Update app.py

app.py

@@ -24,8 +24,9 @@ lora_model_path = "./lora_model.pth"  # Assuming the file is saved locally
 
 # Custom method to load and apply LoRA weights to the Stable Diffusion pipeline
 def load_lora_model(pipe, lora_model_path):
-    # Load the LoRA weights
-    lora_weights = torch.load(lora_model_path, map_location=pipe.device)  # Load LoRA model to the correct device
+    # When loading the LoRA weights
+    lora_weights = torch.load(lora_model_path, map_location=device, weights_only=True)
+
     
     # Print available attributes of the model to check access to `unet` (optional)
     print(dir(pipe))  # This will list all attributes and methods of the `pipe` object