From 8e7097d06a6a261580d34375c9d2a9e4ffc63ffa Mon Sep 17 00:00:00 2001
From: random_thoughtss <random_thoughtss@proton.me>
Date: Wed, 19 Oct 2022 13:47:45 -0700
Subject: Added support for RunwayML inpainting model

---
 modules/processing.py | 34 ++++++++++++++++++++++++++++++++--
 1 file changed, 32 insertions(+), 2 deletions(-)

(limited to 'modules/processing.py')

diff --git a/modules/processing.py b/modules/processing.py
index bcb0c32c..a6c308f9 100644
--- a/modules/processing.py
+++ b/modules/processing.py
@@ -546,7 +546,16 @@ class StableDiffusionProcessingTxt2Img(StableDiffusionProcessing):
 
         if not self.enable_hr:
             x = create_random_tensors([opt_C, self.height // opt_f, self.width // opt_f], seeds=seeds, subseeds=subseeds, subseed_strength=self.subseed_strength, seed_resize_from_h=self.seed_resize_from_h, seed_resize_from_w=self.seed_resize_from_w, p=self)
-            samples = self.sampler.sample(self, x, conditioning, unconditional_conditioning)
+            
+            # 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, self.height, self.width, device=x.device)
+            image_conditioning = self.sd_model.get_first_stage_encoding(self.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)
+            image_conditioning = image_conditioning.to(x.dtype)
+
+            samples = self.sampler.sample(self, x, conditioning, unconditional_conditioning, image_conditioning=image_conditioning)
             return samples
 
         x = create_random_tensors([opt_C, self.firstphase_height // opt_f, self.firstphase_width // opt_f], seeds=seeds, subseeds=subseeds, subseed_strength=self.subseed_strength, seed_resize_from_h=self.seed_resize_from_h, seed_resize_from_w=self.seed_resize_from_w, p=self)
@@ -714,10 +723,31 @@ class StableDiffusionProcessingImg2Img(StableDiffusionProcessing):
             elif self.inpainting_fill == 3:
                 self.init_latent = self.init_latent * self.mask
 
+        if self.image_mask is not None:
+            conditioning_mask = np.array(self.image_mask.convert("L"))
+            conditioning_mask = conditioning_mask.astype(np.float32) / 255.0
+            conditioning_mask = torch.from_numpy(conditioning_mask[None, None])
+
+            # Inpainting model uses a discretized mask as input, so we round to either 1.0 or 0.0
+            conditioning_mask = torch.round(conditioning_mask)
+        else:
+            conditioning_mask = torch.ones(1, 1, *image.shape[-2:])
+
+        # Create another latent image, this time with a masked version of the original input.
+        conditioning_mask = conditioning_mask.to(image.device)
+        conditioning_image = image * (1.0 - conditioning_mask)
+        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=self.init_latent.shape[-2:])
+        conditioning_mask = conditioning_mask.expand(conditioning_image.shape[0], -1, -1, -1)
+        self.image_conditioning = torch.cat([conditioning_mask, conditioning_image], dim=1)
+        self.image_conditioning = self.image_conditioning.to(shared.device).type(self.sd_model.dtype)
+
     def sample(self, conditioning, unconditional_conditioning, seeds, subseeds, subseed_strength):
         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_img2img(self, self.init_latent, x, conditioning, unconditional_conditioning)
+        samples = self.sampler.sample_img2img(self, self.init_latent, x, conditioning, unconditional_conditioning, image_conditioning=self.image_conditioning)
 
         if self.mask is not None:
             samples = samples * self.nmask + self.init_latent * self.mask
-- 
cgit v1.2.3


From c418467c03db916c3e5312e6ac4a67365e196dbd Mon Sep 17 00:00:00 2001
From: random_thoughtss <random_thoughtss@proton.me>
Date: Wed, 19 Oct 2022 15:09:43 -0700
Subject: Don't compute latent mask if were not using it. Also added support
 for fixed highres_fix generation.

---
 modules/processing.py | 72 ++++++++++++++++++++++++++++++++-------------------
 1 file changed, 45 insertions(+), 27 deletions(-)

(limited to 'modules/processing.py')

diff --git a/modules/processing.py b/modules/processing.py
index a6c308f9..684e5833 100644
--- a/modules/processing.py
+++ b/modules/processing.py
@@ -541,12 +541,8 @@ class StableDiffusionProcessingTxt2Img(StableDiffusionProcessing):
             self.truncate_y = int(self.firstphase_height - firstphase_height_truncated) // opt_f
 
 
-    def sample(self, conditioning, unconditional_conditioning, seeds, subseeds, subseed_strength):
-        self.sampler = sd_samplers.create_sampler_with_index(sd_samplers.samplers, self.sampler_index, self.sd_model)
-
-        if not self.enable_hr:
-            x = create_random_tensors([opt_C, self.height // opt_f, self.width // opt_f], seeds=seeds, subseeds=subseeds, subseed_strength=self.subseed_strength, seed_resize_from_h=self.seed_resize_from_h, seed_resize_from_w=self.seed_resize_from_w, p=self)
-            
+    def create_dummy_mask(self, x):
+        if self.sampler.conditioning_key in {'hybrid', 'concat'}:
             # 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, self.height, self.width, device=x.device)
             image_conditioning = self.sd_model.get_first_stage_encoding(self.sd_model.encode_first_stage(image_conditioning)) 
@@ -555,11 +551,23 @@ class StableDiffusionProcessingTxt2Img(StableDiffusionProcessing):
             image_conditioning = torch.nn.functional.pad(image_conditioning, (0, 0, 0, 0, 1, 0), value=1.0)
             image_conditioning = image_conditioning.to(x.dtype)
 
-            samples = self.sampler.sample(self, x, conditioning, unconditional_conditioning, image_conditioning=image_conditioning)
+        else:
+            # Dummy zero conditioning if we're not using inpainting model.
+            # Still takes up a bit of memory, but no encoder call.
+            image_conditioning = torch.zeros(x.shape[0], 5, x.shape[-2], x.shape[-1], dtype=x.dtype, device=x.device)
+
+        return image_conditioning
+
+    def sample(self, conditioning, unconditional_conditioning, seeds, subseeds, subseed_strength):
+        self.sampler = sd_samplers.create_sampler_with_index(sd_samplers.samplers, self.sampler_index, self.sd_model)
+
+        if not self.enable_hr:
+            x = create_random_tensors([opt_C, self.height // opt_f, self.width // opt_f], seeds=seeds, subseeds=subseeds, subseed_strength=self.subseed_strength, seed_resize_from_h=self.seed_resize_from_h, seed_resize_from_w=self.seed_resize_from_w, p=self)
+            samples = self.sampler.sample(self, x, conditioning, unconditional_conditioning, image_conditioning=self.create_dummy_mask(x))
             return samples
 
         x = create_random_tensors([opt_C, self.firstphase_height // opt_f, self.firstphase_width // opt_f], seeds=seeds, subseeds=subseeds, subseed_strength=self.subseed_strength, seed_resize_from_h=self.seed_resize_from_h, seed_resize_from_w=self.seed_resize_from_w, p=self)
-        samples = self.sampler.sample(self, x, conditioning, unconditional_conditioning)
+        samples = self.sampler.sample(self, x, conditioning, unconditional_conditioning, image_conditioning=self.create_dummy_mask(x))
 
         samples = samples[:, :, self.truncate_y//2:samples.shape[2]-self.truncate_y//2, self.truncate_x//2:samples.shape[3]-self.truncate_x//2]
 
