| license: mit | |
| task_categories: | |
| - text-to-image | |
| # Scale RAE Data | |
| [Project Page](https://rae-dit.github.io/scale-rae/) | [Paper](https://huggingface.co/papers/2601.16208) | [Code](https://github.com/ZitengWangNYU/Scale-RAE) | |
| This repository contains data associated with the paper "Scaling Text-to-Image Diffusion Transformers with Representation Autoencoders". | |
| The dataset is used for training and evaluating Scale-RAE, a framework that investigates scaling Representation Autoencoders (RAEs) for large-scale, freeform text-to-image (T2I) generation. It includes data used for scaling RAE decoders beyond ImageNet, featuring web, synthetic, and text-rendering data, as well as high-quality instruction datasets for fine-tuning. | |
| ## Citation | |
| If you find this work useful, please cite: | |
| ```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} | |
| } | |
| ``` |