Upload app.py

app.py

@@ -38,13 +38,11 @@ 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 LoRA weights
-        lora_weights_path = hf_hub_download(
-            repo_id=model_id,
-            filename="pytorch_lora_weights.safetensors",  # Verify this filename
-            repo_type="model"
-        )
-        pipe.load_lora_weights(lora_weights_path)
+        # Load the LoRA weights into the U-Net.
+        # This assumes that load_attn_procs loads the LoRA weights.
+        # pipe.unet.load_attn_procs(model_id)
+        pipe.load_lora_weights(model_id, weight_name="text_encoder/adapter_model.safetensors")
+        pipe.load_lora_weights(model_id, weight_name="unet/adapter_model.safetensors")
     else:
         pipe = DiffusionPipeline.from_pretrained(model_id, torch_dtype=torch_dtype)