@@ -596,7 +604,7 @@ class StableDiffusionProcessingTxt2Img(StableDiffusionProcessing):
         x = None
         devices.torch_gc()
 
-        samples = self.sampler.sample_img2img(self, samples, noise, conditioning, unconditional_conditioning, steps=self.steps)
+        samples = self.sampler.sample_img2img(self, samples, noise, conditioning, unconditional_conditioning, steps=self.steps, image_conditioning=self.create_dummy_mask(samples))
 
         return samples
 
@@ -723,26 +731,36 @@ class StableDiffusionProcessingImg2Img(StableDiffusionProcessing):
             elif self.inpainting_fill == 3:
                 self.init_latent = self.init_latent * self.mask
 
-        if self.image_mask is not None:
-            conditioning_mask = np.array(self.image_mask.convert("L"))
-            conditioning_mask = conditioning_mask.astype(np.float32) / 255.0
-            conditioning_mask = torch.from_numpy(conditioning_mask[None, None])
+        conditioning_key = self.sampler.conditioning_key
 
-            # Inpainting model uses a discretized mask as input, so we round to either 1.0 or 0.0
-            conditioning_mask = torch.round(conditioning_mask)
+        if conditioning_key in {'hybrid', 'concat'}:
+            if self.image_mask is not None:
+                conditioning_mask = np.array(self.image_mask.convert("L"))
+                conditioning_mask = conditioning_mask.astype(np.float32) / 255.0
+                conditioning_mask = torch.from_numpy(conditioning_mask[None, None])
+
+                # Inpainting model uses a discretized mask as input, so we round to either 1.0 or 0.0
+                conditioning_mask = torch.round(conditioning_mask)
+            else:
+                conditioning_mask = torch.ones(1, 1, *image.shape[-2:])
+
+            # Create another latent image, this time with a masked version of the original input.
+            conditioning_mask = conditioning_mask.to(image.device)
+            conditioning_image = image * (1.0 - conditioning_mask)
+            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=self.init_latent.shape[-2:])
+            conditioning_mask = conditioning_mask.expand(conditioning_image.shape[0], -1, -1, -1)
+            self.image_conditioning = torch.cat([conditioning_mask, conditioning_image], dim=1)
+            self.image_conditioning = self.image_conditioning.to(shared.device).type(self.sd_model.dtype)
         else:
-            conditioning_mask = torch.ones(1, 1, *image.shape[-2:])
-
-        # Create another latent image, this time with a masked version of the original input.
-        conditioning_mask = conditioning_mask.to(image.device)
-        conditioning_image = image * (1.0 - conditioning_mask)
-        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=self.init_latent.shape[-2:])
-        conditioning_mask = conditioning_mask.expand(conditioning_image.shape[0], -1, -1, -1)
-        self.image_conditioning = torch.cat([conditioning_mask, conditioning_image], dim=1)
-        self.image_conditioning = self.image_conditioning.to(shared.device).type(self.sd_model.dtype)
+            self.image_conditioning = torch.zeros(
+                self.init_latent.shape[0], 5, self.init_latent.shape[-2], self.init_latent.shape[-1], 
+                dtype=self.init_latent.dtype, 
+                device=self.init_latent.device
+            )
+
 
     def sample(self, conditioning, unconditional_conditioning, seeds, subseeds, subseed_strength):
         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)
-- 
cgit v1.2.3


From aa7ff2a1972f3865883e10ba28c5414cdebe8e3b Mon Sep 17 00:00:00 2001
From: random_thoughtss <random_thoughtss@proton.me>
Date: Wed, 19 Oct 2022 21:46:13 -0700
Subject: Fixed non-square highres fix generation

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

(limited to 'modules/processing.py')

diff --git a/modules/processing.py b/modules/processing.py
index 684e5833..3caac25e 100644
--- a/modules/processing.py
+++ b/modules/processing.py
@@ -541,10 +541,13 @@ class StableDiffusionProcessingTxt2Img(StableDiffusionProcessing):
             self.truncate_y = int(self.firstphase_height - firstphase_height_truncated) // opt_f
 
 
-    def create_dummy_mask(self, x):
+    def create_dummy_mask(self, x, first_phase: bool = False):
         if self.sampler.conditioning_key in {'hybrid', 'concat'}:
+            height = self.firstphase_height if first_phase else self.height
+            width = self.firstphase_width if first_phase else self.width
+
             # 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, self.height, self.width, device=x.device)
+            image_conditioning = torch.zeros(x.shape[0], 3, height, width, device=x.device)
             image_conditioning = self.sd_model.get_first_stage_encoding(self.sd_model.encode_first_stage(image_conditioning)) 
 
             # Add the fake full 1s mask to the first dimension.
@@ -567,7 +570,7 @@ class StableDiffusionProcessingTxt2Img(StableDiffusionProcessing):
             return samples
 
         x = create_random_tensors([opt_C, self.firstphase_height // opt_f, self.firstphase_width // opt_f], seeds=seeds, subseeds=subseeds, subseed_strength=self.subseed_strength, seed_resize_from_h=self.seed_resize_from_h, seed_resize_from_w=self.seed_resize_from_w, p=self)
-        samples = self.sampler.sample(self, x, conditioning, unconditional_conditioning, image_conditioning=self.create_dummy_mask(x))
+        samples = self.sampler.sample(self, x, conditioning, unconditional_conditioning, image_conditioning=self.create_dummy_mask(x, first_phase=True))
 
         samples = samples[:, :, self.truncate_y//2:samples.shape[2]-self.truncate_y//2, self.truncate_x//2:samples.shape[3]-self.truncate_x//2]
 
-- 
cgit v1.2.3


From 92a17a7a4a13fceb3c3e25a2e854b2a7dd6eb5df Mon Sep 17 00:00:00 2001
From: random_thoughtss <random_thoughtss@proton.me>
Date: Thu, 20 Oct 2022 09:45:03 -0700
Subject: Made dummy latents smaller. Minor code cleanups

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

(limited to 'modules/processing.py')

diff --git a/modules/processing.py b/modules/processing.py
index 3caac25e..539cde38 100644
--- a/modules/processing.py
+++ b/modules/processing.py
@@ -557,7 +557,8 @@ class StableDiffusionProcessingTxt2Img(StableDiffusionProcessing):
         else:
             # Dummy zero conditioning if we're not using inpainting model.
             # Still takes up a bit of memory, but no encoder call.
-            image_conditioning = torch.zeros(x.shape[0], 5, x.shape[-2], x.shape[-1], dtype=x.dtype, device=x.device)
+            # Pretty sure we can just make this a 1x1 image since its not going to be used besides its batch size.
+            image_conditioning = torch.zeros(x.shape[0], 5, 1, 1, dtype=x.dtype, device=x.device)
 
         return image_conditioning
 
@@ -759,8 +760,8 @@ class StableDiffusionProcessingImg2Img(StableDiffusionProcessing):
             self.image_conditioning = self.image_conditioning.to(shared.device).type(self.sd_model.dtype)
         else:
             self.image_conditioning = torch.zeros(
-                self.init_latent.shape[0], 5, self.init_latent.shape[-2], self.init_latent.shape[-1], 
-                dtype=self.init_latent.dtype, 
+                self.init_latent.shape[0], 5, 1, 1,
+                dtype=self.init_latent.dtype,
                 device=self.init_latent.device
             )
 
