
+++ b/modules/processing.py
@@ -621,12 +621,12 @@ def create_infotext(p, all_prompts, all_seeds, all_subseeds, comments=None, iter
         "Sampler": p.sampler_name,
         "CFG scale": p.cfg_scale,
         "Image CFG scale": getattr(p, 'image_cfg_scale', None),
-        "Seed": all_seeds[index],
+        "Seed": p.all_seeds[0] if use_main_prompt else all_seeds[index],
         "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_model_name_to_info else shared.sd_model.sd_checkpoint_info.name_for_extra),
-        "Variation seed": (None if p.subseed_strength == 0 else all_subseeds[index]),
+        "Variation seed": (None if p.subseed_strength == 0 else (p.all_subseeds[0] if use_main_prompt else all_subseeds[index])),
         "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),
@@ -807,20 +807,24 @@ def process_images_inner(p: StableDiffusionProcessing) -> Processed:
             if p.scripts is not None:
                 p.scripts.postprocess_batch(p, x_samples_ddim, batch_number=n)
 
-            batch_params = scripts.PostprocessBatchListArgs(
-                list(x_samples_ddim),
-                p.all_prompts[n * p.batch_size:(n + 1) * p.batch_size],
-                p.all_negative_prompts[n * p.batch_size:(n + 1) * p.batch_size],
-                p.seeds,
-                p.subseeds,
-            )
+                batch_params = scripts.PostprocessBatchListArgs(
+                    list(x_samples_ddim),
+                    p.all_prompts[n * p.batch_size:(n + 1) * p.batch_size],
+                    p.all_negative_prompts[n * p.batch_size:(n + 1) * p.batch_size],
+                    p.seeds,
+                    p.subseeds,
+                )
 
-            if p.scripts is not None:
                 p.scripts.postprocess_batch_list(p, batch_params, batch_number=n)
+
                 x_samples_ddim = batch_params.images
+                p.prompts = batch_params.prompts
+                p.negative_prompts = batch_params.negative_prompts
+                p.seeds = batch_params.seeds
+                p.subseeds = batch_params.subseeds
 
             def infotext(index=0, use_main_prompt=False):
-                return create_infotext(p, batch_params.prompts, batch_params.seeds, batch_params.subseeds, use_main_prompt=use_main_prompt, index=index, all_negative_prompts=batch_params.negative_prompts)
+                return create_infotext(p, p.prompts, p.seeds, p.subseeds, use_main_prompt=use_main_prompt, index=index, all_negative_prompts=p.negative_prompts)
 
             for i, x_sample in enumerate(x_samples_ddim):
                 p.batch_index = i
@@ -910,7 +914,7 @@ def process_images_inner(p: StableDiffusionProcessing) -> Processed:
         p,
         images_list=output_images,
         seed=p.all_seeds[0],
-        info=infotext(),
+        info=infotexts[0],
         comments="".join(f"{comment}\n" for comment in comments),
         subseed=p.all_subseeds[0],
         index_of_first_image=index_of_first_image,
-- 
cgit v1.2.1


From 835a7dbf0e73c4cdf945b588d319a6c36652cbe5 Mon Sep 17 00:00:00 2001
From: AUTOMATIC1111 <16777216c@gmail.com>
Date: Wed, 26 Jul 2023 07:49:57 +0300
Subject: simplify PostprocessBatchListArgs

---
 modules/processing.py | 15 +++------------
 1 file changed, 3 insertions(+), 12 deletions(-)

(limited to 'modules/processing.py')

diff --git a/modules/processing.py b/modules/processing.py
index e9108f11..b0992ee1 100644
--- a/modules/processing.py
+++ b/modules/processing.py
@@ -807,21 +807,12 @@ def process_images_inner(p: StableDiffusionProcessing) -> Processed:
             if p.scripts is not None:
                 p.scripts.postprocess_batch(p, x_samples_ddim, batch_number=n)
 
-                batch_params = scripts.PostprocessBatchListArgs(
-                    list(x_samples_ddim),
-                    p.all_prompts[n * p.batch_size:(n + 1) * p.batch_size],
-                    p.all_negative_prompts[n * p.batch_size:(n + 1) * p.batch_size],
-                    p.seeds,
-                    p.subseeds,
-                )
+                p.prompts = p.all_prompts[n * p.batch_size:(n + 1) * p.batch_size]
+                p.negative_prompts = p.all_negative_prompts[n * p.batch_size:(n + 1) * p.batch_size]
 
+                batch_params = scripts.PostprocessBatchListArgs(list(x_samples_ddim))
                 p.scripts.postprocess_batch_list(p, batch_params, batch_number=n)
-
                 x_samples_ddim = batch_params.images
-                p.prompts = batch_params.prompts
-                p.negative_prompts = batch_params.negative_prompts
-                p.seeds = batch_params.seeds
-                p.subseeds = batch_params.subseeds
 
             def infotext(index=0, use_main_prompt=False):
                 return create_infotext(p, p.prompts, p.seeds, p.subseeds, use_main_prompt=use_main_prompt, index=index, all_negative_prompts=p.negative_prompts)
-- 
cgit v1.2.1


From 3bca90b249d749ed5429f76e380d2ffa52fc0d41 Mon Sep 17 00:00:00 2001
From: AUTOMATIC1111 <16777216c@gmail.com>
Date: Sun, 30 Jul 2023 13:48:27 +0300
Subject: hires fix checkpoint selection

---
 modules/processing.py | 47 ++++++++++++++++++++++++++++++-----------------
 1 file changed, 30 insertions(+), 17 deletions(-)

(limited to 'modules/processing.py')

diff --git a/modules/processing.py b/modules/processing.py
index b0992ee1..7026487a 100644
--- a/modules/processing.py
+++ b/modules/processing.py
@@ -935,7 +935,7 @@ class StableDiffusionProcessingTxt2Img(StableDiffusionProcessing):
     cached_hr_uc = [None, None]
     cached_hr_c = [None, None]
 
-    def __init__(self, enable_hr: bool = False, denoising_strength: float = 0.75, firstphase_width: int = 0, firstphase_height: int = 0, hr_scale: float = 2.0, hr_upscaler: str = None, hr_second_pass_steps: int = 0, hr_resize_x: int = 0, hr_resize_y: int = 0, hr_sampler_name: str = None, hr_prompt: str = '', hr_negative_prompt: str = '', **kwargs):
+    def __init__(self, enable_hr: bool = False, denoising_strength: float = 0.75, firstphase_width: int = 0, firstphase_height: int = 0, hr_scale: float = 2.0, hr_upscaler: str = None, hr_second_pass_steps: int = 0, hr_resize_x: int = 0, hr_resize_y: int = 0, hr_checkpoint_name: str = None, hr_sampler_name: str = None, hr_prompt: str = '', hr_negative_prompt: str = '', **kwargs):
         super().__init__(**kwargs)
         self.enable_hr = enable_hr
         self.denoising_strength = denoising_strength
@@ -946,11 +946,14 @@ class StableDiffusionProcessingTxt2Img(StableDiffusionProcessing):
         self.hr_resize_y = hr_resize_y
         self.hr_upscale_to_x = hr_resize_x
         self.hr_upscale_to_y = hr_resize_y
+        self.hr_checkpoint_name = hr_checkpoint_name
+        self.hr_checkpoint_info = None
         self.hr_sampler_name = hr_sampler_name
         self.hr_prompt = hr_prompt
         self.hr_negative_prompt = hr_negative_prompt
         self.all_hr_prompts = None
         self.all_hr_negative_prompts = None
+        self.latent_scale_mode = None
 
         if firstphase_width != 0 or firstphase_height != 0:
             self.hr_upscale_to_x = self.width
@@ -973,6 +976,14 @@ class StableDiffusionProcessingTxt2Img(StableDiffusionProcessing):
 
     def init(self, all_prompts, all_seeds, all_subseeds):
         if self.enable_hr:
+            if self.hr_checkpoint_name:
+                self.hr_checkpoint_info = sd_models.get_closet_checkpoint_match(self.hr_checkpoint_name)
+
+                if self.hr_checkpoint_info is None:
+                    raise Exception(f'Could not find checkpoint with name {self.hr_checkpoint_name}')
+
+                self.extra_generation_params["Hires checkpoint"] = self.hr_checkpoint_info.short_title
+
             if self.hr_sampler_name is not None and self.hr_sampler_name != self.sampler_name:
                 self.extra_generation_params["Hires sampler"] = self.hr_sampler_name
 
@@ -982,6 +993,11 @@ class StableDiffusionProcessingTxt2Img(StableDiffusionProcessing):
             if tuple(self.hr_negative_prompt) != tuple(self.negative_prompt):
                 self.extra_generation_params["Hires negative prompt"] = self.hr_negative_prompt
 
+            self.latent_scale_mode = shared.latent_upscale_modes.get(self.hr_upscaler, None) if self.hr_upscaler is not None else shared.latent_upscale_modes.get(shared.latent_upscale_default_mode, "nearest")
+            if self.enable_hr and self.latent_scale_mode is None:
+                if not any(x.name == self.hr_upscaler for x in shared.sd_upscalers):
+                    raise Exception(f"could not find upscaler named {self.hr_upscaler}")
+
             if opts.use_old_hires_fix_width_height and self.applied_old_hires_behavior_to != (self.width, self.height):
                 self.hr_resize_x = self.width
                 self.hr_resize_y = self.height
@@ -1020,14 +1036,6 @@ class StableDiffusionProcessingTxt2Img(StableDiffusionProcessing):
                     self.truncate_x = (self.hr_upscale_to_x - target_w) // opt_f
                     self.truncate_y = (self.hr_upscale_to_y - target_h) // opt_f
 
-            # special case: the user has chosen to do nothing
-            if self.hr_upscale_to_x == self.width and self.hr_upscale_to_y == self.height:
-                self.enable_hr = False
-                self.denoising_strength = None
-                self.extra_generation_params.pop("Hires upscale", None)
-                self.extra_generation_params.pop("Hires resize", None)
-                return
-
             if not state.processing_has_refined_job_count:
                 if state.job_count == -1:
                     state.job_count = self.n_iter
@@ -1045,17 +1053,22 @@ class StableDiffusionProcessingTxt2Img(StableDiffusionProcessing):
     def sample(self, conditioning, unconditional_conditioning, seeds, subseeds, subseed_strength, prompts):
         self.sampler = sd_samplers.create_sampler(self.sampler_name, self.sd_model)
 
-        latent_scale_mode = shared.latent_upscale_modes.get(self.hr_upscaler, None) if self.hr_upscaler is not None else shared.latent_upscale_modes.get(shared.latent_upscale_default_mode, "nearest")
-        if self.enable_hr and latent_scale_mode is None:
-            if not any(x.name == self.hr_upscaler for x in shared.sd_upscalers):
-                raise Exception(f"could not find upscaler named {self.hr_upscaler}")
-
         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)
         samples = self.sampler.sample(self, x, conditioning, unconditional_conditioning, image_conditioning=self.txt2img_image_conditioning(x))
 
         if not self.enable_hr:
             return samples
 
+        current = shared.sd_model.sd_checkpoint_info
+        try:
+            if self.hr_checkpoint_info is not None:
+                sd_models.reload_model_weights(info=self.hr_checkpoint_info)
+
+            return self.sample_hr_pass(samples, seeds, subseeds, subseed_strength, prompts)
+        finally:
+            sd_models.reload_model_weights(info=current)
+
+    def sample_hr_pass(self, samples, seeds, subseeds, subseed_strength, prompts):
         self.is_hr_pass = True
 
         target_width = self.hr_upscale_to_x
@@ -1073,11 +1086,11 @@ class StableDiffusionProcessingTxt2Img(StableDiffusionProcessing):
             info = create_infotext(self, self.all_prompts, self.all_seeds, self.all_subseeds, [], iteration=self.iteration, position_in_batch=index)
             images.save_image(image, self.outpath_samples, "", seeds[index], prompts[index], opts.samples_format, info=info, p=self, suffix="-before-highres-fix")
 
-        if latent_scale_mode is not None:
+        if self.latent_scale_mode is not None:
             for i in range(samples.shape[0]):
                 save_intermediate(samples, i)
 
-            samples = torch.nn.functional.interpolate(samples, size=(target_height // opt_f, target_width // opt_f), mode=latent_scale_mode["mode"], antialias=latent_scale_mode["antialias"])
+            samples = torch.nn.functional.interpolate(samples, size=(target_height // opt_f, target_width // opt_f), mode=self.latent_scale_mode["mode"], antialias=self.latent_scale_mode["antialias"])
 
             # Avoid making the inpainting conditioning unless necessary as
             # this does need some extra compute to decode / encode the image again.
@@ -1193,7 +1206,7 @@ class StableDiffusionProcessingTxt2Img(StableDiffusionProcessing):
         self.hr_uc = None
         self.hr_c = None
 
-        if self.enable_hr:
+        if self.enable_hr and self.hr_checkpoint_info is None:
             if shared.opts.hires_fix_use_firstpass_conds:
                 self.calculate_hr_conds()
 
-- 
cgit v1.2.1


From 40cd59207b96f9e522fdc104b43279880b671ce4 Mon Sep 17 00:00:00 2001
From: AUTOMATIC1111 <16777216c@gmail.com>
Date: Sun, 30 Jul 2023 14:10:26 +0300
Subject: make it work with SDXL

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

(limited to 'modules/processing.py')

diff --git a/modules/processing.py b/modules/processing.py
index 7026487a..b8af1301 100644
--- a/modules/processing.py
+++ b/modules/processing.py
@@ -1197,8 +1197,11 @@ class StableDiffusionProcessingTxt2Img(StableDiffusionProcessing):
         if self.hr_c is not None:
             return
 
-        self.hr_uc = self.get_conds_with_caching(prompt_parser.get_learned_conditioning, self.hr_negative_prompts, self.steps * self.step_multiplier, [self.cached_hr_uc, self.cached_uc], self.hr_extra_network_data)
-        self.hr_c = self.get_conds_with_caching(prompt_parser.get_multicond_learned_conditioning, self.hr_prompts, self.steps * self.step_multiplier, [self.cached_hr_c, self.cached_c], self.hr_extra_network_data)
+        hr_prompts = prompt_parser.SdConditioning(self.hr_prompts, width=self.hr_upscale_to_x, height=self.hr_upscale_to_y)
+        hr_negative_prompts = prompt_parser.SdConditioning(self.hr_negative_prompts, width=self.hr_upscale_to_x, height=self.hr_upscale_to_y, is_negative_prompt=True)
+
+        self.hr_uc = self.get_conds_with_caching(prompt_parser.get_learned_conditioning, hr_negative_prompts, self.steps * self.step_multiplier, [self.cached_hr_uc, self.cached_uc], self.hr_extra_network_data)
+        self.hr_c = self.get_conds_with_caching(prompt_parser.get_multicond_learned_conditioning, hr_prompts, self.steps * self.step_multiplier, [self.cached_hr_c, self.cached_c], self.hr_extra_network_data)
 
     def setup_conds(self):
         super().setup_conds()
-- 
cgit v1.2.1


From 77761e7bad8a7cbffc9028dc0b2f63169aaf25f9 Mon Sep 17 00:00:00 2001
From: AUTOMATIC1111 <16777216c@gmail.com>
Date: Sun, 30 Jul 2023 14:10:33 +0300
Subject: linter

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

(limited to 'modules/processing.py')

diff --git a/modules/processing.py b/modules/processing.py
index b8af1301..21dbef16 100644
--- a/modules/processing.py
+++ b/modules/processing.py
@@ -1055,6 +1055,7 @@ class StableDiffusionProcessingTxt2Img(StableDiffusionProcessing):
 
         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)
         samples = self.sampler.sample(self, x, conditioning, unconditional_conditioning, image_conditioning=self.txt2img_image_conditioning(x))
+        del x
 
         if not self.enable_hr:
             return samples
@@ -1137,7 +1138,6 @@ class StableDiffusionProcessingTxt2Img(StableDiffusionProcessing):
         noise = create_random_tensors(samples.shape[1:], seeds=seeds, subseeds=subseeds, subseed_strength=subseed_strength, p=self)
 
         # GC now before running the next img2img to prevent running out of memory
-        x = None
         devices.torch_gc()
 
         if not self.disable_extra_networks:
-- 
cgit v1.2.1


From eec540b22798ddcf8a03d947519c36635d77d722 Mon Sep 17 00:00:00 2001
From: AUTOMATIC1111 <16777216c@gmail.com>
Date: Sun, 30 Jul 2023 15:04:12 +0300
Subject: repair non-latent upscaling broken for SDXL

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

(limited to 'modules/processing.py')

diff --git a/modules/processing.py b/modules/processing.py
index 21dbef16..6fb14516 100644
--- a/modules/processing.py
+++ b/modules/processing.py
@@ -1119,6 +1119,7 @@ class StableDiffusionProcessingTxt2Img(StableDiffusionProcessing):
             decoded_samples = torch.from_numpy(np.array(batch_images))
             decoded_samples = decoded_samples.to(shared.device)
             decoded_samples = 2. * decoded_samples - 1.
+            decoded_samples = decoded_samples.to(shared.device, dtype=devices.dtype_vae)
 
             samples = self.sd_model.get_first_stage_encoding(self.sd_model.encode_first_stage(decoded_samples))
 
-- 
cgit v1.2.1


From a64fbe89288802f8b5ec8ca7bcab5aaf2c7bfea5 Mon Sep 17 00:00:00 2001
From: AUTOMATIC1111 <16777216c@gmail.com>
Date: Sun, 30 Jul 2023 15:12:09 +0300
Subject: make it possible to use checkpoints of different types (SD1, SDXL) in
 first and second pass of hires fix

---
 modules/processing.py | 10 +++++++---
 1 file changed, 7 insertions(+), 3 deletions(-)

(limited to 'modules/processing.py')

diff --git a/modules/processing.py b/modules/processing.py
index 6fb14516..c4da208f 100644
--- a/modules/processing.py
+++ b/modules/processing.py
@@ -1060,16 +1060,21 @@ class StableDiffusionProcessingTxt2Img(StableDiffusionProcessing):
         if not self.enable_hr:
             return samples
 
+        if self.latent_scale_mode is None:
+            decoded_samples = decode_first_stage(self.sd_model, samples)
+        else:
+            decoded_samples = None
+
         current = shared.sd_model.sd_checkpoint_info
         try:
             if self.hr_checkpoint_info is not None:
                 sd_models.reload_model_weights(info=self.hr_checkpoint_info)
 
-            return self.sample_hr_pass(samples, seeds, subseeds, subseed_strength, prompts)
+            return self.sample_hr_pass(samples, decoded_samples, seeds, subseeds, subseed_strength, prompts)
         finally:
             sd_models.reload_model_weights(info=current)
 
-    def sample_hr_pass(self, samples, seeds, subseeds, subseed_strength, prompts):
+    def sample_hr_pass(self, samples, decoded_samples, seeds, subseeds, subseed_strength, prompts):
         self.is_hr_pass = True
 
         target_width = self.hr_upscale_to_x
@@ -1100,7 +1105,6 @@ class StableDiffusionProcessingTxt2Img(StableDiffusionProcessing):
             else:
                 image_conditioning = self.txt2img_image_conditioning(samples)
         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)
 
             batch_images = []
-- 
cgit v1.2.1


From cc53db6652b11e6f7bca42c3aa93bd6761ed3d3f Mon Sep 17 00:00:00 2001
From: AUTOMATIC1111 <16777216c@gmail.com>
Date: Sun, 30 Jul 2023 15:30:33 +0300
Subject: this time for sure

---
 modules/processing.py | 16 +++++++++++++---
 1 file changed, 13 insertions(+), 3 deletions(-)

(limited to 'modules/processing.py')

diff --git a/modules/processing.py b/modules/processing.py
index c4da208f..3190b964 100644
--- a/modules/processing.py
+++ b/modules/processing.py
@@ -538,8 +538,12 @@ def create_random_tensors(shape, seeds, subseeds=None, subseed_strength=0.0, see
     return x
 
 
+class DecodedSamples(list):
+    already_decoded = True
+
+
 def decode_latent_batch(model, batch, target_device=None, check_for_nans=False):
-    samples = []
+    samples = DecodedSamples()
 
     for i in range(batch.shape[0]):
         sample = decode_first_stage(model, batch[i:i + 1])[0]
@@ -793,7 +797,11 @@ def process_images_inner(p: StableDiffusionProcessing) -> Processed:
             with devices.without_autocast() if devices.unet_needs_upcast else devices.autocast():
                 samples_ddim = p.sample(conditioning=p.c, unconditional_conditioning=p.uc, seeds=p.seeds, subseeds=p.subseeds, subseed_strength=p.subseed_strength, prompts=p.prompts)
 
-            x_samples_ddim = decode_latent_batch(p.sd_model, samples_ddim, target_device=devices.cpu, check_for_nans=True)
+            if getattr(samples_ddim, 'already_decoded', False):
+                x_samples_ddim = samples_ddim
+            else:
+                x_samples_ddim = decode_latent_batch(p.sd_model, samples_ddim, target_device=devices.cpu, check_for_nans=True)
+
             x_samples_ddim = torch.stack(x_samples_ddim).float()
             x_samples_ddim = torch.clamp((x_samples_ddim + 1.0) / 2.0, min=0.0, max=1.0)
 
@@ -1161,9 +1169,11 @@ class StableDiffusionProcessingTxt2Img(StableDiffusionProcessing):
 
         sd_models.apply_token_merging(self.sd_model, self.get_token_merging_ratio())
 
+        decoded_samples = decode_latent_batch(self.sd_model, samples, target_device=devices.cpu, check_for_nans=True)
+
         self.is_hr_pass = False
 
-        return samples
+        return decoded_samples
 
     def close(self):
         super().close()
-- 
cgit v1.2.1


From 02038036ff571e0f04a94c3e279609666e239dec Mon Sep 17 00:00:00 2001
From: AUTOMATIC1111 <16777216c@gmail.com>
Date: Sun, 30 Jul 2023 16:16:31 +0300
Subject: make it so that VAE NaNs autodetection also works during first pass
 of hires fix

