AttGAN: Facial Attribute Editing by Only Changing What You Want
Zhenliang He1,2, Wangmeng Zuo4, Meina Kan1, Shiguang Shan1,3, Xilin Chen1
1Key Lab of Intelligent Information Processing, Institute of Computing Technology, CAS, China
2University of Chinese Academy of Sciences, China
3CAS Center for Excellence in Brain Science and Intelligence Technology, China
4School of Computer Science and Technology, Harbin Institute of Technology, China
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Other implementations of AttGAN
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AttGAN-PyTorch by Yu-Jing Lin
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AttGAN-PaddlePaddle by ceci3 and zhumanyu (AttGAN is one of the official reproduced models of PaddlePaddle)
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Closely related works
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An excellent work built upon our code - STGAN (CVPR 2019) by Ming Liu
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Changing-the-Memorability (CVPR 2019 MBCCV Workshop) by acecreamu
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Fashion-AttGAN (CVPR 2019 FSS-USAD Workshop) by Qing Ping
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An unofficial demo video of AttGAN by 王一凡
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See results.md for more results, we try higher resolution and more attributes (all 40 attributes!!!)
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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
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Environment
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Python 3.6
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TensorFlow 1.15
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OpenCV, scikit-image, tqdm, oyaml
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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 opencv scikit-image tqdm tensorflow-gpu=1.15 conda install -c conda-forge oyaml
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NOTICE: if you create a new conda environment, remember to activate it before any other command
source activate AttGAN
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Data Preparation
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Option 1: CelebA-unaligned (higher quality than the aligned data, 10.2GB)
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download the dataset
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img_celeba.7z (move to ./data/img_celeba/img_celeba.7z): Google Drive or Baidu Netdisk (password rp0s)
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annotations.zip (move to ./data/img_celeba/annotations.zip): Google Drive
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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
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Option 2: CelebA-HQ (we use the data from CelebAMask-HQ, 3.2GB)
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CelebAMask-HQ.zip (move to ./data/CelebAMask-HQ.zip): Google Drive or Baidu Netdisk
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unzip and process the data
unzip ./data/CelebAMask-HQ.zip -d ./data/ python ./scripts/split_CelebA-HQ.py
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Run AttGAN
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training (see examples.md for more training commands)
\\ for CelebA CUDA_VISIBLE_DEVICES=0 \ python train.py \ --load_size 143 \ --crop_size 128 \ --model model_128 \ --experiment_name AttGAN_128 \\ for CelebA-HQ CUDA_VISIBLE_DEVICES=0 \ python train.py \ --img_dir ./data/CelebAMask-HQ/CelebA-HQ-img \ --train_label_path ./data/CelebAMask-HQ/train_label.txt \ --val_label_path ./data/CelebAMask-HQ/val_label.txt \ --load_size 128 \ --crop_size 128 \ --n_epochs 200 \ --epoch_start_decay 100 \ --model model_128 \ --experiment_name AttGAN_128_CelebA-HQ
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testing
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single attribute editing (inversion)
\\ for CelebA CUDA_VISIBLE_DEVICES=0 \ python test.py \ --experiment_name AttGAN_128 \\ for CelebA-HQ CUDA_VISIBLE_DEVICES=0 \ python test.py \ --img_dir ./data/CelebAMask-HQ/CelebA-HQ-img \ --test_label_path ./data/CelebAMask-HQ/test_label.txt \ --experiment_name AttGAN_128_CelebA-HQ
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multiple attribute editing (inversion) example
\\ for CelebA CUDA_VISIBLE_DEVICES=0 \ python test_multi.py \ --test_att_names Bushy_Eyebrows Pale_Skin \ --experiment_name AttGAN_128
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attribute sliding example
\\ for CelebA 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
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loss visualization
CUDA_VISIBLE_DEVICES='' \ tensorboard \ --logdir ./output/AttGAN_128/summaries \ --port 6006
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convert trained model to .pb file
python to_pb.py --experiment_name AttGAN_128
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Using Trained Weights
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alternative trained weights (move to ./output/*.zip)
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AttGAN_128.zip (987.5MB)
- including G, D, and the state of the optimizer
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AttGAN_128_generator_only.zip (161.5MB)
- G only
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AttGAN_384_generator_only.zip (91.1MB)
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unzip the file (AttGAN_128.zip for example)
unzip ./output/AttGAN_128.zip -d ./output/ -
testing (see above)
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Example for Custom Dataset
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},}