Merge remote-tracking branch 'origin/dev' into soft-inpainting

# Conflicts:
#	modules/processing.py

1 files changed, 10 insertions, 10 deletions

diff --git a/modules/processing.py b/modules/processing.py
index ad716e11..66aaab83 100644
--- a/modules/processing.py
+++ b/modules/processing.py
@@ -149,7 +149,7 @@ class StableDiffusionProcessing:
     masks_for_overlay: 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

@@ -303,7 +303,7 @@ 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

 

@@ -537,6 +537,7 @@ 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 = {

@@ -571,6 +572,7 @@ 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)

@@ -713,7 +715,7 @@ 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

@@ -801,7 +803,6 @@ 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)

@@ -876,7 +877,6 @@ 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

-

                 # Generate the mask(s) based on similarity between the original and denoised latent vectors

                 if getattr(p, "image_mask", None) is not None:

                     # latent_mask = p.nmask[0].float().cpu()

@@ -943,6 +943,8 @@ 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)

 

@@ -1025,7 +1027,8 @@ def process_images_inner(p: StableDiffusionProcessing) -> Processed:
 

             devices.torch_gc()

 

-            state.nextjob()

+        if not infotexts:

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

 

         p.color_corrections = None

 

@@ -1211,6 +1214,7 @@ 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)

@@ -1220,8 +1224,6 @@ 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):

@@ -1229,7 +1231,6 @@ class StableDiffusionProcessingTxt2Img(StableDiffusionProcessing):
             return samples

 

         self.is_hr_pass = True

-

         target_width = self.hr_upscale_to_x

         target_height = self.hr_upscale_to_y

 

@@ -1318,7 +1319,6 @@ 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):