  autoencoder = autoencoder_B
  
  # landmark file can be found in http://dlib.net/files/shape_predictor_68_face_landmarks.dat.bz2
  # unzip it in the same folder as the main scripts
  aligner = Aligner("shape_predictor_68_face_landmarks.dat")
  
  def convert_one_image( image ):
      assert image.shape == (256,256,3)
      crop = slice(48,208)
      face = image[crop,crop]
 
</s> remove     def __init__(self, pred):
        self.detector = dlib.get_frontal_face_detector()
 </s> add     def __init__(self, pred, detect):
        self.detector = dlib.cnn_face_detection_model_v1(detect)
 </s> remove         rects = self.detector(img)
 </s> add         rects = self.detector(img, 1)
 </s> remove             return rects[0]
 </s> add             return rects[0].rect

aligner = Aligner("shape_predictor_68_face_landmarks.dat","mmod_human_face_detector.dat")
  autoencoder = autoencoder_B
  
  # landmark file can be found in http://dlib.net/files/shape_predictor_68_face_landmarks.dat.bz2
  # unzip it in the same folder as the main scripts
aligner = Aligner("shape_predictor_68_face_landmarks.dat","mmod_human_face_detector.dat")
  
  def convert_one_image( image ):
      assert image.shape == (256,256,3)
      crop = slice(48,208)
      face = image[crop,crop]
keep keep keep keep replace keep keep keep keep keep
 Different aligner setup

Using cnn_face_detection_model_v1 as per
https://www.reddit.com/r/deepfakes/comments/7kfva4/face_alignment_with_d
lib/

Note this gives good result on trump/cage, but not on the group photos
I tried 
lib/faces_process.py