Update app.py

app.py

@@ -70,11 +70,11 @@ def preprocess_image(image, target_width, target_height): # Преобразуе
 
 pipe_default = get_lora_sd_pipeline(lora_dir='./lora_man_animestyle', base_model_name_or_path=model_default, dtype=torch_dtype).to(device)
 #pipe_controlnet = StableDiffusionControlNetPipeline.from_pretrained(
-pipe_controlnet = StableDiffusionControlNetImg2ImgPipeline.from_pretrained(    
-    model_default,
-    controlnet=controlnet,
-    torch_dtype=torch_dtype
-).to(device)
+# pipe_controlnet = StableDiffusionControlNetImg2ImgPipeline.from_pretrained(    
+#     model_default,
+#     controlnet=controlnet,
+#     torch_dtype=torch_dtype
+# ).to(device)
 
 def infer(
     prompt, 
@@ -102,50 +102,50 @@ def infer(
 
     # Генерация с IP_adapter
     if use_ip_adapter and ip_source_image is not None and ip_adapter_image is not None:
-        # pipe_controlnet = StableDiffusionControlNetImg2ImgPipeline.from_pretrained(    
-        #     model_default,
-        #     controlnet=controlnet,
-        #     torch_dtype=torch_dtype
-        # ).to(device)
+        pipe_ip_adapter = StableDiffusionControlNetImg2ImgPipeline.from_pretrained(    
+            model_default,
+            controlnet=controlnet,
+            torch_dtype=torch_dtype
+        ).to(device)
         
         # Преобразуем изображения
         ip_source_image = preprocess_image(ip_source_image, width, height)
         ip_adapter_image = preprocess_image(ip_adapter_image, width, height)
         
         # Создаём пайплайн IP_adapter с LoRA, если он ещё не создан   ???????????????????????????????????????????????????????????????
-        if not hasattr(pipe_controlnet, 'lora_loaded') or not pipe_controlnet.lora_loaded:
+        if not hasattr(pipe_ip_adapter, 'lora_loaded') or not pipe_ip_adapter.lora_loaded:
             # Загружаем LoRA для UNet
-            pipe_controlnet.unet = PeftModel.from_pretrained(
-                pipe_controlnet.unet, 
+            pipe_ip_adapter.unet = PeftModel.from_pretrained(
+                pipe_ip_adapter.unet, 
                 './lora_man_animestyle/unet', 
                 adapter_name="default"
             )
-            pipe_controlnet.unet.set_adapter("default")
+            pipe_ip_adapter.unet.set_adapter("default")
             
             # Загружаем LoRA для Text Encoder, если она существует
             text_encoder_lora_path = './lora_man_animestyle/text_encoder'
             if os.path.exists(text_encoder_lora_path):
-                pipe_controlnet.text_encoder = PeftModel.from_pretrained(
-                    pipe_controlnet.text_encoder, 
+                pipe_ip_adapter.text_encoder = PeftModel.from_pretrained(
+                    pipe_ip_adapter.text_encoder, 
                     text_encoder_lora_path, 
                     adapter_name="default"
                 )
-                pipe_controlnet.text_encoder.set_adapter("default")
+                pipe_ip_adapter.text_encoder.set_adapter("default")
             
             # Объединяем LoRA с основной моделью
-            pipe_controlnet.fuse_lora(lora_scale=lora_scale)
-            pipe_controlnet.lora_loaded = True  # Помечаем, что LoRA загружена
+            pipe_ip_adapter.fuse_lora(lora_scale=lora_scale)
+            pipe_ip_adapter.lora_loaded = True  # Помечаем, что LoRA загружена
         
         # Убедимся, что ip_adapter_strength имеет тип float
         ip_adapter_strength = float(ip_adapter_strength)
         #strength_ip = float(strength_ip)
         
