An unofficial Pytorch implementation of SNGAN, achieving IS of 8.21 and FID of 14.21 on CIFAR-10.
An unofficial Pytorch implementation of Spectral Normalization for Generative Adversarial Networks. For official Chainer implementation please refer to https://github.com/pfnet-research/sngan_projection
Our implementation achieves Inception score of 8.21 and FID score of 14.21 on unconditional CIFAR-10 image generation task. In comparison, the original paper claims 8.22 and 21.7 respectively.
pip install -r requirements.txt
mkdir fid_stat
Download the pre-calculated statistics for CIFAR10,
fid_stats_cifar10_train.npz, to ./fid_stat
.
sh exps/sngan_cifar10.sh
mkdir pre_trained
Download the pre-trained SNGAN model sngan_cifar10.pth to ./pre_trained
.
Run the following script:
sh exps/eval.sh