-- 
cgit v1.2.3


From 45872181902ada06267e2de601586d512cf5df1a Mon Sep 17 00:00:00 2001
From: AUTOMATIC <16777216c@gmail.com>
Date: Fri, 21 Oct 2022 09:00:39 +0300
Subject: updated readme and some small stylistic changes to code

---
 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 539cde38..21786968 100644
--- a/modules/processing.py
+++ b/modules/processing.py
@@ -540,11 +540,10 @@ class StableDiffusionProcessingTxt2Img(StableDiffusionProcessing):
             self.truncate_x = int(self.firstphase_width - firstphase_width_truncated) // opt_f
             self.truncate_y = int(self.firstphase_height - firstphase_height_truncated) // opt_f
 
-
-    def create_dummy_mask(self, x, first_phase: bool = False):
+    def create_dummy_mask(self, x, width=None, height=None):
         if self.sampler.conditioning_key in {'hybrid', 'concat'}:
-            height = self.firstphase_height if first_phase else self.height
-            width = self.firstphase_width if first_phase else self.width
+            height = height or self.height
+            width = width or self.width
 
             # 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)
@@ -571,7 +570,7 @@ class StableDiffusionProcessingTxt2Img(StableDiffusionProcessing):
             return samples
 
         x = create_random_tensors([opt_C, self.firstphase_height // opt_f, self.firstphase_width // opt_f], seeds=seeds, subseeds=subseeds, subseed_strength=self.subseed_strength, seed_resize_from_h=self.seed_resize_from_h, seed_resize_from_w=self.seed_resize_from_w, p=self)
-        samples = self.sampler.sample(self, x, conditioning, unconditional_conditioning, image_conditioning=self.create_dummy_mask(x, first_phase=True))
+        samples = self.sampler.sample(self, x, conditioning, unconditional_conditioning, image_conditioning=self.create_dummy_mask(x, self.firstphase_width, self.firstphase_height))
 
         samples = samples[:, :, self.truncate_y//2:samples.shape[2]-self.truncate_y//2, self.truncate_x//2:samples.shape[3]-self.truncate_x//2]
 
@@ -634,6 +633,7 @@ class StableDiffusionProcessingImg2Img(StableDiffusionProcessing):
         self.inpainting_mask_invert = inpainting_mask_invert
         self.mask = None
         self.nmask = None
+        self.image_conditioning = None
 
     def init(self, all_prompts, all_seeds, all_subseeds):
         self.sampler = sd_samplers.create_sampler_with_index(sd_samplers.samplers_for_img2img, self.sampler_index, self.sd_model)
@@ -735,9 +735,7 @@ class StableDiffusionProcessingImg2Img(StableDiffusionProcessing):
             elif self.inpainting_fill == 3:
                 self.init_latent = self.init_latent * self.mask
 
-        conditioning_key = self.sampler.conditioning_key
-
-        if conditioning_key in {'hybrid', 'concat'}:
+        if self.sampler.conditioning_key in {'hybrid', 'concat'}:
             if self.image_mask is not None:
                 conditioning_mask = np.array(self.image_mask.convert("L"))
                 conditioning_mask = conditioning_mask.astype(np.float32) / 255.0
-- 
cgit v1.2.3


From bf30673f5132c8f28357b31224c54331e788d3e7 Mon Sep 17 00:00:00 2001
From: AUTOMATIC <16777216c@gmail.com>
Date: Fri, 21 Oct 2022 10:19:25 +0300
Subject: Fix Hypernet infotext string split bug for PR #3283

---
 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 21786968..d1deffa9 100644
--- a/modules/processing.py
+++ b/modules/processing.py
@@ -304,7 +304,7 @@ def create_infotext(p, all_prompts, all_seeds, all_subseeds, comments, iteration
         "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 or not shared.sd_model.sd_checkpoint_info.model_name else shared.sd_model.sd_checkpoint_info.model_name.replace(',', '').replace(':', '')),
-        "Hypernet": (None if shared.loaded_hypernetwork is None else shared.loaded_hypernetwork.filename.split('\\')[-1].split('.')[0]),
+        "Hypernet": (None if shared.loaded_hypernetwork is None else os.path.splitext(os.path.basename(shared.loaded_hypernetwork.filename))[0]),
         "Batch size": (None if p.batch_size < 2 else p.batch_size),
         "Batch pos": (None if p.batch_size < 2 else position_in_batch),
         "Variation seed": (None if p.subseed_strength == 0 else all_subseeds[index]),
-- 
cgit v1.2.3


From df5706409386cc2e88718bd9101045587c39f8bb Mon Sep 17 00:00:00 2001
From: AUTOMATIC <16777216c@gmail.com>
Date: Fri, 21 Oct 2022 16:10:51 +0300
Subject: do not load aesthetic clip model until it's needed add refresh button
 for aesthetic embeddings add aesthetic params to images' infotext

---
 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 d1deffa9..f0852cd5 100644
--- a/modules/processing.py
+++ b/modules/processing.py
@@ -12,7 +12,7 @@ from skimage import exposure
 from typing import Any, Dict, List, Optional
 
 import modules.sd_hijack
-from modules import devices, prompt_parser, masking, sd_samplers, lowvram
+from modules import devices, prompt_parser, masking, sd_samplers, lowvram, generation_parameters_copypaste
 from modules.sd_hijack import model_hijack
 from modules.shared import opts, cmd_opts, state
 import modules.shared as shared
@@ -318,7 +318,7 @@ def create_infotext(p, all_prompts, all_seeds, all_subseeds, comments, iteration
 
     generation_params.update(p.extra_generation_params)
 
-    generation_params_text = ", ".join([k if k == v else f'{k}: {v}' for k, v in generation_params.items() if v is not None])
+    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])
 
     negative_prompt_text = "\nNegative prompt: " + p.negative_prompt if p.negative_prompt else ""
 
-- 
cgit v1.2.3


From fccad18a59e3c2c33fefbbb1763c6a87a3a68eba Mon Sep 17 00:00:00 2001
From: timntorres <timothynarcisotorres@gmail.com>
Date: Fri, 21 Oct 2022 02:17:26 -0700
Subject: Refer to Hypernet's name, sensibly, by its name variable.

