Update app.py

app.py

@@ -37,7 +37,7 @@ IMAGE_SIZE = 1024
 device = "cuda" if torch.cuda.is_available() else "cpu"
 base_model = "black-forest-labs/FLUX.1-dev"
 
-controlnet_model = 'InstantX/FLUX.1-dev-Controlnet-Canny'
+controlnet_model = 'black-forest-labs/FLUX.1-Canny-dev'
 controlnet = FluxControlNetModel.from_pretrained(controlnet_model, torch_dtype=torch.bfloat16)
 
 
@@ -207,7 +207,7 @@ def load_loras(lora_strings_json:str):
     if lora_configs:
         
         with calculateDuration("Loading LoRA weights"):
-            active_adapters = pipe.get_active_adapters()
+            #active_adapters = pipe.get_active_adapters()
             adapter_names = []
             adapter_weights = []
             for lora_info in lora_configs:
@@ -220,9 +220,9 @@ def load_loras(lora_strings_json:str):
                     adapter_names.append(adapter_name)
                     adapter_weights.append(adapter_weight)
                     
-                    if adapter_name in active_adapters:
-                        print(f"Adapter '{adapter_name}' is already loaded, skipping.")
-                        continue
+                    #if adapter_name in active_adapters:
+                    #    print(f"Adapter '{adapter_name}' is already loaded, skipping.")
+                    #    continue
                         
                     try:
                         pipe.load_lora_weights(lora_repo, weight_name=weights, adapter_name=adapter_name)
@@ -231,8 +231,8 @@ def load_loras(lora_strings_json:str):
                         continue
                     
             # set lora weights
-            if len(adapter_names) > 0:
-                pipe.set_adapters(adapter_names, adapter_weights=adapter_weights)
+            #if len(adapter_names) > 0:
+            #    pipe.set_adapters(adapter_names, adapter_weights=adapter_weights)
 
 
 def generate_control_image(image, mask, width, height):