Update app.py

app.py

@@ -36,7 +36,7 @@ def generate_image(prompt, ckpt):
 
     if loaded != num_inference_steps:
         pipe.scheduler = EulerDiscreteScheduler.from_config(pipe.scheduler.config, timestep_spacing="trailing", prediction_type="sample" if num_inference_steps==1 else "epsilon")
-        pipe.unet.load_state_dict(torch.load(hf_hub_download(repo_name, ckpt_name), map_location="cuda"))
+        pipe.unet.load_state_dict(torch.load(hf_hub_download(repo, checkpoint), map_location="cuda"))
         loaded = num_inference_steps
         
     results = pipe(prompt, num_inference_steps=num_inference_steps, guidance_scale=0)