---
 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 f0852cd5..ff1ec4c9 100644
--- a/modules/processing.py
+++ b/modules/processing.py
@@ -304,7 +304,7 @@ def create_infotext(p, all_prompts, all_seeds, all_subseeds, comments, iteration
         "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 or not shared.sd_model.sd_checkpoint_info.model_name else shared.sd_model.sd_checkpoint_info.model_name.replace(',', '').replace(':', '')),
-        "Hypernet": (None if shared.loaded_hypernetwork is None else os.path.splitext(os.path.basename(shared.loaded_hypernetwork.filename))[0]),
+        "Hypernet": (None if shared.loaded_hypernetwork is None else shared.loaded_hypernetwork.name),
         "Batch size": (None if p.batch_size < 2 else p.batch_size),
         "Batch pos": (None if p.batch_size < 2 else position_in_batch),
         "Variation seed": (None if p.subseed_strength == 0 else all_subseeds[index]),
-- 
cgit v1.2.3


From 2b91251637078e04472c91a06a8d9c4db9c1dcf0 Mon Sep 17 00:00:00 2001
From: AUTOMATIC <16777216c@gmail.com>
Date: Sat, 22 Oct 2022 12:23:45 +0300
Subject: removed aesthetic gradients as built-in added support for extensions

---
 modules/processing.py | 35 ++++++++++++++++++++++-------------
 1 file changed, 22 insertions(+), 13 deletions(-)

(limited to 'modules/processing.py')

diff --git a/modules/processing.py b/modules/processing.py
index ff1ec4c9..372489f7 100644
--- a/modules/processing.py
+++ b/modules/processing.py
@@ -104,6 +104,12 @@ class StableDiffusionProcessing():
             self.seed_resize_from_h = 0
             self.seed_resize_from_w = 0
 
+        self.scripts = None
+        self.script_args = None
+        self.all_prompts = None
+        self.all_seeds = None
+        self.all_subseeds = None
+
 
     def init(self, all_prompts, all_seeds, all_subseeds):
         pass
@@ -350,32 +356,35 @@ def process_images(p: StableDiffusionProcessing) -> Processed:
     shared.prompt_styles.apply_styles(p)
 
     if type(p.prompt) == list:
-        all_prompts = p.prompt
+        p.all_prompts = p.prompt
     else:
-        all_prompts = p.batch_size * p.n_iter * [p.prompt]
+        p.all_prompts = p.batch_size * p.n_iter * [p.prompt]
 
     if type(seed) == list:
-        all_seeds = seed
+        p.all_seeds = seed
     else:
-        all_seeds = [int(seed) + (x if p.subseed_strength == 0 else 0) for x in range(len(all_prompts))]
+        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:
-        all_subseeds = subseed
+        p.all_subseeds = subseed
     else:
-        all_subseeds = [int(subseed) + x for x in range(len(all_prompts))]
+        p.all_subseeds = [int(subseed) + x for x in range(len(p.all_prompts))]
 
     def infotext(iteration=0, position_in_batch=0):
-        return create_infotext(p, all_prompts, all_seeds, all_subseeds, comments, iteration, position_in_batch)
+        return create_infotext(p, p.all_prompts, p.all_seeds, p.all_subseeds, comments, iteration, position_in_batch)
 
     if os.path.exists(cmd_opts.embeddings_dir) and not p.do_not_reload_embeddings:
         model_hijack.embedding_db.load_textual_inversion_embeddings()
 
+    if p.scripts is not None:
+        p.scripts.run_alwayson_scripts(p)
+
     infotexts = []
     output_images = []
 
     with torch.no_grad(), p.sd_model.ema_scope():
         with devices.autocast():
-            p.init(all_prompts, all_seeds, all_subseeds)
+            p.init(p.all_prompts, p.all_seeds, p.all_subseeds)
 
         if state.job_count == -1:
             state.job_count = p.n_iter
@@ -387,9 +396,9 @@ def process_images(p: StableDiffusionProcessing) -> Processed:
             if state.interrupted:
                 break
 
-            prompts = all_prompts[n * p.batch_size:(n + 1) * p.batch_size]
-            seeds = all_seeds[n * p.batch_size:(n + 1) * p.batch_size]
-            subseeds = all_subseeds[n * p.batch_size:(n + 1) * p.batch_size]
+            prompts = p.all_prompts[n * p.batch_size:(n + 1) * p.batch_size]
+            seeds = p.all_seeds[n * p.batch_size:(n + 1) * p.batch_size]
+            subseeds = p.all_subseeds[n * p.batch_size:(n + 1) * p.batch_size]
 
             if (len(prompts) == 0):
                 break
@@ -490,10 +499,10 @@ def process_images(p: StableDiffusionProcessing) -> Processed:
                 index_of_first_image = 1
 
             if opts.grid_save:
-                images.save_image(grid, p.outpath_grids, "grid", all_seeds[0], all_prompts[0], opts.grid_format, info=infotext(), short_filename=not opts.grid_extended_filename, p=p, grid=True)
+                images.save_image(grid, p.outpath_grids, "grid", p.all_seeds[0], p.all_prompts[0], opts.grid_format, info=infotext(), short_filename=not opts.grid_extended_filename, p=p, grid=True)
 
     devices.torch_gc()
-    return Processed(p, output_images, all_seeds[0], infotext() + "".join(["\n\n" + x for x in comments]), subseed=all_subseeds[0], all_prompts=all_prompts, all_seeds=all_seeds, all_subseeds=all_subseeds, index_of_first_image=index_of_first_image, infotexts=infotexts)
+    return Processed(p, output_images, p.all_seeds[0], infotext() + "".join(["\n\n" + x for x in comments]), subseed=p.all_subseeds[0], all_prompts=p.all_prompts, all_seeds=p.all_seeds, all_subseeds=p.all_subseeds, index_of_first_image=index_of_first_image, infotexts=infotexts)
 
 
 class StableDiffusionProcessingTxt2Img(StableDiffusionProcessing):
-- 
cgit v1.2.3


From 324c7c732dd9afc3d4c397c354797ae5d655b514 Mon Sep 17 00:00:00 2001
From: AUTOMATIC <16777216c@gmail.com>
Date: Sat, 22 Oct 2022 20:09:37 +0300
Subject: record First pass size as 0x0 for #3328

---
 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 372489f7..27c669b0 100644
--- a/modules/processing.py
+++ b/modules/processing.py
@@ -524,6 +524,8 @@ class StableDiffusionProcessingTxt2Img(StableDiffusionProcessing):
             else:
                 state.job_count = state.job_count * 2
 
+            self.extra_generation_params["First pass size"] = f"{self.firstphase_width}x{self.firstphase_height}"
+
             if self.firstphase_width == 0 or self.firstphase_height == 0:
                 desired_pixel_count = 512 * 512
                 actual_pixel_count = self.width * self.height
@@ -545,7 +547,6 @@ class StableDiffusionProcessingTxt2Img(StableDiffusionProcessing):
                     firstphase_width_truncated = self.firstphase_height * self.width / self.height
                     firstphase_height_truncated = self.firstphase_height
 
-            self.extra_generation_params["First pass size"] = f"{self.firstphase_width}x{self.firstphase_height}"
             self.truncate_x = int(self.firstphase_width - firstphase_width_truncated) // opt_f
             self.truncate_y = int(self.firstphase_height - firstphase_height_truncated) // opt_f
 
