              The mask sized to Faceswap model output with any requested blurring applied.
          """
          if self.mask_type == "none":
              # Return a dummy mask if not using a mask
              mask = np.ones_like(self.dummy[:, :, 1], dtype="uint8") * 255
          elif self.mask_type == "predicted":
              mask = predicted_mask
          else:
              mask = detected_face.mask[self.mask_type]
              mask.set_blur_and_threshold(blur_kernel=self.config["kernel_size"],
 
</s> remove             mask = predicted_mask
 </s> add             mask = predicted_mask[..., None] </s> remove         raw_mask = np.expand_dims(raw_mask, axis=-1) if raw_mask.ndim != 3 else raw_mask
        mask = np.expand_dims(mask, axis=-1) if mask.ndim != 3 else mask
 </s> add  </s> remove         return mask.astype("float32") / 255.0, raw_mask.astype("float32") / 255.0
 </s> add         return mask, raw_mask
            mask = np.ones_like(self.dummy[:, :, 1], dtype="float32")[..., None]
              The mask sized to Faceswap model output with any requested blurring applied.
          """
          if self.mask_type == "none":
              # Return a dummy mask if not using a mask
            mask = np.ones_like(self.dummy[:, :, 1], dtype="float32")[..., None]
          elif self.mask_type == "predicted":
              mask = predicted_mask
          else:
              mask = detected_face.mask[self.mask_type]
              mask.set_blur_and_threshold(blur_kernel=self.config["kernel_size"],
keep keep keep keep replace keep keep keep keep keep
 Convert - bugfixes
- Default mask to an available mask in Preview tool
- Correctly output predicted mask 
plugins/convert/mask/mask_blend.py