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.
Per-pixel attention result of SAGAN on LSUN church-outdoor dataset. It shows that unsupervised training of self-attention module still works, although it is not interpretable with the attention map itself. Better results with regard to the generated images will be added. These are the visualization of self-attention in generator layer3 and layer4, which are in the size of 16 x 16 and 32 x 32 respectively, each for 64 images. To visualize the per-pixel attentions, only a number of pixels are chosen, as shown on the leftmost and the rightmost numbers indicate.
$ 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).