-- 
cgit v1.2.3


From ca5a9e79dc28eeaa3a161427a82e34703bf15765 Mon Sep 17 00:00:00 2001
From: AUTOMATIC <16777216c@gmail.com>
Date: Sat, 22 Oct 2022 22:06:54 +0300
Subject: fix for img2img color correction in a batch #3218

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

(limited to 'modules/processing.py')

diff --git a/modules/processing.py b/modules/processing.py
index 27c669b0..b1877b80 100644
--- a/modules/processing.py
+++ b/modules/processing.py
@@ -403,8 +403,6 @@ def process_images(p: StableDiffusionProcessing) -> Processed:
             if (len(prompts) == 0):
                 break
 
-            #uc = p.sd_model.get_learned_conditioning(len(prompts) * [p.negative_prompt])
-            #c = p.sd_model.get_learned_conditioning(prompts)
             with devices.autocast():
                 uc = prompt_parser.get_learned_conditioning(shared.sd_model, len(prompts) * [p.negative_prompt], p.steps)
                 c = prompt_parser.get_multicond_learned_conditioning(shared.sd_model, prompts, p.steps)
@@ -716,6 +714,10 @@ class StableDiffusionProcessingImg2Img(StableDiffusionProcessing):
             batch_images = np.expand_dims(imgs[0], axis=0).repeat(self.batch_size, axis=0)
             if self.overlay_images is not None:
                 self.overlay_images = self.overlay_images * self.batch_size
+
+            if self.color_corrections is not None and len(self.color_corrections) == 1:
+                self.color_corrections = self.color_corrections * self.batch_size
+
         elif len(imgs) <= self.batch_size:
             self.batch_size = len(imgs)
             batch_images = np.array(imgs)
-- 
cgit v1.2.3


From a7c213d0f5ebb10722629b8490a5863f9ce6c4fa Mon Sep 17 00:00:00 2001
From: Stephen <farrenzo47@gmail.com>
Date: Fri, 21 Oct 2022 19:27:40 -0400
Subject: [API][Feature] - Add img2img API endpoint

---
 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 b1877b80..1557ed8c 100644
--- a/modules/processing.py
+++ b/modules/processing.py
@@ -623,7 +623,7 @@ class StableDiffusionProcessingTxt2Img(StableDiffusionProcessing):
 class StableDiffusionProcessingImg2Img(StableDiffusionProcessing):
     sampler = None
 
-    def __init__(self, init_images=None, resize_mode=0, denoising_strength=0.75, mask=None, mask_blur=4, inpainting_fill=0, inpaint_full_res=True, inpaint_full_res_padding=0, inpainting_mask_invert=0, **kwargs):
+    def __init__(self, init_images: list=None, resize_mode: int=0, denoising_strength: float=0.75, mask: str=None, mask_blur: int=4, inpainting_fill: int=0, inpaint_full_res: bool=True, inpaint_full_res_padding: int=0, inpainting_mask_invert: int=0, **kwargs):
         super().__init__(**kwargs)
 
         self.init_images = init_images
-- 
cgit v1.2.3


From 9e1a8b7734a2881451a2efbf80def011ea41ba49 Mon Sep 17 00:00:00 2001
From: Stephen <farrenzo47@gmail.com>
Date: Sat, 22 Oct 2022 15:42:00 -0400
Subject: non-implemented mask with any type

---
 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 1557ed8c..ff83023c 100644
--- a/modules/processing.py
+++ b/modules/processing.py
@@ -623,7 +623,7 @@ class StableDiffusionProcessingTxt2Img(StableDiffusionProcessing):
 class StableDiffusionProcessingImg2Img(StableDiffusionProcessing):
     sampler = None
 
-    def __init__(self, init_images: list=None, resize_mode: int=0, denoising_strength: float=0.75, mask: str=None, mask_blur: int=4, inpainting_fill: int=0, inpaint_full_res: bool=True, inpaint_full_res_padding: int=0, inpainting_mask_invert: int=0, **kwargs):
+    def __init__(self, init_images: list=None, resize_mode: int=0, denoising_strength: float=0.75, mask: Any=None, mask_blur: int=4, inpainting_fill: int=0, inpaint_full_res: bool=True, inpaint_full_res_padding: int=0, inpainting_mask_invert: int=0, **kwargs):
         super().__init__(**kwargs)
 
         self.init_images = init_images
-- 
cgit v1.2.3


From 974196932583b96b6b76632052fc0d7e70820bf3 Mon Sep 17 00:00:00 2001
From: Vladimir Repin <32306715+mezotaken@users.noreply.github.com>
Date: Sun, 23 Oct 2022 22:38:42 +0300
Subject: Save properly processed image before color correction

---
 modules/processing.py | 33 ++++++++++++++++++---------------
 1 file changed, 18 insertions(+), 15 deletions(-)

(limited to 'modules/processing.py')

diff --git a/modules/processing.py b/modules/processing.py
index ff83023c..15b639e1 100644
--- a/modules/processing.py
+++ b/modules/processing.py
@@ -46,6 +46,20 @@ def apply_color_correction(correction, image):
 
     return image
 
+def apply_overlay(overlay_exists, overlay, paste_loc, image):
+    if overlay_exists:
+        if paste_loc is not None:
+            x, y, w, h = paste_loc
+            base_image = Image.new('RGBA', (overlay.width, overlay.height))
+            image = images.resize_image(1, image, w, h)
+            base_image.paste(image, (x, y))
+            image = base_image
+
+        image = image.convert('RGBA')
+        image.alpha_composite(overlay)
+        image = image.convert('RGB')
+
+    return image
 
 def get_correct_sampler(p):
     if isinstance(p, modules.processing.StableDiffusionProcessingTxt2Img):
@@ -446,25 +460,14 @@ def process_images(p: StableDiffusionProcessing) -> Processed:
                     devices.torch_gc()
 
                 image = Image.fromarray(x_sample)
-
+                
                 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:
-                        images.save_image(image, p.outpath_samples, "", seeds[i], prompts[i], opts.samples_format, info=infotext(n, i), p=p, suffix="-before-color-correction")
+                        image_without_cc = apply_overlay(p.overlay_images is not None and i < len(p.overlay_images), p.overlay_images[i], p.paste_to, image)
+                        images.save_image(image_without_cc, p.outpath_samples, "", seeds[i], prompts[i], opts.samples_format, info=infotext(n, i), p=p, suffix="-before-color-correction")
                     image = apply_color_correction(p.color_corrections[i], image)
 
-                if p.overlay_images is not None and i < len(p.overlay_images):
-                    overlay = p.overlay_images[i]
-
-                    if p.paste_to is not None:
-                        x, y, w, h = p.paste_to
-                        base_image = Image.new('RGBA', (overlay.width, overlay.height))
-                        image = images.resize_image(1, image, w, h)
-                        base_image.paste(image, (x, y))
-                        image = base_image
-
-                    image = image.convert('RGBA')
-                    image.alpha_composite(overlay)
-                    image = image.convert('RGB')
+                image = apply_overlay(p.overlay_images is not None and i < len(p.overlay_images), p.overlay_images[i], p.paste_to, image)
 
                 if opts.samples_save and not p.do_not_save_samples:
                     images.save_image(image, p.outpath_samples, "", seeds[i], prompts[i], opts.samples_format, info=infotext(n, i), p=p)
-- 
cgit v1.2.3


