Update app.py

app.py

@@ -11,9 +11,9 @@ def generate_image(prompt, num_inference_steps):
     ckpt = "sdxl_lightning_2step_unet.safetensors" # Use the correct ckpt for your step setting!
 
     # Load model.
-    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")
+    unet = UNet2DConditionModel.from_config(base, subfolder="unet").to(torch.device("cpu"), torch.float16)
+    unet.load_state_dict(load_file(hf_hub_download(repo, ckpt), device=torch.device("cpu")))
+    pipe = StableDiffusionXLPipeline.from_pretrained(base, unet=unet, torch_dtype=torch.float16, variant="fp16").to(torch.device("cpu"))
 
     # Ensure sampler uses "trailing" timesteps.
     pipe.scheduler = EulerDiscreteScheduler.from_config(pipe.scheduler.config, timestep_spacing="trailing")