Upload app.py

app.py

@@ -39,9 +39,9 @@ def load_pipeline(model_id: str):
         # Use the specified base model for your LoRA adapter.
         base_model = "CompVis/stable-diffusion-v1-4"
         pipe = DiffusionPipeline.from_pretrained(base_model, torch_dtype=torch_dtype)
-        # Load the LoRA weights into the U-Net.
-        pipe.unet = PeftModel.from_pretrained(pipe.unet, model_id, torch_dtype=torch_dtype)
-
+        # Load the LoRA weights
+        pipe.unet = PeftModel.from_pretrained(pipe.unet, f"{model_id}/unet", torch_dtype=torch_dtype)
+        pipe.text_encoder = PeftModel.from_pretrained(pipe.text_encoder, f"{model_id}/text_encoder", torch_dtype=torch_dtype)
     else:
         pipe = DiffusionPipeline.from_pretrained(model_id, torch_dtype=torch_dtype)