Update app.py

app.py

@@ -17,11 +17,15 @@ def get_lora_sd_pipeline(
     base_model_name_or_path=None, 
     dtype=torch.float16, 
     adapter_name="default"
-    ):
-
+):
     unet_sub_dir = os.path.join(lora_dir, "unet")
     text_encoder_sub_dir = os.path.join(lora_dir, "text_encoder")
     
+    # Проверка существования директорий LoRA
+    print(f"LoRA directory exists: {os.path.exists(lora_dir)}")
+    print(f"UNet LoRA exists: {os.path.exists(unet_sub_dir)}")
+    print(f"Text encoder LoRA exists: {os.path.exists(text_encoder_sub_dir)}")
+    
     if os.path.exists(text_encoder_sub_dir) and base_model_name_or_path is None:
         config = LoraConfig.from_pretrained(text_encoder_sub_dir)
         base_model_name_or_path = config.base_model_name_or_path
@@ -30,14 +34,30 @@ def get_lora_sd_pipeline(
         raise ValueError("Укажите название базовой модели или путь к ней")
     
     pipe = StableDiffusionPipeline.from_pretrained(base_model_name_or_path, torch_dtype=dtype)
-    before_params = pipe.unet.parameters()
-    pipe.unet = PeftModel.from_pretrained(pipe.unet, unet_sub_dir, adapter_name=adapter_name)
-    pipe.unet.set_adapter(adapter_name)
-    after_params = pipe.unet.parameters()
     
+    # Логирование параметров до применения LoRA
+    before_params = list(pipe.unet.parameters())
+    
+    # Применение LoRA к UNet
+    if os.path.exists(unet_sub_dir):
+        pipe.unet = PeftModel.from_pretrained(pipe.unet, unet_sub_dir, adapter_name=adapter_name)
+        pipe.unet.set_adapter(adapter_name)
+    
+    # Применение LoRA к текстовому энкодеру (если есть)
     if os.path.exists(text_encoder_sub_dir):
         pipe.text_encoder = PeftModel.from_pretrained(pipe.text_encoder, text_encoder_sub_dir, adapter_name=adapter_name)
     
+    # Логирование параметров после применения LoRA
+    after_params = list(pipe.unet.parameters())
+    print(f"Parameters changed: {before_params != after_params}")
+    
+    # Детальное сравнение параметров
+    for (name1, param1), (name2, param2) in zip(before_params, after_params):
+        if not torch.equal(param1, param2):
+            print(f"Parameter {name1} changed.")
+        else:
+            print(f"Parameter {name1} did not change.")
+    
     if dtype in (torch.float16, torch.bfloat16):
         pipe.unet.half()
         pipe.text_encoder.half()