          model.load(swapped=False)
  
          images_A = get_image_paths(self.arguments.input_A)
          images_B = get_image_paths(self.arguments.input_B)
          trainer = PluginLoader.get_trainer(variant)(model, images_A, images_B)
  
          try:
              print('Starting. Press "Enter" to stop training and save model')
  
              for epoch in range(0, 1000000):
 
</s> remove         model = PluginLoader.get_model(variant)(self.arguments.model_dir)
 </s> add         model = PluginLoader.get_model(self.arguments.trainer)(self.arguments.model_dir) </s> remove                 trainer.train_one_step(epoch, self.show if save_iteration else None)
 </s> add                 trainer.train_one_step(epoch, self.show if (save_iteration or self.save_now) else None) </s> remove                 for name, image in self.preview_buffer.items():
                    cv2.imshow(name, image)

                key = cv2.waitKey(1000)
                if key == ord('\n') or key == ord('\r'):
 </s> add                 try:
                    for name, image in self.preview_buffer.items():
                        cv2.imshow(name, image)

                    key = cv2.waitKey(1000)
                    if key == ord('\n') or key == ord('\r'):
                        break
                    if key == ord('s'):
                        self.save_now = True
                except KeyboardInterrupt: </s> remove         variant = "Original" # TODO Pass as argument
        
 </s> add 
        trainer = PluginLoader.get_trainer(self.arguments.trainer)(model,
                                                                   images_A,
                                                                   images_B,
                                                                   batch_size=self.arguments.batch_size)
          model.load(swapped=False)
  
          images_A = get_image_paths(self.arguments.input_A)
          images_B = get_image_paths(self.arguments.input_B)
        trainer = PluginLoader.get_trainer(self.arguments.trainer)(model,
                                                                   images_A,
                                                                   images_B,
                                                                   batch_size=self.arguments.batch_size)
  
          try:
              print('Starting. Press "Enter" to stop training and save model')
  
              for epoch in range(0, 1000000):
keep keep keep keep replace keep keep keep keep keep
 added batch-size cli argumet.
neatened up train.py and added LowMem trainer option.
Added save_now/show key to preview. 
scripts/train.py