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arXiv · 2602.17270

Unified Latents (UL): How to train your latents

Jonathan Heek·Emiel Hoogeboom·Thomas Mensink·Tim Salimans
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Abstract

We present Unified Latents (UL), a framework for learning latent representations that are jointly regularized by a diffusion prior and decoded by a diffusion model. By linking the encoder's output noise to the prior's minimum noise level, we obtain a simple training objective that provides a tight upper bound on the latent bitrate. On ImageNet-512, our approach achieves competitive FID of 1.4, with high reconstruction quality (PSNR) while requiring fewer training FLOPs than models trained on Stable Diffusion latents. On Kinetics-600, we set a new state-of-the-art FVD of 1.3.

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Jonathan Heek, Emiel Hoogeboom, Thomas Mensink, Tim Salimans. 2026-02-19. Unified Latents (UL): How to train your latents. https://arxiv.org/abs/2602.17270

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