title: EQ-VAE: Equivariance Regularized Latent Space for Improved Generative
    Image Modeling
summary: 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/.
paper_authors._id: 67b4e4259beded220ad14729
paper_authors.name: Theodoros Kouzelis
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2025-02-21T16:15:22.578Z
paper_authors._id: 6383aa17834d3558a3955186
paper_authors.avatarUrl: /avatars/1f6aed0a762379df334bc6a734d42f86.svg
paper_authors.fullname: Kouzelis
paper_authors.type: user
paper_authors.user: zelaki
paper_authors._id: 67b4e4259beded220ad1472a
paper_authors.name: Ioannis Kakogeorgiou
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2025-02-19T09:03:49.086Z
paper_authors._id: 661ba524bd9243bf7e598355
paper_authors.avatarUrl: https://cdn-avatars.huggingface.co/v1/production/uploads/661ba524bd9243bf7e598355/i77yD4XgJn2vUbn_mIsT8.jpeg
paper_authors.fullname: Ioannis Kakogeorgiou
paper_authors.type: user
paper_authors.user: gkakogeorgiou
paper_authors._id: 67b4e4259beded220ad1472b
paper_authors.name: Spyros Gidaris
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2025-04-28T13:50:15.651Z
paper_authors._id: 678cd947d13c19c917644792
paper_authors.avatarUrl: https://cdn-avatars.huggingface.co/v1/production/uploads/no-auth/010zyg-JoIjaqVn9lSDRT.png
paper_authors.fullname: Spyros Gidaris
paper_authors.type: user
paper_authors.user: sgidaris
paper_authors._id: 67b4e4259beded220ad1472c
paper_authors.name: Nikos Komodakis
paper_title: EQ-VAE: Equivariance Regularized Latent Space for Improved Generative
    Image Modeling
paper_summary: 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/.
paper_ai_summary: EQ-VAE enhances latent generative models by enforcing equivariance in the latent space, improving performance without degrading reconstruction quality.
paper_ai_keywords: latent generative models autoencoder latent space equivariance semantic-preserving transformations scaling rotation generative performance EQ-VAE regularization continuous autoencoders discrete autoencoders DiT SiT REPA MaskGIT SD-VAE fine-tuning