  
      def _set_preview_feed(self):
          """ Set the preview feed for this feeder.
  
          Creates a generator from :class:`lib.training_data.TrainingDataGenerator` specifically
          for previews for the feeder.
  
          Returns
          -------
          dict
 
</s> remove         Creates a generator from :class:`lib.training_data.TrainingDataGenerator` specifically
 </s> add         Creates a generator from :class:`lib.training_data.PreviewDataGenerator` specifically </s> remove         logger.debug("Loading generator")
        input_size = self._model.model.input_shape[output_index][1]
        output_shapes = self._model.output_shapes[output_index]
        logger.debug("input_size: %s, output_shapes: %s", input_size, output_shapes)
        generator = TrainingDataGenerator(input_size,
                                          output_shapes,
                                          self._model.coverage_ratio,
                                          self._model.color_order,
                                          not self._model.command_line_arguments.no_augment_color,
                                          self._model.command_line_arguments.no_flip,
                                          self._model.command_line_arguments.no_warp,
                                          self._model.command_line_arguments.warp_to_landmarks,
                                          self._config)
        return generator

    def _set_preview_feed(self):
 </s> add         logger.debug("Loading generator, side: %s, is_display: %s,  batch_size: %s",
                     side, is_display, batch_size)
        generator = PreviewDataGenerator if is_display else TrainingDataGenerator
        retval = generator(self._config,
                           self._model,
                           side,
                           self._images[side] if images is None else images,
                           self._batch_size if batch_size is None else batch_size)
        return retval

    def _set_preview_feed(self) -> Dict[Literal["a", "b"], Generator[BatchType, None, None]]: </s> remove     def compile_timelapse_sample(self):
        """ Compile the sample images for creating a time-lapse frame.

        Returns
        -------
        dict
            For sides "a" and "b"; The list of samples, targets and masks as
            :class:`numpy.ndarrays` for creating a time-lapse frame
        """
        batchsizes = []
        samples = {}
        images = {}
        masks = {}
        for side in ("a", "b"):
            batch = next(self._display_feeds["timelapse"][side])
            batchsizes.append(len(batch["samples"]))
            samples[side] = batch["samples"]
            images[side] = batch["targets"][-1]
            masks[side] = batch["masks"]
        batchsize = min(batchsizes)
        sample = self.compile_sample(batchsize, samples=samples, images=images, masks=masks)
        return sample

    def set_timelapse_feed(self, images, batch_size):
 </s> add     def set_timelapse_feed(self,
                           images: Dict[Literal["a", "b"], List[str]],
                           batch_size: int) -> None: </s> remove         images: list
            The list of full paths to the images for creating the time-lapse for this
            :class:`_Feeder`
 </s> add         images: dict
            The list of full paths to the images for creating the time-lapse for each side </s> remove         output_index: int
            The output index from the model to get output shapes for
 </s> add         side: ["a", "b"]
            The side of the model to load the generator for
        is_display: bool
            ``True`` if the generator is for creating preview/time-lapse images. ``False`` if it is
            for creating training images
        batch_size: int, optional
            If ``None`` then the batch size selected in command line arguments is used, otherwise
            the batch size provided here is used.
        images: list, optional. Default: ``None``
            If provided then this will be used as the list of images for the generator. If ``None``
            then the training folder images for the side will be used. Default: ``None``
        Creates a generator from :class:`lib.training_data.PreviewDataGenerator` specifically
  
      def _set_preview_feed(self):
          """ Set the preview feed for this feeder.
  
        Creates a generator from :class:`lib.training_data.PreviewDataGenerator` specifically
          for previews for the feeder.
  
          Returns
          -------
          dict
keep keep keep keep replace keep keep keep keep keep
 Data Augmentation update (#1263)

- lib.detected_face
    - Subclass Masks for Landmark based masks
    - Add training mask propery + methods to DetectedFace
  - lib.training_training
    - subclass TrainingDataGenerator for training and preview data
    - Split cache into own module
    - Reduce thread count to 1 to prevent image corruption + data re-use
    - Process on largest model input/output size rather than stored image size
    - Size and crop masks during caching stage
    - Implement ring buffer for data flow
    - Fix preview reload bug
  - augmentation
    - typing
    - switch color aug order
    - better initialization
    - Fix warp + landmark warp to correctly apply at different image scales
    - Slightly improved warp caching
    - Don't store whether image is_preview. Handle all data as training images implicitly
  - plugins.trainer: Typing and fixes to work with trainingdata refactor 
plugins/train/trainer/_base.py