                   pingpong=False,
                   memory_saving_gradients=False,
                   predict=False):
          logger.debug("Initializing ModelBase (%s): (model_dir: '%s', gpus: %s, no_logs: %s"
                       "training_image_size, %s, alignments_paths: %s, preview_scale: %s, "
                       "input_shape: %s, encoder_dim: %s, trainer: %s, pingpong: %s, "
                       "memory_saving_gradients: %s, predict: %s)",
                       self.__class__.__name__, model_dir, gpus, no_logs, training_image_size,
                       alignments_paths, preview_scale, input_shape, encoder_dim, trainer,
                       pingpong, memory_saving_gradients, predict)
  
          self.predict = predict
          self.model_dir = model_dir
          self.gpus = gpus
          self.blocks = NNBlocks(use_subpixel=self.config["subpixel_upscaling"],
 
</s> remove                      "is_timelapse: %s)", len(images), batchsize, side, do_shuffle, is_timelapse)
 </s> add                      "is_preview, %s, is_timelapse: %s)", len(images), batchsize, side, do_shuffle,
                     is_preview, is_timelapse) </s> remove         logger.debug("Loading batch: (image_count: %s, side: '%s', is_timelapse: %s, "
                     "do_shuffle: %s)", len(images), side, is_timelapse, do_shuffle)
 </s> add         logger.debug("Loading batch: (image_count: %s, side: '%s', is_display: %s, "
                     "do_shuffle: %s)", len(images), side, is_display, do_shuffle) </s> remove     def minibatch_ab(self, images, batchsize, side, do_shuffle=True, is_timelapse=False):
 </s> add     def minibatch_ab(self, images, batchsize, side,
                     do_shuffle=True, is_preview=False, is_timelapse=False): </s> remove         queue_in, queue_out = self.make_queues(side, is_timelapse)
 </s> add         is_display = is_preview or is_timelapse
        queue_in, queue_out = self.make_queues(side, is_preview, is_timelapse) </s> remove     def load_batches(self, mem_gen, images, side, is_timelapse,
 </s> add     def load_batches(self, mem_gen, images, side, is_display,
                     "warp_to_landmarks: %s, augment_color: %s, no_flip: %s, training_image_size, "
                     "%s, alignments_paths: %s, preview_scale: %s, input_shape: %s, encoder_dim: "
                     "%s, trainer: %s, pingpong: %s, memory_saving_gradients: %s, predict: %s)",
                     self.__class__.__name__, model_dir, gpus, no_logs, warp_to_landmarks,
                     augment_color, no_flip, training_image_size, alignments_paths, preview_scale,
                     input_shape, encoder_dim, trainer, pingpong, memory_saving_gradients, predict)
                   pingpong=False,
                   memory_saving_gradients=False,
                   predict=False):
          logger.debug("Initializing ModelBase (%s): (model_dir: '%s', gpus: %s, no_logs: %s"
                     "warp_to_landmarks: %s, augment_color: %s, no_flip: %s, training_image_size, "
                     "%s, alignments_paths: %s, preview_scale: %s, input_shape: %s, encoder_dim: "
                     "%s, trainer: %s, pingpong: %s, memory_saving_gradients: %s, predict: %s)",
                     self.__class__.__name__, model_dir, gpus, no_logs, warp_to_landmarks,
                     augment_color, no_flip, training_image_size, alignments_paths, preview_scale,
                     input_shape, encoder_dim, trainer, pingpong, memory_saving_gradients, predict)
                     "warp_to_landmarks: %s, augment_color: %s, no_flip: %s, training_image_size, "
                     "%s, alignments_paths: %s, preview_scale: %s, input_shape: %s, encoder_dim: "
                     "%s, trainer: %s, pingpong: %s, memory_saving_gradients: %s, predict: %s)",
                     self.__class__.__name__, model_dir, gpus, no_logs, warp_to_landmarks,
                     augment_color, no_flip, training_image_size, alignments_paths, preview_scale,
                     input_shape, encoder_dim, trainer, pingpong, memory_saving_gradients, predict)
                     "warp_to_landmarks: %s, augment_color: %s, no_flip: %s, training_image_size, "
                     "%s, alignments_paths: %s, preview_scale: %s, input_shape: %s, encoder_dim: "
                     "%s, trainer: %s, pingpong: %s, memory_saving_gradients: %s, predict: %s)",
                     self.__class__.__name__, model_dir, gpus, no_logs, warp_to_landmarks,
                     augment_color, no_flip, training_image_size, alignments_paths, preview_scale,
                     input_shape, encoder_dim, trainer, pingpong, memory_saving_gradients, predict)
                     "warp_to_landmarks: %s, augment_color: %s, no_flip: %s, training_image_size, "
                     "%s, alignments_paths: %s, preview_scale: %s, input_shape: %s, encoder_dim: "
                     "%s, trainer: %s, pingpong: %s, memory_saving_gradients: %s, predict: %s)",
                     self.__class__.__name__, model_dir, gpus, no_logs, warp_to_landmarks,
                     augment_color, no_flip, training_image_size, alignments_paths, preview_scale,
                     input_shape, encoder_dim, trainer, pingpong, memory_saving_gradients, predict)
                     "warp_to_landmarks: %s, augment_color: %s, no_flip: %s, training_image_size, "
                     "%s, alignments_paths: %s, preview_scale: %s, input_shape: %s, encoder_dim: "
                     "%s, trainer: %s, pingpong: %s, memory_saving_gradients: %s, predict: %s)",
                     self.__class__.__name__, model_dir, gpus, no_logs, warp_to_landmarks,
                     augment_color, no_flip, training_image_size, alignments_paths, preview_scale,
                     input_shape, encoder_dim, trainer, pingpong, memory_saving_gradients, predict)
                     "warp_to_landmarks: %s, augment_color: %s, no_flip: %s, training_image_size, "
                     "%s, alignments_paths: %s, preview_scale: %s, input_shape: %s, encoder_dim: "
                     "%s, trainer: %s, pingpong: %s, memory_saving_gradients: %s, predict: %s)",
                     self.__class__.__name__, model_dir, gpus, no_logs, warp_to_landmarks,
                     augment_color, no_flip, training_image_size, alignments_paths, preview_scale,
                     input_shape, encoder_dim, trainer, pingpong, memory_saving_gradients, predict)
  
          self.predict = predict
          self.model_dir = model_dir
          self.gpus = gpus
          self.blocks = NNBlocks(use_subpixel=self.config["subpixel_upscaling"],
keep keep keep keep replace replace replace replace replace replace keep keep keep keep keep
 Color Augmentation method (#752)

* Color Augmentation: Implement testing code

* Tweak Contrast and Lighting augmentation amounts

* Prevent color augmentation from entering timelapse

* Remove all augmentations from preview images 
plugins/train/model/_base.py