Update utils.py

utils.py

@@ -161,7 +161,8 @@ def generate_with_embs(text_input, text_embeddings, output=None, generator=None,
     latents = latents * scheduler.init_noise_sigma
 
     # Loop
-    for i, t in tqdm(enumerate(scheduler.timesteps), total=len(scheduler.timesteps)):
+    #for i, t in tqdm(enumerate(scheduler.timesteps), total=len(scheduler.timesteps)):
+    for i, t in enumerate(scheduler.timesteps):
         # expand the latents if we are doing classifier-free guidance to avoid doing two forward passes.
         latent_model_input = torch.cat([latents] * 2)
         sigma = scheduler.sigmas[i]