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

(limited to 'modules/processing.py')

diff --git a/modules/processing.py b/modules/processing.py
index 3190b964..0677de81 100644
--- a/modules/processing.py
+++ b/modules/processing.py
@@ -1069,7 +1069,7 @@ class StableDiffusionProcessingTxt2Img(StableDiffusionProcessing):
             return samples
 
         if self.latent_scale_mode is None:
-            decoded_samples = decode_first_stage(self.sd_model, samples)
+            decoded_samples = torch.stack(decode_latent_batch(self.sd_model, samples, target_device=devices.cpu, check_for_nans=True))
         else:
             decoded_samples = None
 
-- 
cgit v1.2.1


From 0af4127fd14360ebb12c6569d98aebf8047abbfc Mon Sep 17 00:00:00 2001
From: AUTOMATIC1111 <16777216c@gmail.com>
Date: Sun, 30 Jul 2023 19:36:24 +0300
Subject: delete the field that is preventing the model from being unloaded and
 is causing increased RAM usage

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

(limited to 'modules/processing.py')

diff --git a/modules/processing.py b/modules/processing.py
index 0677de81..b09433b0 100644
--- a/modules/processing.py
+++ b/modules/processing.py
@@ -1076,11 +1076,15 @@ class StableDiffusionProcessingTxt2Img(StableDiffusionProcessing):
         current = shared.sd_model.sd_checkpoint_info
         try:
             if self.hr_checkpoint_info is not None:
+                del self.sampler
                 sd_models.reload_model_weights(info=self.hr_checkpoint_info)
+                devices.torch_gc()
 
             return self.sample_hr_pass(samples, decoded_samples, seeds, subseeds, subseed_strength, prompts)
         finally:
+            del self.sampler
             sd_models.reload_model_weights(info=current)
+            devices.torch_gc()
 
     def sample_hr_pass(self, samples, decoded_samples, seeds, subseeds, subseed_strength, prompts):
         self.is_hr_pass = True
-- 
cgit v1.2.1


From dca121e9035ba36b3f7484c8a31a7776d85c0960 Mon Sep 17 00:00:00 2001
From: AUTOMATIC1111 <16777216c@gmail.com>
Date: Mon, 31 Jul 2023 09:13:07 +0300
Subject: set the field to None instead

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

(limited to 'modules/processing.py')

diff --git a/modules/processing.py b/modules/processing.py
index b09433b0..35e7b87e 100644
--- a/modules/processing.py
+++ b/modules/processing.py
@@ -1076,13 +1076,13 @@ class StableDiffusionProcessingTxt2Img(StableDiffusionProcessing):
         current = shared.sd_model.sd_checkpoint_info
         try:
             if self.hr_checkpoint_info is not None:
-                del self.sampler
+                self.sampler = None
                 sd_models.reload_model_weights(info=self.hr_checkpoint_info)
                 devices.torch_gc()
 
             return self.sample_hr_pass(samples, decoded_samples, seeds, subseeds, subseed_strength, prompts)
         finally:
-            del self.sampler
+            self.sampler = None
             sd_models.reload_model_weights(info=current)
             devices.torch_gc()
 
-- 
cgit v1.2.1


From 29d7e31d89e9d686784eacbdbfc5b15959eb4449 Mon Sep 17 00:00:00 2001
From: AUTOMATIC1111 <16777216c@gmail.com>
Date: Mon, 31 Jul 2023 10:43:26 +0300
Subject: repair AttributeError: 'NoneType' object has no attribute
 'conditioning_key'

---
 modules/processing.py | 14 +++++++-------
 1 file changed, 7 insertions(+), 7 deletions(-)

(limited to 'modules/processing.py')

diff --git a/modules/processing.py b/modules/processing.py
index 35e7b87e..1f0c0b3b 100644
--- a/modules/processing.py
+++ b/modules/processing.py
@@ -1104,6 +1104,13 @@ class StableDiffusionProcessingTxt2Img(StableDiffusionProcessing):
             info = create_infotext(self, self.all_prompts, self.all_seeds, self.all_subseeds, [], iteration=self.iteration, position_in_batch=index)
             images.save_image(image, self.outpath_samples, "", seeds[index], prompts[index], opts.samples_format, info=info, p=self, suffix="-before-highres-fix")
 
+        img2img_sampler_name = self.hr_sampler_name or self.sampler_name
+
+        if self.sampler_name in ['PLMS', 'UniPC']:  # PLMS/UniPC do not support img2img so we just silently switch to DDIM
+            img2img_sampler_name = 'DDIM'
+
+        self.sampler = sd_samplers.create_sampler(img2img_sampler_name, self.sd_model)
+
         if self.latent_scale_mode is not None:
             for i in range(samples.shape[0]):
                 save_intermediate(samples, i)
@@ -1143,13 +1150,6 @@ class StableDiffusionProcessingTxt2Img(StableDiffusionProcessing):
 
         shared.state.nextjob()
 
-        img2img_sampler_name = self.hr_sampler_name or self.sampler_name
-
-        if self.sampler_name in ['PLMS', 'UniPC']:  # PLMS/UniPC do not support img2img so we just silently switch to DDIM
-            img2img_sampler_name = 'DDIM'
-
-        self.sampler = sd_samplers.create_sampler(img2img_sampler_name, self.sd_model)
-
         samples = samples[:, :, self.truncate_y//2:samples.shape[2]-(self.truncate_y+1)//2, self.truncate_x//2:samples.shape[3]-(self.truncate_x+1)//2]
 
         noise = create_random_tensors(samples.shape[1:], seeds=seeds, subseeds=subseeds, subseed_strength=subseed_strength, p=self)
-- 
cgit v1.2.1


From c09bc2c60856ca1ab2243386176badf909affdbe Mon Sep 17 00:00:00 2001
From: AUTOMATIC1111 <16777216c@gmail.com>
Date: Mon, 31 Jul 2023 13:20:26 +0300
Subject: fix "clamp_scalar_cpu" not implemented for 'Half'

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

(limited to 'modules/processing.py')

diff --git a/modules/processing.py b/modules/processing.py
index 1f0c0b3b..f8f8bddc 100644
--- a/modules/processing.py
+++ b/modules/processing.py
@@ -1069,7 +1069,7 @@ class StableDiffusionProcessingTxt2Img(StableDiffusionProcessing):
             return samples
 
         if self.latent_scale_mode is None:
-            decoded_samples = torch.stack(decode_latent_batch(self.sd_model, samples, target_device=devices.cpu, check_for_nans=True))
+            decoded_samples = torch.stack(decode_latent_batch(self.sd_model, samples, target_device=devices.cpu, check_for_nans=True)).to(dtype=torch.float32)
         else:
             decoded_samples = None
 
-- 
cgit v1.2.1


From ccb92339348f6973de39cde062982a51a4cd0818 Mon Sep 17 00:00:00 2001
From: AUTOMATIC1111 <16777216c@gmail.com>
Date: Wed, 2 Aug 2023 18:53:09 +0300
Subject: add yet another torch_gc to reclaim some of VRAM after the initial
 stage of img2img

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

(limited to 'modules/processing.py')

diff --git a/modules/processing.py b/modules/processing.py
index b0992ee1..0b66cd2a 100644
--- a/modules/processing.py
+++ b/modules/processing.py
@@ -1348,6 +1348,7 @@ class StableDiffusionProcessingImg2Img(StableDiffusionProcessing):
         image = image.to(shared.device, dtype=devices.dtype_vae)
 
         self.init_latent = self.sd_model.get_first_stage_encoding(self.sd_model.encode_first_stage(image))
+        devices.torch_gc()
 
         if self.resize_mode == 3:
             self.init_latent = torch.nn.functional.interpolate(self.init_latent, size=(self.height // opt_f, self.width // opt_f), mode="bilinear")
-- 
cgit v1.2.1


From 84b6fcd02ca6d6ab48c4b6be4bb8724b1c2e7014 Mon Sep 17 00:00:00 2001
From: AUTOMATIC1111 <16777216c@gmail.com>
Date: Thu, 3 Aug 2023 00:00:23 +0300
Subject: add NV option for Random number generator source setting, which
 allows to generate same pictures on CPU/AMD/Mac as on NVidia  videocards.

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

(limited to 'modules/processing.py')

diff --git a/modules/processing.py b/modules/processing.py
index 0b66cd2a..8f34c8b4 100644
--- a/modules/processing.py
+++ b/modules/processing.py
@@ -492,7 +492,7 @@ def create_random_tensors(shape, seeds, subseeds=None, subseed_strength=0.0, see
         noise_shape = shape if seed_resize_from_h <= 0 or seed_resize_from_w <= 0 else (shape[0], seed_resize_from_h//8, seed_resize_from_w//8)
 
         subnoise = None
-        if subseeds is not None:
+        if subseeds is not None and subseed_strength != 0:
             subseed = 0 if i >= len(subseeds) else subseeds[i]
 
             subnoise = devices.randn(subseed, noise_shape)
@@ -524,7 +524,7 @@ def create_random_tensors(shape, seeds, subseeds=None, subseed_strength=0.0, see
             cnt = p.sampler.number_of_needed_noises(p)
 
             if eta_noise_seed_delta > 0:
-                torch.manual_seed(seed + eta_noise_seed_delta)
+                devices.manual_seed(seed + eta_noise_seed_delta)
 
             for j in range(cnt):
                 sampler_noises[j].append(devices.randn_without_seed(tuple(noise_shape)))
@@ -636,7 +636,7 @@ def create_infotext(p, all_prompts, all_seeds, all_subseeds, comments=None, iter
         "Token merging ratio": None if token_merging_ratio == 0 else token_merging_ratio,
         "Token merging ratio hr": None if not enable_hr or token_merging_ratio_hr == 0 else token_merging_ratio_hr,
         "Init image hash": getattr(p, 'init_img_hash', None),
-        "RNG": opts.randn_source if opts.randn_source != "GPU" else None,
+        "RNG": opts.randn_source if opts.randn_source != "GPU" and opts.randn_source != "NV" else None,
         "NGMS": None if p.s_min_uncond == 0 else p.s_min_uncond,
         **p.extra_generation_params,
         "Version": program_version() if opts.add_version_to_infotext else None,
-- 
cgit v1.2.1


From a1825ee741bb21b35561d58db8cb316d7f5d0c79 Mon Sep 17 00:00:00 2001
From: Splendide Imaginarius
 <119545140+Splendide-Imaginarius@users.noreply.github.com>
Date: Thu, 3 Aug 2023 02:03:35 +0000
Subject: Make StableDiffusionProcessingImg2Img.mask_blur a property
MIME-Version: 1.0
Content-Type: text/plain; charset=UTF-8
Content-Transfer-Encoding: 8bit

Fixes breakage when mask_blur is set after construction.

See https://github.com/Coyote-A/ultimate-upscale-for-automatic1111/issues/111#issuecomment-1652091424

Thanks to Алексей Трофимов and eunnone for reporting the issue.
---
 modules/processing.py | 21 ++++++++++++++++++---
 1 file changed, 18 insertions(+), 3 deletions(-)
 mode change 100644 => 100755 modules/processing.py

(limited to 'modules/processing.py')

diff --git a/modules/processing.py b/modules/processing.py
old mode 100644
new mode 100755
index b0992ee1..44d20fb7
--- a/modules/processing.py
+++ b/modules/processing.py
@@ -1232,11 +1232,10 @@ class StableDiffusionProcessingImg2Img(StableDiffusionProcessing):
         self.image_mask = mask
         self.latent_mask = None
         self.mask_for_overlay = None
-        if mask_blur is not None:
-            mask_blur_x = mask_blur
-            mask_blur_y = mask_blur
         self.mask_blur_x = mask_blur_x
         self.mask_blur_y = mask_blur_y
+        if mask_blur is not None:
+            self.mask_blur = mask_blur
         self.inpainting_fill = inpainting_fill
         self.inpaint_full_res = inpaint_full_res
         self.inpaint_full_res_padding = inpaint_full_res_padding
@@ -1246,6 +1245,22 @@ class StableDiffusionProcessingImg2Img(StableDiffusionProcessing):
         self.nmask = None
         self.image_conditioning = None
 
+    @property
+    def mask_blur(self):
+        if self.mask_blur_x == self.mask_blur_y:
+            return self.mask_blur_x
+        return None
+
+    @mask_blur.setter
+    def mask_blur(self, value):
+        self.mask_blur_x = value
+        self.mask_blur_y = value
+
+    @mask_blur.deleter
+    def mask_blur(self):
+        del self.mask_blur_x
+        del self.mask_blur_y
+
     def init(self, all_prompts, all_seeds, all_subseeds):
         self.sampler = sd_samplers.create_sampler(self.sampler_name, self.sd_model)
         crop_region = None
-- 
cgit v1.2.1


From 75336dfc84cae280036bc52a6805eb10d9ae30ba Mon Sep 17 00:00:00 2001
From: Kohaku-Blueleaf <59680068+KohakuBlueleaf@users.noreply.github.com>
Date: Fri, 4 Aug 2023 13:38:52 +0800
Subject: add TAESD for i2i and t2i

---
 modules/processing.py | 13 ++++++-------
 1 file changed, 6 insertions(+), 7 deletions(-)

(limited to 'modules/processing.py')

diff --git a/modules/processing.py b/modules/processing.py
index 8f34c8b4..099d86b7 100644
--- a/modules/processing.py
+++ b/modules/processing.py
@@ -573,9 +573,10 @@ def decode_latent_batch(model, batch, target_device=None, check_for_nans=False):
 
 
 def decode_first_stage(model, x):
-    x = model.decode_first_stage(x.to(devices.dtype_vae))
-
-    return x
+    from modules.sd_samplers_common import samples_to_images_tensor, approximation_indexes
+    x = x.to(devices.dtype_vae)
+    approx_index = approximation_indexes.get(opts.sd_vae_decode_method, 0)
+    return samples_to_images_tensor(x, approx_index, model)
 
 
 def get_fixed_seed(seed):
@@ -1344,10 +1345,8 @@ class StableDiffusionProcessingImg2Img(StableDiffusionProcessing):
             raise RuntimeError(f"bad number of images passed: {len(imgs)}; expecting {self.batch_size} or less")
 
         image = torch.from_numpy(batch_images)
-        image = 2. * image - 1.
-        image = image.to(shared.device, dtype=devices.dtype_vae)
-
-        self.init_latent = self.sd_model.get_first_stage_encoding(self.sd_model.encode_first_stage(image))
+        from modules.sd_samplers_common import images_tensor_to_samples, approximation_indexes
+        self.init_latent = images_tensor_to_samples(image, approximation_indexes.get(opts.sd_vae_encode_method), self.sd_model)
         devices.torch_gc()
 
         if self.resize_mode == 3:
-- 
cgit v1.2.1


From f0c1063a707a4a43823b0ed00e2a8eeb22a9ed0a Mon Sep 17 00:00:00 2001
From: AUTOMATIC1111 <16777216c@gmail.com>
Date: Fri, 4 Aug 2023 09:09:09 +0300
Subject: resolve some of circular import issues for kohaku

---
 modules/processing.py | 7 +------
 1 file changed, 1 insertion(+), 6 deletions(-)

(limited to 'modules/processing.py')

diff --git a/modules/processing.py b/modules/processing.py
index 8f34c8b4..8086a2b0 100644
--- a/modules/processing.py
+++ b/modules/processing.py
@@ -30,6 +30,7 @@ from ldm.models.diffusion.ddpm import LatentDepth2ImageDiffusion
 from einops import repeat, rearrange
 from blendmodes.blend import blendLayers, BlendType
 
+decode_first_stage = sd_samplers_common.decode_first_stage
 
 # some of those options should not be changed at all because they would break the model, so I removed them from options.
 opt_C = 4
@@ -572,12 +573,6 @@ def decode_latent_batch(model, batch, target_device=None, check_for_nans=False):
     return samples
 
 
-def decode_first_stage(model, x):
-    x = model.decode_first_stage(x.to(devices.dtype_vae))
-
-    return x
-
-
 def get_fixed_seed(seed):
     if seed is None or seed == '' or seed == -1:
         return int(random.randrange(4294967294))
-- 
cgit v1.2.1


From 094c416a801b16c7d8e1944e2e9fae2c9e98bf12 Mon Sep 17 00:00:00 2001
From: Kohaku-Blueleaf <59680068+KohakuBlueleaf@users.noreply.github.com>
Date: Fri, 4 Aug 2023 17:53:16 +0800
Subject: change all encode

---
 modules/processing.py | 14 ++++++--------
 1 file changed, 6 insertions(+), 8 deletions(-)

(limited to 'modules/processing.py')

diff --git a/modules/processing.py b/modules/processing.py
index aae39866..544667a4 100644
--- a/modules/processing.py
+++ b/modules/processing.py
@@ -16,6 +16,7 @@ from typing import Any, Dict, List
 import modules.sd_hijack
 from modules import devices, prompt_parser, masking, sd_samplers, lowvram, generation_parameters_copypaste, extra_networks, sd_vae_approx, scripts, sd_samplers_common, sd_unet, errors
 from modules.sd_hijack import model_hijack
+from modules.sd_samplers_common import images_tensor_to_samples, decode_first_stage, approximation_indexes
 from modules.shared import opts, cmd_opts, state
 import modules.shared as shared
 import modules.paths as paths
@@ -30,7 +31,6 @@ from ldm.models.diffusion.ddpm import LatentDepth2ImageDiffusion
 from einops import repeat, rearrange
 from blendmodes.blend import blendLayers, BlendType
 
-decode_first_stage = sd_samplers_common.decode_first_stage
 
 # some of those options should not be changed at all because they would break the model, so I removed them from options.
 opt_C = 4
@@ -84,7 +84,7 @@ def txt2img_image_conditioning(sd_model, x, width, height):
 
         # The "masked-image" in this case will just be all zeros since the entire image is masked.
         image_conditioning = torch.zeros(x.shape[0], 3, height, width, device=x.device)
-        image_conditioning = sd_model.get_first_stage_encoding(sd_model.encode_first_stage(image_conditioning))
+        image_conditioning = images_tensor_to_samples(image_conditioning, approximation_indexes.get(opts.sd_vae_encode_method))
 
         # Add the fake full 1s mask to the first dimension.
         image_conditioning = torch.nn.functional.pad(image_conditioning, (0, 0, 0, 0, 1, 0), value=1.0)
@@ -203,7 +203,7 @@ class StableDiffusionProcessing:
         midas_in = torch.from_numpy(transformed["midas_in"][None, ...]).to(device=shared.device)
         midas_in = repeat(midas_in, "1 ... -> n ...", n=self.batch_size)
 
-        conditioning_image = self.sd_model.get_first_stage_encoding(self.sd_model.encode_first_stage(source_image))
+        conditioning_image = images_tensor_to_samples(source_image*0.5+0.5, approximation_indexes.get(opts.sd_vae_encode_method))
         conditioning = torch.nn.functional.interpolate(
             self.sd_model.depth_model(midas_in),
             size=conditioning_image.shape[2:],
@@ -216,7 +216,7 @@ class StableDiffusionProcessing:
         return conditioning
 
     def edit_image_conditioning(self, source_image):
-        conditioning_image = self.sd_model.encode_first_stage(source_image).mode()
+        conditioning_image = images_tensor_to_samples(source_image*0.5+0.5, approximation_indexes.get(opts.sd_vae_encode_method))
 
         return conditioning_image
 
@@ -255,7 +255,7 @@ class StableDiffusionProcessing:
         )
 
         # Encode the new masked image using first stage of network.
-        conditioning_image = self.sd_model.get_first_stage_encoding(self.sd_model.encode_first_stage(conditioning_image))
+        conditioning_image = images_tensor_to_samples(conditioning_image*0.5+0.5, approximation_indexes.get(opts.sd_vae_encode_method))
 
         # Create the concatenated conditioning tensor to be fed to `c_concat`
         conditioning_mask = torch.nn.functional.interpolate(conditioning_mask, size=latent_image.shape[-2:])
@@ -1099,9 +1099,8 @@ class StableDiffusionProcessingTxt2Img(StableDiffusionProcessing):
 
             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))
+            samples = images_tensor_to_samples(decoded_samples, approximation_indexes.get(opts.sd_vae_encode_method))
 
             image_conditioning = self.img2img_image_conditioning(decoded_samples, samples)
 
@@ -1339,7 +1338,6 @@ class StableDiffusionProcessingImg2Img(StableDiffusionProcessing):
             raise RuntimeError(f"bad number of images passed: {len(imgs)}; expecting {self.batch_size} or less")
 
         image = torch.from_numpy(batch_images)
-        from modules.sd_samplers_common import images_tensor_to_samples, approximation_indexes
         self.init_latent = images_tensor_to_samples(image, approximation_indexes.get(opts.sd_vae_encode_method), self.sd_model)
         devices.torch_gc()
 
-- 
cgit v1.2.1


From 6346d8eeaa17ba0f7e41618908519f6e9bfe07e0 Mon Sep 17 00:00:00 2001
From: Kohaku-Blueleaf <59680068+KohakuBlueleaf@users.noreply.github.com>
Date: Fri, 4 Aug 2023 17:53:30 +0800
Subject: Revert "change all encode"

This reverts commit 094c416a801b16c7d8e1944e2e9fae2c9e98bf12.
---
 modules/processing.py | 14 ++++++++------
 1 file changed, 8 insertions(+), 6 deletions(-)

(limited to 'modules/processing.py')

diff --git a/modules/processing.py b/modules/processing.py
index 544667a4..aae39866 100644
--- a/modules/processing.py
+++ b/modules/processing.py
@@ -16,7 +16,6 @@ from typing import Any, Dict, List
 import modules.sd_hijack
 from modules import devices, prompt_parser, masking, sd_samplers, lowvram, generation_parameters_copypaste, extra_networks, sd_vae_approx, scripts, sd_samplers_common, sd_unet, errors
 from modules.sd_hijack import model_hijack
-from modules.sd_samplers_common import images_tensor_to_samples, decode_first_stage, approximation_indexes
 from modules.shared import opts, cmd_opts, state
 import modules.shared as shared
 import modules.paths as paths
@@ -31,6 +30,7 @@ from ldm.models.diffusion.ddpm import LatentDepth2ImageDiffusion
 from einops import repeat, rearrange
 from blendmodes.blend import blendLayers, BlendType
 
+decode_first_stage = sd_samplers_common.decode_first_stage
 
 # some of those options should not be changed at all because they would break the model, so I removed them from options.
 opt_C = 4
@@ -84,7 +84,7 @@ def txt2img_image_conditioning(sd_model, x, width, height):
 
         # The "masked-image" in this case will just be all zeros since the entire image is masked.
         image_conditioning = torch.zeros(x.shape[0], 3, height, width, device=x.device)
-        image_conditioning = images_tensor_to_samples(image_conditioning, approximation_indexes.get(opts.sd_vae_encode_method))
+        image_conditioning = sd_model.get_first_stage_encoding(sd_model.encode_first_stage(image_conditioning))
 
         # Add the fake full 1s mask to the first dimension.
         image_conditioning = torch.nn.functional.pad(image_conditioning, (0, 0, 0, 0, 1, 0), value=1.0)
@@ -203,7 +203,7 @@ class StableDiffusionProcessing:
         midas_in = torch.from_numpy(transformed["midas_in"][None, ...]).to(device=shared.device)
         midas_in = repeat(midas_in, "1 ... -> n ...", n=self.batch_size)
 
-        conditioning_image = images_tensor_to_samples(source_image*0.5+0.5, approximation_indexes.get(opts.sd_vae_encode_method))
+        conditioning_image = self.sd_model.get_first_stage_encoding(self.sd_model.encode_first_stage(source_image))
         conditioning = torch.nn.functional.interpolate(
             self.sd_model.depth_model(midas_in),
             size=conditioning_image.shape[2:],
@@ -216,7 +216,7 @@ class StableDiffusionProcessing:
         return conditioning
 
     def edit_image_conditioning(self, source_image):
-        conditioning_image = images_tensor_to_samples(source_image*0.5+0.5, approximation_indexes.get(opts.sd_vae_encode_method))
+        conditioning_image = self.sd_model.encode_first_stage(source_image).mode()
 
         return conditioning_image
 
@@ -255,7 +255,7 @@ class StableDiffusionProcessing:
         )
 
         # Encode the new masked image using first stage of network.
-        conditioning_image = images_tensor_to_samples(conditioning_image*0.5+0.5, approximation_indexes.get(opts.sd_vae_encode_method))
+        conditioning_image = self.sd_model.get_first_stage_encoding(self.sd_model.encode_first_stage(conditioning_image))
 
         # Create the concatenated conditioning tensor to be fed to `c_concat`
         conditioning_mask = torch.nn.functional.interpolate(conditioning_mask, size=latent_image.shape[-2:])
@@ -1099,8 +1099,9 @@ class StableDiffusionProcessingTxt2Img(StableDiffusionProcessing):
 
             decoded_samples = torch.from_numpy(np.array(batch_images))
             decoded_samples = decoded_samples.to(shared.device)
+            decoded_samples = 2. * decoded_samples - 1.
 
