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PA-GAN

TensorFlow implementation of PA-GAN: Progressive Attention Generative Adversarial Network for Facial Attribute Editing

Usage

Environment
- Python 3.6
- TensorFlow 1.13+, TensorFlow Graphics
- OpenCV, scikit-image, tqdm, oyaml
- we recommend Anaconda or Miniconda, then you can create the PA-GAN environment with commands below
```
conda create -n PA-GAN python=3.6

source activate PA-GAN

conda install opencv scikit-image tqdm tensorflow-gpu=1.13

conda install -c conda-forge oyaml

pip install tensorflow-graphics-gpu --no-deps
```
- NOTICE: if you create a new conda environment, remember to activate it before any other command
```
source activate PA-GAN
```
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 (password rp0s)
    - 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 PA-GAN

training

CUDA_VISIBLE_DEVICES=0 \
python train.py \
--experiment_name PA-GAN_128

testing

single attribute editing (inversion)

CUDA_VISIBLE_DEVICES=0 \
python test.py \
--experiment_name PA-GAN_128

multiple attribute editing (inversion) example

CUDA_VISIBLE_DEVICES=0 \
python test_multi.py \
--test_att_names Bushy_Eyebrows Mustache \
--experiment_name PA-GAN_128

loss visualization

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

Using Trained Weights
- alternative trained weights (move to ./output/*.zip)
  - PA-GAN_128.zip (953.9MB)
    - including G, D, and the state of the optimizer
  - PA-GAN_128_generator_only.zip (151.5MB)
    - G only
  - PA-GAN_256_generator_only.zip (70.8MB)
- unzip the file (PA-GAN_128.zip for example)
```
unzip ./output/PA-GAN_128.zip -d ./output/
```
- testing (see above)

Citation

If you find PA-GAN useful in your research work, please consider citing:

@article{he2020pagan,
  title={PA-GAN: Progressive Attention Generative Adversarial Network for Facial Attribute Editing},
  author={He, Zhenliang and Kan, Meina and Zhang, Jichao and Shan, Shiguang},
  journal={arXiv preprint arXiv:2007.05892},
  year={2020}
}

Awesome

<p align="center"> PA-GAN </p>

Usage

Citation