Update app.py

app.py

@@ -16,22 +16,21 @@ model_id = "stabilityai/stable-diffusion-3.5-medium"
 pipe = StableDiffusion3Pipeline.from_pretrained(model_id, torch_dtype=torch.float16)
 pipe.to("cpu")
 
-# Define the path to the LoRA model
-lora_model_path = "https://huggingface.co/spaces/DonImages/Testing2/resolve/main/lora_model.pth"  # LoRA model path
-
 # 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 (assuming it's a PyTorch .pth file)
     lora_weights = torch.load(lora_model_path, map_location="cpu")
 
-    # Modify this section based on how LoRA is intended to interact with your Stable Diffusion model
-    # Here, we just load the weights into the model's parameters (this is a conceptual approach)
+    # Apply LoRA weights to the parameters of the model
     for name, param in pipe.named_parameters():
         if name in lora_weights:
             param.data += lora_weights[name]  # Apply LoRA weights to the parameters
 
     return pipe  # Return the updated model
 
+# Define the path to the LoRA model (Local path in your Hugging Face Space)
+lora_model_path = "./lora_model.pth"  # Local path to LoRA model
+
 # Load and apply the LoRA model weights
 pipe = load_lora_model(pipe, lora_model_path)