Pytorch implementation of Self-Attention Generative Adversarial Networks (SAGAN)
This repository provides a PyTorch implementation of SAGAN. Both wgan-gp and wgan-hinge loss are ready, but note that wgan-gp is somehow not compatible with the spectral normalization. Remove all the spectral normalization at the model for the adoption of wgan-gp.
Self-attentions are applied to later two layers of both discriminator and generator.
$ git clone https://github.com/heykeetae/Self-Attention-GAN.git
$ cd Self-Attention-GAN
$ bash download.sh CelebA
or
$ bash download.sh LSUN
$ python python main.py --batch_size 64 --imsize 64 --dataset celeb --adv_loss hinge --version sagan_celeb
or
$ python python main.py --batch_size 64 --imsize 64 --dataset lsun --adv_loss hinge --version sagan_lsun
$ cd samples/sagan_celeb
or
$ cd samples/sagan_lsun
Samples generated every 100 iterations are located. The rate of sampling could be controlled via --sample_step (ex, --sample_step 100).