Documentation

512x512 flowers after 12 hours of training, 1 gpu

256x256 flowers after 12 hours of training, 1 gpu

'Lightweight' GAN

PyPI version

Implementation of 'lightweight' GAN proposed in ICLR 2021, in Pytorch. The main contributions of the paper is a skip-layer excitation in the generator, paired with autoencoding self-supervised learning in the discriminator. Quoting the one-line summary "converge on single gpu with few hours' training, on 1024 resolution sub-hundred images".

Install

$ pip install lightweight-gan

Use

One command

$ lightweight_gan --data ./path/to/images --image-size 512

Model will be saved to ./models/{name} every 1000 iterations, and samples from the model saved to ./results/{name}. name will be default, by default.

Training settings

Pretty self explanatory for deep learning practitioners

$ lightweight_gan \
    --data ./path/to/images \
    --name {name of run} \
    --batch-size 16 \
    --gradient-accumulate-every 4 \
    --num-train-steps 200000

Augmentation

Augmentation is essential for Lightweight GAN to work effectively in a low data setting

By default, the augmentation types is set to translation and cutout, with color omitted. You can include color as well with the following.

$ lightweight_gan --data ./path/to/images --aug-prob 0.25 --aug-types [translation,cutout,color]

Mixed precision

You can turn on automatic mixed precision with one flag --amp

You should expect it to be 33% faster and save up to 40% memory

Multiple GPUs

Also one flag to use --multi-gpus

Generating

Once you have finished training, you can generate samples with one command. You can select which checkpoint number to load from. If --load-from is not specified, will default to the latest.

$ lightweight_gan --name {name of run} --load-from {checkpoint num} --generate

You can also generate interpolations

$ lightweight_gan --name {name of run} --generate-interpolation

Discriminator output size

The author has kindly let me know that the discriminator output size (5x5 vs 1x1) leads to different results on different datasets. (5x5 works better for art than for faces, as an example). You can toggle this with a single flag

# disc output size is by default 1x1
$ lightweight_gan --data ./path/to/art --image-size 512 --disc-output-size 5

Attention

You can add linear + axial attention to specific resolution layers with the following

# make sure there are no spaces between the values within the brackets []
$ lightweight_gan --data ./path/to/images --image-size 512 --attn-res-layers [32,64] --aug-prob 0.25

Alternatives

If you want the current state of the art GAN, you can find it at https://github.com/lucidrains/stylegan2-pytorch

Citations

@inproceedings{
    anonymous2021towards,
    title={Towards Faster and Stabilized {\{}GAN{\}} Training for High-fidelity Few-shot Image Synthesis},
    author={Anonymous},
    booktitle={Submitted to International Conference on Learning Representations},
    year={2021},
    url={https://openreview.net/forum?id=1Fqg133qRaI},
    note={under review}
}
@inproceedings{
    anonymous2021global,
    title={Global Self-Attention Networks},
    author={Anonymous},
    booktitle={Submitted to International Conference on Learning Representations},
    year={2021},
    url={https://openreview.net/forum?id=KiFeuZu24k},
    note={under review}
}
@misc{woo2018cbam,
    title={CBAM: Convolutional Block Attention Module}, 
    author={Sanghyun Woo and Jongchan Park and Joon-Young Lee and In So Kweon},
    year={2018},
    eprint={1807.06521},
    archivePrefix={arXiv},
    primaryClass={cs.CV}
}
@misc{sinha2020topk,
    title={Top-k Training of GANs: Improving GAN Performance by Throwing Away Bad Samples},
    author={Samarth Sinha and Zhengli Zhao and Anirudh Goyal and Colin Raffel and Augustus Odena},
    year={2020},
    eprint={2002.06224},
    archivePrefix={arXiv},
    primaryClass={stat.ML}
}

What I cannot create, I do not understand - Richard Feynman