          for key in ("masks", "eyes", "mouths"):
              item = self._masks[key]
              if item is None and key != "masks":
                  continue
              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
  
              # 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)
  
 
</s> remove             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)
 </s> add             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) </s> remove         if track_modified and self._command in ("train", "convert") and self.title == "Model Dir":
 </s> add         if track_modified and self._command == "train" and self.title == "Model Dir": </s> remove         if self._model.config["eye_multiplier"] > 1:
 </s> add         if penalized_loss and self._model.config["eye_multiplier"] > 1:
          for key in ("masks", "eyes", "mouths"):
              item = self._masks[key]
              if item is None and key != "masks":
                  continue




  
              # 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)
  
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