Diffusion Transformers with Representation Autoencoders

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 convolutional 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.