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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}
}
``` |