-            samples = images_tensor_to_samples(decoded_samples, approximation_indexes.get(opts.sd_vae_encode_method))
+            samples = self.sd_model.get_first_stage_encoding(self.sd_model.encode_first_stage(decoded_samples))
 
             image_conditioning = self.img2img_image_conditioning(decoded_samples, samples)
 
@@ -1338,6 +1339,7 @@ class StableDiffusionProcessingImg2Img(StableDiffusionProcessing):
             raise RuntimeError(f"bad number of images passed: {len(imgs)}; expecting {self.batch_size} or less")
 
         image = torch.from_numpy(batch_images)
+        from modules.sd_samplers_common import images_tensor_to_samples, approximation_indexes
         self.init_latent = images_tensor_to_samples(image, approximation_indexes.get(opts.sd_vae_encode_method), self.sd_model)
         devices.torch_gc()
 
-- 
cgit v1.2.1


From 21000f13a169263a7da2c66b300130d7a6339c7d Mon Sep 17 00:00:00 2001
From: Kohaku-Blueleaf <59680068+KohakuBlueleaf@users.noreply.github.com>
Date: Fri, 4 Aug 2023 18:23:14 +0800
Subject: replace get_first_stage_encoding

---
 modules/processing.py | 12 +++++-------
 1 file changed, 5 insertions(+), 7 deletions(-)

(limited to 'modules/processing.py')

diff --git a/modules/processing.py b/modules/processing.py
index aae39866..aa6d4d2a 100644
--- a/modules/processing.py
+++ b/modules/processing.py
@@ -16,6 +16,7 @@ from typing import Any, Dict, List
 import modules.sd_hijack
 from modules import devices, prompt_parser, masking, sd_samplers, lowvram, generation_parameters_copypaste, extra_networks, sd_vae_approx, scripts, sd_samplers_common, sd_unet, errors
 from modules.sd_hijack import model_hijack
+from modules.sd_samplers_common import images_tensor_to_samples, decode_first_stage, approximation_indexes
 from modules.shared import opts, cmd_opts, state
 import modules.shared as shared
 import modules.paths as paths
@@ -30,7 +31,6 @@ from ldm.models.diffusion.ddpm import LatentDepth2ImageDiffusion
 from einops import repeat, rearrange
 from blendmodes.blend import blendLayers, BlendType
 
-decode_first_stage = sd_samplers_common.decode_first_stage
 
 # some of those options should not be changed at all because they would break the model, so I removed them from options.
 opt_C = 4
@@ -84,7 +84,7 @@ def txt2img_image_conditioning(sd_model, x, width, height):
 
         # The "masked-image" in this case will just be all zeros since the entire image is masked.
         image_conditioning = torch.zeros(x.shape[0], 3, height, width, device=x.device)
-        image_conditioning = sd_model.get_first_stage_encoding(sd_model.encode_first_stage(image_conditioning))
+        image_conditioning = images_tensor_to_samples(image_conditioning, approximation_indexes.get(opts.sd_vae_encode_method))
 
         # Add the fake full 1s mask to the first dimension.
         image_conditioning = torch.nn.functional.pad(image_conditioning, (0, 0, 0, 0, 1, 0), value=1.0)
@@ -203,7 +203,7 @@ class StableDiffusionProcessing:
         midas_in = torch.from_numpy(transformed["midas_in"][None, ...]).to(device=shared.device)
         midas_in = repeat(midas_in, "1 ... -> n ...", n=self.batch_size)
 
-        conditioning_image = self.sd_model.get_first_stage_encoding(self.sd_model.encode_first_stage(source_image))
+        conditioning_image = images_tensor_to_samples(source_image*0.5+0.5, approximation_indexes.get(opts.sd_vae_encode_method))
         conditioning = torch.nn.functional.interpolate(
             self.sd_model.depth_model(midas_in),
             size=conditioning_image.shape[2:],
@@ -216,7 +216,7 @@ class StableDiffusionProcessing:
         return conditioning
 
     def edit_image_conditioning(self, source_image):
-        conditioning_image = self.sd_model.encode_first_stage(source_image).mode()
+        conditioning_image = images_tensor_to_samples(source_image*0.5+0.5, approximation_indexes.get(opts.sd_vae_encode_method))
 
         return conditioning_image
 
@@ -1099,9 +1099,8 @@ class StableDiffusionProcessingTxt2Img(StableDiffusionProcessing):
 
             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))
+            samples = images_tensor_to_samples(decoded_samples, approximation_indexes.get(opts.sd_vae_encode_method))
 
             image_conditioning = self.img2img_image_conditioning(decoded_samples, samples)
 
@@ -1339,7 +1338,6 @@ class StableDiffusionProcessingImg2Img(StableDiffusionProcessing):
             raise RuntimeError(f"bad number of images passed: {len(imgs)}; expecting {self.batch_size} or less")
 
         image = torch.from_numpy(batch_images)
-        from modules.sd_samplers_common import images_tensor_to_samples, approximation_indexes
         self.init_latent = images_tensor_to_samples(image, approximation_indexes.get(opts.sd_vae_encode_method), self.sd_model)
         devices.torch_gc()
 
-- 
cgit v1.2.1


From aa744cadc8e357e696a608c8d0c77a7bfc1c9f39 Mon Sep 17 00:00:00 2001
From: Kohaku-Blueleaf <59680068+KohakuBlueleaf@users.noreply.github.com>
Date: Sat, 5 Aug 2023 12:35:40 +0800
Subject: add infotext

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

(limited to 'modules/processing.py')

diff --git a/modules/processing.py b/modules/processing.py
index aa6d4d2a..a9ee7507 100644
--- a/modules/processing.py
+++ b/modules/processing.py
@@ -788,6 +788,7 @@ def process_images_inner(p: StableDiffusionProcessing) -> Processed:
             with devices.without_autocast() if devices.unet_needs_upcast else devices.autocast():
                 samples_ddim = p.sample(conditioning=p.c, unconditional_conditioning=p.uc, seeds=p.seeds, subseeds=p.subseeds, subseed_strength=p.subseed_strength, prompts=p.prompts)
 
+            p.extra_generation_params['VAE Decoder'] = opts.sd_vae_decode_method
             x_samples_ddim = decode_latent_batch(p.sd_model, samples_ddim, target_device=devices.cpu, check_for_nans=True)
             x_samples_ddim = torch.stack(x_samples_ddim).float()
             x_samples_ddim = torch.clamp((x_samples_ddim + 1.0) / 2.0, min=0.0, max=1.0)
@@ -1100,6 +1101,7 @@ class StableDiffusionProcessingTxt2Img(StableDiffusionProcessing):
             decoded_samples = torch.from_numpy(np.array(batch_images))
             decoded_samples = decoded_samples.to(shared.device)
 
+            self.extra_generation_params['VAE Encoder'] = opts.sd_vae_encode_method
             samples = images_tensor_to_samples(decoded_samples, approximation_indexes.get(opts.sd_vae_encode_method))
 
             image_conditioning = self.img2img_image_conditioning(decoded_samples, samples)
@@ -1338,6 +1340,7 @@ class StableDiffusionProcessingImg2Img(StableDiffusionProcessing):
             raise RuntimeError(f"bad number of images passed: {len(imgs)}; expecting {self.batch_size} or less")
 
         image = torch.from_numpy(batch_images)
+        self.extra_generation_params['VAE Encoder'] = opts.sd_vae_encode_method
         self.init_latent = images_tensor_to_samples(image, approximation_indexes.get(opts.sd_vae_encode_method), self.sd_model)
         devices.torch_gc()
 
-- 
cgit v1.2.1


From 56888644a67298253260eda84ceb2d6cd0ce5099 Mon Sep 17 00:00:00 2001
From: Splendide Imaginarius
 <119545140+Splendide-Imaginarius@users.noreply.github.com>
Date: Sat, 5 Aug 2023 04:54:23 +0000
Subject: Reduce mask blur kernel size to 2.5 sigmas
MIME-Version: 1.0
Content-Type: text/plain; charset=UTF-8
Content-Transfer-Encoding: 8bit

This more closely matches the old behavior of PIL's Gaussian blur, and
fixes breakage when tiling.

See https://github.com/Coyote-A/ultimate-upscale-for-automatic1111/issues/111#issuecomment-1663504109

Thanks to Алексей Трофимов and eunnone for reporting the issue.
---
 modules/processing.py | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

(limited to 'modules/processing.py')

diff --git a/modules/processing.py b/modules/processing.py
index 44d20fb7..63cd025c 100755
--- a/modules/processing.py
+++ b/modules/processing.py
@@ -1275,13 +1275,13 @@ class StableDiffusionProcessingImg2Img(StableDiffusionProcessing):
 
             if self.mask_blur_x > 0:
                 np_mask = np.array(image_mask)
-                kernel_size = 2 * int(4 * self.mask_blur_x + 0.5) + 1
+                kernel_size = 2 * int(2.5 * self.mask_blur_x + 0.5) + 1
                 np_mask = cv2.GaussianBlur(np_mask, (kernel_size, 1), self.mask_blur_x)
                 image_mask = Image.fromarray(np_mask)
 
             if self.mask_blur_y > 0:
                 np_mask = np.array(image_mask)
-                kernel_size = 2 * int(4 * self.mask_blur_y + 0.5) + 1
+                kernel_size = 2 * int(2.5 * self.mask_blur_y + 0.5) + 1
                 np_mask = cv2.GaussianBlur(np_mask, (1, kernel_size), self.mask_blur_y)
                 image_mask = Image.fromarray(np_mask)
 
-- 
cgit v1.2.1


From b85ec2b9b66492ff9bf3d40a4d9b424390067f0f Mon Sep 17 00:00:00 2001
From: Kohaku-Blueleaf <59680068+KohakuBlueleaf@users.noreply.github.com>
Date: Sat, 5 Aug 2023 13:14:00 +0800
Subject: Fix some merge mistakes

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

(limited to 'modules/processing.py')

diff --git a/modules/processing.py b/modules/processing.py
index b9900ded..43cb763f 100644
--- a/modules/processing.py
+++ b/modules/processing.py
@@ -1136,7 +1136,7 @@ class StableDiffusionProcessingTxt2Img(StableDiffusionProcessing):
                 batch_images.append(image)
 
             decoded_samples = torch.from_numpy(np.array(batch_images))
-            decoded_samples = decoded_samples.to(shared.device)
+            decoded_samples = decoded_samples.to(shared.device, dtype=devices.dtype_vae)
 
             self.extra_generation_params['VAE Encoder'] = opts.sd_vae_encode_method
             samples = images_tensor_to_samples(decoded_samples, approximation_indexes.get(opts.sd_vae_encode_method))
@@ -1374,6 +1374,7 @@ class StableDiffusionProcessingImg2Img(StableDiffusionProcessing):
             raise RuntimeError(f"bad number of images passed: {len(imgs)}; expecting {self.batch_size} or less")
 
         image = torch.from_numpy(batch_images)
+        image = image.to(shared.device, dtype=devices.dtype_vae)
         self.extra_generation_params['VAE Encoder'] = opts.sd_vae_encode_method
         self.init_latent = images_tensor_to_samples(image, approximation_indexes.get(opts.sd_vae_encode_method), self.sd_model)
         devices.torch_gc()
-- 
cgit v1.2.1


From 3f451f3042cc69a751cebc329c11f9f145da5186 Mon Sep 17 00:00:00 2001
From: AUTOMATIC1111 <16777216c@gmail.com>
Date: Sat, 5 Aug 2023 10:36:26 +0300
Subject: do not add VAE Encoder/Decoder to infotext if it's the default

---
 modules/processing.py | 12 +++++++++---
 1 file changed, 9 insertions(+), 3 deletions(-)

(limited to 'modules/processing.py')

diff --git a/modules/processing.py b/modules/processing.py
index 43cb763f..a9d66005 100644
--- a/modules/processing.py
+++ b/modules/processing.py
@@ -795,7 +795,9 @@ def process_images_inner(p: StableDiffusionProcessing) -> Processed:
             if getattr(samples_ddim, 'already_decoded', False):
                 x_samples_ddim = samples_ddim
             else:
-                p.extra_generation_params['VAE Decoder'] = opts.sd_vae_decode_method
+                if opts.sd_vae_decode_method != 'Full':
+                    p.extra_generation_params['VAE Decoder'] = opts.sd_vae_decode_method
+
                 x_samples_ddim = decode_latent_batch(p.sd_model, samples_ddim, target_device=devices.cpu, check_for_nans=True)
 
             x_samples_ddim = torch.stack(x_samples_ddim).float()
@@ -1138,7 +1140,8 @@ class StableDiffusionProcessingTxt2Img(StableDiffusionProcessing):
             decoded_samples = torch.from_numpy(np.array(batch_images))
             decoded_samples = decoded_samples.to(shared.device, dtype=devices.dtype_vae)
 
-            self.extra_generation_params['VAE Encoder'] = opts.sd_vae_encode_method
+            if opts.sd_vae_encode_method != 'Full':
+                self.extra_generation_params['VAE Encoder'] = opts.sd_vae_encode_method
             samples = images_tensor_to_samples(decoded_samples, approximation_indexes.get(opts.sd_vae_encode_method))
 
             image_conditioning = self.img2img_image_conditioning(decoded_samples, samples)
@@ -1375,7 +1378,10 @@ class StableDiffusionProcessingImg2Img(StableDiffusionProcessing):
 
         image = torch.from_numpy(batch_images)
         image = image.to(shared.device, dtype=devices.dtype_vae)
-        self.extra_generation_params['VAE Encoder'] = opts.sd_vae_encode_method
+
+        if opts.sd_vae_encode_method != 'Full':
+            self.extra_generation_params['VAE Encoder'] = opts.sd_vae_encode_method
+
         self.init_latent = images_tensor_to_samples(image, approximation_indexes.get(opts.sd_vae_encode_method), self.sd_model)
         devices.torch_gc()
 
-- 
cgit v1.2.1


From 1d7dcdb6c38c7bca945b3fa8a5d4a1f93446f22a Mon Sep 17 00:00:00 2001
From: w-e-w <40751091+w-e-w@users.noreply.github.com>
Date: Sat, 5 Aug 2023 19:07:39 +0900
Subject: Option to not save incomplete images

---
 modules/processing.py | 19 +++++++++++--------
 1 file changed, 11 insertions(+), 8 deletions(-)

(limited to 'modules/processing.py')

diff --git a/modules/processing.py b/modules/processing.py
index 43cb763f..bf4f938b 100644
--- a/modules/processing.py
+++ b/modules/processing.py
@@ -103,6 +103,10 @@ def txt2img_image_conditioning(sd_model, x, width, height):
         return x.new_zeros(x.shape[0], 5, 1, 1, dtype=x.dtype, device=x.device)
 
 
+def save_images_if_interrupted():
+    return not (opts.dont_save_interrupted_images and (state.interrupted or state.skipped))
+
+
 class StableDiffusionProcessing:
     """
     The first set of paramaters: sd_models -> do_not_reload_embeddings represent the minimum required to create a StableDiffusionProcessing
@@ -821,6 +825,8 @@ def process_images_inner(p: StableDiffusionProcessing) -> Processed:
             def infotext(index=0, use_main_prompt=False):
                 return create_infotext(p, p.prompts, p.seeds, p.subseeds, use_main_prompt=use_main_prompt, index=index, all_negative_prompts=p.negative_prompts)
 
+            save_images_if_interrupt = save_images_if_interrupted()
+
             for i, x_sample in enumerate(x_samples_ddim):
                 p.batch_index = i
 
@@ -828,7 +834,7 @@ def process_images_inner(p: StableDiffusionProcessing) -> Processed:
                 x_sample = x_sample.astype(np.uint8)
 
                 if p.restore_faces:
-                    if opts.save and not p.do_not_save_samples and opts.save_images_before_face_restoration:
+                    if opts.save and not p.do_not_save_samples and opts.save_images_before_face_restoration and save_images_if_interrupt:
                         images.save_image(Image.fromarray(x_sample), p.outpath_samples, "", p.seeds[i], p.prompts[i], opts.samples_format, info=infotext(i), p=p, suffix="-before-face-restoration")
 
                     devices.torch_gc()
@@ -842,16 +848,15 @@ def process_images_inner(p: StableDiffusionProcessing) -> Processed:
                     pp = scripts.PostprocessImageArgs(image)
                     p.scripts.postprocess_image(p, pp)
                     image = pp.image
-
                 if p.color_corrections is not None and i < len(p.color_corrections):
-                    if opts.save and not p.do_not_save_samples and opts.save_images_before_color_correction:
+                    if opts.save and not p.do_not_save_samples and opts.save_images_before_color_correction and save_images_if_interrupt:
                         image_without_cc = apply_overlay(image, p.paste_to, i, p.overlay_images)
                         images.save_image(image_without_cc, p.outpath_samples, "", p.seeds[i], p.prompts[i], opts.samples_format, info=infotext(i), p=p, suffix="-before-color-correction")
                     image = apply_color_correction(p.color_corrections[i], image)
 
                 image = apply_overlay(image, p.paste_to, i, p.overlay_images)
 
-                if opts.samples_save and not p.do_not_save_samples:
+                if opts.samples_save and not p.do_not_save_samples and save_images_if_interrupt:
                     images.save_image(image, p.outpath_samples, "", p.seeds[i], p.prompts[i], opts.samples_format, info=infotext(i), p=p)
 
                 text = infotext(i)
@@ -859,8 +864,7 @@ def process_images_inner(p: StableDiffusionProcessing) -> Processed:
                 if opts.enable_pnginfo:
                     image.info["parameters"] = text
                 output_images.append(image)
-
-                if hasattr(p, 'mask_for_overlay') and p.mask_for_overlay and any([opts.save_mask, opts.save_mask_composite, opts.return_mask, opts.return_mask_composite]):
+                if hasattr(p, 'mask_for_overlay') and p.mask_for_overlay and any([opts.save_mask, opts.save_mask_composite, opts.return_mask, opts.return_mask_composite]) and save_images_if_interrupt:
                     image_mask = p.mask_for_overlay.convert('RGB')
                     image_mask_composite = Image.composite(image.convert('RGBA').convert('RGBa'), Image.new('RGBa', image.size), images.resize_image(2, p.mask_for_overlay, image.width, image.height).convert('L')).convert('RGBA')
 
@@ -896,7 +900,6 @@ def process_images_inner(p: StableDiffusionProcessing) -> Processed:
                     grid.info["parameters"] = text
                 output_images.insert(0, grid)
                 index_of_first_image = 1
-
             if opts.grid_save:
                 images.save_image(grid, p.outpath_grids, "grid", p.all_seeds[0], p.all_prompts[0], opts.grid_format, info=infotext(use_main_prompt=True), short_filename=not opts.grid_extended_filename, p=p, grid=True)
 
@@ -1091,7 +1094,7 @@ class StableDiffusionProcessingTxt2Img(StableDiffusionProcessing):
         def save_intermediate(image, index):
             """saves image before applying hires fix, if enabled in options; takes as an argument either an image or batch with latent space images"""
 
-            if not opts.save or self.do_not_save_samples or not opts.save_images_before_highres_fix:
+            if not opts.save or self.do_not_save_samples or not opts.save_images_before_highres_fix or not save_images_if_interrupted():
                 return
 
             if not isinstance(image, Image.Image):
-- 
cgit v1.2.1


From 496cef956b3337c4745b1224e244706bd747195a Mon Sep 17 00:00:00 2001
From: catboxanon <122327233+catboxanon@users.noreply.github.com>
Date: Sat, 5 Aug 2023 21:14:13 -0400
Subject: Allow s_noise override to actually be used

