CoVAE: Consistency Training of Variational Autoencoders

• URL: http://arxiv.org/abs/2507.09103v1
• Date: Sat, 12 Jul 2025 01:32:08 GMT
• Title: CoVAE: Consistency Training of Variational Autoencoders
• Authors: Gianluigi Silvestri, Luca Ambrogioni,
• Abstract summary: We propose a novel single-stage generative autoencoding framework that adopts techniques from consistency models to train a VAE architecture.<n>We show that CoVAE can generate high-quality samples in one or few steps without the use of a learned prior.<n>Our approach provides a unified framework for autoencoding and diffusion-style generative modeling and provides a viable route for one-step generative high-performance autoencoding.
• Score: 9.358185536754537
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Current state-of-the-art generative approaches frequently rely on a two-stage training procedure, where an autoencoder (often a VAE) first performs dimensionality reduction, followed by training a generative model on the learned latent space. While effective, this introduces computational overhead and increased sampling times. We challenge this paradigm by proposing Consistency Training of Variational AutoEncoders (CoVAE), a novel single-stage generative autoencoding framework that adopts techniques from consistency models to train a VAE architecture. The CoVAE encoder learns a progressive series of latent representations with increasing encoding noise levels, mirroring the forward processes of diffusion and flow matching models. This sequence of representations is regulated by a time dependent $\beta$ parameter that scales the KL loss. The decoder is trained using a consistency loss with variational regularization, which reduces to a conventional VAE loss at the earliest latent time. We show that CoVAE can generate high-quality samples in one or few steps without the use of a learned prior, significantly outperforming equivalent VAEs and other single-stage VAEs methods. Our approach provides a unified framework for autoencoding and diffusion-style generative modeling and provides a viable route for one-step generative high-performance autoencoding. Our code is publicly available at https://github.com/gisilvs/covae.

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