Update app.py

app.py

@@ -38,7 +38,6 @@ with open("flux_loras.json", "r") as file:
     ]
 print(f"Loaded {len(flux_loras_raw)} LoRAs from JSON")
 # Global variables for LoRA management
-current_lora = None
 lora_cache = {}
 
 def load_lora_weights(repo_id, weights_filename):
@@ -144,7 +143,7 @@ def infer_with_lora_wrapper(input_image, prompt, selected_index, lora_state, cus
 @spaces.GPU
 def infer_with_lora(input_image, prompt, selected_index, lora_state, custom_lora, seed=42, randomize_seed=False, guidance_scale=2.5, lora_scale=1.0, portrait_mode=False, flux_loras=None,  progress=gr.Progress(track_tqdm=True)):
     """Generate image with selected LoRA"""
-    global current_lora, pipe
+    global pipe
     
     if randomize_seed:
         seed = random.randint(0, MAX_SEED)
@@ -157,10 +156,10 @@ def infer_with_lora(input_image, prompt, selected_index, lora_state, custom_lora
         lora_to_use = flux_loras[selected_index]
     print(f"Loaded {len(flux_loras)} LoRAs from JSON")
     # Load LoRA if needed
-    print(f"LoRA to use: {lora_to_use}, Current LoRA: {current_lora}")
-    if lora_to_use and lora_to_use != current_lora:
+    print(f"LoRA to use: {lora_to_use}")
+    if lora_to_use:
         try:
-            if current_lora:
+            if "selected_lora" in pipe.get_active_adapters():
                 pipe.unload_lora_weights()
             
             lora_path = load_lora_weights(lora_to_use["repo"], lora_to_use["weights"])
@@ -168,14 +167,9 @@ def infer_with_lora(input_image, prompt, selected_index, lora_state, custom_lora
                 pipe.load_lora_weights(lora_path, adapter_name="selected_lora")
                 pipe.set_adapters(["selected_lora"], adapter_weights=[lora_scale])
                 print(f"loaded: {lora_path} with scale {lora_scale}")
-                current_lora = lora_to_use
             
         except Exception as e:
             print(f"Error loading LoRA: {e}")
-            # Continue without LoRA
-    elif lora_scale != lora_state:
-        print(f"using already loaded lora: {lora_to_use}, udpated {lora_scale} based on user preference")
-        pipe.set_adapters(["selected_lora"], adapter_weights=[lora_scale])
         
     
     input_image = input_image.convert("RGB")