Update app.py

app.py

@@ -46,7 +46,7 @@ def generate(schedul):
     scheduler.set_timesteps(num_inference_steps=40)
     x = torch.randn(1, 4, 64, 64).to(device)
     # Minimal sampling loop
-    for i, t in enumerate(scheduler.timesteps):
+    for i, t in tqdm(enumerate(scheduler.timesteps)):
         model_input = scheduler.scale_model_input(x, t)
         with torch.no_grad():
             noise_pred = image_pipe.unet(model_input, t)["sample"]