Update app.py

app.py

@@ -13,6 +13,7 @@ repo = "ByteDance/SDXL-Lightning"
 ckpt = "sdxl_lightning_4step_unet.safetensors" # Use the correct ckpt for your step setting!
 
 # Load model.
+@spaces.GPU
 unet = UNet2DConditionModel.from_config(base, subfolder="unet").to("cuda", torch.float16)
 unet.load_state_dict(load_file(hf_hub_download(repo, ckpt), device="cuda"))
 pipe = StableDiffusionXLPipeline.from_pretrained(base, unet=unet, torch_dtype=torch.float16, variant="fp16").to("cuda")
@@ -20,7 +21,6 @@ pipe = StableDiffusionXLPipeline.from_pretrained(base, unet=unet, torch_dtype=to
 # Ensure sampler uses "trailing" timesteps.
 pipe.scheduler = EulerDiscreteScheduler.from_config(pipe.scheduler.config, timestep_spacing="trailing")
 
-@spaces.GPU
 def run():
     # Ensure using the same inference steps as the loaded model and CFG set to 0.
     return pipe("A cat", num_inference_steps=4, guidance_scale=0).images[0].save("output.png")