RAE — Representation Autoencoders

Representation Autoencoders from Saining Xie's group replace the VAE latents in diffusion transformers with frozen pretrained representation encoders paired with lightweight decoders. The result is a semantically structured latent space that trains faster and generates better than VAE-based DiTs, extending the REPA representation-alignment line and scaled up in Scale-RAE.