Update app.py

app.py

@@ -1,7 +1,9 @@
 import gradio as gr
+import spaces
 from diffusers import StableDiffusionControlNetPipeline, ControlNetModel, UniPCMultistepScheduler
 import torch
 
+# Initialize models outside the GPU function
 controlnet = ControlNetModel.from_pretrained(
     "lllyasviel/control_v11p_sd15_openpose", torch_dtype=torch.float16
 )
@@ -12,20 +14,22 @@ pipe = StableDiffusionControlNetPipeline.from_pretrained(
     torch_dtype=torch.float16,
     safety_checker=None
 )
-
 pipe.scheduler = UniPCMultistepScheduler.from_config(pipe.scheduler.config)
-pipe.to("cuda" if torch.cuda.is_available() else "cpu")
 
+# Move model to GPU inside the decorated function
+@spaces.GPU(duration=60)  # Request GPU for 60 seconds per call
 def generate(image, prompt="a person posing"):
+    pipe.to("cuda")
     result = pipe(prompt=prompt, image=image, num_inference_steps=20).images[0]
     return result
 
 demo = gr.Interface(
     fn=generate,
-    inputs=[gr.Image(type="pil"), gr.Textbox(label="Prompt")],
+    inputs=[gr.Image(type="pil"), gr.Textbox(label="Prompt", value="a person posing")],
     outputs="image",
     title="Pose Generator",
     description="Upload an image and enter a prompt to generate a ControlNet-based pose output."
 )
 
-demo.launch()
+if __name__ == "__main__":
+    demo.launch()