Update app.py

app.py

@@ -45,15 +45,19 @@ def get_pipe(model_id: str, lora_scale: float = 1.0):
     if cache_key in PIPE_CACHE:
         return PIPE_CACHE[cache_key]
 
+
     # Check if the selected model is the LoRA adapter
     if model_id == LORA_MODEL_ID:
-        # Load the base model for LoRA
+        # Укажите правильные имена файлов
         pipe = DiffusionPipeline.from_pretrained(
             BASE_MODEL_FOR_LORA,
             torch_dtype=torch_dtype
         ).to(device)
-        # Load and merge the LoRA weights with the specified scale
-        pipe.load_lora_weights(LORA_MODEL_ID)
+        pipe.load_lora_weights(
+            LORA_MODEL_ID,
+            weight_name=["adapter_model_unet.safetensors", "adapter_model_text_encoder.safetensors"]  # Замените на ваши имена файлов
+        )
+        # Применяем масштаб LoRA
         pipe.fuse_lora(lora_scale=lora_scale)
     else:
         # Load a standard model without LoRA
@@ -77,6 +81,7 @@ def infer(
     guidance_scale: float = 7.0,
     num_inference_steps: int = 20,
     scheduler_name: Optional[str] = None,
+    lora_scale: float = 1.0,
     progress=gr.Progress(track_tqdm=True),
 ):
     # получаем/загружаем нужный pipe