Update app.py

app.py

@@ -105,6 +105,18 @@ def download_file(url, directory=None):
     
     return filepath
 
+def get_lora_weights(lora_repo, weight_name=None):
+    try:
+        # Download the weights from Hugging Face Hub
+        file_path = hf_hub_download(
+            repo_id=lora_repo, 
+            filename=weight_name if weight_name else "pytorch_model.bin"
+        )
+        return file_path
+    except Exception as e:
+        print(f"Failed to fetch weights for {lora_repo}: {e}")
+        raise
+
 def update_selection(evt: gr.SelectData, selected_indices, loras_state, width, height):
     selected_index = evt.index
     selected_indices = selected_indices or []
@@ -462,7 +474,9 @@ def run_lora(prompt, cfg_scale, steps, selected_indices, lora_scale_1, lora_scal
     for idx, lora in enumerate(selected_loras):
         print(f"Inspecting LoRA {idx + 1}: {lora['title']}")
         try:
-            lora_weights = torch.load(lora['repo'])  # Load the LoRA weights
+            lora_weights_path = get_lora_weights(lora['repo'], lora.get("weights"))
+            print(f"LoRA weights fetched from: {lora_weights_path}")
+            lora_weights = torch.load(lora_weights_path, weights_only=True) #lora_weights = torch.load(lora_weights_path)
             print(f"LoRA Parameter Keys for {lora['title']}: {list(lora_weights.keys())}")
         except Exception as e:
             print(f"Error loading LoRA weights for {lora['title']} from {lora['repo']}: {e}")
@@ -499,16 +513,14 @@ def run_lora(prompt, cfg_scale, steps, selected_indices, lora_scale_1, lora_scal
     with calculateDuration("Loading LoRA weights"):
         for idx, lora in enumerate(selected_loras):
             lora_name = f"lora_{idx}"
-            lora_weights_path = lora.get("weights")
-            print(f"Loading LoRA: {lora['title']} from {lora['repo']} with adapter name: {lora_name}")
-        
+            print(f"Loading LoRA: {lora['title']} with adapter name: {lora_name}")
+            lora_weights_path = get_lora_weights(lora['repo'], lora.get("weights"))
             pipe.load_lora_weights(
-                lora['repo'],
-                weight_name=lora_weights_path,
+                lora_weights_path,
                 low_cpu_mem_usage=True,
                 adapter_name=lora_name,
-                merge_and_unload=True,  # Explicitly merge weights to avoid runtime conflicts
-        )
+                merge_and_unload=True,
+            )
         
         print("Adapter weights:", lora_weights)
     try: