Merge branch 'master' of https://github.com/AUTOMATIC1111/stable-diffusion-webui into TI_optimizations

1 files changed, 3 insertions, 5 deletions

diff --git a/modules/processing.py b/modules/processing.py
index 50343846..b1df4918 100644
--- a/modules/processing.py
+++ b/modules/processing.py
@@ -396,6 +396,7 @@ 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),

+        "Hypernet strength": (None if shared.loaded_hypernetwork is None or shared.opts.sd_hypernetwork_strength >= 1 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]),

@@ -686,15 +687,12 @@ class StableDiffusionProcessingTxt2Img(StableDiffusionProcessing):
 

         noise = create_random_tensors(samples.shape[1:], seeds=seeds, subseeds=subseeds, subseed_strength=subseed_strength, seed_resize_from_h=self.seed_resize_from_h, seed_resize_from_w=self.seed_resize_from_w, p=self)

 

+        image_conditioning = self.txt2img_image_conditioning(x)

+

         # GC now before running the next img2img to prevent running out of memory

         x = None

         devices.torch_gc()

 

-        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