Recommendation

• Our GAN based work for facial attribute editing - AttGAN.

News

• 28 June 2019: We re-implement these GANs by Pytorch 1.1! The old version is here: v0 or in the "v0" directory.
• Tensorflow 2 Version

GANs - Pytorch

Pytorch implementations of DCGAN, LSGAN, WGAN-GP(LP) and DRAGAN.

Exemplar results

Fashion-MNIST

DCGAN	LSGAN	WGAN-GP	DRAGAN

CelebA

DCGAN	LSGAN
WGAN-GP	DRAGAN
WGAN-LP	DRAGAN-LP

Anime

WGAN-GP	DRAGAN

Usage

• Prerequisites

  • PyTorch 1.1
  • tensorboardX
  • scikit-image, oyaml, tqdm
  • Python 3.6

• Datasets

  • Fashion-MNIST will be automatically downloaded
  • CelebA should be prepared by yourself in ./data/img_align_celeba/*.jpg
    • dataset link: Dropbox
    • the above link might be inaccessible, the alternatives are (find "img_align_celeba.zip")
      • Baidu Netdisk or
      • Google Drive
  • the Anime dataset should be prepared by yourself in ./data/faces/*.jpg
    • dataset link: https://pan.baidu.com/s/1eSifHcA, password: g5qa
    • reference: https://zhuanlan.zhihu.com/p/24767059

• Examples of training

  • Fashion-MNIST DCGAN

    CUDA_VISIBLE_DEVICES=0 python train.py --dataset=fashion_mnist --epoch=25 --adversarial_loss_mode=gan
    

  • CelebA DRAGAN

    CUDA_VISIBLE_DEVICES=0 python train.py --dataset=celeba --epoch=25 --adversarial_loss_mode=gan --gradient_penalty_mode=1-gp --gradient_penalty_sample_mode=dragan
    

  • Anime WGAN-GP

    CUDA_VISIBLE_DEVICES=0 python train.py --dataset=anime --epoch=200 --adversarial_loss_mode=wgan --gradient_penalty_mode=1-gp --gradient_penalty_sample_mode=line --n_d=5
    

  • see more training exampls in commands.sh

  • tensorboard for loss visualization

    tensorboard --logdir ./output/fashion_mnist_gan/summaries --port 6006