From f2cc3f32d5bc8538e95edec54d7dc1b9efdf769a Mon Sep 17 00:00:00 2001
From: Vladimir Repin <32306715+mezotaken@users.noreply.github.com>
Date: Sun, 23 Oct 2022 22:44:46 +0300
Subject: fix whitespaces

---
 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 15b639e1..2a332514 100644
--- a/modules/processing.py
+++ b/modules/processing.py
@@ -460,7 +460,7 @@ def process_images(p: StableDiffusionProcessing) -> Processed:
                     devices.torch_gc()
 
                 image = Image.fromarray(x_sample)
-                
+
                 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(p.overlay_images is not None and i < len(p.overlay_images), p.overlay_images[i], p.paste_to, image)
-- 
cgit v1.2.3


From 6cbb04f7a5e675cf1f6dfc247aa9c9e8df7dc5ce Mon Sep 17 00:00:00 2001
From: AUTOMATIC <16777216c@gmail.com>
Date: Mon, 24 Oct 2022 09:15:26 +0300
Subject: fix #3517 breaking txt2img

---
 modules/processing.py | 33 +++++++++++++++++++--------------
 1 file changed, 19 insertions(+), 14 deletions(-)

(limited to 'modules/processing.py')

diff --git a/modules/processing.py b/modules/processing.py
index 2a332514..c61bbfbd 100644
--- a/modules/processing.py
+++ b/modules/processing.py
@@ -46,18 +46,23 @@ def apply_color_correction(correction, image):
 
     return image
 
-def apply_overlay(overlay_exists, overlay, paste_loc, image):
-    if overlay_exists:
-        if paste_loc is not None:
-            x, y, w, h = paste_loc
-            base_image = Image.new('RGBA', (overlay.width, overlay.height))
-            image = images.resize_image(1, image, w, h)
-            base_image.paste(image, (x, y))
-            image = base_image
-
-        image = image.convert('RGBA')
-        image.alpha_composite(overlay)
-        image = image.convert('RGB')
+
+def apply_overlay(image, paste_loc, index, overlays):
+    if overlays is None or index >= len(overlays):
+        return image
+
+    overlay = overlays[index]
+
+    if paste_loc is not None:
+        x, y, w, h = paste_loc
+        base_image = Image.new('RGBA', (overlay.width, overlay.height))
+        image = images.resize_image(1, image, w, h)
+        base_image.paste(image, (x, y))
+        image = base_image
+
+    image = image.convert('RGBA')
+    image.alpha_composite(overlay)
+    image = image.convert('RGB')
 
     return image
 
@@ -463,11 +468,11 @@ def process_images(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(p.overlay_images is not None and i < len(p.overlay_images), p.overlay_images[i], p.paste_to, image)
+                        image_without_cc = apply_overlay(image, p.paste_to, i, p.overlay_images)
                         images.save_image(image_without_cc, p.outpath_samples, "", seeds[i], prompts[i], opts.samples_format, info=infotext(n, i), p=p, suffix="-before-color-correction")
                     image = apply_color_correction(p.color_corrections[i], image)
 
-                image = apply_overlay(p.overlay_images is not None and i < len(p.overlay_images), p.overlay_images[i], p.paste_to, 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, "", seeds[i], prompts[i], opts.samples_format, info=infotext(n, i), p=p)
-- 
cgit v1.2.3


From f9549d1cbb3f1d7d1f0fb70375a06e31f9c5dd9d Mon Sep 17 00:00:00 2001
From: random_thoughtss <random_thoughtss@proton.me>
Date: Tue, 25 Oct 2022 11:14:12 -0700
Subject: Added option to use unmasked conditioning image.

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

(limited to 'modules/processing.py')

diff --git a/modules/processing.py b/modules/processing.py
index c61bbfbd..96f56b0d 100644
--- a/modules/processing.py
+++ b/modules/processing.py
@@ -768,7 +768,11 @@ class StableDiffusionProcessingImg2Img(StableDiffusionProcessing):
 
             # Create another latent image, this time with a masked version of the original input.
             conditioning_mask = conditioning_mask.to(image.device)
-            conditioning_image = image * (1.0 - conditioning_mask)
+
+            conditioning_image = image
+            if shared.opts.inpainting_mask_image:
+                conditioning_image = conditioning_image * (1.0 - conditioning_mask)                
+            
             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`
-- 
cgit v1.2.3


From 605d27687f433c0fefb9025aace7dc94f0ebd454 Mon Sep 17 00:00:00 2001
From: random_thoughtss <random_thoughtss@proton.me>
Date: Tue, 25 Oct 2022 12:20:54 -0700
Subject: Added conditioning image masking to xy_grid. Use `True` and `False`
 to select values.

---
 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 96f56b0d..23ee5e02 100644
--- a/modules/processing.py
+++ b/modules/processing.py
@@ -770,7 +770,7 @@ class StableDiffusionProcessingImg2Img(StableDiffusionProcessing):
             conditioning_mask = conditioning_mask.to(image.device)
 
             conditioning_image = image
-            if shared.opts.inpainting_mask_image:
+            if getattr(self, "inpainting_mask_image", shared.opts.inpainting_mask_image):
                 conditioning_image = conditioning_image * (1.0 - conditioning_mask)                
             
             conditioning_image = self.sd_model.get_first_stage_encoding(self.sd_model.encode_first_stage(conditioning_image))
-- 
cgit v1.2.3


From 8b4f32779f28010fc8077e8fcfb85a3205b36bc2 Mon Sep 17 00:00:00 2001
From: random_thoughtss <random_thoughtss@proton.me>
Date: Tue, 25 Oct 2022 13:15:08 -0700
Subject: Switch to a continous blend for cond. image.

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

(limited to 'modules/processing.py')

diff --git a/modules/processing.py b/modules/processing.py
index 23ee5e02..02292bdc 100644
--- a/modules/processing.py
+++ b/modules/processing.py
@@ -769,9 +769,12 @@ class StableDiffusionProcessingImg2Img(StableDiffusionProcessing):
             # Create another latent image, this time with a masked version of the original input.
             conditioning_mask = conditioning_mask.to(image.device)
 
-            conditioning_image = image
-            if getattr(self, "inpainting_mask_image", shared.opts.inpainting_mask_image):
-                conditioning_image = conditioning_image * (1.0 - conditioning_mask)                
+            # Smoothly interpolate between the masked and unmasked latent conditioning image.
+            conditioning_image = torch.lerp(
+                image,
+                image * (1.0 - conditioning_mask),
+                getattr(self, "inpainting_mask_weight", shared.opts.inpainting_mask_weight)
+            )
             
             conditioning_image = self.sd_model.get_first_stage_encoding(self.sd_model.encode_first_stage(conditioning_image))
 
-- 
cgit v1.2.3


From 1e428238db4e399b7a06ad5251cb16eef23a014d Mon Sep 17 00:00:00 2001
From: AUTOMATIC <16777216c@gmail.com>
Date: Wed, 26 Oct 2022 11:47:07 +0300
Subject: add override_settings to API as an alternative to #3629