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

(limited to 'modules/processing.py')

diff --git a/modules/processing.py b/modules/processing.py
index a9d66005..1601a14c 100644
--- a/modules/processing.py
+++ b/modules/processing.py
@@ -149,7 +149,7 @@ class StableDiffusionProcessing:
         self.s_churn = s_churn or opts.s_churn
         self.s_tmin = s_tmin or opts.s_tmin
         self.s_tmax = s_tmax or float('inf')  # not representable as a standard ui option
-        self.s_noise = s_noise or opts.s_noise
+        self.s_noise = opts.data.get('s_noise', s_noise)
         self.override_settings = {k: v for k, v in (override_settings or {}).items() if k not in shared.restricted_opts}
         self.override_settings_restore_afterwards = override_settings_restore_afterwards
         self.is_using_inpainting_conditioning = False
-- 
cgit v1.2.1


From c11104fed5ffee7b9a22674889580028296c5e55 Mon Sep 17 00:00:00 2001
From: catboxanon <122327233+catboxanon@users.noreply.github.com>
Date: Sat, 5 Aug 2023 21:42:03 -0400
Subject: Add s_tmax

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

(limited to 'modules/processing.py')

diff --git a/modules/processing.py b/modules/processing.py
index a9d66005..a5cd2a47 100644
--- a/modules/processing.py
+++ b/modules/processing.py
@@ -148,7 +148,7 @@ class StableDiffusionProcessing:
         self.s_min_uncond = s_min_uncond or opts.s_min_uncond
         self.s_churn = s_churn or opts.s_churn
         self.s_tmin = s_tmin or opts.s_tmin
-        self.s_tmax = s_tmax or float('inf')  # not representable as a standard ui option
+        self.s_tmax = opts.data.get('s_tmax', 0) or float('inf')  # not representable as a standard ui option
         self.s_noise = s_noise or opts.s_noise
         self.override_settings = {k: v for k, v in (override_settings or {}).items() if k not in shared.restricted_opts}
         self.override_settings_restore_afterwards = override_settings_restore_afterwards
-- 
cgit v1.2.1


From 85c2c138d2f5f853c79bde3775d69628e86c65ca Mon Sep 17 00:00:00 2001
From: catboxanon <122327233+catboxanon@users.noreply.github.com>
Date: Sat, 5 Aug 2023 21:51:46 -0400
Subject: Attempt to read s_tmax from arg first if option not found

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

(limited to 'modules/processing.py')

diff --git a/modules/processing.py b/modules/processing.py
index a5cd2a47..2003532d 100644
--- a/modules/processing.py
+++ b/modules/processing.py
@@ -148,7 +148,7 @@ class StableDiffusionProcessing:
         self.s_min_uncond = s_min_uncond or opts.s_min_uncond
         self.s_churn = s_churn or opts.s_churn
         self.s_tmin = s_tmin or opts.s_tmin
-        self.s_tmax = opts.data.get('s_tmax', 0) or float('inf')  # not representable as a standard ui option
+        self.s_tmax = opts.data.get('s_tmax', s_tmax or 0) or float('inf')  # not representable as a standard ui option
         self.s_noise = s_noise or opts.s_noise
         self.override_settings = {k: v for k, v in (override_settings or {}).items() if k not in shared.restricted_opts}
         self.override_settings_restore_afterwards = override_settings_restore_afterwards
-- 
cgit v1.2.1


From f18a03219039ebaa48ce96adc5b50e0e5ed5d84f Mon Sep 17 00:00:00 2001
From: catboxanon <122327233+catboxanon@users.noreply.github.com>
Date: Sat, 5 Aug 2023 23:05:25 -0400
Subject: Correct s_noise fix

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

(limited to 'modules/processing.py')

diff --git a/modules/processing.py b/modules/processing.py
index 1601a14c..bb268db7 100644
--- a/modules/processing.py
+++ b/modules/processing.py
@@ -110,7 +110,7 @@ class StableDiffusionProcessing:
     cached_uc = [None, None]
     cached_c = [None, None]
 
-    def __init__(self, sd_model=None, outpath_samples=None, outpath_grids=None, prompt: str = "", styles: List[str] = None, seed: int = -1, subseed: int = -1, subseed_strength: float = 0, seed_resize_from_h: int = -1, seed_resize_from_w: int = -1, seed_enable_extras: bool = True, sampler_name: str = None, batch_size: int = 1, n_iter: int = 1, steps: int = 50, cfg_scale: float = 7.0, width: int = 512, height: int = 512, restore_faces: bool = False, tiling: bool = False, do_not_save_samples: bool = False, do_not_save_grid: bool = False, extra_generation_params: Dict[Any, Any] = None, overlay_images: Any = None, negative_prompt: str = None, eta: float = None, do_not_reload_embeddings: bool = False, denoising_strength: float = 0, ddim_discretize: str = None, s_min_uncond: float = 0.0, s_churn: float = 0.0, s_tmax: float = None, s_tmin: float = 0.0, s_noise: float = 1.0, override_settings: Dict[str, Any] = None, override_settings_restore_afterwards: bool = True, sampler_index: int = None, script_args: list = None):
+    def __init__(self, sd_model=None, outpath_samples=None, outpath_grids=None, prompt: str = "", styles: List[str] = None, seed: int = -1, subseed: int = -1, subseed_strength: float = 0, seed_resize_from_h: int = -1, seed_resize_from_w: int = -1, seed_enable_extras: bool = True, sampler_name: str = None, batch_size: int = 1, n_iter: int = 1, steps: int = 50, cfg_scale: float = 7.0, width: int = 512, height: int = 512, restore_faces: bool = False, tiling: bool = False, do_not_save_samples: bool = False, do_not_save_grid: bool = False, extra_generation_params: Dict[Any, Any] = None, overlay_images: Any = None, negative_prompt: str = None, eta: float = None, do_not_reload_embeddings: bool = False, denoising_strength: float = 0, ddim_discretize: str = None, s_min_uncond: float = 0.0, s_churn: float = 0.0, s_tmax: float = None, s_tmin: float = 0.0, s_noise: float = None, override_settings: Dict[str, Any] = None, override_settings_restore_afterwards: bool = True, sampler_index: int = None, script_args: list = None):
         if sampler_index is not None:
             print("sampler_index argument for StableDiffusionProcessing does not do anything; use sampler_name", file=sys.stderr)
 
@@ -149,7 +149,7 @@ class StableDiffusionProcessing:
         self.s_churn = s_churn or opts.s_churn
         self.s_tmin = s_tmin or opts.s_tmin
         self.s_tmax = s_tmax or float('inf')  # not representable as a standard ui option
-        self.s_noise = opts.data.get('s_noise', s_noise)
+        self.s_noise = s_noise if s_noise is not None else opts.data.get('s_noise', 1.0)
         self.override_settings = {k: v for k, v in (override_settings or {}).items() if k not in shared.restricted_opts}
         self.override_settings_restore_afterwards = override_settings_restore_afterwards
         self.is_using_inpainting_conditioning = False
-- 
cgit v1.2.1


From d86d12e9117772f041682124badc7baac7c57911 Mon Sep 17 00:00:00 2001
From: AUTOMATIC1111 <16777216c@gmail.com>
Date: Sun, 6 Aug 2023 06:21:36 +0300
Subject: rework saving incomplete images

---
 modules/processing.py | 18 +++++++++---------
 1 file changed, 9 insertions(+), 9 deletions(-)

(limited to 'modules/processing.py')

diff --git a/modules/processing.py b/modules/processing.py
index 8f26621b..aef8fafd 100644
--- a/modules/processing.py
+++ b/modules/processing.py
@@ -103,10 +103,6 @@ def txt2img_image_conditioning(sd_model, x, width, height):
         return x.new_zeros(x.shape[0], 5, 1, 1, dtype=x.dtype, device=x.device)
 
 
-def save_images_if_interrupted():
-    return not (opts.dont_save_interrupted_images and (state.interrupted or state.skipped))
-
-
 class StableDiffusionProcessing:
     """
     The first set of paramaters: sd_models -> do_not_reload_embeddings represent the minimum required to create a StableDiffusionProcessing
@@ -372,6 +368,10 @@ class StableDiffusionProcessing:
     def parse_extra_network_prompts(self):
         self.prompts, self.extra_network_data = extra_networks.parse_prompts(self.prompts)
 
+    def save_samples(self) -> bool:
+        """Returns whether generated images need to be written to disk"""
+        return opts.samples_save and not self.do_not_save_samples and (opts.save_incomplete_images or not state.interrupted and not state.skipped)
+
 
 class Processed:
     def __init__(self, p: StableDiffusionProcessing, images_list, seed=-1, info="", subseed=None, all_prompts=None, all_negative_prompts=None, all_seeds=None, all_subseeds=None, index_of_first_image=0, infotexts=None, comments=""):
@@ -827,7 +827,7 @@ def process_images_inner(p: StableDiffusionProcessing) -> Processed:
             def infotext(index=0, use_main_prompt=False):
                 return create_infotext(p, p.prompts, p.seeds, p.subseeds, use_main_prompt=use_main_prompt, index=index, all_negative_prompts=p.negative_prompts)
 
-            save_images_if_interrupt = save_images_if_interrupted()
+            save_samples = p.save_samples()
 
             for i, x_sample in enumerate(x_samples_ddim):
                 p.batch_index = i
@@ -836,7 +836,7 @@ def process_images_inner(p: StableDiffusionProcessing) -> Processed:
                 x_sample = x_sample.astype(np.uint8)
 
                 if p.restore_faces:
-                    if opts.save and not p.do_not_save_samples and opts.save_images_before_face_restoration and save_images_if_interrupt:
+                    if save_samples and opts.save_images_before_face_restoration:
                         images.save_image(Image.fromarray(x_sample), p.outpath_samples, "", p.seeds[i], p.prompts[i], opts.samples_format, info=infotext(i), p=p, suffix="-before-face-restoration")
 
                     devices.torch_gc()
@@ -851,14 +851,14 @@ def process_images_inner(p: StableDiffusionProcessing) -> Processed:
                     p.scripts.postprocess_image(p, pp)
                     image = pp.image
                 if p.color_corrections is not None and i < len(p.color_corrections):
-                    if opts.save and not p.do_not_save_samples and opts.save_images_before_color_correction and save_images_if_interrupt:
+                    if save_samples and opts.save_images_before_color_correction:
                         image_without_cc = apply_overlay(image, p.paste_to, i, p.overlay_images)
                         images.save_image(image_without_cc, p.outpath_samples, "", p.seeds[i], p.prompts[i], opts.samples_format, info=infotext(i), p=p, suffix="-before-color-correction")
                     image = apply_color_correction(p.color_corrections[i], image)
 
                 image = apply_overlay(image, p.paste_to, i, p.overlay_images)
 
-                if opts.samples_save and not p.do_not_save_samples and save_images_if_interrupt:
+                if save_samples:
                     images.save_image(image, p.outpath_samples, "", p.seeds[i], p.prompts[i], opts.samples_format, info=infotext(i), p=p)
 
                 text = infotext(i)
@@ -1096,7 +1096,7 @@ class StableDiffusionProcessingTxt2Img(StableDiffusionProcessing):
         def save_intermediate(image, index):
             """saves image before applying hires fix, if enabled in options; takes as an argument either an image or batch with latent space images"""
 
-            if not opts.save or self.do_not_save_samples or not opts.save_images_before_highres_fix or not save_images_if_interrupted():
+            if not self.save_samples() or not opts.save_images_before_highres_fix:
                 return
 
             if not isinstance(image, Image.Image):
-- 
cgit v1.2.1


From ee96a6a58852e6db30afac129570efb88e991957 Mon Sep 17 00:00:00 2001
From: AUTOMATIC1111 <16777216c@gmail.com>
Date: Sun, 6 Aug 2023 06:32:41 +0300
Subject: do the same for s_tmax #12345

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

(limited to 'modules/processing.py')

diff --git a/modules/processing.py b/modules/processing.py
index 8743ac0a..6625f2de 100644
--- a/modules/processing.py
+++ b/modules/processing.py
@@ -148,8 +148,8 @@ class StableDiffusionProcessing:
         self.s_min_uncond = s_min_uncond or opts.s_min_uncond
         self.s_churn = s_churn or opts.s_churn
         self.s_tmin = s_tmin or opts.s_tmin
-        self.s_tmax = opts.data.get('s_tmax', s_tmax or 0) or float('inf')  # not representable as a standard ui option
-        self.s_noise = s_noise if s_noise is not None else opts.data.get('s_noise', 1.0)
+        self.s_tmax = (s_tmax if s_tmax is not None else opts.s_tmax) or float('inf')
+        self.s_noise = s_noise if s_noise is not None else opts.s_noise
         self.override_settings = {k: v for k, v in (override_settings or {}).items() if k not in shared.restricted_opts}
         self.override_settings_restore_afterwards = override_settings_restore_afterwards
         self.is_using_inpainting_conditioning = False
-- 
cgit v1.2.1


From 5cae08f2c32b63989aa04fba32e5c56d9ff4fa6e Mon Sep 17 00:00:00 2001
From: AUTOMATIC1111 <16777216c@gmail.com>
Date: Sun, 6 Aug 2023 06:55:19 +0300
Subject: fix rework saving incomplete images

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

(limited to 'modules/processing.py')

diff --git a/modules/processing.py b/modules/processing.py
index 6625f2de..7d21fb12 100644
--- a/modules/processing.py
+++ b/modules/processing.py
@@ -866,7 +866,7 @@ def process_images_inner(p: StableDiffusionProcessing) -> Processed:
                 if opts.enable_pnginfo:
                     image.info["parameters"] = text
                 output_images.append(image)
-                if hasattr(p, 'mask_for_overlay') and p.mask_for_overlay and any([opts.save_mask, opts.save_mask_composite, opts.return_mask, opts.return_mask_composite]) and save_images_if_interrupt:
+                if save_samples and hasattr(p, 'mask_for_overlay') and p.mask_for_overlay and any([opts.save_mask, opts.save_mask_composite, opts.return_mask, opts.return_mask_composite]):
                     image_mask = p.mask_for_overlay.convert('RGB')
                     image_mask_composite = Image.composite(image.convert('RGBA').convert('RGBa'), Image.new('RGBa', image.size), images.resize_image(2, p.mask_for_overlay, image.width, image.height).convert('L')).convert('RGBA')
 
-- 
cgit v1.2.1


From 57e8a11d17a6646fdf551320f5f714fba752987a Mon Sep 17 00:00:00 2001
From: AUTOMATIC1111 <16777216c@gmail.com>
Date: Sun, 6 Aug 2023 13:25:51 +0300
Subject: enable cond cache by default

---
 modules/processing.py | 31 ++++++++++++++++++-------------
 1 file changed, 18 insertions(+), 13 deletions(-)

(limited to 'modules/processing.py')

diff --git a/modules/processing.py b/modules/processing.py
index 7d21fb12..31745006 100644
--- a/modules/processing.py
+++ b/modules/processing.py
@@ -295,7 +295,7 @@ class StableDiffusionProcessing:
         self.sampler = None
         self.c = None
         self.uc = None
-        if not opts.experimental_persistent_cond_cache:
+        if not opts.persistent_cond_cache:
             StableDiffusionProcessing.cached_c = [None, None]
             StableDiffusionProcessing.cached_uc = [None, None]
 
@@ -319,6 +319,21 @@ class StableDiffusionProcessing:
         self.all_prompts = [shared.prompt_styles.apply_styles_to_prompt(x, self.styles) for x in self.all_prompts]
         self.all_negative_prompts = [shared.prompt_styles.apply_negative_styles_to_prompt(x, self.styles) for x in self.all_negative_prompts]
 
+    def cached_params(self, required_prompts, steps, extra_network_data):
+        """Returns parameters that invalidate the cond cache if changed"""
+
+        return (
+            required_prompts,
+            steps,
+            opts.CLIP_stop_at_last_layers,
+            shared.sd_model.sd_checkpoint_info,
+            extra_network_data,
+            opts.sdxl_crop_left,
+            opts.sdxl_crop_top,
+            self.width,
+            self.height,
+        )
+
     def get_conds_with_caching(self, function, required_prompts, steps, caches, extra_network_data):
         """
         Returns the result of calling function(shared.sd_model, required_prompts, steps)
@@ -332,17 +347,7 @@ class StableDiffusionProcessing:
         caches is a list with items described above.
         """
 
-        cached_params = (
-            required_prompts,
-            steps,
-            opts.CLIP_stop_at_last_layers,
-            shared.sd_model.sd_checkpoint_info,
-            extra_network_data,
-            opts.sdxl_crop_left,
-            opts.sdxl_crop_top,
-            self.width,
-            self.height,
-        )
+        cached_params = self.cached_params(required_prompts, steps, extra_network_data)
 
         for cache in caches:
             if cache[0] is not None and cached_params == cache[0]:
@@ -1184,7 +1189,7 @@ class StableDiffusionProcessingTxt2Img(StableDiffusionProcessing):
         super().close()
         self.hr_c = None
         self.hr_uc = None
-        if not opts.experimental_persistent_cond_cache:
+        if not opts.persistent_cond_cache:
             StableDiffusionProcessingTxt2Img.cached_hr_uc = [None, None]
             StableDiffusionProcessingTxt2Img.cached_hr_c = [None, None]
 
-- 
cgit v1.2.1


From f1975b0213f5be400889ec04b3891d1cb571fe20 Mon Sep 17 00:00:00 2001
From: AUTOMATIC1111 <16777216c@gmail.com>
Date: Sun, 6 Aug 2023 17:01:07 +0300
Subject: initial refiner support

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

(limited to 'modules/processing.py')

diff --git a/modules/processing.py b/modules/processing.py
index 31745006..f4748d6d 100644
--- a/modules/processing.py
+++ b/modules/processing.py
@@ -666,6 +666,10 @@ def process_images(p: StableDiffusionProcessing) -> Processed:
     stored_opts = {k: opts.data[k] for k in p.override_settings.keys()}
 
     try:
+        # after running refiner, the refiner model is not unloaded - webui swaps back to main model here
+        if shared.sd_model.sd_checkpoint_info.title != opts.sd_model_checkpoint:
+            sd_models.reload_model_weights()
+
         # if no checkpoint override or the override checkpoint can't be found, remove override entry and load opts checkpoint
         if sd_models.checkpoint_aliases.get(p.override_settings.get('sd_model_checkpoint')) is None:
             p.override_settings.pop('sd_model_checkpoint', None)
-- 
cgit v1.2.1


From 5a0db84b6c7322082c7532df11a29a95a59a612b Mon Sep 17 00:00:00 2001
From: AUTOMATIC1111 <16777216c@gmail.com>
Date: Sun, 6 Aug 2023 17:53:33 +0300
Subject: add infotext add proper support for recalculating conds in
 k-diffusion samplers remove support for compvis samplers

---
 modules/processing.py | 10 ++++++++++
 1 file changed, 10 insertions(+)

(limited to 'modules/processing.py')

diff --git a/modules/processing.py b/modules/processing.py
index f4748d6d..ec66fd8e 100644
--- a/modules/processing.py
+++ b/modules/processing.py
@@ -370,6 +370,9 @@ class StableDiffusionProcessing:
         self.uc = self.get_conds_with_caching(prompt_parser.get_learned_conditioning, negative_prompts, self.steps * self.step_multiplier, [self.cached_uc], self.extra_network_data)
         self.c = self.get_conds_with_caching(prompt_parser.get_multicond_learned_conditioning, prompts, self.steps * self.step_multiplier, [self.cached_c], self.extra_network_data)
 
+    def get_conds(self):
+        return self.c, self.uc
+
     def parse_extra_network_prompts(self):
         self.prompts, self.extra_network_data = extra_networks.parse_prompts(self.prompts)
 
@@ -1251,6 +1254,13 @@ class StableDiffusionProcessingTxt2Img(StableDiffusionProcessing):
                 with devices.autocast():
                     extra_networks.activate(self, self.extra_network_data)
 
+    def get_conds(self):
+        if self.is_hr_pass:
+            return self.hr_c, self.hr_uc
+
+        return super().get_conds()
+
+
     def parse_extra_network_prompts(self):
         res = super().parse_extra_network_prompts()
 
-- 
cgit v1.2.1


From 2a72d76d6f3d34b1ffccec7736b19e7d52033dad Mon Sep 17 00:00:00 2001
From: dhwz <dhwz@gmx.net>
Date: Tue, 8 Aug 2023 19:08:37 +0200
Subject: fix typo

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

(limited to 'modules/processing.py')

diff --git a/modules/processing.py b/modules/processing.py
index 31745006..dc6e8ff1 100644
--- a/modules/processing.py
+++ b/modules/processing.py
@@ -568,7 +568,7 @@ def decode_latent_batch(model, batch, target_device=None, check_for_nans=False):
                 errors.print_error_explanation(
                     "A tensor with all NaNs was produced in VAE.\n"
                     "Web UI will now convert VAE into 32-bit float and retry.\n"
-                    "To disable this behavior, disable the 'Automaticlly revert VAE to 32-bit floats' setting.\n"
+                    "To disable this behavior, disable the 'Automatically revert VAE to 32-bit floats' setting.\n"
                     "To always start with 32-bit VAE, use --no-half-vae commandline flag."
                 )
 
-- 
cgit v1.2.1


From a8a256f9b5b445206818bfc8a363ed5a1ba50c86 Mon Sep 17 00:00:00 2001
From: AUTOMATIC1111 <16777216c@gmail.com>
Date: Tue, 8 Aug 2023 21:07:18 +0300
Subject: REMOVE

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

(limited to 'modules/processing.py')

diff --git a/modules/processing.py b/modules/processing.py
index 31745006..61ba5f11 100644
--- a/modules/processing.py
+++ b/modules/processing.py
@@ -1112,9 +1112,6 @@ class StableDiffusionProcessingTxt2Img(StableDiffusionProcessing):
 
         img2img_sampler_name = self.hr_sampler_name or self.sampler_name
 
-        if self.sampler_name in ['PLMS', 'UniPC']:  # PLMS/UniPC do not support img2img so we just silently switch to DDIM
-            img2img_sampler_name = 'DDIM'
-
         self.sampler = sd_samplers.create_sampler(img2img_sampler_name, self.sd_model)
 
         if self.latent_scale_mode is not None:
-- 
cgit v1.2.1


From ec194b637476855ea5918a44a65e85fb587483ab Mon Sep 17 00:00:00 2001
From: AUTOMATIC1111 <16777216c@gmail.com>
Date: Tue, 8 Aug 2023 22:14:02 +0300
Subject: fix webui not switching back to original model from refiner when
 batch count is greater than 1

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

(limited to 'modules/processing.py')

diff --git a/modules/processing.py b/modules/processing.py
index b635cc74..cf62cdd3 100644
--- a/modules/processing.py
+++ b/modules/processing.py
@@ -766,6 +766,8 @@ def process_images_inner(p: StableDiffusionProcessing) -> Processed:
             if state.interrupted:
                 break
 
