          self.state.training_size = 256
          self.state.config["coverage"] = 62.5
          self.state.config["subpixel_upscaling"] = False
          self.state.config["reflect_padding"] = False
          self.state.config["replicate_input_mask"] = False
          self.state.config["lowmem"] = False
          self.encoder_dim = 1024
  
          if set_lowmem:
              logger.debug("Setting encoder_dim and lowmem flag for legacy lowmem model")
 
</s> remove 
        if self.config.get("replicate_input_mask", False):
 </s> add         # Mask
        if self.config.get("mask_type", None): </s> remove         self.target = batch["targets"][self.model.largest_face_index]
        self.masks = batch["masks"][0]
 </s> add         self.target = [batch["targets"][self.model.largest_face_index]]
        if self.use_mask:
            self.target += [batch["masks"]] </s> remove         images = batch["targets"][self.model.largest_face_index]
        masks = batch["masks"][0]
        sample = self.compile_sample(batchsize,
                                     samples=batch["samples"],
                                     images=images,
                                     masks=masks)
 </s> add         images = [batch["targets"][self.model.largest_face_index]]
        if self.use_mask:
            images = images + [batch["masks"]]
        sample = self.compile_sample(batchsize, samples=batch["samples"], images=images) </s> remove             batch[..., :3] = self._processing.color_adjust(batch[..., :3])
 </s> add             batch = self._processing.color_adjust(batch)

        # Add mask to batch prior to transforms and warps
        if self._mask_class:
            batch = np.array([self._mask_class(src_pts, image, channels=4).mask
                              for src_pts, image in zip(batch_src_pts, batch)])
        self.state.config["mask_type"] = None
          self.state.training_size = 256
          self.state.config["coverage"] = 62.5
          self.state.config["subpixel_upscaling"] = False
          self.state.config["reflect_padding"] = False
        self.state.config["mask_type"] = None
          self.state.config["lowmem"] = False
          self.encoder_dim = 1024
  
          if set_lowmem:
              logger.debug("Setting encoder_dim and lowmem flag for legacy lowmem model")
keep keep keep keep replace keep keep keep keep keep
 Serialize masks to alignments file

- Add new serializers (npy + compressed)
- Remove Serializer option from cli
- Revert get_aligned call in scripts/extract
- Default alignments to compressed
- Size masks to 128px and compress
- Remove mask thresholding/blur from generation code
- Add Mask class to lib/faces_detect
- Revert debug landmarks to aligned face
- Revert non-extraction code to staging version 
plugins/train/model/_base.py