Update app.py

app.py

@@ -9,15 +9,14 @@ import spaces
 base = "stabilityai/stable-diffusion-xl-base-1.0"
 repo = "ByteDance/SDXL-Lightning"
 ckpt = "sdxl_lightning_1step_unet_x0.safetensors" # Use the correct ckpt for your step setting!
-
+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 and "sample" prediction type.
+pipe.scheduler = EulerDiscreteScheduler.from_config(pipe.scheduler.config, timestep_spacing="trailing", prediction_type="sample")
 # Load model.
 @spaces.GPU
 def generate(prompt):
-    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 and "sample" prediction type.
-    pipe.scheduler = EulerDiscreteScheduler.from_config(pipe.scheduler.config, timestep_spacing="trailing", prediction_type="sample")
     image = pipe(prompt, num_inference_steps=1, guidance_scale=0).images[0]
     return image
 # Ensure using the same inference steps as the loaded model and CFG set to 0.