+            sd_models.reload_model_weights()  # model can be changed for example by refiner
+
             p.prompts = p.all_prompts[n * p.batch_size:(n + 1) * p.batch_size]
             p.negative_prompts = p.all_negative_prompts[n * p.batch_size:(n + 1) * p.batch_size]
             p.seeds = p.all_seeds[n * p.batch_size:(n + 1) * p.batch_size]
-- 
cgit v1.2.1


From d81d3fa8cde83ce1421889ed481a69c950c0c6f6 Mon Sep 17 00:00:00 2001
From: AUTOMATIC1111 <16777216c@gmail.com>
Date: Wed, 9 Aug 2023 07:45:06 +0300
Subject: fix styles missing from the prompt in infotext when making a grid of
 batch of multiplie images

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

(limited to 'modules/processing.py')

diff --git a/modules/processing.py b/modules/processing.py
index d7266307..aa72b132 100644
--- a/modules/processing.py
+++ b/modules/processing.py
@@ -172,6 +172,8 @@ class StableDiffusionProcessing:
         self.iteration = 0
         self.is_hr_pass = False
         self.sampler = None
+        self.main_prompt = None
+        self.main_negative_prompt = None
 
         self.prompts = None
         self.negative_prompts = None
@@ -319,6 +321,9 @@ class StableDiffusionProcessing:
         self.all_prompts = [shared.prompt_styles.apply_styles_to_prompt(x, self.styles) for x in self.all_prompts]
         self.all_negative_prompts = [shared.prompt_styles.apply_negative_styles_to_prompt(x, self.styles) for x in self.all_negative_prompts]
 
+        self.main_prompt = self.all_prompts[0]
+        self.main_negative_prompt = self.all_negative_prompts[0]
+
     def cached_params(self, required_prompts, steps, extra_network_data):
         """Returns parameters that invalidate the cond cache if changed"""
 
@@ -653,8 +658,8 @@ def create_infotext(p, all_prompts, all_seeds, all_subseeds, comments=None, iter
 
     generation_params_text = ", ".join([k if k == v else f'{k}: {generation_parameters_copypaste.quote(v)}' for k, v in generation_params.items() if v is not None])
 
-    prompt_text = p.prompt if use_main_prompt else all_prompts[index]
-    negative_prompt_text = f"\nNegative prompt: {all_negative_prompts[index]}" if all_negative_prompts[index] else ""
+    prompt_text = p.main_prompt if use_main_prompt else all_prompts[index]
+    negative_prompt_text = f"\nNegative prompt: {p.main_negative_prompt if use_main_prompt else all_negative_prompts[index]}" if all_negative_prompts[index] else ""
 
     return f"{prompt_text}{negative_prompt_text}\n{generation_params_text}".strip()
 
-- 
cgit v1.2.1


From 0d5dc9a6e7f6362e423a06bf0e75dd5854025394 Mon Sep 17 00:00:00 2001
From: AUTOMATIC1111 <16777216c@gmail.com>
Date: Wed, 9 Aug 2023 08:43:31 +0300
Subject: rework RNG to use generators instead of generating noises beforehand

---
 modules/processing.py | 89 ++++++---------------------------------------------
 1 file changed, 10 insertions(+), 79 deletions(-)

(limited to 'modules/processing.py')

diff --git a/modules/processing.py b/modules/processing.py
index aa72b132..2df5e8c7 100644
--- a/modules/processing.py
+++ b/modules/processing.py
@@ -14,7 +14,7 @@ from skimage import exposure
 from typing import Any, Dict, List
 
 import modules.sd_hijack
-from modules import devices, prompt_parser, masking, sd_samplers, lowvram, generation_parameters_copypaste, extra_networks, sd_vae_approx, scripts, sd_samplers_common, sd_unet, errors
+from modules import devices, prompt_parser, masking, sd_samplers, lowvram, generation_parameters_copypaste, extra_networks, sd_vae_approx, scripts, sd_samplers_common, sd_unet, errors, rng
 from modules.sd_hijack import model_hijack
 from modules.sd_samplers_common import images_tensor_to_samples, decode_first_stage, approximation_indexes
 from modules.shared import opts, cmd_opts, state
@@ -186,6 +186,7 @@ class StableDiffusionProcessing:
         self.cached_c = StableDiffusionProcessing.cached_c
         self.uc = None
         self.c = None
+        self.rng: rng.ImageRNG = None
 
         self.user = None
 
@@ -475,82 +476,9 @@ class Processed:
         return self.token_merging_ratio_hr if for_hr else self.token_merging_ratio
 
 
-# from https://discuss.pytorch.org/t/help-regarding-slerp-function-for-generative-model-sampling/32475/3
-def slerp(val, low, high):
-    low_norm = low/torch.norm(low, dim=1, keepdim=True)
-    high_norm = high/torch.norm(high, dim=1, keepdim=True)
-    dot = (low_norm*high_norm).sum(1)
-
-    if dot.mean() > 0.9995:
-        return low * val + high * (1 - val)
-
-    omega = torch.acos(dot)
-    so = torch.sin(omega)
-    res = (torch.sin((1.0-val)*omega)/so).unsqueeze(1)*low + (torch.sin(val*omega)/so).unsqueeze(1) * high
-    return res
-
-
 def create_random_tensors(shape, seeds, subseeds=None, subseed_strength=0.0, seed_resize_from_h=0, seed_resize_from_w=0, p=None):
-    eta_noise_seed_delta = opts.eta_noise_seed_delta or 0
-    xs = []
-
-    # if we have multiple seeds, this means we are working with batch size>1; this then
-    # 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 or eta_noise_seed_delta > 0):
-        sampler_noises = [[] for _ in range(p.sampler.number_of_needed_noises(p))]
-    else:
-        sampler_noises = None
-
-    for i, seed in enumerate(seeds):
-        noise_shape = shape if seed_resize_from_h <= 0 or seed_resize_from_w <= 0 else (shape[0], seed_resize_from_h//8, seed_resize_from_w//8)
-
-        subnoise = None
-        if subseeds is not None and subseed_strength != 0:
-            subseed = 0 if i >= len(subseeds) else subseeds[i]
-
-            subnoise = devices.randn(subseed, noise_shape)
-
-        # randn results depend on device; gpu and cpu get different results for same seed;
-        # the way I see it, it's better to do this on CPU, so that everyone gets same result;
-        # but the original script had it like this, so I do not dare change it for now because
-        # it will break everyone's seeds.
-        noise = devices.randn(seed, noise_shape)
-
-        if subnoise is not None:
-            noise = slerp(subseed_strength, noise, subnoise)
-
-        if noise_shape != shape:
-            x = devices.randn(seed, shape)
-            dx = (shape[2] - noise_shape[2]) // 2
-            dy = (shape[1] - noise_shape[1]) // 2
-            w = noise_shape[2] if dx >= 0 else noise_shape[2] + 2 * dx
-            h = noise_shape[1] if dy >= 0 else noise_shape[1] + 2 * dy
-            tx = 0 if dx < 0 else dx
-            ty = 0 if dy < 0 else dy
-            dx = max(-dx, 0)
-            dy = max(-dy, 0)
-
-            x[:, ty:ty+h, tx:tx+w] = noise[:, dy:dy+h, dx:dx+w]
-            noise = x
-
-        if sampler_noises is not None:
-            cnt = p.sampler.number_of_needed_noises(p)
-
-            if eta_noise_seed_delta > 0:
-                devices.manual_seed(seed + eta_noise_seed_delta)
-
-            for j in range(cnt):
-                sampler_noises[j].append(devices.randn_without_seed(tuple(noise_shape)))
-
-        xs.append(noise)
-
-    if sampler_noises is not None:
-        p.sampler.sampler_noises = [torch.stack(n).to(shared.device) for n in sampler_noises]
-
-    x = torch.stack(xs).to(shared.device)
-    return x
+    g = rng.ImageRNG(shape, seeds, subseeds=subseeds, subseed_strength=subseed_strength, seed_resize_from_h=seed_resize_from_h, seed_resize_from_w=seed_resize_from_w)
+    return g.next()
 
 
 class DecodedSamples(list):
@@ -769,6 +697,8 @@ def process_images_inner(p: StableDiffusionProcessing) -> Processed:
             p.seeds = p.all_seeds[n * p.batch_size:(n + 1) * p.batch_size]
             p.subseeds = p.all_subseeds[n * p.batch_size:(n + 1) * p.batch_size]
 
+            p.rng = rng.ImageRNG((opt_C, p.height // opt_f, p.width // opt_f), p.seeds, subseeds=p.subseeds, subseed_strength=p.subseed_strength, seed_resize_from_h=p.seed_resize_from_h, seed_resize_from_w=p.seed_resize_from_w)
+
             if p.scripts is not None:
                 p.scripts.before_process_batch(p, batch_number=n, prompts=p.prompts, seeds=p.seeds, subseeds=p.subseeds)
 
@@ -1072,7 +1002,7 @@ class StableDiffusionProcessingTxt2Img(StableDiffusionProcessing):
     def sample(self, conditioning, unconditional_conditioning, seeds, subseeds, subseed_strength, prompts):
         self.sampler = sd_samplers.create_sampler(self.sampler_name, self.sd_model)
 
-        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)
+        x = self.rng.next()
         samples = self.sampler.sample(self, x, conditioning, unconditional_conditioning, image_conditioning=self.txt2img_image_conditioning(x))
         del x
 
@@ -1160,7 +1090,8 @@ class StableDiffusionProcessingTxt2Img(StableDiffusionProcessing):
 
         samples = samples[:, :, self.truncate_y//2:samples.shape[2]-(self.truncate_y+1)//2, self.truncate_x//2:samples.shape[3]-(self.truncate_x+1)//2]
 
-        noise = create_random_tensors(samples.shape[1:], seeds=seeds, subseeds=subseeds, subseed_strength=subseed_strength, p=self)
+        self.rng = rng.ImageRNG(samples.shape[1:], self.seeds, subseeds=self.subseeds, subseed_strength=self.subseed_strength, seed_resize_from_h=self.seed_resize_from_h, seed_resize_from_w=self.seed_resize_from_w)
+        noise = self.rng.next()
 
         # GC now before running the next img2img to prevent running out of memory
         devices.torch_gc()
@@ -1418,7 +1349,7 @@ class StableDiffusionProcessingImg2Img(StableDiffusionProcessing):
         self.image_conditioning = self.img2img_image_conditioning(image, self.init_latent, image_mask)
 
     def sample(self, conditioning, unconditional_conditioning, seeds, subseeds, subseed_strength, prompts):
-        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)
+        x = self.rng.next()
 
         if self.initial_noise_multiplier != 1.0:
             self.extra_generation_params["Noise multiplier"] = self.initial_noise_multiplier
-- 
cgit v1.2.1


From d1ba46b6e15d2f2917e7d392955c8c6f988527ba Mon Sep 17 00:00:00 2001
From: Robert Barron <rubberbaron@robustspread.com>
Date: Wed, 9 Aug 2023 07:46:30 -0700
Subject: allow first pass and hires pass to use a single prompt to do
 different prompt editing, hires is 1.0..2.0:   relative time range is [1..2] 
  absolute time range is [steps+1..steps+hire_steps], e.g. with 30 steps and
 20 hires steps, '20' is 2/3rds through first pass, and 40 is halfway through
 hires pass

---
 modules/processing.py | 20 ++++++++++++--------
 1 file changed, 12 insertions(+), 8 deletions(-)

(limited to 'modules/processing.py')

diff --git a/modules/processing.py b/modules/processing.py
index 31745006..0750b299 100644
--- a/modules/processing.py
+++ b/modules/processing.py
@@ -319,12 +319,14 @@ class StableDiffusionProcessing:
         self.all_prompts = [shared.prompt_styles.apply_styles_to_prompt(x, self.styles) for x in self.all_prompts]
         self.all_negative_prompts = [shared.prompt_styles.apply_negative_styles_to_prompt(x, self.styles) for x in self.all_negative_prompts]
 
-    def cached_params(self, required_prompts, steps, extra_network_data):
+    def cached_params(self, required_prompts, steps, hires_steps, extra_network_data, use_old_scheduling):
         """Returns parameters that invalidate the cond cache if changed"""
 
         return (
             required_prompts,
             steps,
+            hires_steps,
+            use_old_scheduling,
             opts.CLIP_stop_at_last_layers,
             shared.sd_model.sd_checkpoint_info,
             extra_network_data,
@@ -334,7 +336,7 @@ class StableDiffusionProcessing:
             self.height,
         )
 
-    def get_conds_with_caching(self, function, required_prompts, steps, caches, extra_network_data):
+    def get_conds_with_caching(self, function, required_prompts, steps, hires_steps, caches, extra_network_data):
         """
         Returns the result of calling function(shared.sd_model, required_prompts, steps)
         using a cache to store the result if the same arguments have been used before.
@@ -347,7 +349,7 @@ class StableDiffusionProcessing:
         caches is a list with items described above.
         """
 
-        cached_params = self.cached_params(required_prompts, steps, extra_network_data)
+        cached_params = self.cached_params(required_prompts, steps, hires_steps, extra_network_data, shared.opts.use_old_scheduling)
 
         for cache in caches:
             if cache[0] is not None and cached_params == cache[0]:
@@ -356,7 +358,7 @@ class StableDiffusionProcessing:
         cache = caches[0]
 
         with devices.autocast():
-            cache[1] = function(shared.sd_model, required_prompts, steps)
+            cache[1] = function(shared.sd_model, required_prompts, steps, hires_steps, shared.opts.use_old_scheduling)
 
         cache[0] = cached_params
         return cache[1]
@@ -367,8 +369,9 @@ class StableDiffusionProcessing:
 
         sampler_config = sd_samplers.find_sampler_config(self.sampler_name)
         self.step_multiplier = 2 if sampler_config and sampler_config.options.get("second_order", False) else 1
-        self.uc = self.get_conds_with_caching(prompt_parser.get_learned_conditioning, negative_prompts, self.steps * self.step_multiplier, [self.cached_uc], self.extra_network_data)
-        self.c = self.get_conds_with_caching(prompt_parser.get_multicond_learned_conditioning, prompts, self.steps * self.step_multiplier, [self.cached_c], self.extra_network_data)
+        self.firstpass_steps = self.steps * self.step_multiplier
+        self.uc = self.get_conds_with_caching(prompt_parser.get_learned_conditioning, negative_prompts, self.firstpass_steps, None, [self.cached_uc], self.extra_network_data)
+        self.c = self.get_conds_with_caching(prompt_parser.get_multicond_learned_conditioning, prompts, self.firstpass_steps, None, [self.cached_c], self.extra_network_data)
 
     def parse_extra_network_prompts(self):
         self.prompts, self.extra_network_data = extra_networks.parse_prompts(self.prompts)
@@ -1225,8 +1228,9 @@ class StableDiffusionProcessingTxt2Img(StableDiffusionProcessing):
         hr_prompts = prompt_parser.SdConditioning(self.hr_prompts, width=self.hr_upscale_to_x, height=self.hr_upscale_to_y)
         hr_negative_prompts = prompt_parser.SdConditioning(self.hr_negative_prompts, width=self.hr_upscale_to_x, height=self.hr_upscale_to_y, is_negative_prompt=True)
 
-        self.hr_uc = self.get_conds_with_caching(prompt_parser.get_learned_conditioning, hr_negative_prompts, self.steps * self.step_multiplier, [self.cached_hr_uc, self.cached_uc], self.hr_extra_network_data)
-        self.hr_c = self.get_conds_with_caching(prompt_parser.get_multicond_learned_conditioning, hr_prompts, self.steps * self.step_multiplier, [self.cached_hr_c, self.cached_c], self.hr_extra_network_data)
+        hires_steps = (self.hr_second_pass_steps or self.steps) * self.step_multiplier
+        self.hr_uc = self.get_conds_with_caching(prompt_parser.get_learned_conditioning, hr_negative_prompts, self.firstpass_steps, hires_steps, [self.cached_hr_uc, self.cached_uc], self.hr_extra_network_data)
+        self.hr_c = self.get_conds_with_caching(prompt_parser.get_multicond_learned_conditioning, hr_prompts, self.firstpass_steps, hires_steps, [self.cached_hr_c, self.cached_c], self.hr_extra_network_data)
 
     def setup_conds(self):
         super().setup_conds()
-- 
cgit v1.2.1


From 259805947e4e567abcf6a4be20fe520d456c251f Mon Sep 17 00:00:00 2001
From: catboxanon <122327233+catboxanon@users.noreply.github.com>
Date: Wed, 9 Aug 2023 14:24:16 -0400
Subject: Add slerp import for extension backwards compat

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

(limited to 'modules/processing.py')

diff --git a/modules/processing.py b/modules/processing.py
index 2df5e8c7..6961b7b1 100644
--- a/modules/processing.py
+++ b/modules/processing.py
@@ -15,6 +15,7 @@ from typing import Any, Dict, List
 
 import modules.sd_hijack
 from modules import devices, prompt_parser, masking, sd_samplers, lowvram, generation_parameters_copypaste, extra_networks, sd_vae_approx, scripts, sd_samplers_common, sd_unet, errors, rng
+from modules.rng import slerp # noqa: F401
 from modules.sd_hijack import model_hijack
 from modules.sd_samplers_common import images_tensor_to_samples, decode_first_stage, approximation_indexes
 from modules.shared import opts, cmd_opts, state
-- 
cgit v1.2.1


From 9199b6b7ebe96cdf09571ba874a103e8ed8c90ef Mon Sep 17 00:00:00 2001
From: AUTOMATIC1111 <16777216c@gmail.com>
Date: Thu, 10 Aug 2023 11:20:46 +0300
Subject: add a custom UI element that combines accordion and checkbox rework
 hires fix UI to use accordion prevent bogus progress output in console when
 calculating hires fix dimensions

---
 modules/processing.py | 77 ++++++++++++++++++++++++++-------------------------
 1 file changed, 40 insertions(+), 37 deletions(-)

(limited to 'modules/processing.py')

diff --git a/modules/processing.py b/modules/processing.py
index 6961b7b1..7819644c 100644
--- a/modules/processing.py
+++ b/modules/processing.py
@@ -924,6 +924,45 @@ class StableDiffusionProcessingTxt2Img(StableDiffusionProcessing):
         self.hr_c = None
         self.hr_uc = None
 
+    def calculate_target_resolution(self):
+        if opts.use_old_hires_fix_width_height and self.applied_old_hires_behavior_to != (self.width, self.height):
+            self.hr_resize_x = self.width
+            self.hr_resize_y = self.height
+            self.hr_upscale_to_x = self.width
+            self.hr_upscale_to_y = self.height
+
+            self.width, self.height = old_hires_fix_first_pass_dimensions(self.width, self.height)
+            self.applied_old_hires_behavior_to = (self.width, self.height)
+
+        if self.hr_resize_x == 0 and self.hr_resize_y == 0:
+            self.extra_generation_params["Hires upscale"] = self.hr_scale
+            self.hr_upscale_to_x = int(self.width * self.hr_scale)
+            self.hr_upscale_to_y = int(self.height * self.hr_scale)
+        else:
+            self.extra_generation_params["Hires resize"] = f"{self.hr_resize_x}x{self.hr_resize_y}"
+
+            if self.hr_resize_y == 0:
+                self.hr_upscale_to_x = self.hr_resize_x
+                self.hr_upscale_to_y = self.hr_resize_x * self.height // self.width
+            elif self.hr_resize_x == 0:
+                self.hr_upscale_to_x = self.hr_resize_y * self.width // self.height
+                self.hr_upscale_to_y = self.hr_resize_y
+            else:
+                target_w = self.hr_resize_x
+                target_h = self.hr_resize_y
+                src_ratio = self.width / self.height
+                dst_ratio = self.hr_resize_x / self.hr_resize_y
+
+                if src_ratio < dst_ratio:
+                    self.hr_upscale_to_x = self.hr_resize_x
+                    self.hr_upscale_to_y = self.hr_resize_x * self.height // self.width
+                else:
+                    self.hr_upscale_to_x = self.hr_resize_y * self.width // self.height
+                    self.hr_upscale_to_y = self.hr_resize_y
+
+                self.truncate_x = (self.hr_upscale_to_x - target_w) // opt_f
+                self.truncate_y = (self.hr_upscale_to_y - target_h) // opt_f
+
     def init(self, all_prompts, all_seeds, all_subseeds):
         if self.enable_hr:
             if self.hr_checkpoint_name:
@@ -948,43 +987,7 @@ class StableDiffusionProcessingTxt2Img(StableDiffusionProcessing):
                 if not any(x.name == self.hr_upscaler for x in shared.sd_upscalers):
                     raise Exception(f"could not find upscaler named {self.hr_upscaler}")
 
-            if opts.use_old_hires_fix_width_height and self.applied_old_hires_behavior_to != (self.width, self.height):
-                self.hr_resize_x = self.width
-                self.hr_resize_y = self.height
-                self.hr_upscale_to_x = self.width
-                self.hr_upscale_to_y = self.height
-
-                self.width, self.height = old_hires_fix_first_pass_dimensions(self.width, self.height)
-                self.applied_old_hires_behavior_to = (self.width, self.height)
-
-            if self.hr_resize_x == 0 and self.hr_resize_y == 0:
-                self.extra_generation_params["Hires upscale"] = self.hr_scale
-                self.hr_upscale_to_x = int(self.width * self.hr_scale)
-                self.hr_upscale_to_y = int(self.height * self.hr_scale)
-            else:
-                self.extra_generation_params["Hires resize"] = f"{self.hr_resize_x}x{self.hr_resize_y}"
-
-                if self.hr_resize_y == 0:
-                    self.hr_upscale_to_x = self.hr_resize_x
-                    self.hr_upscale_to_y = self.hr_resize_x * self.height // self.width
-                elif self.hr_resize_x == 0:
-                    self.hr_upscale_to_x = self.hr_resize_y * self.width // self.height
-                    self.hr_upscale_to_y = self.hr_resize_y
-                else:
-                    target_w = self.hr_resize_x
-                    target_h = self.hr_resize_y
-                    src_ratio = self.width / self.height
-                    dst_ratio = self.hr_resize_x / self.hr_resize_y
-
-                    if src_ratio < dst_ratio:
-                        self.hr_upscale_to_x = self.hr_resize_x
-                        self.hr_upscale_to_y = self.hr_resize_x * self.height // self.width
-                    else:
-                        self.hr_upscale_to_x = self.hr_resize_y * self.width // self.height
-                        self.hr_upscale_to_y = self.hr_resize_y
-
-                    self.truncate_x = (self.hr_upscale_to_x - target_w) // opt_f
-                    self.truncate_y = (self.hr_upscale_to_y - target_h) // opt_f
+            self.calculate_target_resolution()
 
             if not state.processing_has_refined_job_count:
                 if state.job_count == -1:
-- 
cgit v1.2.1