         # Используем IP_adapter с LoRA  ????????????????????????????????????????????????????????????????????????
-        pipe = pipe_controlnet
-        prompt_embeds = long_prompt_encoder(prompt, pipe.tokenizer, pipe.text_encoder)
-        negative_prompt_embeds = long_prompt_encoder(negative_prompt, pipe.tokenizer, pipe.text_encoder)
+        #pipe = pipe_ip_adapter
+        prompt_embeds = long_prompt_encoder(prompt, pipe_ip_adapter.tokenizer, pipe_ip_adapter.text_encoder)
+        negative_prompt_embeds = long_prompt_encoder(negative_prompt, pipe_ip_adapter.tokenizer, pipe_ip_adapter.text_encoder)
         prompt_embeds, negative_prompt_embeds = align_embeddings(prompt_embeds, negative_prompt_embeds)
-        image = pipe_controlnet(
+        image = pipe_ip_adapter(
             prompt_embeds=prompt_embeds,
             negative_prompt_embeds=negative_prompt_embeds,
             image=ip_source_image,     
@@ -161,11 +161,11 @@ def infer(
     else:
         # Генерация с ControlNet
         if use_control_net and control_image is not None and cn_source_image is not None:
-            # pipe_controlnet = StableDiffusionControlNetImg2ImgPipeline.from_pretrained(    
-            #     model_default,
-            #     controlnet=controlnet,
-            #     torch_dtype=torch_dtype
-            # ).to(device)
+            pipe_controlnet = StableDiffusionControlNetImg2ImgPipeline.from_pretrained(    
+                model_default,
+                controlnet=controlnet,
+                torch_dtype=torch_dtype
+            ).to(device)
         
             # Преобразуем изображения
             cn_source_image = preprocess_image(cn_source_image, width, height)
@@ -200,9 +200,9 @@ def infer(
             #strength_sn = float(strength_sn)
         
             # Используем ControlNet с LoRA
-            pipe = pipe_controlnet
-            prompt_embeds = long_prompt_encoder(prompt, pipe.tokenizer, pipe.text_encoder)
-            negative_prompt_embeds = long_prompt_encoder(negative_prompt, pipe.tokenizer, pipe.text_encoder)
+            #pipe = pipe_controlnet
+            prompt_embeds = long_prompt_encoder(prompt, pipe_controlnet.tokenizer, pipe_controlnet.text_encoder)
+            negative_prompt_embeds = long_prompt_encoder(negative_prompt, pipe_controlnet.tokenizer, pipe_controlnet.text_encoder)
             prompt_embeds, negative_prompt_embeds = align_embeddings(prompt_embeds, negative_prompt_embeds)
             image = pipe_controlnet(
                 prompt_embeds=prompt_embeds,
@@ -423,7 +423,6 @@ with gr.Blocks(css=css) as demo:
                 outputs=ip_adapter_options
             )
         # --------------------------------------------------------------------------------------
-         
         
         gr.Examples(examples=examples, inputs=[prompt])
         gr.Examples(examples=examples_negative, inputs=[negative_prompt])
@@ -444,11 +443,11 @@ with gr.Blocks(css=css) as demo:
             seed,
             guidance_scale,
             lora_scale,
-            strength_cn,       # Коэфф. зашумления ControlNet
-            use_control_net,   # Чекбокс для ControlNet
-            control_strength,  # Контроль силы ControlNet
-            cn_source_image,   # Исходное изображение ControlNet
-            control_image,     # Контрольное изображение ControlNet
+            strength_cn,        # Коэфф. зашумления ControlNet
+            use_control_net,    # Чекбокс для ControlNet
+            control_strength,   # Контроль силы ControlNet
+            cn_source_image,    # Исходное изображение ControlNet
+            control_image,      # Контрольное изображение ControlNet
             strength_ip,        # Коэфф. зашумления IP_adapter
             use_ip_adapter,     # Параметр для включения IP_adapter
             ip_adapter_strength,# Сила влияния IP_adapter