---
 modules/processing.py | 25 ++++++++++++++++++++-----
 1 file changed, 20 insertions(+), 5 deletions(-)

(limited to 'modules/processing.py')

diff --git a/modules/processing.py b/modules/processing.py
index c61bbfbd..4efba946 100644
--- a/modules/processing.py
+++ b/modules/processing.py
@@ -77,9 +77,8 @@ def get_correct_sampler(p):
 class StableDiffusionProcessing():
     """
     The first set of paramaters: sd_models -> do_not_reload_embeddings represent the minimum required to create a StableDiffusionProcessing
-    
     """
-    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_index: int=0, 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 = "uniform", s_churn: float = 0.0, s_tmax: float = None, s_tmin: float = 0.0, s_noise: float = 1.0):
+    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_index: int = 0, 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_churn: float = 0.0, s_tmax: float = None, s_tmin: float = 0.0, s_noise: float = 1.0, override_settings: Dict[str, Any] = None):
         self.sd_model = sd_model
         self.outpath_samples: str = outpath_samples
         self.outpath_grids: str = outpath_grids
@@ -109,13 +108,14 @@ class StableDiffusionProcessing():
         self.do_not_reload_embeddings = do_not_reload_embeddings
         self.paste_to = None
         self.color_corrections = None
-        self.denoising_strength: float = 0
+        self.denoising_strength: float = denoising_strength
         self.sampler_noise_scheduler_override = None
-        self.ddim_discretize = opts.ddim_discretize
+        self.ddim_discretize = ddim_discretize or opts.ddim_discretize
         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.override_settings = {k: v for k, v in (override_settings or {}).items() if k not in shared.restricted_opts}
 
         if not seed_enable_extras:
             self.subseed = -1
@@ -129,7 +129,6 @@ class StableDiffusionProcessing():
         self.all_seeds = None
         self.all_subseeds = None
 
-
     def init(self, all_prompts, all_seeds, all_subseeds):
         pass
 
@@ -351,6 +350,22 @@ def create_infotext(p, all_prompts, all_seeds, all_subseeds, comments, iteration
 
 
 def process_images(p: StableDiffusionProcessing) -> Processed:
+    stored_opts = {k: opts.data[k] for k in p.override_settings.keys()}
+
+    try:
+        for k, v in p.override_settings.items():
+            opts.data[k] = v  # we don't call onchange for simplicity which makes changing model, hypernet impossible
+
+        res = process_images_inner(p)
+
+    finally:
+        for k, v in stored_opts.items():
+            opts.data[k] = v
+
+    return res
+
+
+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:
-- 
cgit v1.2.3


From 26a3fd2fe9314330336fb0e28d1e9ca7d2abe10e Mon Sep 17 00:00:00 2001
From: random_thoughtss <random_thoughtss@proton.me>
Date: Thu, 27 Oct 2022 11:27:59 -0700
Subject: Highres fix works with unmasked latent. Also refactor the mask
 creation to make it more accesible.

---
 modules/processing.py | 134 ++++++++++++++++++++++++++++----------------------
 1 file changed, 76 insertions(+), 58 deletions(-)

(limited to 'modules/processing.py')

diff --git a/modules/processing.py b/modules/processing.py
index f72185ac..548eec29 100644
--- a/modules/processing.py
+++ b/modules/processing.py
@@ -129,6 +129,73 @@ class StableDiffusionProcessing():
         self.all_seeds = None
         self.all_subseeds = None
 
+    def txt2img_image_conditioning(self, x, width=None, height=None):
+        if self.sampler.conditioning_key not in {'hybrid', 'concat'}:
+            # Dummy zero conditioning if we're not using inpainting model.
+            # Still takes up a bit of memory, but no encoder call.
+            # Pretty sure we can just make this a 1x1 image since its not going to be used besides its batch size.
+            return torch.zeros(
+                x.shape[0], 5, 1, 1, 
+                dtype=x.dtype, 
+                device=x.device
+            )
+
+        height = height or self.height
+        width = width or self.width
+
+        # 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 = self.sd_model.get_first_stage_encoding(self.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)
+        image_conditioning = image_conditioning.to(x.dtype)            
+
+        return image_conditioning
+
+    def img2img_image_conditioning(self, source_image, latent_image, image_mask = None):
+        if self.sampler.conditioning_key not in {'hybrid', 'concat'}:
+            # Dummy zero conditioning if we're not using inpainting model.
+            return torch.zeros(
+                latent_image.shape[0], 5, 1, 1,
+                dtype=latent_image.dtype,
+                device=latent_image.device
+            )
+
+        # Handle the different mask inputs
+        if image_mask is not None:
+            if torch.is_tensor(image_mask):
+                conditioning_mask = image_mask
+            else:
+                conditioning_mask = np.array(image_mask.convert("L"))
+                conditioning_mask = conditioning_mask.astype(np.float32) / 255.0
+                conditioning_mask = torch.from_numpy(conditioning_mask[None, None])
+
+                # Inpainting model uses a discretized mask as input, so we round to either 1.0 or 0.0
+                conditioning_mask = torch.round(conditioning_mask)
+        else:
+            conditioning_mask = torch.ones(1, 1, *source_image.shape[-2:])
+
+        # Create another latent image, this time with a masked version of the original input.
+        # Smoothly interpolate between the masked and unmasked latent conditioning image using a parameter.
+        conditioning_mask = conditioning_mask.to(source_image.device)
+        conditioning_image = torch.lerp(
+            source_image,
+            source_image * (1.0 - conditioning_mask),
+            getattr(self, "inpainting_mask_weight", shared.opts.inpainting_mask_weight)
+        )
+        
+        # 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))
+
+        # Create the concatenated conditioning tensor to be fed to `c_concat`
+        conditioning_mask = torch.nn.functional.interpolate(conditioning_mask, size=latent_image.shape[-2:])
+        conditioning_mask = conditioning_mask.expand(conditioning_image.shape[0], -1, -1, -1)
+        image_conditioning = torch.cat([conditioning_mask, conditioning_image], dim=1)
+        image_conditioning = image_conditioning.to(shared.device).type(self.sd_model.dtype)
+
+        return image_conditioning
+
     def init(self, all_prompts, all_seeds, all_subseeds):
         pass
 
@@ -571,37 +638,16 @@ class StableDiffusionProcessingTxt2Img(StableDiffusionProcessing):
             self.truncate_x = int(self.firstphase_width - firstphase_width_truncated) // opt_f
             self.truncate_y = int(self.firstphase_height - firstphase_height_truncated) // opt_f
 