From 33446acf47a8c3e0c0964782189562df3c4bcf4f Mon Sep 17 00:00:00 2001
From: AUTOMATIC1111 <16777216c@gmail.com>
Date: Thu, 10 Aug 2023 12:41:41 +0300
Subject: face restoration and tiling moved to settings - use "Options in main
 UI" setting if you want them back

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

(limited to 'modules/processing.py')

diff --git a/modules/processing.py b/modules/processing.py
index 7819644c..68a8f1c6 100644
--- a/modules/processing.py
+++ b/modules/processing.py
@@ -111,7 +111,7 @@ class StableDiffusionProcessing:
     cached_uc = [None, None]
     cached_c = [None, None]
 
-    def __init__(self, sd_model=None, outpath_samples=None, outpath_grids=None, prompt: str = "", styles: List[str] = None, seed: int = -1, subseed: int = -1, subseed_strength: float = 0, seed_resize_from_h: int = -1, seed_resize_from_w: int = -1, seed_enable_extras: bool = True, sampler_name: str = None, batch_size: int = 1, n_iter: int = 1, steps: int = 50, cfg_scale: float = 7.0, width: int = 512, height: int = 512, restore_faces: bool = False, tiling: bool = False, do_not_save_samples: bool = False, do_not_save_grid: bool = False, extra_generation_params: Dict[Any, Any] = None, overlay_images: Any = None, negative_prompt: str = None, eta: float = None, do_not_reload_embeddings: bool = False, denoising_strength: float = 0, ddim_discretize: str = None, s_min_uncond: float = 0.0, s_churn: float = 0.0, s_tmax: float = None, s_tmin: float = 0.0, s_noise: float = None, override_settings: Dict[str, Any] = None, override_settings_restore_afterwards: bool = True, sampler_index: int = None, script_args: list = None):
+    def __init__(self, sd_model=None, outpath_samples=None, outpath_grids=None, prompt: str = "", styles: List[str] = None, seed: int = -1, subseed: int = -1, subseed_strength: float = 0, seed_resize_from_h: int = -1, seed_resize_from_w: int = -1, seed_enable_extras: bool = True, sampler_name: str = None, batch_size: int = 1, n_iter: int = 1, steps: int = 50, cfg_scale: float = 7.0, width: int = 512, height: int = 512, restore_faces: bool = None, tiling: bool = None, do_not_save_samples: bool = False, do_not_save_grid: bool = False, extra_generation_params: Dict[Any, Any] = None, overlay_images: Any = None, negative_prompt: str = None, eta: float = None, do_not_reload_embeddings: bool = False, denoising_strength: float = 0, ddim_discretize: str = None, s_min_uncond: float = 0.0, s_churn: float = 0.0, s_tmax: float = None, s_tmin: float = 0.0, s_noise: float = None, override_settings: Dict[str, Any] = None, override_settings_restore_afterwards: bool = True, sampler_index: int = None, script_args: list = None):
         if sampler_index is not None:
             print("sampler_index argument for StableDiffusionProcessing does not do anything; use sampler_name", file=sys.stderr)
 
@@ -564,7 +564,7 @@ def create_infotext(p, all_prompts, all_seeds, all_subseeds, comments=None, iter
         "CFG scale": p.cfg_scale,
         "Image CFG scale": getattr(p, 'image_cfg_scale', None),
         "Seed": p.all_seeds[0] if use_main_prompt else all_seeds[index],
-        "Face restoration": (opts.face_restoration_model if p.restore_faces else None),
+        "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_model_name_to_info else shared.sd_model.sd_checkpoint_info.name_for_extra),
@@ -580,6 +580,7 @@ def create_infotext(p, all_prompts, all_seeds, all_subseeds, comments=None, iter
         "Init image hash": getattr(p, 'init_img_hash', None),
         "RNG": opts.randn_source if opts.randn_source != "GPU" and opts.randn_source != "NV" else None,
         "NGMS": None if p.s_min_uncond == 0 else p.s_min_uncond,
+        "Tiling": "True" if p.tiling else None,
         **p.extra_generation_params,
         "Version": program_version() if opts.add_version_to_infotext else None,
         "User": p.user if opts.add_user_name_to_info else None,
@@ -645,6 +646,12 @@ def process_images_inner(p: StableDiffusionProcessing) -> Processed:
     seed = get_fixed_seed(p.seed)
     subseed = get_fixed_seed(p.subseed)
 
+    if p.restore_faces is None:
+        p.restore_faces = opts.face_restoration
+
+    if p.tiling is None:
+        p.tiling = opts.tiling
+
     modules.sd_hijack.model_hijack.apply_circular(p.tiling)
     modules.sd_hijack.model_hijack.clear_comments()
 
-- 
cgit v1.2.1


From 1b3093fe3aedb20aa8d505ceeea7900ac592e6fe Mon Sep 17 00:00:00 2001
From: AUTOMATIC1111 <16777216c@gmail.com>
Date: Thu, 10 Aug 2023 15:58:53 +0300
Subject: fix  --use-textbox-seed

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

(limited to 'modules/processing.py')

diff --git a/modules/processing.py b/modules/processing.py
index 68a8f1c6..f06c374a 100644
--- a/modules/processing.py
+++ b/modules/processing.py
@@ -521,7 +521,15 @@ def decode_latent_batch(model, batch, target_device=None, check_for_nans=False):
 
 
 def get_fixed_seed(seed):
-    if seed is None or seed == '' or seed == -1:
+    if seed == '' or seed is None:
+        seed = -1
+    elif isinstance(seed, str):
+        try:
+            seed = int(seed)
+        except Exception:
+            seed = -1
+
+    if seed == -1:
         return int(random.randrange(4294967294))
 
     return seed
@@ -728,7 +736,7 @@ def process_images_inner(p: StableDiffusionProcessing) -> Processed:
             # strength, which is saved as "Model Strength: 1.0" in the infotext
             if n == 0:
                 with open(os.path.join(paths.data_path, "params.txt"), "w", encoding="utf8") as file:
-                    processed = Processed(p, [], p.seed, "")
+                    processed = Processed(p, [])
                     file.write(processed.infotext(p, 0))
 
             p.setup_conds()
-- 
cgit v1.2.1


From 9d78d317ae492db59ebf8b31fda9a049f6c9bd14 Mon Sep 17 00:00:00 2001
From: AUTOMATIC1111 <16777216c@gmail.com>
Date: Thu, 10 Aug 2023 16:22:10 +0300
Subject: add VAE to infotext

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

(limited to 'modules/processing.py')

diff --git a/modules/processing.py b/modules/processing.py
index f06c374a..44d47e8c 100644
--- a/modules/processing.py
+++ b/modules/processing.py
@@ -576,6 +576,8 @@ def create_infotext(p, all_prompts, all_seeds, all_subseeds, comments=None, iter
         "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_model_name_to_info else shared.sd_model.sd_checkpoint_info.name_for_extra),
+        "VAE hash": sd_vae.get_loaded_vae_hash() if opts.add_model_hash_to_info else None,
+        "VAE": sd_vae.get_loaded_vae_name() if opts.add_model_name_to_info else None,
         "Variation seed": (None if p.subseed_strength == 0 else (p.all_subseeds[0] if use_main_prompt else all_subseeds[index])),
         "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}"),
-- 
cgit v1.2.1


From af27b716e53671c52308d4e101214b0fd4fd5e80 Mon Sep 17 00:00:00 2001
From: catboxanon <122327233+catboxanon@users.noreply.github.com>
Date: Fri, 11 Aug 2023 12:22:11 -0400
Subject: Fix color correction by converting image to RGB

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

(limited to 'modules/processing.py')

diff --git a/modules/processing.py b/modules/processing.py
index c048ca25..131c4c3c 100755
--- a/modules/processing.py
+++ b/modules/processing.py
@@ -57,7 +57,7 @@ def apply_color_correction(correction, original_image):
 
     image = blendLayers(image, original_image, BlendType.LUMINOSITY)
 
-    return image
+    return image.convert('RGB')
 
 
 def apply_overlay(image, paste_loc, index, overlays):
-- 
cgit v1.2.1


From 64311faa6848d641cc452115e4e1eb47d2a7b519 Mon Sep 17 00:00:00 2001
From: AUTOMATIC1111 <16777216c@gmail.com>
Date: Sat, 12 Aug 2023 12:39:59 +0300
Subject: put refiner into main UI, into the new accordions section add VAE
 from main model into infotext, not from refiner model option to make scripts
 UI without gr.Group fix inconsistencies with refiner when usings samplers
 that do more denoising than steps

---
 modules/processing.py | 22 +++++++++++++++-------
 1 file changed, 15 insertions(+), 7 deletions(-)

(limited to 'modules/processing.py')

diff --git a/modules/processing.py b/modules/processing.py
index 131c4c3c..5996cbac 100755
--- a/modules/processing.py
+++ b/modules/processing.py
@@ -373,9 +373,10 @@ class StableDiffusionProcessing:
         negative_prompts = prompt_parser.SdConditioning(self.negative_prompts, width=self.width, height=self.height, is_negative_prompt=True)
 
         sampler_config = sd_samplers.find_sampler_config(self.sampler_name)
-        self.step_multiplier = 2 if sampler_config and sampler_config.options.get("second_order", False) else 1
-        self.uc = self.get_conds_with_caching(prompt_parser.get_learned_conditioning, negative_prompts, self.steps * self.step_multiplier, [self.cached_uc], self.extra_network_data)
-        self.c = self.get_conds_with_caching(prompt_parser.get_multicond_learned_conditioning, prompts, self.steps * self.step_multiplier, [self.cached_c], self.extra_network_data)
+        total_steps = sampler_config.total_steps(self.steps) if sampler_config else self.steps
+        self.step_multiplier = total_steps // self.steps
+        self.uc = self.get_conds_with_caching(prompt_parser.get_learned_conditioning, negative_prompts, total_steps, [self.cached_uc], self.extra_network_data)
+        self.c = self.get_conds_with_caching(prompt_parser.get_multicond_learned_conditioning, prompts, total_steps, [self.cached_c], self.extra_network_data)
 
     def get_conds(self):
         return self.c, self.uc
@@ -579,8 +580,8 @@ def create_infotext(p, all_prompts, all_seeds, all_subseeds, comments=None, iter
         "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_model_name_to_info else shared.sd_model.sd_checkpoint_info.name_for_extra),
-        "VAE hash": sd_vae.get_loaded_vae_hash() if opts.add_model_hash_to_info else None,
-        "VAE": sd_vae.get_loaded_vae_name() if opts.add_model_name_to_info else None,
+        "VAE hash": p.loaded_vae_hash if opts.add_model_hash_to_info else None,
+        "VAE": p.loaded_vae_name if opts.add_model_name_to_info else None,
         "Variation seed": (None if p.subseed_strength == 0 else (p.all_subseeds[0] if use_main_prompt else all_subseeds[index])),
         "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}"),
@@ -669,6 +670,9 @@ def process_images_inner(p: StableDiffusionProcessing) -> Processed:
     if p.tiling is None:
         p.tiling = opts.tiling
 
+    p.loaded_vae_name = sd_vae.get_loaded_vae_name()
+    p.loaded_vae_hash = sd_vae.get_loaded_vae_hash()
+
     modules.sd_hijack.model_hijack.apply_circular(p.tiling)
     modules.sd_hijack.model_hijack.clear_comments()
 
@@ -1188,8 +1192,12 @@ class StableDiffusionProcessingTxt2Img(StableDiffusionProcessing):
         hr_prompts = prompt_parser.SdConditioning(self.hr_prompts, width=self.hr_upscale_to_x, height=self.hr_upscale_to_y)
         hr_negative_prompts = prompt_parser.SdConditioning(self.hr_negative_prompts, width=self.hr_upscale_to_x, height=self.hr_upscale_to_y, is_negative_prompt=True)
 
-        self.hr_uc = self.get_conds_with_caching(prompt_parser.get_learned_conditioning, hr_negative_prompts, self.steps * self.step_multiplier, [self.cached_hr_uc, self.cached_uc], self.hr_extra_network_data)
-        self.hr_c = self.get_conds_with_caching(prompt_parser.get_multicond_learned_conditioning, hr_prompts, self.steps * self.step_multiplier, [self.cached_hr_c, self.cached_c], self.hr_extra_network_data)
+        sampler_config = sd_samplers.find_sampler_config(self.hr_sampler_name or self.sampler_name)
+        steps = self.hr_second_pass_steps or self.steps
+        total_steps = sampler_config.total_steps(steps) if sampler_config else steps
+
+        self.hr_uc = self.get_conds_with_caching(prompt_parser.get_learned_conditioning, hr_negative_prompts, total_steps, [self.cached_hr_uc, self.cached_uc], self.hr_extra_network_data)
+        self.hr_c = self.get_conds_with_caching(prompt_parser.get_multicond_learned_conditioning, hr_prompts, total_steps, [self.cached_hr_c, self.cached_c], self.hr_extra_network_data)
 
     def setup_conds(self):
         super().setup_conds()
-- 
cgit v1.2.1


From b293ed30610c040e621e1840d63047ae298f0650 Mon Sep 17 00:00:00 2001
From: AUTOMATIC1111 <16777216c@gmail.com>
Date: Sat, 12 Aug 2023 12:54:32 +0300
Subject: make it possible to use hires fix together with refiner

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

(limited to 'modules/processing.py')

diff --git a/modules/processing.py b/modules/processing.py
index 5996cbac..6ad105d7 100755
--- a/modules/processing.py
+++ b/modules/processing.py
@@ -1200,6 +1200,12 @@ class StableDiffusionProcessingTxt2Img(StableDiffusionProcessing):
         self.hr_c = self.get_conds_with_caching(prompt_parser.get_multicond_learned_conditioning, hr_prompts, total_steps, [self.cached_hr_c, self.cached_c], self.hr_extra_network_data)
 
     def setup_conds(self):
+        if self.is_hr_pass:
+            # if we are in hr pass right now, the call is being made from the refiner, and we don't need to setup firstpass cons or switch model
+            self.hr_c = None
+            self.calculate_hr_conds()
+            return
+
         super().setup_conds()
 
         self.hr_uc = None
-- 
cgit v1.2.1


From fa9370b7411166c19e8e386400dc4e6082f47b2d Mon Sep 17 00:00:00 2001
From: AUTOMATIC1111 <16777216c@gmail.com>
Date: Sun, 13 Aug 2023 06:07:30 +0300
Subject: add refiner to StableDiffusionProcessing class write out correct
 model name in infotext, rather than the refiner model

---
 modules/processing.py | 38 ++++++++++++++++++++++++++++++--------
 1 file changed, 30 insertions(+), 8 deletions(-)

(limited to 'modules/processing.py')

diff --git a/modules/processing.py b/modules/processing.py
index 6ad105d7..b47ddaa8 100755
--- a/modules/processing.py
+++ b/modules/processing.py
@@ -111,7 +111,7 @@ class StableDiffusionProcessing:
     cached_uc = [None, None]
     cached_c = [None, None]
 
-    def __init__(self, sd_model=None, outpath_samples=None, outpath_grids=None, prompt: str = "", styles: List[str] = None, seed: int = -1, subseed: int = -1, subseed_strength: float = 0, seed_resize_from_h: int = -1, seed_resize_from_w: int = -1, seed_enable_extras: bool = True, sampler_name: str = None, batch_size: int = 1, n_iter: int = 1, steps: int = 50, cfg_scale: float = 7.0, width: int = 512, height: int = 512, restore_faces: bool = None, tiling: bool = None, do_not_save_samples: bool = False, do_not_save_grid: bool = False, extra_generation_params: Dict[Any, Any] = None, overlay_images: Any = None, negative_prompt: str = None, eta: float = None, do_not_reload_embeddings: bool = False, denoising_strength: float = 0, ddim_discretize: str = None, s_min_uncond: float = 0.0, s_churn: float = 0.0, s_tmax: float = None, s_tmin: float = 0.0, s_noise: float = None, override_settings: Dict[str, Any] = None, override_settings_restore_afterwards: bool = True, sampler_index: int = None, script_args: list = None):
+    def __init__(self, sd_model=None, outpath_samples=None, outpath_grids=None, prompt: str = "", styles: List[str] = None, seed: int = -1, subseed: int = -1, subseed_strength: float = 0, seed_resize_from_h: int = -1, seed_resize_from_w: int = -1, seed_enable_extras: bool = True, sampler_name: str = None, batch_size: int = 1, n_iter: int = 1, steps: int = 50, cfg_scale: float = 7.0, width: int = 512, height: int = 512, restore_faces: bool = None, tiling: bool = None, do_not_save_samples: bool = False, do_not_save_grid: bool = False, extra_generation_params: Dict[Any, Any] = None, overlay_images: Any = None, negative_prompt: str = None, eta: float = None, do_not_reload_embeddings: bool = False, denoising_strength: float = 0, ddim_discretize: str = None, s_min_uncond: float = 0.0, s_churn: float = 0.0, s_tmax: float = None, s_tmin: float = 0.0, s_noise: float = None, override_settings: Dict[str, Any] = None, override_settings_restore_afterwards: bool = True, sampler_index: int = None, refiner_checkpoint: str = None, refiner_switch_at: float = None, script_args: list = None):
         if sampler_index is not None:
             print("sampler_index argument for StableDiffusionProcessing does not do anything; use sampler_name", file=sys.stderr)
 
@@ -153,10 +153,14 @@ class StableDiffusionProcessing:
         self.s_noise = s_noise if s_noise is not None else opts.s_noise
         self.override_settings = {k: v for k, v in (override_settings or {}).items() if k not in shared.restricted_opts}
         self.override_settings_restore_afterwards = override_settings_restore_afterwards
+        self.refiner_checkpoint = refiner_checkpoint
+        self.refiner_switch_at = refiner_switch_at
+
         self.is_using_inpainting_conditioning = False
         self.disable_extra_networks = False
         self.token_merging_ratio = 0
         self.token_merging_ratio_hr = 0
+        self.refiner_checkpoint_info = None
 
         if not seed_enable_extras:
             self.subseed = -1
@@ -191,6 +195,11 @@ class StableDiffusionProcessing:
 
         self.user = None
 
+        self.sd_model_name = None
+        self.sd_model_hash = None
+        self.sd_vae_name = None
+        self.sd_vae_hash = None
+
     @property
     def sd_model(self):
         return shared.sd_model
@@ -408,7 +417,10 @@ class Processed:
         self.batch_size = p.batch_size
         self.restore_faces = p.restore_faces
         self.face_restoration_model = opts.face_restoration_model if p.restore_faces else None
-        self.sd_model_hash = shared.sd_model.sd_model_hash
+        self.sd_model_name = p.sd_model_name
+        self.sd_model_hash = p.sd_model_hash
+        self.sd_vae_name = p.sd_vae_name
+        self.sd_vae_hash = p.sd_vae_hash
         self.seed_resize_from_w = p.seed_resize_from_w
         self.seed_resize_from_h = p.seed_resize_from_h
         self.denoising_strength = getattr(p, 'denoising_strength', None)
@@ -459,7 +471,10 @@ class Processed:
             "batch_size": self.batch_size,
             "restore_faces": self.restore_faces,
             "face_restoration_model": self.face_restoration_model,
+            "sd_model_name": self.sd_model_name,
             "sd_model_hash": self.sd_model_hash,
+            "sd_vae_name": self.sd_vae_name,
+            "sd_vae_hash": self.sd_vae_hash,
             "seed_resize_from_w": self.seed_resize_from_w,
             "seed_resize_from_h": self.seed_resize_from_h,
             "denoising_strength": self.denoising_strength,
@@ -578,10 +593,10 @@ def create_infotext(p, all_prompts, all_seeds, all_subseeds, comments=None, iter
         "Seed": p.all_seeds[0] if use_main_prompt else all_seeds[index],
         "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_model_name_to_info else shared.sd_model.sd_checkpoint_info.name_for_extra),
-        "VAE hash": p.loaded_vae_hash if opts.add_model_hash_to_info else None,
-        "VAE": p.loaded_vae_name if opts.add_model_name_to_info else None,
+        "Model hash": p.sd_model_hash if opts.add_model_hash_to_info else None,
+        "Model": p.sd_model_name if opts.add_model_name_to_info else None,
+        "VAE hash": p.sd_vae_hash if opts.add_model_hash_to_info else None,
+        "VAE": p.sd_vae_name if opts.add_model_name_to_info else None,
         "Variation seed": (None if p.subseed_strength == 0 else (p.all_subseeds[0] if use_main_prompt else all_subseeds[index])),
         "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}"),
@@ -670,8 +685,15 @@ def process_images_inner(p: StableDiffusionProcessing) -> Processed:
     if p.tiling is None:
         p.tiling = opts.tiling
 
-    p.loaded_vae_name = sd_vae.get_loaded_vae_name()
-    p.loaded_vae_hash = sd_vae.get_loaded_vae_hash()
+    if p.refiner_checkpoint not in (None, "", "None"):
+        p.refiner_checkpoint_info = sd_models.get_closet_checkpoint_match(p.refiner_checkpoint)
+        if p.refiner_checkpoint_info is None:
+            raise Exception(f'Could not find checkpoint with name {p.refiner_checkpoint}')
+
+    p.sd_model_name = shared.sd_model.sd_checkpoint_info.name_for_extra
+    p.sd_model_hash = shared.sd_model.sd_model_hash
+    p.sd_vae_name = sd_vae.get_loaded_vae_name()
+    p.sd_vae_hash = sd_vae.get_loaded_vae_hash()
 
     modules.sd_hijack.model_hijack.apply_circular(p.tiling)
     modules.sd_hijack.model_hijack.clear_comments()
-- 
cgit v1.2.1


