shallowdream204/BitDance-ImageNet

BitDance: Scaling Autoregressive Generative Models with Binary Tokens

<p align="center"> <a href="https://bitdance.csuhan.com/"> <img src="https://img.shields.io/badge/Project-Page-0A66C2?logo=chromewebstore&logoColor=0A66C2" alt="Project Page" /> </a> <a href="https://arxiv.org/abs/2602.14041"> <img src="https://img.shields.io/badge/arXiv paper-2602.14041-red?logo=arxiv&logoColor=red" alt="BitDance Paper on arXiv" /> </a> <a href="https://github.com/shallowdream204/BitDance"> <img src="https://img.shields.io/badge/Github-Code-blue?logo=github&logoColor=white" alt="BitDance GitHub" /> </a> <a href="https://huggingface.co/collections/shallowdream204/bitdance"> <img src="https://img.shields.io/badge/Weights-BitDance-yellow?logo=huggingface&logoColor=yellow" alt="BitDance Model" /> </a> <a href="https://huggingface.co/spaces/shallowdream204/BitDance-14B-64x"> <img src="https://img.shields.io/badge/Play with BitDance!-Demo-orange?logo=huggingface&logoColor=yellow" alt="BitDance Demo" /> </a> </p>

<p align="center"><img src="https://github.com/shallowdream204/BitDance/raw/main/assets/speed.webp" width=90%"></p>

Yuang Ai*, Jiaming Han*, Shaobin Zhuang*, Weijia Mao, Xuefeng Hu, Ziyan Yang, Zhenheng Yang, Huaibo Huang†, Xiangyu Yue†, Hao Chen*†‡  Equal Contribution&nbsp;&nbsp;^† Corresponding Author&nbsp;&nbsp;^‡ Project Lead For visual generation, discrete autoregressive models often struggle with poor tokenizer reconstruction, difficulties in sampling from large vocabularies, and slow token-by-token generation speeds. We present BitDance*, which addresses these challenges via a large-vocabulary binary tokenizer, a binary diffusion head for sampling in large discrete space, and a next-patch diffusion paradigm that enables efficient multitoken prediction. BitDance is an open-source discrete autoregressive foundation model with 14B parameters, trained on large-scale multimodal tokens. While maintaining the standard language modeling paradigm for text tokens, BitDance employs a next-patch diffusion paradigm for visual tokens to predict multiple tokens in parallel—up to 64 per step. This unified multimodal framework is simple, scalable, and capable of efficiently generating high-resolution, photorealistic images.

This repository hosts the BitDance model weights for class-conditional image generation on ImageNet. For detailed instructions, please visit our GitHub repository.

🪪 License

BitDance is licensed under the Apache 2.0 license.

📖 Citation

If you find our work useful for your research, please consider citing our paper:

@article{ai2026bitdance,
  title   = {BitDance: Scaling Autoregressive Generative Models with Binary Tokens},
  author  = {Ai, Yuang and Han, Jiaming and Zhuang, Shaobin and Hu, Xuefeng and Yang, Ziyan and Yang, Zhenheng and Huang, Huaibo and Yue, Xiangyu and Chen, Hao},
  journal = {arXiv preprint arXiv:2602.14041},
  year    = {2026}
}