Tensorflow implementation of AttGAN - Arbitrary Facial Attribute Editing: Only Change What You Want

• 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 or 1.8
  • 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 alternative is
      • img_align_celeba.zip
        • https://pan.baidu.com/s/1eSNpdRG#list/path=%2FCelebA%2FImg or
        • https://drive.google.com/drive/folders/0B7EVK8r0v71pTUZsaXdaSnZBZzg
      • list_attr_celeba.txt
        • https://pan.baidu.com/s/1eSNpdRG#list/path=%2FCelebA%2FAnno&parentPath=%2F or
        • https://drive.google.com/drive/folders/0B7EVK8r0v71pOC0wOVZlQnFfaGs

  • 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{he2017arbitrary,
  title={Arbitrary Facial Attribute Editing: Only Change What You Want},
  author={He, Zhenliang and Zuo, Wangmeng and Kan, Meina and Shan, Shiguang and Chen, Xilin},
  journal={arXiv preprint arXiv:1711.10678},
  year={2017}
}