Update app.py

app.py

@@ -9,8 +9,6 @@ from huggingface_hub import hf_hub_download
 from safetensors.torch import load_file
 from PIL import Image
 
-assert torch.cuda.is_available()
-
 device = "cuda"
 dtype = torch.float16
 
@@ -27,11 +25,11 @@ opts = {
 step_loaded = 4
 unet = UNet2DConditionModel.from_config(base, subfolder="unet")
 unet.load_state_dict(load_file(hf_hub_download(repo, opts["4 Steps"][0])))
-pipe = StableDiffusionXLPipeline.from_pretrained(base, unet=unet, torch_dtype=dtype, variant="fp16").to(device, dtype)
+pipe = StableDiffusionXLPipeline.from_pretrained(base, unet=unet, torch_dtype=dtype, variant="fp16")
 pipe.scheduler = EulerDiscreteScheduler.from_config(pipe.scheduler.config, timestep_spacing="trailing")
 
 # Safety checker.
-safety_checker=StableDiffusionSafetyChecker.from_pretrained("CompVis/stable-diffusion-safety-checker").to(device, dtype)
+safety_checker=StableDiffusionSafetyChecker.from_pretrained("CompVis/stable-diffusion-safety-checker")
 feature_extractor=CLIPImageProcessor.from_pretrained("openai/clip-vit-base-patch32")
 image_processor = VaeImageProcessor(vae_scale_factor=8)
 
@@ -42,6 +40,10 @@ def generate(prompt, option, progress=gr.Progress()):
     print(prompt, option)
     ckpt, step = opts[option]
     progress((0, step))
+
+    pipe.to(device, dtype)
+    safety_checker.to(device, dtype)
+
     if step != step_loaded:
         print(f"Switching checkpoint from {step_loaded} to {step}")
         pipe.scheduler = EulerDiscreteScheduler.from_config(pipe.scheduler.config, timestep_spacing="trailing", prediction_type="sample" if step == 1 else "epsilon")