diff --git a/denoising_diffusion_pytorch/denoising_diffusion_pytorch.py b/denoising_diffusion_pytorch/denoising_diffusion_pytorch.py
index 8a49a69..e8c1e9a 100644
--- a/denoising_diffusion_pytorch/denoising_diffusion_pytorch.py
+++ b/denoising_diffusion_pytorch/denoising_diffusion_pytorch.py
@@ -603,34 +603,37 @@ class Trainer(object):
         self.scaler.load_state_dict(data['scaler'])
 
     def train(self):
-        while self.step < self.train_num_steps:
-            for i in range(self.gradient_accumulate_every):
-                data = next(self.dl).cuda()
+        with tqdm(initial = self.step, total = self.train_num_steps) as pbar:
 
-                with autocast(enabled = self.amp):
-                    loss = self.model(data)
-                    self.scaler.scale(loss / self.gradient_accumulate_every).backward()
+            while self.step < self.train_num_steps:
+                for i in range(self.gradient_accumulate_every):
+                    data = next(self.dl).cuda()
 
-                print(f'{self.step}: {loss.item()}')
+                    with autocast(enabled = self.amp):
+                        loss = self.model(data)
+                        self.scaler.scale(loss / self.gradient_accumulate_every).backward()
 
-            self.scaler.step(self.opt)
-            self.scaler.update()
-            self.opt.zero_grad()
+                    pbar.set_description(f'loss: {loss.item():.4f}')
 
-            if self.step % self.update_ema_every == 0:
-                self.step_ema()
+                self.scaler.step(self.opt)
+                self.scaler.update()
+                self.opt.zero_grad()
 
-            if self.step != 0 and self.step % self.save_and_sample_every == 0:
-                self.ema_model.eval()
+                if self.step % self.update_ema_every == 0:
+                    self.step_ema()
 
-                milestone = self.step // self.save_and_sample_every
-                batches = num_to_groups(36, self.batch_size)
-                all_images_list = list(map(lambda n: self.ema_model.sample(batch_size=n), batches))
-                all_images = torch.cat(all_images_list, dim=0)
-                all_images = unnormalize_to_zero_to_one(all_images)
-                utils.save_image(all_images, str(self.results_folder / f'sample-{milestone}.png'), nrow = 6)
-                self.save(milestone)
+                if self.step != 0 and self.step % self.save_and_sample_every == 0:
+                    self.ema_model.eval()
 
-            self.step += 1
+                    milestone = self.step // self.save_and_sample_every
+                    batches = num_to_groups(36, self.batch_size)
+                    all_images_list = list(map(lambda n: self.ema_model.sample(batch_size=n), batches))
+                    all_images = torch.cat(all_images_list, dim=0)
+                    all_images = unnormalize_to_zero_to_one(all_images)
+                    utils.save_image(all_images, str(self.results_folder / f'sample-{milestone}.png'), nrow = 6)
+                    self.save(milestone)
 
-        print('training completed')
+                self.step += 1
+                pbar.update(1)
+
+        print('training complete')
diff --git a/setup.py b/setup.py
index a507209..f9605bf 100644
--- a/setup.py
+++ b/setup.py
@@ -3,7 +3,7 @@ from setuptools import setup, find_packages
 setup(
   name = 'denoising-diffusion-pytorch',
   packages = find_packages(),
-  version = '0.15.2',
+  version = '0.15.3',
   license='MIT',
   description = 'Denoising Diffusion Probabilistic Models - Pytorch',
   author = 'Phil Wang',