          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')
  
          if viewer is not None:
              viewer(self.show_sample(target_A[0:8], target_B[0:8]), "training using {}, bs={}".format(self.model, self.batch_size))
              
 
</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         loss_B = self.model.autoencoder_B.train_on_batch(warped_B, target_B)        
 </s> add         loss_B = self.model.autoencoder_B.train_on_batch(warped_B, target_B)
        
        self.model._epoch_no += 1         </s> remove #     
 </s> add     
            time.strftime("%H:%M:%S"), self.model._epoch_no, self._clock()-when, loss_A, loss_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"), self.model._epoch_no, self._clock()-when, loss_A, loss_B),
              end='\r')
  
          if viewer is not None:
              viewer(self.show_sample(target_A[0:8], target_B[0:8]), "training using {}, bs={}".format(self.model, self.batch_size))
              
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