-    def create_dummy_mask(self, x, width=None, height=None):
-        if self.sampler.conditioning_key in {'hybrid', 'concat'}:
-            height = height or self.height
-            width = width or self.width
-
-            # 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 = self.sd_model.get_first_stage_encoding(self.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)
-            image_conditioning = image_conditioning.to(x.dtype)
-
-        else:
-            # Dummy zero conditioning if we're not using inpainting model.
-            # Still takes up a bit of memory, but no encoder call.
-            # Pretty sure we can just make this a 1x1 image since its not going to be used besides its batch size.
-            image_conditioning = torch.zeros(x.shape[0], 5, 1, 1, dtype=x.dtype, device=x.device)
-
-        return image_conditioning
-
     def sample(self, conditioning, unconditional_conditioning, seeds, subseeds, subseed_strength):
         self.sampler = sd_samplers.create_sampler_with_index(sd_samplers.samplers, self.sampler_index, self.sd_model)
 
         if not self.enable_hr:
             x = create_random_tensors([opt_C, self.height // opt_f, self.width // opt_f], seeds=seeds, subseeds=subseeds, subseed_strength=self.subseed_strength, seed_resize_from_h=self.seed_resize_from_h, seed_resize_from_w=self.seed_resize_from_w, p=self)
-            samples = self.sampler.sample(self, x, conditioning, unconditional_conditioning, image_conditioning=self.create_dummy_mask(x))
+            samples = self.sampler.sample(self, x, conditioning, unconditional_conditioning, image_conditioning=self.txt2img_image_conditioning(x))
             return samples
 
         x = create_random_tensors([opt_C, self.firstphase_height // opt_f, self.firstphase_width // opt_f], seeds=seeds, subseeds=subseeds, subseed_strength=self.subseed_strength, seed_resize_from_h=self.seed_resize_from_h, seed_resize_from_w=self.seed_resize_from_w, p=self)
-        samples = self.sampler.sample(self, x, conditioning, unconditional_conditioning, image_conditioning=self.create_dummy_mask(x, self.firstphase_width, self.firstphase_height))
+        samples = self.sampler.sample(self, x, conditioning, unconditional_conditioning, image_conditioning=self.txt2img_image_conditioning(x, self.firstphase_width, self.firstphase_height))
 
         samples = samples[:, :, self.truncate_y//2:samples.shape[2]-self.truncate_y//2, self.truncate_x//2:samples.shape[3]-self.truncate_x//2]
 
@@ -638,7 +684,12 @@ class StableDiffusionProcessingTxt2Img(StableDiffusionProcessing):
         x = None
         devices.torch_gc()
 
-        samples = self.sampler.sample_img2img(self, samples, noise, conditioning, unconditional_conditioning, steps=self.steps, image_conditioning=self.create_dummy_mask(samples))
+        image_conditioning = self.img2img_image_conditioning(
+            decoded_samples, 
+            samples, 
+            decoded_samples.new_ones(decoded_samples.shape[0], 1, decoded_samples.shape[2], decoded_samples.shape[3])
+        )
+        samples = self.sampler.sample_img2img(self, samples, noise, conditioning, unconditional_conditioning, steps=self.steps, image_conditioning=image_conditioning)
 
         return samples
 
@@ -770,40 +821,7 @@ class StableDiffusionProcessingImg2Img(StableDiffusionProcessing):
             elif self.inpainting_fill == 3:
                 self.init_latent = self.init_latent * self.mask
 
-        if self.sampler.conditioning_key in {'hybrid', 'concat'}:
-            if self.image_mask is not None:
-                conditioning_mask = np.array(self.image_mask.convert("L"))
-                conditioning_mask = conditioning_mask.astype(np.float32) / 255.0
-                conditioning_mask = torch.from_numpy(conditioning_mask[None, None])
-
-                # Inpainting model uses a discretized mask as input, so we round to either 1.0 or 0.0
-                conditioning_mask = torch.round(conditioning_mask)
-            else:
-                conditioning_mask = torch.ones(1, 1, *image.shape[-2:])
-
-            # Create another latent image, this time with a masked version of the original input.
-            conditioning_mask = conditioning_mask.to(image.device)
-
-            # Smoothly interpolate between the masked and unmasked latent conditioning image.
-            conditioning_image = torch.lerp(
-                image,
-                image * (1.0 - conditioning_mask),
-                getattr(self, "inpainting_mask_weight", shared.opts.inpainting_mask_weight)
-            )
-            
-            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=self.init_latent.shape[-2:])
-            conditioning_mask = conditioning_mask.expand(conditioning_image.shape[0], -1, -1, -1)
-            self.image_conditioning = torch.cat([conditioning_mask, conditioning_image], dim=1)
-            self.image_conditioning = self.image_conditioning.to(shared.device).type(self.sd_model.dtype)
-        else:
-            self.image_conditioning = torch.zeros(
-                self.init_latent.shape[0], 5, 1, 1,
-                dtype=self.init_latent.dtype,
-                device=self.init_latent.device
-            )
+        self.image_conditioning = self.img2img_image_conditioning(image, self.init_latent, self.image_mask)
 
 
     def sample(self, conditioning, unconditional_conditioning, seeds, subseeds, subseed_strength):
-- 
cgit v1.2.3


From 9e465c8aa5616df4c6723bee007ffd3910404f12 Mon Sep 17 00:00:00 2001
From: timntorres <timothynarcisotorres@gmail.com>
Date: Thu, 27 Oct 2022 23:03:34 -0700
Subject: Add strength to textinfo.

---
 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 4efba946..93066522 100644
--- a/modules/processing.py
+++ b/modules/processing.py
@@ -329,6 +329,7 @@ def create_infotext(p, all_prompts, all_seeds, all_subseeds, comments, iteration
         "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 or not shared.sd_model.sd_checkpoint_info.model_name else shared.sd_model.sd_checkpoint_info.model_name.replace(',', '').replace(':', '')),
         "Hypernet": (None if shared.loaded_hypernetwork is None else shared.loaded_hypernetwork.name),
+        "Hypernetwork strength": (None if shared.loaded_hypernetwork is None else shared.opts.sd_hypernetwork_strength),
         "Batch size": (None if p.batch_size < 2 else p.batch_size),
         "Batch pos": (None if p.batch_size < 2 else position_in_batch),
         "Variation seed": (None if p.subseed_strength == 0 else all_subseeds[index]),
-- 
cgit v1.2.3


From c0677b33161f04c3ed1a7a78f4c7288fb95787b7 Mon Sep 17 00:00:00 2001
From: timntorres <timothynarcisotorres@gmail.com>
Date: Thu, 27 Oct 2022 23:31:45 -0700
Subject: Explicitly state when Hypernet is none.

---
 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 93066522..74a0cd64 100644
--- a/modules/processing.py
+++ b/modules/processing.py
@@ -328,7 +328,7 @@ def create_infotext(p, all_prompts, all_seeds, all_subseeds, comments, iteration
         "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 or not shared.sd_model.sd_checkpoint_info.model_name else shared.sd_model.sd_checkpoint_info.model_name.replace(',', '').replace(':', '')),
-        "Hypernet": (None if shared.loaded_hypernetwork is None else shared.loaded_hypernetwork.name),
+        "Hypernet": ("None" if shared.loaded_hypernetwork is None else shared.loaded_hypernetwork.name),
         "Hypernetwork strength": (None if shared.loaded_hypernetwork is None else shared.opts.sd_hypernetwork_strength),
         "Batch size": (None if p.batch_size < 2 else p.batch_size),
         "Batch pos": (None if p.batch_size < 2 else position_in_batch),