Title: Diffusion Transformers with Representation Autoencoders
Primary area: generative models
Keywords: Generative Models, Diffusion Models, Representation Learning, High-dimensional Diffusion

Abstract: Latent generative modeling has become the standard strategy for Diffusion Transformers (DiTs), but the autoencoder has barely evolved. Most DiTs still use the legacy VAE encoder, which introduces several limitations: large UNet backbones that compromise architectural simplicity, low-dimensional latent spaces that restrict information capacity, and weak representations resulting from purely reconstruction-based training. In this work, we investigate replacing the VAE encoder–decoder with pretrained representation encoders (e.g., DINO, SigLIP, MAE) combined with trained decoders, forming what we call \emph{Representation Autoencoders} (RAEs). These models provide both high-quality reconstructions and semantically rich latent spaces, while allowing for a scalable transformer-based architecture. A key challenge arises in enabling diffusion transformers to operate effectively within these high-dimensional representations. We analyze the sources of this difficulty, propose theoretically motivated solutions, and validate them empirically. Our approach achieves faster convergence without auxiliary representation alignment losses. Using a DiT variant with a lightweight wide DDT-head, we demonstrate state-of-the-art image generation performance, reaching FIDs of 1.18 @256 resolution and 1.13 @512 on ImageNet.

TL;DR: Pretrained representation encoder as autoencoder for diffusion models