EQ-VAE: Equivariance Regularized Latent Space for Improved Generative
  Image Modeling
Authors: name: Theodoros Kouzelis, name: Ioannis Kakogeorgiou, name: Spyros Gidaris, name: Nikos Komodakis

Latent generative models have emerged as a leading approach for high-quality
image synthesis. These models rely on an autoencoder to compress images into a
latent space, followed by a generative model to learn the latent distribution.
We identify that existing autoencoders lack equivariance to semantic-preserving
transformations like scaling and rotation, resulting in complex latent spaces
that hinder generative performance. To address this, we propose EQ-VAE, a
simple regularization approach that enforces equivariance in the latent space,
reducing its complexity without degrading reconstruction quality. By finetuning
pre-trained autoencoders with EQ-VAE, we enhance the performance of several
state-of-the-art generative models, including DiT, SiT, REPA and MaskGIT,
achieving a 7 speedup on DiT-XL/2 with only five epochs of SD-VAE fine-tuning.
EQ-VAE is compatible with both continuous and discrete autoencoders, thus
offering a versatile enhancement for a wide range of latent generative models.
Project page and code: https://eq-vae.github.io/.