Update app.py

app.py

@@ -20,7 +20,7 @@ def get_lora_sd_pipeline(
     dtype=torch.float16, 
     adapter_name="default"
     ):
-
+    
     unet_sub_dir = os.path.join(ckpt_dir, "unet")
     text_encoder_sub_dir = os.path.join(ckpt_dir, "text_encoder")
     
@@ -33,7 +33,17 @@ def get_lora_sd_pipeline(
     
     pipe = DiffusionPipeline.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 = PeftModel.from_pretrained(pipe.unet, unet_sub_dir, adapter_name=adapter_name)
+    # Исправляем загрузку конфигурации
+    config = LoraConfig.from_pretrained(unet_sub_dir)
+    
+    pipe.unet = PeftModel.from_pretrained(
+        pipe.unet, 
+        unet_sub_dir, 
+        adapter_name=adapter_name,
+        config=config  # Явно передаем конфигурацию
+    )
+    
     pipe.unet.set_adapter(adapter_name)
     after_params = pipe.unet.parameters()
     print("UNet Parameters changed:", any(torch.any(b != a) for b, a in zip(before_params, after_params)))
@@ -141,6 +151,7 @@ def infer(
             )
         
         print(f"Active adapters - UNet: {pipe.unet.active_adapters}, Text Encoder: {pipe.text_encoder.active_adapters if hasattr(pipe, 'text_encoder') else None}")
+        print("UNet first layer weights:", pipe.unet.base_model.model[0].weight.data[0,0,:5])
         print(f"LoRA scale applied: {lora_scale}")