updated loop

app.py

@@ -5,6 +5,7 @@ from diffusers import ControlNetModel, DiffusionPipeline
 from diffusers.utils import load_image
 import gradio as gr 
 import warnings
+import spaces
 warnings.filterwarnings("ignore")
 def resize_for_condition_image(input_image: Image, resolution: int):
     input_image = input_image.convert("RGB")
@@ -26,6 +27,7 @@ pipe = DiffusionPipeline.from_pretrained("runwayml/stable-diffusion-v1-5",
                                          torch_dtype=torch.float16).to(device)
 #pipe.enable_xformers_memory_efficient_attention()
 
+@spaces.GPU(enable_queue=True)
 def super_esr(source_image,prompt,negative_prompt,strength,seed,num_inference_steps):
     condition_image = resize_for_condition_image(source_image, 1024)
     generator =  torch.Generator(device="cuda").manual_seed(seed)
@@ -63,6 +65,10 @@ examples=[
     ["https://i.imgur.com/9IqyX1F.png","best quality","blur, lowres, bad anatomy, bad hands, cropped, worst quality",1.0,0,100],
 ]
 # create a queue of the requests
-x=gr.Interface(fn=super_esr,inputs=inputs,outputs=outputs,title=title,description=description,examples=examples)
-x.launch()
+
+demo=gr.Interface(fn=super_esr,inputs=inputs,outputs=outputs,title=title,description=description,examples=examples)
+
+
+if __name__ == "__main__":
+    demo.queue(max_size=20, api_open=False).launch(show_api=False)