              # Expand out partials for eye and mouth masks on first epoch
              if item is not None and key in ("eyes", "mouths"):
                  self._expand_partials(side, item, filenames)
  
              logger.trace("Obtaining masks for batch. (key: %s side: %s)", key, side)
              masks = np.array([self._get_mask(item[side][filename], size)
                                for filename in filenames], dtype=batch.dtype)
              masks = self._resize_masks(size, masks)
  
              logger.trace("masks: (key: %s, shape: %s)", key, masks.shape)
              batch = np.concatenate((batch, masks), axis=-1)
          logger.trace("Output batch shape: %s, side: %s", batch.shape, side)
          return batch
 
</s> remove             if item is None and key == "masks":
                logger.trace("Creating dummy masks. side: %s", side)
                masks = np.ones_like(batch[..., :1], dtype=batch.dtype)
                continue
 </s> add  </s> remove         if self._model.config["mouth_multiplier"] > 1:
 </s> add         if penalized_loss and self._model.config["mouth_multiplier"] > 1: </s> remove         if self._model.config["eye_multiplier"] > 1:
 </s> add         if penalized_loss and self._model.config["eye_multiplier"] > 1:
            if item is None and key == "masks":
                logger.trace("Creating dummy masks. side: %s", side)
                masks = np.ones_like(batch[..., :1], dtype=batch.dtype)
            else:
                logger.trace("Obtaining masks for batch. (key: %s side: %s)", key, side)
                masks = np.array([self._get_mask(item[side][filename], size)
                                  for filename in filenames], dtype=batch.dtype)
                masks = self._resize_masks(size, masks)
              # Expand out partials for eye and mouth masks on first epoch
              if item is not None and key in ("eyes", "mouths"):
                  self._expand_partials(side, item, filenames)
  
            if item is None and key == "masks":
                logger.trace("Creating dummy masks. side: %s", side)
                masks = np.ones_like(batch[..., :1], dtype=batch.dtype)
            else:
                logger.trace("Obtaining masks for batch. (key: %s side: %s)", key, side)
                masks = np.array([self._get_mask(item[side][filename], size)
                                  for filename in filenames], dtype=batch.dtype)
                masks = self._resize_masks(size, masks)
            if item is None and key == "masks":
                logger.trace("Creating dummy masks. side: %s", side)
                masks = np.ones_like(batch[..., :1], dtype=batch.dtype)
            else:
                logger.trace("Obtaining masks for batch. (key: %s side: %s)", key, side)
                masks = np.array([self._get_mask(item[side][filename], size)
                                  for filename in filenames], dtype=batch.dtype)
                masks = self._resize_masks(size, masks)
            if item is None and key == "masks":
                logger.trace("Creating dummy masks. side: %s", side)
                masks = np.ones_like(batch[..., :1], dtype=batch.dtype)
            else:
                logger.trace("Obtaining masks for batch. (key: %s side: %s)", key, side)
                masks = np.array([self._get_mask(item[side][filename], size)
                                  for filename in filenames], dtype=batch.dtype)
                masks = self._resize_masks(size, masks)
            if item is None and key == "masks":
                logger.trace("Creating dummy masks. side: %s", side)
                masks = np.ones_like(batch[..., :1], dtype=batch.dtype)
            else:
                logger.trace("Obtaining masks for batch. (key: %s side: %s)", key, side)
                masks = np.array([self._get_mask(item[side][filename], size)
                                  for filename in filenames], dtype=batch.dtype)
                masks = self._resize_masks(size, masks)
  
              logger.trace("masks: (key: %s, shape: %s)", key, masks.shape)
              batch = np.concatenate((batch, masks), axis=-1)
          logger.trace("Output batch shape: %s, side: %s", batch.shape, side)
          return batch
keep keep keep keep replace replace replace replace keep keep keep keep keep
 - Bugfix: Training - Disable loss multipliers if penalized loss not selected
- Training: Half mouth/eye multiplier defaults
- GUI: Remove analysis callback from convert tab 
lib/training_data.py