[WIP] HakuLatent

A comprehensive codebase for training and finetuning Image <> Latent models.

TODOs

• Trainer for VAE or VQ-VAE or direct AE
  • Basic Trainer
  • Decoder-only finetuning
  • PEFT
• Equivariance Regularization EQ-VAE
  • Rotate
  • Scale down
  • Scale up + crop
  • crop
  • random affine
  • blending
• Adversarial Loss
  • Investigate better discriminator setup
• Latent Regularization
  • Discrete VAE
  • Kepler Codebook Regularization Loss
• Models
  • MAE for latent
  • windowed/natten attention for commonly used VAE setup

EQ-VAE

KBlueLeaf/EQ-SDXL-VAE · Hugging Face

Quick PoC run (significant quality degrad but also significant smoother latent):

Before EQ-VAE	After EQ-VAE

The 1~4 row are: original image, transformed image, decoded image from transformed latent, transformed latent

Cite

@misc{kohakublueleaf_hakulatent,
    author       = {Shih-Ying Yeh (KohakuBlueLeaf)},
    title        = {HakuLatent: A comprehensive codebase for training and finetuning Image <> Latent models},
    year         = {2024},
    publisher    = {GitHub},
    journal      = {GitHub repository},
    url          = {https://github.com/KohakuBlueleaf/HakuLatent},
    note         = {Python library for training and finetuning Variational Autoencoders and related latent models, implementing EQ-VAE and other techniques.}
}