TensorFlow implementation of AttGAN: Facial Attribute Editing by Only Changing What You Want

• Other AttGAN implementations

  • AttGAN-PyTorch by Yu-Jing Lin
  • AttGAN-PaddlePaddle by ceci3 and zhumanyu (our AttGAN is one of the official reproduced models of PaddlePaddle)

• Closely related works

  • An excellent work built upon our code - STGAN (CVPR 2019) by Ming Liu
  • Changing-the-Memorability (CVPR 2019 MBCCV Workshop) by acecreamu
  • Fashion-AttGAN (CVPR 2019 FSS-USAD Workshop) by Qing Ping

• An unofficial demo video of AttGAN by 王一凡

• See results.md for more results, we try higher resolution and more attributes (all 40 attributes!!!) here

• Inverting 13 attributes respectively

  from left to right: Input, Reconstruction, Bald, Bangs, Black_Hair, Blond_Hair, Brown_Hair, Bushy_Eyebrows, Eyeglasses, Male, Mouth_Slightly_Open, Mustache, No_Beard, Pale_Skin, Young

  

• Comparisons with VAE/GAN and IcGAN on inverting single attribute

  

• Comparisons with VAE/GAN and IcGAN on simultaneously inverting multiple attributes

  

• Prerequisites

  • TensorFlow 1.7+
  • Python 2.7 or 3.6

• Dataset

  • Celeba dataset

    • Images should be placed in ./data/img_align_celeba/*.jpg
    • Attribute labels should be placed in ./data/list_attr_celeba.txt
    • the above links might be inaccessible, the alternatives are
      • img_align_celeba.zip
        • Baidu Netdisk or
        • Google Drive
      • list_attr_celeba.txt
        • Baidu Netdisk or
        • Google Drive

  • HD-Celeba (optional)

    • the images of img_align_celeba.zip are low resolution and uncropped, higher resolution and cropped images are available here
    • the high quality data should be placed in ./data/img_crop_celeba/*.jpg

• Well-trained models: download the models you need and unzip the files to ./output/ as below,

  output
  ├── 128_shortcut1_inject1_none
  └── 384_shortcut1_inject1_none_hd
  

• Examples of training

  • see examples.md for more examples

  • training

    • for 128x128 images

      CUDA_VISIBLE_DEVICES=0 \
      python train.py \
      --img_size 128 \
      --shortcut_layers 1 \
      --inject_layers 1 \
      --experiment_name 128_shortcut1_inject1_none

    • for 384x384 images

      CUDA_VISIBLE_DEVICES=0 \
      python train.py \
      --img_size 384 \
      --enc_dim 48 \
      --dec_dim 48 \
      --dis_dim 48 \
      --dis_fc_dim 512 \
      --shortcut_layers 1 \
      --inject_layers 1 \
      --n_sample 24 \
      --experiment_name 384_shortcut1_inject1_none

    • for 384x384 HD images (need HD-Celeba)

      CUDA_VISIBLE_DEVICES=0 \
      python train.py \
      --img_size 384 \
      --enc_dim 48 \
      --dec_dim 48 \
      --dis_dim 48 \
      --dis_fc_dim 512 \
      --shortcut_layers 1 \
      --inject_layers 1 \
      --n_sample 24 \
      --use_cropped_img \
      --experiment_name 384_shortcut1_inject1_none_hd

  • tensorboard for loss visualization

    CUDA_VISIBLE_DEVICES='' \
    tensorboard \
    --logdir ./output/128_shortcut1_inject1_none/summaries \
    --port 6006

• Example of testing single attribute

  CUDA_VISIBLE_DEVICES=0 \
  python test.py \
  --experiment_name 128_shortcut1_inject1_none \
  --test_int 1.0

• Example of testing multiple attributes

  CUDA_VISIBLE_DEVICES=0 \
  python test_multi.py \
  --experiment_name 128_shortcut1_inject1_none \
  --test_atts Pale_Skin Male \
  --test_ints 0.5 0.5

• Example of attribute intensity control

  CUDA_VISIBLE_DEVICES=0 \
  python test_slide.py \
  --experiment_name 128_shortcut1_inject1_none \
  --test_att Male \
  --test_int_min -1.0 \
  --test_int_max 1.0 \
  --n_slide 10

If you find AttGAN useful in your research work, please consider citing:

@ARTICLE{8718508,
author={Z. {He} and W. {Zuo} and M. {Kan} and S. {Shan} and X. {Chen}},
journal={IEEE Transactions on Image Processing},
title={AttGAN: Facial Attribute Editing by Only Changing What You Want},
year={2019},
volume={28},
number={11},
pages={5464-5478},
keywords={Face;Facial features;Task analysis;Decoding;Image reconstruction;Hair;Gallium nitride;Facial attribute editing;attribute style manipulation;adversarial learning},
doi={10.1109/TIP.2019.2916751},
ISSN={1057-7149},
month={Nov},}