Update app.py

app.py

@@ -30,7 +30,7 @@ def generate(color, guidance_loss_scale):
   target_color = ImageColor.getcolor(color, "RGB") # Target color as RGB
   target_color = [a/255 for a in target_color] # Rescale from (0, 255) to (0, 1)
   x = torch.randn(1, 3, 256, 256).to(device) 
-  for i, t in tqdm(enumerate(scheduler.timesteps)):
+  for i, t in enumerate(scheduler.timesteps):
     model_input = scheduler.scale_model_input(x, t)
     with torch.no_grad():
         noise_pred = image_pipe.unet(model_input, t)["sample"]