          _, warped_A, target_A = next(self.images_A)
          _, warped_B, target_B = next(self.images_B)
  
          loss_A = self.model.autoencoder_A.train_on_batch(warped_A, target_A)
          loss_B = self.model.autoencoder_B.train_on_batch(warped_B, target_B)        
                      
          print("[{0}] [#{1:05d}] [{2:.3f}s] loss_A: {3:.5f}, loss_B: {4:.5f}".format(
              time.strftime("%H:%M:%S"), iter_no, self._clock()-when, loss_A, loss_B),
              end='\r')
  
 
</s> remove 
        #generator = TrainingDataGenerator(self.random_transform_args, 160)                
        # make sre to keep zoom=2 or you won't get 128x128 vectors as input
        #generator = TrainingDataGenerator(self.random_transform_args, 220, 5, zoom=2)
        #generator = TrainingDataGenerator(self.random_transform_args, 180, 7, zoom=2)
 </s> add  </s> remove #     
 </s> add      </s> remove             time.strftime("%H:%M:%S"), iter_no, self._clock()-when, loss_A, loss_B),
 </s> add             time.strftime("%H:%M:%S"), self.model._epoch_no, self._clock()-when, loss_A, loss_B),
        loss_B = self.model.autoencoder_B.train_on_batch(warped_B, target_B)
        
        self.model._epoch_no += 1        
          _, warped_A, target_A = next(self.images_A)
          _, warped_B, target_B = next(self.images_B)
  
          loss_A = self.model.autoencoder_A.train_on_batch(warped_A, target_A)
        loss_B = self.model.autoencoder_B.train_on_batch(warped_B, target_B)
        
        self.model._epoch_no += 1        
                      
          print("[{0}] [#{1:05d}] [{2:.3f}s] loss_A: {3:.5f}, loss_B: {4:.5f}".format(
              time.strftime("%H:%M:%S"), iter_no, self._clock()-when, loss_A, loss_B),
              end='\r')
  
keep keep keep keep replace keep keep keep keep keep
 altered trainer (#425)

altered trainer to accommodate with model change 
plugins/Model_OriginalHighRes/Trainer.py