lucidrains/gigagan-pytorch

GigaGAN - Pytorch (wip)

Implementation of GigaGAN (project page), new SOTA GAN out of Adobe.

I will also add a few findings from lightweight gan, for faster convergence (skip layer excitation), better stability (reconstruction auxiliary loss in discriminator), as well as improved results (GLU in generator).

It will also contain the code for the 1k - 4k upsamplers, which I find to be the highlight of this paper.

Please join if you are interested in helping out with the replication with the LAION community

Appreciation

• StabilityAI for the sponsorship, as well as my other sponsors, for affording me the independence to open source artificial intelligence.

• 🤗 Huggingface for their accelerate library

• All the maintainers at OpenClip, for their SOTA open sourced contrastive learning text-image models

Todo

• make sure it can be trained unconditionally
• read the relevant papers and knock out all 3 auxiliary losses
  • matching aware loss
  • clip loss
  • vision-aided discriminator loss
  • add reconstruction losses on arbitrary stages in the discriminator (lightweight gan)
  • figure out how the random projections are used from projected-gan
  • vision aided discriminator needs to extract N layers from the vision model in CLIP
  • figure out whether to discard CLS token and reshape into image dimensions for convolution, or stick with attention and condition with adaptive layernorm - also turn off vision aided gan in unconditional case
• do a review of the auxiliary losses
• get a code review for the multi-scale inputs and outputs, as the paper was a bit vague
• add upsampling network architecture
• port over CLI from lightweight|stylegan2-pytorch
• hook up laion dataset for text-image

Citations

@misc{https://doi.org/10.48550/arxiv.2303.05511,
    url     = {https://arxiv.org/abs/2303.05511},
    author  = {Kang, Minguk and Zhu, Jun-Yan and Zhang, Richard and Park, Jaesik and Shechtman, Eli and Paris, Sylvain and Park, Taesung},  
    title   = {Scaling up GANs for Text-to-Image Synthesis},
    publisher = {arXiv},
    year    = {2023},
    copyright = {arXiv.org perpetual, non-exclusive license}
}

@article{Liu2021TowardsFA,
    title   = {Towards Faster and Stabilized GAN Training for High-fidelity Few-shot Image Synthesis},
    author  = {Bingchen Liu and Yizhe Zhu and Kunpeng Song and A. Elgammal},
    journal = {ArXiv},
    year    = {2021},
    volume  = {abs/2101.04775}
}