Update app.py

app.py

@@ -11,17 +11,19 @@ from safetensors.torch import load_file
 base = "stabilityai/stable-diffusion-xl-base-1.0"
 repo = "ByteDance/SDXL-Lightning"
 ckpt = "sdxl_lightning_4step_unet.safetensors" # Use the correct ckpt for your step setting!
-
 # Load model.
+pipe_box=[]
 @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")
-
-# Ensure sampler uses "trailing" timesteps.
-pipe.scheduler = EulerDiscreteScheduler.from_config(pipe.scheduler.config, timestep_spacing="trailing")
-
+def inti():
+    device="cuda"
+    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")
+    # Ensure sampler uses "trailing" timesteps.
+    pipe.scheduler = EulerDiscreteScheduler.from_config(pipe.scheduler.config, timestep_spacing="trailing")
+    pipe_box.append(pipe)
 def run():
+    pipe=pipe_box[0]
     # 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")