Add dataset card and metadata

This PR adds a dataset card for the Scale RAE data, including:
- Metadata for task categories (`text-to-image`) and license (MIT).
- Links to the paper, project page, and official GitHub repository.
- A brief description of the dataset's role in scaling representation autoencoders for text-to-image generation.
- The BibTeX citation for the paper.

README.md

@@ -0,0 +1,26 @@
+---
+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}
+}
+```