          # Currently Tensorflow does not release vram after it has been allocated
          # Whilst this vram can still be used, the pipeline for the extraction process can't see
          # it so thinks there is not enough vram available.
          # Either the pipeline will need to be changed to be re-usable by face-filter and extraction
          # Or another vram measurement technique will need to be implemented to for when TF has
          # already performed allocation. For now we force CPU detectors.
  
          # self.align_faces(detector, aligner, multiprocess)
          self.align_faces("cv2-dnn", "cv2-dnn", "none", multiprocess)
  
          self.get_filter_encodings()
 
</s> remove         argument_list.append(dict(
            opts=("-d", "--distributed"),
            action="store_true",
            default=False,
            backend="nvidia",
            group="settings",
            help="Use the Tensorflow Mirrored Distrubution Strategy to train on multiple GPUs."))
 </s> add  </s> remove         rounding:  [partial] For <class 'int'> and <class 'float'> datatypes this is
 </s> add         rounding:  [partial] For <class 'int'> and <class 'float'> data types this is </s> remove         rounding:  [partial] For <class 'int'> and <class 'float'> datatypes this is
 </s> add         rounding:  [partial] For <class 'int'> and <class 'float'> data types this is </s> remove         min_max:   [partial] For <class 'int'> and <class 'float'> datatypes this is required
 </s> add         min_max:   [partial] For <class 'int'> and <class 'float'> data types this is required
        # Or another vram measurement technique will need to be implemented to for when tensorflow
        # has already performed allocation. For now we force CPU detectors.

          # Currently Tensorflow does not release vram after it has been allocated
          # Whilst this vram can still be used, the pipeline for the extraction process can't see
          # it so thinks there is not enough vram available.
          # Either the pipeline will need to be changed to be re-usable by face-filter and extraction
        # Or another vram measurement technique will need to be implemented to for when tensorflow
        # has already performed allocation. For now we force CPU detectors.
        # Or another vram measurement technique will need to be implemented to for when tensorflow
        # has already performed allocation. For now we force CPU detectors.
  
          # self.align_faces(detector, aligner, multiprocess)
          self.align_faces("cv2-dnn", "cv2-dnn", "none", multiprocess)
  
          self.get_filter_encodings()
keep keep keep keep replace 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 
lib/face_filter.py