AttGAN-PyTorch

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

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

The original TensorFlow version can be found here.

Requirements

• Python 3
• PyTorch 0.4.0
• TensorboardX

pip3 install -r requirements.txt

• Dataset
  • CelebA dataset
    • Images should be placed in ./data/img_align_celeba/*.jpg
    • Attribute labels should be placed in ./data/list_attr_celeba.txt
  • HD-CelebA (optional)
    • Please see here.
  • CelebA-HQ dataset (optional)
    • Please see here.
    • Images should be placed in ./data/celeba-hq/celeba-*/*.jpg
    • Image list should be placed in ./data/image_list.txt
• Pretrained models: download the models you need and unzip the files to ./output/ as below,

  output
  ├── 128_shortcut1_inject0_none
  ├── 128_shortcut1_inject1_none
  ├── 256_shortcut1_inject0_none_hq
  └── 256_shortcut1_inject1_none_hq
  

Usage

To train an AttGAN on CelebA 128x128

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

To train an AttGAN on CelebA-HQ 256x256

CUDA_VISIBLE_DEVICES=0 \ 
python train.py \ 
--data CelebA-HQ \ 
--img_size 256 \ 
--shortcut_layers 1 \ 
--inject_layers 1 \ 
--experiment_name 256_shortcut1_inject1_none_hq \ 
--gpu

To visualize training details

tensorboard \
--logdir ./output

To test with single attribute editing

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

To test with multiple attributes editing

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 \ 
--gpu

To test with 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 \ 
--gpu