Update app.py

app.py

@@ -516,24 +516,52 @@ def run_lora(face_image, prompt, negative, lora_scale, selected_state, face_stre
     """
     VRAM optimized generation with enhanced error reporting
     """
+    print("=" * 80)
+    print("🚀 FUNCTION CALLED: run_lora")
+    print(f"📊 Inputs received:")
+    print(f"  - face_image: {type(face_image)} - {face_image.size if face_image else 'None'}")
+    print(f"  - prompt: '{prompt}'")
+    print(f"  - selected_state: {type(selected_state)} - {selected_state}")
+    print(f"  - custom_lora: {custom_lora}")
+    print(f"  - use_multiple_faces: {use_multiple_faces}")
+    print("=" * 80)
+    
     try:
-        print("=" * 80)
         print("Starting generation...")
         print("Custom LoRA:", custom_lora)
         custom_lora_path = custom_lora[0] if custom_lora else None
-        selected_state_index = selected_state.index if selected_state else -1
+        
+        # Extract index from selected_state (handle Gradio SelectData object)
+        if selected_state:
+            print(f"  selected_state exists: {selected_state}")
+            print(f"  selected_state type: {type(selected_state)}")
+            print(f"  selected_state dir: {dir(selected_state)}")
+            if hasattr(selected_state, 'index'):
+                selected_state_index = selected_state.index
+                print(f"  ✓ Extracted index: {selected_state_index}")
+            else:
+                selected_state_index = -1
+                print(f"  ❌ No index attribute, using -1")
+        else:
+            selected_state_index = -1
+            print(f"  ❌ selected_state is None or False")
+        
+        print(f"🔍 VALIDATION CHECK:")
+        print(f"  - selected_state_index: {selected_state_index}")
+        print(f"  - custom_lora_path: {custom_lora_path}")
+        print(f"  - len(sdxl_loras): {len(sdxl_loras)}")
 
         # Validate selection immediately
-        if not selected_state and not custom_lora_path:
+        if (selected_state_index is None or selected_state_index < 0) and not custom_lora_path:
             error_msg = "❌ You must select a style before generating"
             print(error_msg)
-            return None, gr.update(visible=False), gr.update(visible=True, value=error_msg)
+            return gr.update(), gr.update(visible=False), gr.update(visible=True, value=error_msg)
         
-        # Validate selected_state_index is valid
-        if not custom_lora_path and (selected_state_index < 0 or selected_state_index >= len(sdxl_loras)):
+        # Validate selected_state_index is valid (only check positive indices)
+        if not custom_lora_path and selected_state_index >= 0 and selected_state_index >= len(sdxl_loras):
             error_msg = f"❌ Invalid style selection (index: {selected_state_index}, available: {len(sdxl_loras)})"
             print(error_msg)
-            return None, gr.update(visible=False), gr.update(visible=True, value=error_msg)
+            return gr.update(), gr.update(visible=False), gr.update(visible=True, value=error_msg)
 
         st = time.time()
         
@@ -602,7 +630,7 @@ def run_lora(face_image, prompt, negative, lora_scale, selected_state, face_stre
             if repo_name not in state_dicts:
                 error_msg = f"❌ LoRA not loaded: {repo_name}\nAvailable: {list(state_dicts.keys())[:5]}"
                 print(error_msg)
-                return None, gr.update(visible=False), gr.update(visible=True, value=error_msg)
+                return gr.update(), gr.update(visible=False), gr.update(visible=True, value=error_msg)
             full_path_lora = state_dicts[repo_name]["saved_name"]
 
         repo_name = repo_name.rstrip("/").lower()
@@ -651,7 +679,7 @@ def run_lora(face_image, prompt, negative, lora_scale, selected_state, face_stre
         print(error_msg)
         print("=" * 80)
         torch.cuda.empty_cache()
-        return None, gr.update(visible=False), gr.update(visible=True, value=error_msg)
+        return gr.update(), gr.update(visible=False), gr.update(visible=True, value=error_msg)
     except RuntimeError as e:
         if "out of memory" in str(e).lower():
             error_msg = (
@@ -668,7 +696,7 @@ def run_lora(face_image, prompt, negative, lora_scale, selected_state, face_stre
         print(error_msg)
         print("=" * 80)
         torch.cuda.empty_cache()
-        return None, gr.update(visible=False), gr.update(visible=True, value=error_msg)
+        return gr.update(), gr.update(visible=False), gr.update(visible=True, value=error_msg)
     except Exception as e:
         error_msg = f"Generation failed: {str(e)}\n\nFull error:\n{traceback.format_exc()}"
         print("=" * 80)
@@ -676,7 +704,7 @@ def run_lora(face_image, prompt, negative, lora_scale, selected_state, face_stre
         print(error_msg)
         print("=" * 80)
         torch.cuda.empty_cache()
-        return None, gr.update(visible=False), gr.update(visible=True, value=error_msg)
+        return gr.update(), gr.update(visible=False), gr.update(visible=True, value=error_msg)
 
 
 def generate_image_inline(prompt, negative, face_emb, face_image, face_kps, image_strength,