diff --git a/modules/models/diffusion/uni_pc/sampler.py b/modules/models/diffusion/uni_pc/sampler.py
index bf346ff48..a241c8a7c 100644
--- a/modules/models/diffusion/uni_pc/sampler.py
+++ b/modules/models/diffusion/uni_pc/sampler.py
@@ -71,7 +71,7 @@ class UniPCSampler(object):
         # sampling
         C, H, W = shape
         size = (batch_size, C, H, W)
-        print(f'Data shape for UniPC sampling is {size}')
+        # print(f'Data shape for UniPC sampling is {size}')
 
         device = self.model.betas.device
         if x_T is None:
diff --git a/modules/models/diffusion/uni_pc/uni_pc.py b/modules/models/diffusion/uni_pc/uni_pc.py
index e9a093a2b..eb5f4e762 100644
--- a/modules/models/diffusion/uni_pc/uni_pc.py
+++ b/modules/models/diffusion/uni_pc/uni_pc.py
@@ -1,6 +1,7 @@
 import torch
 import torch.nn.functional as F
 import math
+from tqdm.auto import trange
 
 
 class NoiseScheduleVP:
@@ -750,7 +751,7 @@ class UniPC:
         if method == 'multistep':
             assert steps >= order, "UniPC order must be < sampling steps"
             timesteps = self.get_time_steps(skip_type=skip_type, t_T=t_T, t_0=t_0, N=steps, device=device)
-            print(f"Running UniPC Sampling with {timesteps.shape[0]} timesteps, order {order}")
+            #print(f"Running UniPC Sampling with {timesteps.shape[0]} timesteps, order {order}")
             assert timesteps.shape[0] - 1 == steps
             with torch.no_grad():
                 vec_t = timesteps[0].expand((x.shape[0]))
@@ -766,7 +767,7 @@ class UniPC:
                         self.after_update(x, model_x)
                     model_prev_list.append(model_x)
                     t_prev_list.append(vec_t)
-                for step in range(order, steps + 1):
+                for step in trange(order, steps + 1):
                     vec_t = timesteps[step].expand(x.shape[0])
                     if lower_order_final:
                         step_order = min(order, steps + 1 - step)