Merge branch 'release_candidate'

1 files changed, 26 insertions, 27 deletions

diff --git a/modules/processing.py b/modules/processing.py
index e124e7f0..6f01c95f 100644
--- a/modules/processing.py
+++ b/modules/processing.py
@@ -142,7 +142,7 @@ class StableDiffusionProcessing:
     overlay_images: list = None

     eta: float = None

     do_not_reload_embeddings: bool = False

-    denoising_strength: float = 0

+    denoising_strength: float = None

     ddim_discretize: str = None

     s_min_uncond: float = None

     s_churn: float = None

@@ -296,7 +296,7 @@ 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 = shared.sd_model.encode_first_stage(source_image).mode()

 

         return conditioning_image

 

@@ -533,6 +533,7 @@ class Processed:
         self.all_seeds = all_seeds or p.all_seeds or [self.seed]

         self.all_subseeds = all_subseeds or p.all_subseeds or [self.subseed]

         self.infotexts = infotexts or [info]

+        self.version = program_version()

 

     def js(self):

         obj = {

@@ -567,6 +568,7 @@ class Processed:
             "job_timestamp": self.job_timestamp,

             "clip_skip": self.clip_skip,

             "is_using_inpainting_conditioning": self.is_using_inpainting_conditioning,

+            "version": self.version,

         }

 

         return json.dumps(obj)

@@ -677,8 +679,8 @@ def create_infotext(p, all_prompts, all_seeds, all_subseeds, comments=None, iter
         "Size": f"{p.width}x{p.height}",

         "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,

+        "VAE hash": p.sd_vae_hash if opts.add_vae_hash_to_info else None,

+        "VAE": p.sd_vae_name if opts.add_vae_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}"),

@@ -709,7 +711,7 @@ def process_images(p: StableDiffusionProcessing) -> Processed:
     if p.scripts is not None:

         p.scripts.before_process(p)

 

-    stored_opts = {k: opts.data[k] for k in p.override_settings.keys()}

+    stored_opts = {k: opts.data[k] if k in opts.data else opts.get_default(k) for k in p.override_settings.keys() if k in opts.data}

 

     try:

         # if no checkpoint override or the override checkpoint can't be found, remove override entry and load opts checkpoint

@@ -797,7 +799,6 @@ def process_images_inner(p: StableDiffusionProcessing) -> Processed:
 

     infotexts = []

     output_images = []

-

     with torch.no_grad(), p.sd_model.ema_scope():

         with devices.autocast():

             p.init(p.all_prompts, p.all_seeds, p.all_subseeds)

@@ -871,7 +872,6 @@ def process_images_inner(p: StableDiffusionProcessing) -> Processed:
             else:

                 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()

@@ -884,6 +884,8 @@ def process_images_inner(p: StableDiffusionProcessing) -> Processed:
 

             devices.torch_gc()

 

+            state.nextjob()

+

             if p.scripts is not None:

                 p.scripts.postprocess_batch(p, x_samples_ddim, batch_number=n)

 

@@ -936,27 +938,27 @@ def process_images_inner(p: StableDiffusionProcessing) -> Processed:
                 if opts.enable_pnginfo:

                     image.info["parameters"] = text

                 output_images.append(image)

-                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')

-

-                    if opts.save_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(i), p=p, suffix="-mask-composite")

-

-                    if opts.return_mask:

-                        output_images.append(image_mask)

-

-                    if opts.return_mask_composite:

-                        output_images.append(image_mask_composite)

+                if hasattr(p, 'mask_for_overlay') and p.mask_for_overlay:

+                    if opts.return_mask or opts.save_mask:

+                        image_mask = p.mask_for_overlay.convert('RGB')

+                        if save_samples and opts.save_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.return_mask:

+                            output_images.append(image_mask)

+

+                    if opts.return_mask_composite or opts.save_mask_composite:

+                        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 save_samples and opts.save_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_composite:

+                            output_images.append(image_mask_composite)

 

             del x_samples_ddim

 

             devices.torch_gc()

 

-            state.nextjob()

+        if not infotexts:

+            infotexts.append(Processed(p, []).infotext(p, 0))

 

         p.color_corrections = None

 

@@ -1142,6 +1144,7 @@ class StableDiffusionProcessingTxt2Img(StableDiffusionProcessing):
 

         if not self.enable_hr:

             return samples

+        devices.torch_gc()

 

         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)).to(dtype=torch.float32)

@@ -1151,8 +1154,6 @@ class StableDiffusionProcessingTxt2Img(StableDiffusionProcessing):
         with sd_models.SkipWritingToConfig():

             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)

 

     def sample_hr_pass(self, samples, decoded_samples, seeds, subseeds, subseed_strength, prompts):

@@ -1160,7 +1161,6 @@ class StableDiffusionProcessingTxt2Img(StableDiffusionProcessing):
             return samples

 

         self.is_hr_pass = True

-

         target_width = self.hr_upscale_to_x

         target_height = self.hr_upscale_to_y

 

@@ -1249,7 +1249,6 @@ class StableDiffusionProcessingTxt2Img(StableDiffusionProcessing):
         decoded_samples = decode_latent_batch(self.sd_model, samples, target_device=devices.cpu, check_for_nans=True)

 

         self.is_hr_pass = False

-

         return decoded_samples

 

     def close(self):