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+ ---
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+ license: mit
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+ task_categories:
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+ - text-to-image
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+ ---
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+
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+ # Scale RAE Data
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+ [Project Page](https://rae-dit.github.io/scale-rae/) | [Paper](https://huggingface.co/papers/2601.16208) | [Code](https://github.com/ZitengWangNYU/Scale-RAE)
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+ This repository contains data associated with the paper "Scaling Text-to-Image Diffusion Transformers with Representation Autoencoders".
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+ 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.
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+ ## Citation
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+ If you find this work useful, please cite:
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+ ```bibtex
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+ @article{scale-rae-2026,
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+ title={Scaling Text-to-Image Diffusion Transformers with Representation Autoencoders},
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+ 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},
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+ journal={arXiv preprint arXiv:2601.16208},
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+ year={2026}
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+ }
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+ ```