From 599f61a1e0bddf463dd3c6adb84509b3d9db1941 Mon Sep 17 00:00:00 2001
From: AUTOMATIC1111 <16777216c@gmail.com>
Date: Sun, 13 Aug 2023 08:24:16 +0300
Subject: use dataclass for StableDiffusionProcessing

---
 modules/processing.py | 318 +++++++++++++++++++++++++++-----------------------
 1 file changed, 172 insertions(+), 146 deletions(-)

(limited to 'modules/processing.py')

diff --git a/modules/processing.py b/modules/processing.py
index b47ddaa8..007a4e05 100755
--- a/modules/processing.py
+++ b/modules/processing.py
@@ -1,9 +1,11 @@
+from __future__ import annotations
 import json
 import logging
 import math
 import os
 import sys
 import hashlib
+from dataclasses import dataclass, field
 
 import torch
 import numpy as np
@@ -11,7 +13,7 @@ from PIL import Image, ImageOps
 import random
 import cv2
 from skimage import exposure
-from typing import Any, Dict, List
+from typing import Any
 
 import modules.sd_hijack
 from modules import devices, prompt_parser, masking, sd_samplers, lowvram, generation_parameters_copypaste, extra_networks, sd_vae_approx, scripts, sd_samplers_common, sd_unet, errors, rng
@@ -104,106 +106,126 @@ def txt2img_image_conditioning(sd_model, x, width, height):
         return x.new_zeros(x.shape[0], 5, 1, 1, dtype=x.dtype, device=x.device)
 
 
+@dataclass(repr=False)
 class StableDiffusionProcessing:
-    """
-    The first set of paramaters: sd_models -> do_not_reload_embeddings represent the minimum required to create a StableDiffusionProcessing
-    """
+    sd_model: object = None
+    outpath_samples: str = None
+    outpath_grids: str = None
+    prompt: str = ""
+    prompt_for_display: str = None
+    negative_prompt: str = ""
+    styles: list[str] = field(default_factory=list)
+    seed: int = -1
+    subseed: int = -1
+    subseed_strength: float = 0
+    seed_resize_from_h: int = -1
+    seed_resize_from_w: int = -1
+    seed_enable_extras: bool = True
+    sampler_name: str = None
+    batch_size: int = 1
+    n_iter: int = 1
+    steps: int = 50
+    cfg_scale: float = 7.0
+    width: int = 512
+    height: int = 512
+    restore_faces: bool = None
+    tiling: bool = None
+    do_not_save_samples: bool = False
+    do_not_save_grid: bool = False
+    extra_generation_params: dict[str, Any] = None
+    overlay_images: list = None
+    eta: float = None
+    do_not_reload_embeddings: bool = False
+    denoising_strength: float = 0
+    ddim_discretize: str = None
+    s_min_uncond: float = None
+    s_churn: float = None
+    s_tmax: float = None
+    s_tmin: float = None
+    s_noise: float = None
+    override_settings: dict[str, Any] = None
+    override_settings_restore_afterwards: bool = True
+    sampler_index: int = None
+    refiner_checkpoint: str = None
+    refiner_switch_at: float = None
+    token_merging_ratio = 0
+    token_merging_ratio_hr = 0
+    disable_extra_networks: bool = False
+
+    script_args: list = None
+
     cached_uc = [None, None]
     cached_c = [None, None]
 
-    def __init__(self, sd_model=None, outpath_samples=None, outpath_grids=None, prompt: str = "", styles: List[str] = None, seed: int = -1, subseed: int = -1, subseed_strength: float = 0, seed_resize_from_h: int = -1, seed_resize_from_w: int = -1, seed_enable_extras: bool = True, sampler_name: str = None, batch_size: int = 1, n_iter: int = 1, steps: int = 50, cfg_scale: float = 7.0, width: int = 512, height: int = 512, restore_faces: bool = None, tiling: bool = None, do_not_save_samples: bool = False, do_not_save_grid: bool = False, extra_generation_params: Dict[Any, Any] = None, overlay_images: Any = None, negative_prompt: str = None, eta: float = None, do_not_reload_embeddings: bool = False, denoising_strength: float = 0, ddim_discretize: str = None, s_min_uncond: float = 0.0, s_churn: float = 0.0, s_tmax: float = None, s_tmin: float = 0.0, s_noise: float = None, override_settings: Dict[str, Any] = None, override_settings_restore_afterwards: bool = True, sampler_index: int = None, refiner_checkpoint: str = None, refiner_switch_at: float = None, script_args: list = None):
-        if sampler_index is not None:
+    sampler: sd_samplers_common.Sampler | None = field(default=None, init=False)
+    is_using_inpainting_conditioning: bool = field(default=False, init=False)
+    paste_to: tuple | None = field(default=None, init=False)
+
+    is_hr_pass: bool = field(default=False, init=False)
+
+    c: tuple = field(default=None, init=False)
+    uc: tuple = field(default=None, init=False)
+
+    rng: rng.ImageRNG | None = field(default=None, init=False)
+    step_multiplier: int = field(default=1, init=False)
+    color_corrections: list = field(default=None, init=False)
+
+    scripts: list = field(default=None, init=False)
+    all_prompts: list = field(default=None, init=False)
+    all_negative_prompts: list = field(default=None, init=False)
+    all_seeds: list = field(default=None, init=False)
+    all_subseeds: list = field(default=None, init=False)
+    iteration: int = field(default=0, init=False)
+    main_prompt: str = field(default=None, init=False)
+    main_negative_prompt: str = field(default=None, init=False)
+
+    prompts: list = field(default=None, init=False)
+    negative_prompts: list = field(default=None, init=False)
+    seeds: list = field(default=None, init=False)
+    subseeds: list = field(default=None, init=False)
+    extra_network_data: dict = field(default=None, init=False)
+
+    user: str = field(default=None, init=False)
+
+    sd_model_name: str = field(default=None, init=False)
+    sd_model_hash: str = field(default=None, init=False)
+    sd_vae_name: str = field(default=None, init=False)
+    sd_vae_hash: str = field(default=None, init=False)
+
+    def __post_init__(self):
+        if self.sampler_index is not None:
             print("sampler_index argument for StableDiffusionProcessing does not do anything; use sampler_name", file=sys.stderr)
 
-        self.outpath_samples: str = outpath_samples
-        self.outpath_grids: str = outpath_grids
-        self.prompt: str = prompt
-        self.prompt_for_display: str = None
-        self.negative_prompt: str = (negative_prompt or "")
-        self.styles: list = styles or []
-        self.seed: int = seed
-        self.subseed: int = subseed
-        self.subseed_strength: float = subseed_strength
-        self.seed_resize_from_h: int = seed_resize_from_h
-        self.seed_resize_from_w: int = seed_resize_from_w
-        self.sampler_name: str = sampler_name
-        self.batch_size: int = batch_size
-        self.n_iter: int = n_iter
-        self.steps: int = steps
-        self.cfg_scale: float = cfg_scale
-        self.width: int = width
-        self.height: int = height
-        self.restore_faces: bool = restore_faces
-        self.tiling: bool = tiling
-        self.do_not_save_samples: bool = do_not_save_samples
-        self.do_not_save_grid: bool = do_not_save_grid
-        self.extra_generation_params: dict = extra_generation_params or {}
-        self.overlay_images = overlay_images
-        self.eta = eta
-        self.do_not_reload_embeddings = do_not_reload_embeddings
-        self.paste_to = None
-        self.color_corrections = None
-        self.denoising_strength: float = denoising_strength
         self.sampler_noise_scheduler_override = None
-        self.ddim_discretize = ddim_discretize or opts.ddim_discretize
-        self.s_min_uncond = s_min_uncond or opts.s_min_uncond
-        self.s_churn = s_churn or opts.s_churn
-        self.s_tmin = s_tmin or opts.s_tmin
-        self.s_tmax = (s_tmax if s_tmax is not None else opts.s_tmax) or float('inf')
-        self.s_noise = s_noise if s_noise is not None else opts.s_noise
-        self.override_settings = {k: v for k, v in (override_settings or {}).items() if k not in shared.restricted_opts}
-        self.override_settings_restore_afterwards = override_settings_restore_afterwards
-        self.refiner_checkpoint = refiner_checkpoint
-        self.refiner_switch_at = refiner_switch_at
-
-        self.is_using_inpainting_conditioning = False
-        self.disable_extra_networks = False
-        self.token_merging_ratio = 0
-        self.token_merging_ratio_hr = 0
+        self.s_min_uncond = self.s_min_uncond if self.s_min_uncond is not None else opts.s_min_uncond
+        self.s_churn = self.s_churn if self.s_churn is not None else opts.s_churn
+        self.s_tmin = self.s_tmin if self.s_tmin is not None else opts.s_tmin
+        self.s_tmax = (self.s_tmax if self.s_tmax is not None else opts.s_tmax) or float('inf')
+        self.s_noise = self.s_noise if self.s_noise is not None else opts.s_noise
+
+        self.extra_generation_params = self.extra_generation_params or {}
+        self.override_settings = self.override_settings or {}
+        self.script_args = self.script_args or {}
+
         self.refiner_checkpoint_info = None
 
-        if not seed_enable_extras:
+        if not self.seed_enable_extras:
             self.subseed = -1
             self.subseed_strength = 0
             self.seed_resize_from_h = 0
             self.seed_resize_from_w = 0
 
-        self.scripts = None
-        self.script_args = script_args
-        self.all_prompts = None
-        self.all_negative_prompts = None
-        self.all_seeds = None
-        self.all_subseeds = None
-        self.iteration = 0
-        self.is_hr_pass = False
-        self.sampler = None
-        self.main_prompt = None
-        self.main_negative_prompt = None
-
-        self.prompts = None
-        self.negative_prompts = None
-        self.extra_network_data = None
-        self.seeds = None
-        self.subseeds = None
-
-        self.step_multiplier = 1
         self.cached_uc = StableDiffusionProcessing.cached_uc
         self.cached_c = StableDiffusionProcessing.cached_c
-        self.uc = None
-        self.c = None
-        self.rng: rng.ImageRNG = None
-
-        self.user = None
-
-        self.sd_model_name = None
-        self.sd_model_hash = None
-        self.sd_vae_name = None
-        self.sd_vae_hash = None
 
     @property
     def sd_model(self):
         return shared.sd_model
 
+    @sd_model.setter
+    def sd_model(self, value):
+        pass
+
     def txt2img_image_conditioning(self, x, width=None, height=None):
         self.is_using_inpainting_conditioning = self.sd_model.model.conditioning_key in {'hybrid', 'concat'}
 
@@ -932,49 +954,51 @@ def old_hires_fix_first_pass_dimensions(width, height):
     return width, height
 
 
+@dataclass(repr=False)
 class StableDiffusionProcessingTxt2Img(StableDiffusionProcessing):
-    sampler = None
+    enable_hr: bool = False
+    denoising_strength: float = 0.75
+    firstphase_width: int = 0
+    firstphase_height: int = 0
+    hr_scale: float = 2.0
+    hr_upscaler: str = None
+    hr_second_pass_steps: int = 0
+    hr_resize_x: int = 0
+    hr_resize_y: int = 0
+    hr_checkpoint_name: str = None
+    hr_sampler_name: str = None
+    hr_prompt: str = ''
+    hr_negative_prompt: str = ''
+
     cached_hr_uc = [None, None]
     cached_hr_c = [None, None]
 
-    def __init__(self, enable_hr: bool = False, denoising_strength: float = 0.75, firstphase_width: int = 0, firstphase_height: int = 0, hr_scale: float = 2.0, hr_upscaler: str = None, hr_second_pass_steps: int = 0, hr_resize_x: int = 0, hr_resize_y: int = 0, hr_checkpoint_name: str = None, hr_sampler_name: str = None, hr_prompt: str = '', hr_negative_prompt: str = '', **kwargs):
-        super().__init__(**kwargs)
-        self.enable_hr = enable_hr
-        self.denoising_strength = denoising_strength
-        self.hr_scale = hr_scale
-        self.hr_upscaler = hr_upscaler
-        self.hr_second_pass_steps = hr_second_pass_steps
-        self.hr_resize_x = hr_resize_x
-        self.hr_resize_y = hr_resize_y
-        self.hr_upscale_to_x = hr_resize_x
-        self.hr_upscale_to_y = hr_resize_y
-        self.hr_checkpoint_name = hr_checkpoint_name
-        self.hr_checkpoint_info = None
-        self.hr_sampler_name = hr_sampler_name
-        self.hr_prompt = hr_prompt
-        self.hr_negative_prompt = hr_negative_prompt
-        self.all_hr_prompts = None
-        self.all_hr_negative_prompts = None
-        self.latent_scale_mode = None
-
-        if firstphase_width != 0 or firstphase_height != 0:
+    hr_checkpoint_info: dict = field(default=None, init=False)
+    hr_upscale_to_x: int = field(default=0, init=False)
+    hr_upscale_to_y: int = field(default=0, init=False)
+    truncate_x: int = field(default=0, init=False)
+    truncate_y: int = field(default=0, init=False)
+    applied_old_hires_behavior_to: tuple = field(default=None, init=False)
+    latent_scale_mode: dict = field(default=None, init=False)
+    hr_c: tuple | None = field(default=None, init=False)
+    hr_uc: tuple | None = field(default=None, init=False)
+    all_hr_prompts: list = field(default=None, init=False)
+    all_hr_negative_prompts: list = field(default=None, init=False)
+    hr_prompts: list = field(default=None, init=False)
+    hr_negative_prompts: list = field(default=None, init=False)
+    hr_extra_network_data: list = field(default=None, init=False)
+
+    def __post_init__(self):
+        super().__post_init__()
+
+        if self.firstphase_width != 0 or self.firstphase_height != 0:
             self.hr_upscale_to_x = self.width
             self.hr_upscale_to_y = self.height
-            self.width = firstphase_width
-            self.height = firstphase_height
-
-        self.truncate_x = 0
-        self.truncate_y = 0
-        self.applied_old_hires_behavior_to = None
-
-        self.hr_prompts = None
-        self.hr_negative_prompts = None
-        self.hr_extra_network_data = None
+            self.width = self.firstphase_width
+            self.height = self.firstphase_height
 
         self.cached_hr_uc = StableDiffusionProcessingTxt2Img.cached_hr_uc
         self.cached_hr_c = StableDiffusionProcessingTxt2Img.cached_hr_c
-        self.hr_c = None
-        self.hr_uc = None
 
     def calculate_target_resolution(self):
         if opts.use_old_hires_fix_width_height and self.applied_old_hires_behavior_to != (self.width, self.height):
@@ -1252,7 +1276,6 @@ class StableDiffusionProcessingTxt2Img(StableDiffusionProcessing):
 
         return super().get_conds()
 
-
     def parse_extra_network_prompts(self):
         res = super().parse_extra_network_prompts()
 
@@ -1265,32 +1288,37 @@ class StableDiffusionProcessingTxt2Img(StableDiffusionProcessing):
         return res
 
 
+@dataclass(repr=False)
 class StableDiffusionProcessingImg2Img(StableDiffusionProcessing):
-    sampler = None
-
-    def __init__(self, init_images: list = None, resize_mode: int = 0, denoising_strength: float = 0.75, image_cfg_scale: float = None, mask: Any = None, mask_blur: int = None, mask_blur_x: int = 4, mask_blur_y: int = 4, inpainting_fill: int = 0, inpaint_full_res: bool = True, inpaint_full_res_padding: int = 0, inpainting_mask_invert: int = 0, initial_noise_multiplier: float = None, **kwargs):
-        super().__init__(**kwargs)
-
-        self.init_images = init_images
-        self.resize_mode: int = resize_mode
-        self.denoising_strength: float = denoising_strength
-        self.image_cfg_scale: float = image_cfg_scale if shared.sd_model.cond_stage_key == "edit" else None
-        self.init_latent = None
-        self.image_mask = mask
-        self.latent_mask = None
-        self.mask_for_overlay = None
-        self.mask_blur_x = mask_blur_x
-        self.mask_blur_y = mask_blur_y
-        if mask_blur is not None:
-            self.mask_blur = mask_blur
-        self.inpainting_fill = inpainting_fill
-        self.inpaint_full_res = inpaint_full_res
-        self.inpaint_full_res_padding = inpaint_full_res_padding
-        self.inpainting_mask_invert = inpainting_mask_invert
-        self.initial_noise_multiplier = opts.initial_noise_multiplier if initial_noise_multiplier is None else initial_noise_multiplier
+    init_images: list = None
+    resize_mode: int = 0
+    denoising_strength: float = 0.75
+    image_cfg_scale: float = None
+    mask: Any = None
+    mask_blur_x: int = 4
+    mask_blur_y: int = 4
+    mask_blur: int = None
+    inpainting_fill: int = 0
+    inpaint_full_res: bool = True
+    inpaint_full_res_padding: int = 0
+    inpainting_mask_invert: int = 0
+    initial_noise_multiplier: float = None
+    latent_mask: Image = None
+
+    image_mask: Any = field(default=None, init=False)
+
+    nmask: torch.Tensor = field(default=None, init=False)
+    image_conditioning: torch.Tensor = field(default=None, init=False)
+    init_img_hash: str = field(default=None, init=False)
+    mask_for_overlay: Image = field(default=None, init=False)
+    init_latent: torch.Tensor = field(default=None, init=False)
+
+    def __post_init__(self):
+        super().__post_init__()
+
+        self.image_mask = self.mask
         self.mask = None
-        self.nmask = None
-        self.image_conditioning = None
+        self.initial_noise_multiplier = opts.initial_noise_multiplier if self.initial_noise_multiplier is None else self.initial_noise_multiplier
 
     @property
     def mask_blur(self):
@@ -1300,15 +1328,13 @@ class StableDiffusionProcessingImg2Img(StableDiffusionProcessing):
 
     @mask_blur.setter
     def mask_blur(self, value):
-        self.mask_blur_x = value
-        self.mask_blur_y = value
-
-    @mask_blur.deleter
-    def mask_blur(self):
-        del self.mask_blur_x
-        del self.mask_blur_y
+        if isinstance(value, int):
+            self.mask_blur_x = value
+            self.mask_blur_y = value
 
     def init(self, all_prompts, all_seeds, all_subseeds):
+        self.image_cfg_scale: float = self.image_cfg_scale if shared.sd_model.cond_stage_key == "edit" else None
+
         self.sampler = sd_samplers.create_sampler(self.sampler_name, self.sd_model)
         crop_region = None
 
-- 
cgit v1.2.1


From 69f49c8d394220331eaa6609825b477eed60e0f4 Mon Sep 17 00:00:00 2001
From: catboxanon <122327233+catboxanon@users.noreply.github.com>
Date: Sun, 13 Aug 2023 04:40:34 -0400
Subject: Clear sampler before decoding images

More significant VRAM reduction.
---
 modules/processing.py | 3 +++
 1 file changed, 3 insertions(+)

(limited to 'modules/processing.py')

diff --git a/modules/processing.py b/modules/processing.py
index 007a4e05..16104f92 100755
--- a/modules/processing.py
+++ b/modules/processing.py
@@ -1192,6 +1192,9 @@ class StableDiffusionProcessingTxt2Img(StableDiffusionProcessing):
 
         sd_models.apply_token_merging(self.sd_model, self.get_token_merging_ratio())
 
+        self.sampler = None
+        devices.torch_gc()
+
         decoded_samples = decode_latent_batch(self.sd_model, samples, target_device=devices.cpu, check_for_nans=True)
 
         self.is_hr_pass = False
-- 
cgit v1.2.1


From d8419762c1454ba51baa710d9ce8e762efc056ef Mon Sep 17 00:00:00 2001
From: AUTOMATIC1111 <16777216c@gmail.com>
Date: Sun, 13 Aug 2023 15:07:37 +0300
Subject: Lora: output warnings in UI rather than fail for unfitting loras;
 switch to logging for error output in console

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

(limited to 'modules/processing.py')

diff --git a/modules/processing.py b/modules/processing.py
index 007a4e05..10749aa2 100755
--- a/modules/processing.py
+++ b/modules/processing.py
@@ -157,6 +157,7 @@ class StableDiffusionProcessing:
     cached_uc = [None, None]
     cached_c = [None, None]
 
+    comments: dict = None
     sampler: sd_samplers_common.Sampler | None = field(default=None, init=False)
     is_using_inpainting_conditioning: bool = field(default=False, init=False)
     paste_to: tuple | None = field(default=None, init=False)
@@ -196,6 +197,8 @@ class StableDiffusionProcessing:
         if self.sampler_index is not None:
             print("sampler_index argument for StableDiffusionProcessing does not do anything; use sampler_name", file=sys.stderr)
 
+        self.comments = {}
+
         self.sampler_noise_scheduler_override = None
         self.s_min_uncond = self.s_min_uncond if self.s_min_uncond is not None else opts.s_min_uncond
         self.s_churn = self.s_churn if self.s_churn is not None else opts.s_churn
@@ -226,6 +229,9 @@ class StableDiffusionProcessing:
     def sd_model(self, value):
         pass
 
+    def comment(self, text):
+        self.comments[text] = 1
+
     def txt2img_image_conditioning(self, x, width=None, height=None):
         self.is_using_inpainting_conditioning = self.sd_model.model.conditioning_key in {'hybrid', 'concat'}
 
@@ -429,7 +435,7 @@ class Processed:
         self.subseed = subseed
         self.subseed_strength = p.subseed_strength
         self.info = info
-        self.comments = comments
+        self.comments = "".join(f"{comment}\n" for comment in p.comments)
         self.width = p.width
         self.height = p.height
         self.sampler_name = p.sampler_name
@@ -720,8 +726,6 @@ def process_images_inner(p: StableDiffusionProcessing) -> Processed:
     modules.sd_hijack.model_hijack.apply_circular(p.tiling)
     modules.sd_hijack.model_hijack.clear_comments()
 
-    comments = {}
-
     p.setup_prompts()
 
     if type(seed) == list:
@@ -801,7 +805,7 @@ def process_images_inner(p: StableDiffusionProcessing) -> Processed:
             p.setup_conds()
 
             for comment in model_hijack.comments:
-                comments[comment] = 1
+                p.comment(comment)
 
             p.extra_generation_params.update(model_hijack.extra_generation_params)
 
@@ -930,7 +934,6 @@ def process_images_inner(p: StableDiffusionProcessing) -> Processed:
         images_list=output_images,
         seed=p.all_seeds[0],
         info=infotexts[0],
-        comments="".join(f"{comment}\n" for comment in comments),
         subseed=p.all_subseeds[0],
         index_of_first_image=index_of_first_image,
         infotexts=infotexts,
-- 
cgit v1.2.1


