This repository implements a two-stage generative image model: Representation Autoencoders (RAE) followed by Diffusion Transformers (DiT^DH). Stage 1 trains a ViT decoder to reconstruct images from frozen pretrained encoders (DINOv2, MAE, SigLIP), with optional GAN and LPIPS losses. Stage 2 trains a class-conditional diffusion transformer on the resulting latent space, supporting both classifier-free guidance and autoguidance. The codebase is config-driven (OmegaConf), PyTorch DDP, and provides training, distributed sampling, FID evaluation, and latent statistics computation. It is a research codebase with strong engineering quality, modular design (transport, discriminator, encoders, decoders), and reproducibility through pinned dependencies and checkpointing.