          # for queue) at each point. Adding more just stacks RAM with no speed benefit.
          self._queue_size = 1
          self._vram_stats = self._get_vram_stats()
          self._detect = self._load_detect(detector, rotate_images, min_size, configfile)
          self._align = self._load_align(aligner, configfile, normalize_method)
          self._mask = [self._load_mask(mask, image_is_aligned, configfile) for mask in masker]
          self._is_parallel = self._set_parallel_processing(multiprocess)
          self._phases = self._set_phases(multiprocess)
          self._phase_index = 0
          self._set_extractor_batchsize()
 
</s> remove                      exclude_gpus, rotate_images, min_size, normalize_method, image_is_aligned)
 </s> add                      exclude_gpus, rotate_images, min_size, normalize_method, re_feed,
                     image_is_aligned) </s> remove         mask = self._get_mask(detected_face, predicted_mask)
 </s> add         mask = self._get_mask(detected_face, predicted_mask, sub_crop_offset) </s> remove                      "normalize_method: %s, image_is_aligned: %s)",
 </s> add                      "normalize_method: %s, re_feed: %s, image_is_aligned: %s)", </s> remove         for key in tqdm(sorted(self.alignments.data.keys()), desc="Compiling"):
            val = self.alignments.data[key]["faces"]
 </s> add         for key in tqdm(sorted(self._alignments.data.keys()), desc="Compiling"):
            val = self._alignments.data[key]["faces"] </s> remove         for alignments in tqdm(self.alignments, desc="Filtering out faces"):
 </s> add         for alignments in tqdm(self._alignments, desc="Filtering out faces"):
        self._align = self._load_align(aligner, configfile, normalize_method, re_feed)
          # for queue) at each point. Adding more just stacks RAM with no speed benefit.
          self._queue_size = 1
          self._vram_stats = self._get_vram_stats()
          self._detect = self._load_detect(detector, rotate_images, min_size, configfile)
        self._align = self._load_align(aligner, configfile, normalize_method, re_feed)
          self._mask = [self._load_mask(mask, image_is_aligned, configfile) for mask in masker]
          self._is_parallel = self._set_parallel_processing(multiprocess)
          self._phases = self._set_phases(multiprocess)
          self._phase_index = 0
          self._set_extractor_batchsize()
keep keep keep keep replace keep keep keep keep keep
 Extract - Increase area and move centering (#1095)

* Extract
  - Implement aligner re-feeding
  - Add extract type to pipeline.ExtractMedia
  - Add pose annotation to debug
* Convert
  - implement centering
  - remove usage of feed and reference face properties
  - Remove distributed option from convert
  - Force update of alignments file on legacy receive
* Train
  - Resize preview image to model output size
  - Force legacy centering if centering does not exist in model's state file
  - Enable training on legacy face sets

* Alignments Tool
  - Update draw to include head/pose
  - Remove DFL drop + linting
  - Remove remove-frames job
  - remove align-eyes option
  - Update legacy masks to new extract type
  - Exit if attempting to merge version 1.0 alignments files with version 2.0 alignments files
  - Re-generate thumbnails on legacy upgrade
* Mask Tool
  - Update for new extract + bugfix full frame
* Manual Tool
  - Update to new extraction method
   - Disable legacy alignments,
   - extract box bugfix
   - extract faces - size to 512 and center on head
* Preview Tool
  - Display based on model centering
* Sort Tool
  - Use alignments for sort by face

* lib.aligner
  - Add Pose Class
  - Add AlignedFace Class
  - center _MEAN_FACE on x
  - Add meta information with versioning to alignments file
  - lib.aligner.get_align_matrix to use landmarks not face
  - Refactor aligned faces in lib.faces_detect
* lib.logger
  - larger file log padding
* lib.config
  - Fix global changeable_items
* lib.face_filter
  - Use new extracted face images
* lib.image
  - bump thumbnail default size to 96px 
plugins/extract/pipeline.py