          self.vram_warnings = 256
          self.vram_per_batch = 64
          self.batchsize = self.config["batch-size"]
  
          self._storage_centering = "head" if self.config["include_hair"] else "face"
          # Separate storage for face and head masks
          self._storage_name = f"{self._storage_name}_{self._storage_centering}"
  
      def _check_weights_selection(self, configfile):
 
</s> remove         self._segment_indices = self._get_segment_indices()
 </s> add  </s> remove         mean = (0.485, 0.456, 0.406)
        std = (0.229, 0.224, 0.225)
 </s> add         mean = (0.384, 0.314, 0.279) if self._is_faceswap else (0.485, 0.456, 0.406)
        std = (0.324, 0.286, 0.275) if self._is_faceswap else (0.229, 0.224, 0.225) </s> remove         model_filename = "bisnet_face_parsing_v1.h5"
 </s> add         model_filename = f"bisnet_face_parsing_v{'2' if self._is_faceswap else '1'}.h5"
        self._segment_indices = self._get_segment_indices()
          self.vram_warnings = 256
          self.vram_per_batch = 64
          self.batchsize = self.config["batch-size"]
  
        self._segment_indices = self._get_segment_indices()
          self._storage_centering = "head" if self.config["include_hair"] else "face"
          # Separate storage for face and head masks
          self._storage_name = f"{self._storage_name}_{self._storage_centering}"
  
      def _check_weights_selection(self, configfile):
keep keep keep add keep keep keep keep keep
 Bisenet-FP - Improved weights for masker (#1210)

* Add option to load faceswap trained weights

* Switch to original weights if fs weights not present

* typofix 
plugins/extract/mask/bisenet_fp.py