Core Viewpoint - The era of Variational Autoencoders (VAE) is coming to an end, with Representation Autoencoders (RAE) set to take over in the field of diffusion models [1][3]. Summary by Sections RAE Introduction - RAE is a new type of autoencoder designed for training diffusion Transformers (DiT), utilizing pre-trained representation encoders (like DINO, SigLIP, MAE) paired with lightweight decoders, replacing the traditional VAE [3][9]. Advantages of RAE - RAE provides high-quality reconstruction results and a semantically rich latent space, supporting scalable transformer-based architectures. It achieves faster convergence without the need for additional representation alignment losses [4][10]. Performance Metrics - At a resolution of 256×256, the FID score without guidance is 1.51, and with guidance, it is 1.13 for both 256×256 and 512×512 resolutions [6]. Limitations of VAE - VAE has outdated backbone networks, leading to overly complex architectures, requiring 450 GFLOPs compared to only 22 GFLOPs for a simple ViT-B encoder [7]. - The compressed latent space of VAE (only 4 channels) severely limits information capacity, resulting in minimal improvement in information carrying ability [7]. - VAE's weak representation capability, relying solely on reconstruction training, leads to low feature quality and slows down convergence, negatively impacting generation quality [7]. RAE's Design and Training - RAE combines pre-trained representation encoders with trained decoders without requiring additional training or alignment phases, and it does not introduce auxiliary loss functions [9]. - RAE outperforms SD-VAE in reconstruction quality despite its simplicity [10]. Model Comparisons - RAE models such as DINOv2-B, SigLIP2-B, and MAE-B show significant improvements in rFID and Top-1 accuracy compared to SD-VAE [11]. Adjustments for Diffusion Models - RAE requires simple adjustments for effective performance in high-dimensional spaces, including a wide DiT design, noise scheduling, and noise injection in the decoder training [13][17]. - The DiT-XL model trained with RAE surpasses REPA without any auxiliary losses or additional training phases, achieving convergence speeds up to 16 times faster than REPA based on SD-VAE [18][19]. Scalability and Efficiency - The new architecture enhances the scalability of DiT in terms of training computation and model size, outperforming both standard DiT based on RAE and traditional methods based on VAE [24].