Add model card for Scale-RAE

README.md

@@ -0,0 +1,30 @@
+---
+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}
+}
+```