Update app.py

app.py

@@ -37,7 +37,7 @@ def generate_image(prompt, ckpt):
 
     if loaded != num_inference_steps:
         unet = UNet2DConditionModel.from_config(base, subfolder="unet").to("cuda", torch.float16)
-        unet.load_state_dict(torch.load(hf_hub_download(repo, checkpoints)), map_location="cuda"))
+        unet.load_state_dict(torch.load(hf_hub_download(repo, checkpoints)), map_location="cuda")
         pipe.scheduler = LCMScheduler.from_config(pipe.scheduler.config, timestep_spacing="trailing", prediction_type="sample" if num_inference_steps==1 else "epsilon")
         loaded = num_inference_steps