Update app.py

app.py

@@ -50,7 +50,7 @@ image_examples = [
     
 ]
 
-@spaces.GPU(duration=120)
+@spaces.GPU(enable_queue=True)
 def load_model(base_model_path, lora_path):
     global pipe 
     transformer = FluxTransformer2DModel.from_pretrained(base_model_path, subfolder='transformer', torch_dtype=torch.bfloat16)
@@ -84,7 +84,7 @@ def load_model(base_model_path, lora_path):
     gr.Info(str(f"Inject LoRA: {lora_path}"))
     pipe.load_lora_weights(lora_path, weight_name="pytorch_lora_weights.safetensors")
     gr.Info(str(f"Model loading: {int((100 / 100) * 100)}%"))
-@spaces.GPU(duration=120)
+@spaces.GPU(enable_queue=True)
 def set_seed(seed):
     torch.manual_seed(seed)
     torch.cuda.manual_seed(seed)
@@ -92,7 +92,7 @@ def set_seed(seed):
     np.random.seed(seed)
     random.seed(seed)
 
-@spaces.GPU(duration=120)   
+@spaces.GPU(enable_queue=True)   
 def predict(
     input_image,
     prompt,
@@ -147,7 +147,6 @@ def predict(
         gray_image_pil = Image.fromarray(gray_image).convert('L')
     else:
         gray_image_pil = input_image["layers"][0]
-    load_model(base_model_path=base_model_path, lora_path=lora_path)
     result = pipe(
         prompt=prompt,
         control_image=input_image["background"].convert("RGB"),