AttGAN
_{_{TIP Nov. 2019, arXiv Nov. 2017}}

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

Other implementations of AttGAN
- AttGAN-PyTorch by Yu-Jing Lin
- AttGAN-PaddlePaddle by ceci3 and zhumanyu (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

Exemplar Results

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

Usage

Environment

Python 3.6
TensorFlow 1.15
OpenCV, scikit-image, tqdm, oyaml

we recommend Anaconda or Miniconda, then you can create the AttGAN environment with commands below

conda create -n AttGAN python=3.6

source activate AttGAN

conda install -c anaconda tensorflow-gpu=1.15

conda install -c anaconda opencv

conda install -c anaconda scikit-image

conda install -c anaconda tqdm

conda install -c conda-forge oyaml

Data Preparation
- CelebA-unaligned (10.2GB, higher quality than the aligned data)
  - download the dataset
    - img_celeba.7z (move to ./data/img_celeba/img_celeba.7z): Google Drive or Baidu Netdisk
    - annotations.zip (move to ./data/img_celeba/annotations.zip): Google Drive
  - unzip and process the data
```
7z x ./data/img_celeba/img_celeba.7z/img_celeba.7z.001 -o./data/img_celeba/

unzip ./data/img_celeba/annotations.zip -d ./data/img_celeba/

python ./scripts/align.py
```

Run AttGAN

NOTICE: if you create a new conda environment, remember to activate it before any command
```
source activate AttGAN
```

training (see examples.md for more training commands)

CUDA_VISIBLE_DEVICES=0 \
python train.py \
--load_size 143 \
--crop_size 128 \
--model model_128 \
--experiment_name AttGAN_128

testing

single attribute editing (inversion)

CUDA_VISIBLE_DEVICES=0 \
python test.py \
--experiment_name AttGAN_128

multiple attribute editing (inversion) example

CUDA_VISIBLE_DEVICES=0 \
python test_multi.py \
--test_att_names Bushy_Eyebrows Pale_Skin \
--experiment_name AttGAN_128

attribute sliding example

CUDA_VISIBLE_DEVICES=0 \
python test_slide.py \
--test_att_name Pale_Skin \
--test_int_min -2 \
--test_int_max 2 \
--test_int_step 0.5 \
--experiment_name AttGAN_128

loss visualization

CUDA_VISIBLE_DEVICES='' \
tensorboard \
--logdir ./output/AttGAN_128/summaries \
--port 6006

convert trained model to .pb file

python to_pb.py --experiment_name AttGAN_128

Using Trained Weights
- alternative trained weights (move to ./output/*.zip)
  - AttGAN_128.zip (987.5MB)
    - including G, D, and the state of the optimizer
  - AttGAN_128_generator_only.zip (161.5MB)
    - G only
  - AttGAN_384_generator_only.zip (91.1MB)
- unzip the file (AttGAN_128.zip for example)
```
unzip ./output/AttGAN_128.zip -d ./output/
```
- testing (see above)
Example for Custom Dataset
- AttGAN-Cartoon

Citation

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},}

AttGAN TIP Nov. 2019, arXiv Nov. 2017

Related

Exemplar Results

Usage

Citation

AttGAN
_{_{TIP Nov. 2019, arXiv Nov. 2017}}