Update app.py

app.py

@@ -494,62 +494,62 @@ def infer(
         else:
             # Генерация изображений с LCM_Adapter ---------------------------------------------------------------------------------------------
             
-            # if use_LCM_adapter:
+            if use_LCM_adapter:
             
-            #     if LCM_adapter == "lcm-lora-sdv1-5":
-            #         adapter_id = "latent-consistency/lcm-lora-sdv1-5"
-
-            #     generator = torch.Generator(device).manual_seed(seed)
-
-            #     pipe_default = get_lora_sd_pipeline(lora_dir='lora_man_animestyle', base_model_name_or_path=model_default, dtype=torch_dtype).to(device)
-            #     pipe = pipe_default
-            #     pipe.scheduler = LCMScheduler.from_config(pipe.scheduler.config)
-            #     pipe.to(device)
-
-            #     pipe.load_lora_weights(adapter_id)
-            #     pipe.fuse_lora()
-
-            #     prompt_embeds = long_prompt_encoder(prompt, pipe.tokenizer, pipe.text_encoder)
-            #     negative_prompt_embeds = long_prompt_encoder(negative_prompt, pipe.tokenizer, pipe.text_encoder)
-            #     prompt_embeds, negative_prompt_embeds = align_embeddings(prompt_embeds, negative_prompt_embeds)
-
-            #     params = {
-            #         'prompt_embeds': prompt_embeds,
-            #         'negative_prompt_embeds': negative_prompt_embeds,
-            #         'guidance_scale': guidance_scale,
-            #         'num_inference_steps': num_inference_steps,
-            #         'width': width,
-            #         'height': height,
-            #         'generator': generator,
-            #     }
-
-            #     image = pipe(**params).images[0]    
-            # else:        
-            #     # Генерация изображений с DDIMScheduler ---------------------------------------------------------------------------------------------
-
-            #     if use_DDIMScheduler:
-
-            #         generator = torch.Generator(device).manual_seed(seed)
-
-            #         pipe = StableDiffusionPipeline.from_pretrained(model_default, torch_dtype=torch_dtype).to(device)
-            #         pipe.scheduler = DPMSolverMultistepScheduler.from_config(pipe.scheduler.config)
-
-            #         prompt_embeds = long_prompt_encoder(prompt, pipe.tokenizer, pipe.text_encoder)
-            #         negative_prompt_embeds = long_prompt_encoder(negative_prompt, pipe.tokenizer, pipe.text_encoder)
-            #         prompt_embeds, negative_prompt_embeds = align_embeddings(prompt_embeds, negative_prompt_embeds)
-
-            #         params = {
-            #             'prompt_embeds': prompt_embeds,
-            #             'negative_prompt_embeds': negative_prompt_embeds,
-            #             'guidance_scale': guidance_scale,
-            #             'num_inference_steps': num_inference_steps,
-            #             'width': width,
-            #             'height': height,
-            #             'generator': generator,
-            #         }
-
-            #         image = pipe(**params).images[0]
-            #     else:    
+                if LCM_adapter == "lcm-lora-sdv1-5":
+                    adapter_id = "latent-consistency/lcm-lora-sdv1-5"
+
+                generator = torch.Generator(device).manual_seed(seed)
+
+                pipe_LCM_default = get_lora_sd_pipeline(lora_dir='lora_man_animestyle', base_model_name_or_path=model_default, dtype=torch_dtype).to(device)
+                pipe_LCM = pipe_LCM_default
+                pipe_LCM.scheduler = LCMScheduler.from_config(pipe.scheduler.config)
+                pipe_LCM.to(device)
+
+                pipe_LCM.load_lora_weights(adapter_id)
+                pipe_LCM.fuse_lora()
+
+                prompt_embeds = long_prompt_encoder(prompt, pipe_LCM.tokenizer, pipe_LCM.text_encoder)
+                negative_prompt_embeds = long_prompt_encoder(negative_prompt, pipe_LCM.tokenizer, pipe_LCM.text_encoder)
+                prompt_embeds, negative_prompt_embeds = align_embeddings(prompt_embeds, negative_prompt_embeds)
+
+                params = {
+                    'prompt_embeds': prompt_embeds,
+                    'negative_prompt_embeds': negative_prompt_embeds,
+                    'guidance_scale': guidance_scale,
+                    'num_inference_steps': num_inference_steps,
+                    'width': width,
+                    'height': height,
+                    'generator': generator,
+                }
+
+                image = pipe_LCM(**params).images[0]    
+            else:        
+                # Генерация изображений с DDIMScheduler ---------------------------------------------------------------------------------------------
+
+                if use_DDIMScheduler:
+
+                    generator = torch.Generator(device).manual_seed(seed)
+
+                    pipe_DDIMS = StableDiffusionPipeline.from_pretrained(model_default, torch_dtype=torch_dtype).to(device)
+                    pipe_DDIMS.scheduler = DPMSolverMultistepScheduler.from_config(pipe.scheduler.config)
+
+                    prompt_embeds = long_prompt_encoder(prompt, pipe_DDIMS.tokenizer, pipe_DDIMS.text_encoder)
+                    negative_prompt_embeds = long_prompt_encoder(negative_prompt, pipe_DDIMS.tokenizer, pipe_DDIMS.text_encoder)
+                    prompt_embeds, negative_prompt_embeds = align_embeddings(prompt_embeds, negative_prompt_embeds)
+
+                    params = {
+                        'prompt_embeds': prompt_embeds,
+                        'negative_prompt_embeds': negative_prompt_embeds,
+                        'guidance_scale': guidance_scale,
+                        'num_inference_steps': num_inference_steps,
+                        'width': width,
+                        'height': height,
+                        'generator': generator,
+                    }
+
+                    image = pipe_DDIMS(**params).images[0]
+                else:    
                     # Генерация изображений с LORA без ControlNet и IP_Adapter ---------------------------------------------------------------------------------------------
 
                     # Инициализация ControlNet