Update app.py

app.py

@@ -13,17 +13,20 @@ repo = "ByteDance/SDXL-Lightning"
 ckpt = "sdxl_lightning_4step_unet.safetensors" # Use the correct ckpt for your step setting!
 # Load model.
 pipe_box=[]
+device="cuda:0"
+
 @spaces.GPU()
 def init():
-    device="cuda:0"
-    unet = UNet2DConditionModel.from_config(base, subfolder="unet").to(device, torch.float16)
-    unet.load_state_dict(load_file(hf_hub_download(repo, ckpt), device=device))
-    pipe = StableDiffusionXLPipeline.from_pretrained(base, unet=unet, torch_dtype=torch.float16, variant="fp16").to(device)
+    #unet = UNet2DConditionModel.from_config(base, subfolder="unet").to(device, torch.float16)
+    #unet.load_state_dict(load_file(hf_hub_download(repo, ckpt), device=device))
+    #pipe = StableDiffusionXLPipeline.from_pretrained(base, unet=unet, torch_dtype=torch.float16, variant="fp16").to(device)
+    pipe = StableDiffusionXLPipeline.from_pretrained(base, torch_dtype=torch.float16, variant="fp16").to(device)
     # Ensure sampler uses "trailing" timesteps.
     pipe.scheduler = EulerDiscreteScheduler.from_config(pipe.scheduler.config, timestep_spacing="trailing")
     pipe_box.append(pipe)
-init()
+#init()
 def run():
+    init()
     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")