          logger.debug("Initializing %s: (model: %s, image counts: %s)",
                       self.__class__.__name__, model, {k: len(v) for k, v in image_list.items()})
          self._args = model.command_line_arguments
          self._config = model.config
          self._alignments_paths = self._get_alignments_paths()
          self._hashes, sizes = self._get_image_hashes(image_list)
  
          self._alignments_version = dict()
          self._detected_faces = dict()
  
          self._load_alignments()
 
</s> remove         self._detected_faces = dict()
 </s> add         self._image_sizes = {key: None for key in image_list}
        self._detected_faces = self._load_detected_faces(image_list)
        self._update_legacy_facesets() </s> remove                                      input_sizes[side])
            for fhash, face in detected_faces.items():
                face.aligned = AlignedFace(face.landmarks_xy,
                                           centering=self._config["centering"],
                                           size=size,
                                           is_aligned=True)
                for filename in self._hash_to_filenames(side, fhash):
                    ret_side[filename] = face.aligned
            retval[side] = ret_side
 </s> add                                      self._image_sizes[side])
            for filename, face in detected_faces.items():
                retval[side][filename] = AlignedFace(face.landmarks_xy,
                                                     centering=self._config["centering"],
                                                     size=size,
                                                     is_aligned=True)
        logger.debug("Loaded aligned faces: %s", {k: len(v) for k, v in retval.items()}) </s> remove         self._load_alignments()
        self._check_all_faces()

        self._aligned_faces = self._get_aligned_faces(sizes)
 </s> add         self._validity_check()
        self._aligned_faces = self._get_aligned_faces()
          logger.debug("Initializing %s: (model: %s, image counts: %s)",
                       self.__class__.__name__, model, {k: len(v) for k, v in image_list.items()})
          self._args = model.command_line_arguments
          self._config = model.config


  
          self._alignments_version = dict()
          self._detected_faces = dict()
  
          self._load_alignments()
keep keep keep keep replace replace keep keep keep keep keep
 Update extracted faces to use PNG EXIF data (#1123)

Documentation
  - Update Usage.md, align.rst and image.rst
lib.image.py
  - read_image - Remove hash return, add metadata return
  - Remove read_image_hash functions
  - Add read_image_meta functios
  - Replace encode_image_with_hash with encode_image (to store metadata)
  - Add png meta reading and writing functions
  - Update Image Loaders/Savers to handle metadata rather than hashes
lib.training_data
  - Naming updates to remove references to hashes
lib.align.Alignments
  - Add versioning notes
  - Increment alignments version to 2.1
  - Deprecate hashing lookup functions
  - Replace filter_hashes with filter_faces
lib.align.detected_face
  - DetectedFace
    - Remove hash property
    - Add png header data serializing/deserializing functions
  - Mask
    - Add png header data serializing/deserializing functions
  - add update_legacy_png_header function to update png meta data
lib.cli.args - Deprecate alignments files for training
- plugins.train.trainer
  - Update alignments/mask code to read png header data
- scripts.convert
  - Aligned images folder - read data from png headers
- scripts.extract
  - Write png header information and no longer store hash of face
- tools.alignments
  - remove leftover-faces, merge and update-hashes jobs
  - Update jobs to use png meta data rather than hashes
- tools.manual
  - Update extract code to output png meta data and don't store hashes
  - Perform check on launch that tool is not pointing at a faces folder
tools.mask
  - Update to use png meta data
tools.sort
  - Update to use png meta data 
plugins/train/trainer/_base.py