| license: mit | |
| library_name: transformers | |
| pipeline_tag: image-to-image | |
| # Scale-RAE: Scaling Text-to-Image Diffusion Transformers with Representation Autoencoders | |
| Official model weights for the paper [Scaling Text-to-Image Diffusion Transformers with Representation Autoencoders](https://huggingface.co/papers/2601.16208). | |
| Representation Autoencoders (RAEs) enable diffusion modeling in high-dimensional semantic latent spaces. Scale-RAE scales this framework to large-scale, freeform text-to-image generation. RAEs consistently outperform traditional VAEs during pretraining across various model scales, offering faster convergence and better generation quality. | |
| - **Project Page:** [https://rae-dit.github.io/scale-rae/](https://rae-dit.github.io/scale-rae/) | |
| - **GitHub Repository:** [https://github.com/ZitengWangNYU/Scale-RAE](https://github.com/ZitengWangNYU/Scale-RAE) | |
| - **Paper:** [https://arxiv.org/abs/2601.16208](https://arxiv.org/abs/2601.16208) | |
| ## Usage | |
| For full text-to-image generation using Scale-RAE, please follow the installation and inference instructions in the [official repository](https://github.com/ZitengWangNYU/Scale-RAE). | |
| ## Citation | |
| ```bibtex | |
| @article{scale-rae-2026, | |
| title={Scaling Text-to-Image Diffusion Transformers with Representation Autoencoders}, | |
| author={Shengbang Tong and Boyang Zheng and Ziteng Wang and Bingda Tang and Nanye Ma and Ellis Brown and Jihan Yang and Rob Fergus and Yann LeCun and Saining Xie}, | |
| journal={arXiv preprint arXiv:2601.16208}, | |
| year={2026} | |
| } | |
| ``` |