From f093c9d39d0fe9951a8f5c570027cecc68778ef2 Mon Sep 17 00:00:00 2001
From: AUTOMATIC1111 <16777216c@gmail.com>
Date: Sun, 13 Aug 2023 17:31:10 +0300
Subject: fix broken XYZ plot seeds add new callback for scripts to be used
 before processing

---
 modules/processing.py | 32 ++++++++++++++++++++++++++++++--
 1 file changed, 30 insertions(+), 2 deletions(-)

(limited to 'modules/processing.py')

diff --git a/modules/processing.py b/modules/processing.py
index fdf49359..74366655 100755
--- a/modules/processing.py
+++ b/modules/processing.py
@@ -152,7 +152,9 @@ class StableDiffusionProcessing:
     token_merging_ratio_hr = 0
     disable_extra_networks: bool = False
 
-    script_args: list = None
+    scripts_value: scripts.ScriptRunner = field(default=None, init=False)
+    script_args_value: list = field(default=None, init=False)
+    scripts_setup_complete: bool = field(default=False, init=False)
 
     cached_uc = [None, None]
     cached_c = [None, None]
@@ -171,7 +173,6 @@ class StableDiffusionProcessing:
     step_multiplier: int = field(default=1, init=False)
     color_corrections: list = field(default=None, init=False)
 
-    scripts: list = field(default=None, init=False)
     all_prompts: list = field(default=None, init=False)
     all_negative_prompts: list = field(default=None, init=False)
     all_seeds: list = field(default=None, init=False)
@@ -229,6 +230,33 @@ class StableDiffusionProcessing:
     def sd_model(self, value):
         pass
 
+    @property
+    def scripts(self):
+        return self.scripts_value
+
+    @scripts.setter
+    def scripts(self, value):
+        self.scripts_value = value
+
+        if self.scripts_value and self.script_args_value and not self.scripts_setup_complete:
+            self.setup_scripts()
+
+    @property
+    def script_args(self):
+        return self.script_args_value
+
+    @script_args.setter
+    def script_args(self, value):
+        self.script_args_value = value
+
+        if self.scripts_value and self.script_args_value and not self.scripts_setup_complete:
+            self.setup_scripts()
+
+    def setup_scripts(self):
+        self.scripts_setup_complete = True
+
+        self.scripts.setup_scrips(self)
+
     def comment(self, text):
         self.comments[text] = 1
 
-- 
cgit v1.2.1


From c7c16f805c9ea0da42d1d993f2ea7bda48beba76 Mon Sep 17 00:00:00 2001
From: AUTOMATIC1111 <16777216c@gmail.com>
Date: Mon, 14 Aug 2023 09:48:40 +0300
Subject: repair /docs page

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

(limited to 'modules/processing.py')

diff --git a/modules/processing.py b/modules/processing.py
index 74366655..69d365b8 100755
--- a/modules/processing.py
+++ b/modules/processing.py
@@ -114,7 +114,7 @@ class StableDiffusionProcessing:
     prompt: str = ""
     prompt_for_display: str = None
     negative_prompt: str = ""
-    styles: list[str] = field(default_factory=list)
+    styles: list[str] = None
     seed: int = -1
     subseed: int = -1
     subseed_strength: float = 0
@@ -199,6 +199,7 @@ class StableDiffusionProcessing:
             print("sampler_index argument for StableDiffusionProcessing does not do anything; use sampler_name", file=sys.stderr)
 
         self.comments = {}
+        self.styles = []
 
         self.sampler_noise_scheduler_override = None
         self.s_min_uncond = self.s_min_uncond if self.s_min_uncond is not None else opts.s_min_uncond
-- 
cgit v1.2.1


From abbecb3e7363e422d6840fbb5746c74fd453ead5 Mon Sep 17 00:00:00 2001
From: AUTOMATIC1111 <16777216c@gmail.com>
Date: Mon, 14 Aug 2023 10:15:10 +0300
Subject: further repair the /docs page to not break styles with the attempted
 fix

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

(limited to 'modules/processing.py')

diff --git a/modules/processing.py b/modules/processing.py
index 69d365b8..f34ba48a 100755
--- a/modules/processing.py
+++ b/modules/processing.py
@@ -199,7 +199,9 @@ class StableDiffusionProcessing:
             print("sampler_index argument for StableDiffusionProcessing does not do anything; use sampler_name", file=sys.stderr)
 
         self.comments = {}
-        self.styles = []
+
+        if self.styles is None:
+            self.styles = []
 
         self.sampler_noise_scheduler_override = None
         self.s_min_uncond = self.s_min_uncond if self.s_min_uncond is not None else opts.s_min_uncond
-- 
cgit v1.2.1


From 353c876172a48c5044130249370c9303e611dd8b Mon Sep 17 00:00:00 2001
From: AUTOMATIC1111 <16777216c@gmail.com>
Date: Mon, 14 Aug 2023 10:43:18 +0300
Subject: fix API always using -1 as seed

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

(limited to 'modules/processing.py')

diff --git a/modules/processing.py b/modules/processing.py
index f34ba48a..b1eac2ab 100755
--- a/modules/processing.py
+++ b/modules/processing.py
@@ -194,6 +194,8 @@ class StableDiffusionProcessing:
     sd_vae_name: str = field(default=None, init=False)
     sd_vae_hash: str = field(default=None, init=False)
 
+    is_api: bool = field(default=False, init=False)
+
     def __post_init__(self):
         if self.sampler_index is not None:
             print("sampler_index argument for StableDiffusionProcessing does not do anything; use sampler_name", file=sys.stderr)
@@ -258,7 +260,7 @@ class StableDiffusionProcessing:
     def setup_scripts(self):
         self.scripts_setup_complete = True
 
-        self.scripts.setup_scrips(self)
+        self.scripts.setup_scrips(self, is_ui=not self.is_api)
 
     def comment(self, text):
         self.comments[text] = 1
-- 
cgit v1.2.1


From 99ab3d43a71e3f66e57d3cd2013b97c97e7ab69b Mon Sep 17 00:00:00 2001
From: Robert Barron <rubberbaron@robustspread.com>
Date: Mon, 14 Aug 2023 00:41:36 -0700
Subject: hires prompt timeline: merge to latests, slightly simplify diff

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

(limited to 'modules/processing.py')

diff --git a/modules/processing.py b/modules/processing.py
index 6d3c9365..75f1d66f 100644
--- a/modules/processing.py
+++ b/modules/processing.py
@@ -394,7 +394,7 @@ class StableDiffusionProcessing:
         self.main_prompt = self.all_prompts[0]
         self.main_negative_prompt = self.all_negative_prompts[0]
 
-    def cached_params(self, required_prompts, steps, hires_steps, extra_network_data, use_old_scheduling):
+    def cached_params(self, required_prompts, steps, extra_network_data, hires_steps=None, use_old_scheduling=False):
         """Returns parameters that invalidate the cond cache if changed"""
 
         return (
@@ -424,7 +424,7 @@ class StableDiffusionProcessing:
         caches is a list with items described above.
         """
 
-        cached_params = self.cached_params(required_prompts, steps, hires_steps, extra_network_data, shared.opts.use_old_scheduling)
+        cached_params = self.cached_params(required_prompts, steps, extra_network_data, hires_steps, shared.opts.use_old_scheduling)
 
         for cache in caches:
             if cache[0] is not None and cached_params == cache[0]:
@@ -447,8 +447,8 @@ class StableDiffusionProcessing:
         self.step_multiplier = total_steps // self.steps
         self.firstpass_steps = total_steps
 
-        self.uc = self.get_conds_with_caching(prompt_parser.get_learned_conditioning, negative_prompts, total_steps, [self.cached_uc], self.extra_network_data, None)
-        self.c = self.get_conds_with_caching(prompt_parser.get_multicond_learned_conditioning, prompts, total_steps, [self.cached_c], self.extra_network_data, None )
+        self.uc = self.get_conds_with_caching(prompt_parser.get_learned_conditioning, negative_prompts, total_steps, [self.cached_uc], self.extra_network_data)
+        self.c = self.get_conds_with_caching(prompt_parser.get_multicond_learned_conditioning, prompts, total_steps, [self.cached_c], self.extra_network_data)
 
     def get_conds(self):
         return self.c, self.uc
-- 
cgit v1.2.1


From f23e5ce2daad86d891a5ecdf3f3bad43d7a09c1a Mon Sep 17 00:00:00 2001
From: AUTOMATIC1111 <16777216c@gmail.com>
Date: Mon, 14 Aug 2023 17:58:54 +0300
Subject: revert changed inpainting mask conditioning calculation after #12311

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

(limited to 'modules/processing.py')

diff --git a/modules/processing.py b/modules/processing.py
index b1eac2ab..c983d001 100755
--- a/modules/processing.py
+++ b/modules/processing.py
@@ -1499,7 +1499,7 @@ class StableDiffusionProcessingImg2Img(StableDiffusionProcessing):
             elif self.inpainting_fill == 3:
                 self.init_latent = self.init_latent * self.mask
 
-        self.image_conditioning = self.img2img_image_conditioning(image, self.init_latent, image_mask)
+        self.image_conditioning = self.img2img_image_conditioning(image * 2 - 1, self.init_latent, image_mask)
 
     def sample(self, conditioning, unconditional_conditioning, seeds, subseeds, subseed_strength, prompts):
         x = self.rng.next()
-- 
cgit v1.2.1


From 7e77a38cbc155baf96db66064afbdd40df715335 Mon Sep 17 00:00:00 2001
From: AUTOMATIC1111 <16777216c@gmail.com>
Date: Tue, 15 Aug 2023 08:27:50 +0300
Subject: get XYZ plot to work with recent changes to refined specified in
 fields of p rather than in settings

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

(limited to 'modules/processing.py')

diff --git a/modules/processing.py b/modules/processing.py
index c983d001..25a19a77 100755
--- a/modules/processing.py
+++ b/modules/processing.py
@@ -746,7 +746,7 @@ def process_images_inner(p: StableDiffusionProcessing) -> Processed:
     if p.tiling is None:
         p.tiling = opts.tiling
 
-    if p.refiner_checkpoint not in (None, "", "None"):
+    if p.refiner_checkpoint not in (None, "", "None", "none"):
         p.refiner_checkpoint_info = sd_models.get_closet_checkpoint_match(p.refiner_checkpoint)
         if p.refiner_checkpoint_info is None:
             raise Exception(f'Could not find checkpoint with name {p.refiner_checkpoint}')
-- 
cgit v1.2.1


From 79d4e81984171a047d6c71d97a67dda7dd87c43c Mon Sep 17 00:00:00 2001
From: AUTOMATIC1111 <16777216c@gmail.com>
Date: Tue, 15 Aug 2023 08:46:17 +0300
Subject: fix processing error that happens if batch_size is not a multiple of
 how many prompts/negative prompts there are #12509

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

(limited to 'modules/processing.py')

diff --git a/modules/processing.py b/modules/processing.py
index 25a19a77..1d098302 100755
--- a/modules/processing.py
+++ b/modules/processing.py
@@ -382,13 +382,18 @@ class StableDiffusionProcessing:
     def setup_prompts(self):
         if type(self.prompt) == list:
             self.all_prompts = self.prompt
+        elif type(self.negative_prompt) == list:
+            self.all_prompts = [self.prompt] * len(self.negative_prompt)
         else:
             self.all_prompts = self.batch_size * self.n_iter * [self.prompt]
 
         if type(self.negative_prompt) == list:
             self.all_negative_prompts = self.negative_prompt
         else:
-            self.all_negative_prompts = self.batch_size * self.n_iter * [self.negative_prompt]
+            self.all_negative_prompts = [self.negative_prompt] * len(self.all_prompts)
+
+        if len(self.all_prompts) != len(self.all_negative_prompts):
+            raise RuntimeError(f"Received a different number of prompts ({len(self.all_prompts)}) and negative prompts ({len(self.all_negative_prompts)})")
 
         self.all_prompts = [shared.prompt_styles.apply_styles_to_prompt(x, self.styles) for x in self.all_prompts]
         self.all_negative_prompts = [shared.prompt_styles.apply_negative_styles_to_prompt(x, self.styles) for x in self.all_negative_prompts]
-- 
cgit v1.2.1


From 0f77139253f5481d62f7c1eddc220355440b2d1f Mon Sep 17 00:00:00 2001
From: catboxanon <122327233+catboxanon@users.noreply.github.com>
Date: Tue, 15 Aug 2023 14:24:55 -0400
Subject: Fix inpaint upload for alpha masks, create reusable function

---
 modules/processing.py | 10 +++++++++-
 1 file changed, 9 insertions(+), 1 deletion(-)

(limited to 'modules/processing.py')

diff --git a/modules/processing.py b/modules/processing.py
index 1d098302..e62db62f 100755
--- a/modules/processing.py
+++ b/modules/processing.py
@@ -81,6 +81,12 @@ def apply_overlay(image, paste_loc, index, overlays):
 
     return image
 
+def create_binary_mask(image):
+    if image.mode == 'RGBA' and image.getextrema()[-1] != (255, 255):
+        image = image.split()[-1].convert("L").point(lambda x: 255 if x > 128 else 0)
+    else:
+        image = image.convert('L')
+    return image
 
 def txt2img_image_conditioning(sd_model, x, width, height):
     if sd_model.model.conditioning_key in {'hybrid', 'concat'}: # Inpainting models
@@ -1385,7 +1391,9 @@ class StableDiffusionProcessingImg2Img(StableDiffusionProcessing):
         image_mask = self.image_mask
 
         if image_mask is not None:
-            image_mask = image_mask.convert('L')
+            # image_mask is passed in as RGBA by Gradio to support alpha masks,
+            # but we still want to support binary masks.
+            image_mask = create_binary_mask(image_mask)
 
             if self.inpainting_mask_invert:
                 image_mask = ImageOps.invert(image_mask)
-- 
cgit v1.2.1


From 640cb1bb8dfbd05a3927753090bc80fb5b9737b1 Mon Sep 17 00:00:00 2001
From: w-e-w <40751091+w-e-w@users.noreply.github.com>
Date: Fri, 18 Aug 2023 17:14:02 +0900
Subject: fix model override logic

do not need extra logic to unload refine model
---
 modules/processing.py | 5 +----
 1 file changed, 1 insertion(+), 4 deletions(-)

(limited to 'modules/processing.py')

diff --git a/modules/processing.py b/modules/processing.py
index e62db62f..263baefc 100755
--- a/modules/processing.py
+++ b/modules/processing.py
@@ -702,11 +702,8 @@ def process_images(p: StableDiffusionProcessing) -> Processed:
     stored_opts = {k: opts.data[k] for k in p.override_settings.keys()}
 
     try:
-        # after running refiner, the refiner model is not unloaded - webui swaps back to main model here
-        if shared.sd_model.sd_checkpoint_info.title != opts.sd_model_checkpoint:
-            sd_models.reload_model_weights()
-
         # if no checkpoint override or the override checkpoint can't be found, remove override entry and load opts checkpoint
+        # and if after running refiner, the refiner model is not unloaded - webui swaps back to main model here, if model over is present it will be reloaded afterwards
         if sd_models.checkpoint_aliases.get(p.override_settings.get('sd_model_checkpoint')) is None:
             p.override_settings.pop('sd_model_checkpoint', None)
             sd_models.reload_model_weights()
-- 
cgit v1.2.1


From 9d1d63afcaffc57e9dff5bdc59643ddd60f377e4 Mon Sep 17 00:00:00 2001
From: catboxanon <122327233+catboxanon@users.noreply.github.com>
Date: Fri, 18 Aug 2023 05:55:10 -0400
Subject: Exit out of hires fix if interrupted earlier

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

(limited to 'modules/processing.py')

diff --git a/modules/processing.py b/modules/processing.py
index e62db62f..b4cb5a82 100755
--- a/modules/processing.py
+++ b/modules/processing.py
@@ -1155,6 +1155,9 @@ class StableDiffusionProcessingTxt2Img(StableDiffusionProcessing):
             devices.torch_gc()
 
     def sample_hr_pass(self, samples, decoded_samples, seeds, subseeds, subseed_strength, prompts):
+        if shared.state.interrupted:
+            return samples
+
         self.is_hr_pass = True
 
         target_width = self.hr_upscale_to_x
-- 
cgit v1.2.1


From 61c1261e4e50385aab68b84c0f886911466044bb Mon Sep 17 00:00:00 2001
From: XDOneDude <106700244+XDOneDude@users.noreply.github.com>
Date: Fri, 18 Aug 2023 21:56:15 -0400
Subject: more grammar fixes

---
 modules/processing.py | 24 ++++++++++++------------
 1 file changed, 12 insertions(+), 12 deletions(-)

(limited to 'modules/processing.py')

diff --git a/modules/processing.py b/modules/processing.py
index e62db62f..0315e1fd 100755
--- a/modules/processing.py
+++ b/modules/processing.py
@@ -386,14 +386,14 @@ class StableDiffusionProcessing:
         return self.token_merging_ratio or opts.token_merging_ratio
 
     def setup_prompts(self):
-        if type(self.prompt) == list:
+        if isinstance(self.prompt,list):
             self.all_prompts = self.prompt
-        elif type(self.negative_prompt) == list:
+        elif isinstance(self.negative_prompt, list):
             self.all_prompts = [self.prompt] * len(self.negative_prompt)
         else:
             self.all_prompts = self.batch_size * self.n_iter * [self.prompt]
 
-        if type(self.negative_prompt) == list:
+        if isinstance(self.negative_prompt, list):
             self.all_negative_prompts = self.negative_prompt
         else:
             self.all_negative_prompts = [self.negative_prompt] * len(self.all_prompts)
@@ -512,10 +512,10 @@ class Processed:
         self.s_noise = p.s_noise
         self.s_min_uncond = p.s_min_uncond
         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]) 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.prompt = self.prompt if not isinstance(self.prompt, list) else self.prompt[0]
+        self.negative_prompt = self.negative_prompt if not isinstance(self.negative_prompt, list) else self.negative_prompt[0]
+        self.seed = int(self.seed if not isinstance(self.seed, list) else self.seed[0]) if self.seed is not None else -1
+        self.subseed = int(self.subseed if not isinstance(self.subseed, list) else self.subseed[0]) if self.subseed is not None else -1
         self.is_using_inpainting_conditioning = p.is_using_inpainting_conditioning
 
         self.all_prompts = all_prompts or p.all_prompts or [self.prompt]
@@ -741,7 +741,7 @@ def process_images(p: StableDiffusionProcessing) -> Processed:
 def process_images_inner(p: StableDiffusionProcessing) -> Processed:
     """this is the main loop that both txt2img and img2img use; it calls func_init once inside all the scopes and func_sample once per batch"""
 
-    if type(p.prompt) == list:
+    if isinstance(p.prompt, list):
         assert(len(p.prompt) > 0)
     else:
         assert p.prompt is not None
@@ -772,12 +772,12 @@ def process_images_inner(p: StableDiffusionProcessing) -> Processed:
 
     p.setup_prompts()
 
-    if type(seed) == list:
+    if isinstance(seed, list):
         p.all_seeds = seed
     else:
         p.all_seeds = [int(seed) + (x if p.subseed_strength == 0 else 0) for x in range(len(p.all_prompts))]
 
-    if type(subseed) == list:
+    if isinstance(subseed, list):
         p.all_subseeds = subseed
     else:
         p.all_subseeds = [int(subseed) + x for x in range(len(p.all_prompts))]
@@ -1268,12 +1268,12 @@ class StableDiffusionProcessingTxt2Img(StableDiffusionProcessing):
         if self.hr_negative_prompt == '':
             self.hr_negative_prompt = self.negative_prompt
 
-        if type(self.hr_prompt) == list:
+        if isinstance(self.hr_prompt, list):
             self.all_hr_prompts = self.hr_prompt
         else:
             self.all_hr_prompts = self.batch_size * self.n_iter * [self.hr_prompt]
 
-        if type(self.hr_negative_prompt) == list:
+        if isinstance(self.hr_negative_prompt, list):
             self.all_hr_negative_prompts = self.hr_negative_prompt
         else:
             self.all_hr_negative_prompts = self.batch_size * self.n_iter * [self.hr_negative_prompt]
-- 
cgit v1.2.1


From d02c4da483a656331f641224fe5da5f694580066 Mon Sep 17 00:00:00 2001
From: AUTOMATIC1111 <16777216c@gmail.com>
Date: Mon, 21 Aug 2023 08:58:15 +0300
Subject: also prevent changing API options via override_settings

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

(limited to 'modules/processing.py')

diff --git a/modules/processing.py b/modules/processing.py
index d4926524..e60cc92b 100755
--- a/modules/processing.py
+++ b/modules/processing.py
@@ -709,7 +709,7 @@ def process_images(p: StableDiffusionProcessing) -> Processed:
             sd_models.reload_model_weights()
 
         for k, v in p.override_settings.items():
-            setattr(opts, k, v)
+            opts.set(k, v, is_api=True, run_callbacks=False)
 
             if k == 'sd_model_checkpoint':
                 sd_models.reload_model_weights()
-- 
cgit v1.2.1


From 995ff5902fe0567e4cb2aa2e8ac3d554fca7b1ab Mon Sep 17 00:00:00 2001
From: AUTOMATIC1111 <16777216c@gmail.com>
Date: Thu, 24 Aug 2023 10:07:54 +0300
Subject: add infotext for use_old_scheduling option

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

(limited to 'modules/processing.py')

diff --git a/modules/processing.py b/modules/processing.py
index 066351c1..7dc931ba 100644
--- a/modules/processing.py
+++ b/modules/processing.py
@@ -437,6 +437,12 @@ class StableDiffusionProcessing:
         caches is a list with items described above.
         """
 
+        if shared.opts.use_old_scheduling:
+            old_schedules = prompt_parser.get_learned_conditioning_prompt_schedules(required_prompts, steps, hires_steps, False)
+            new_schedules = prompt_parser.get_learned_conditioning_prompt_schedules(required_prompts, steps, hires_steps, True)
+            if old_schedules != new_schedules:
+                self.extra_generation_params["Old prompt editing timelines"] = True
+
         cached_params = self.cached_params(required_prompts, steps, extra_network_data, hires_steps, shared.opts.use_old_scheduling)
 
         for cache in caches:
-- 
cgit v1.2.1