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--- |
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license: apache-2.0 |
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pipeline_tag: image-feature-extraction |
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tags: |
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- image-generation |
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- autoregressive |
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- vision |
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--- |
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# BitDance: Scaling Autoregressive Generative Models with Binary Tokens |
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<p align="center"> |
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<a href="https://bitdance.csuhan.com/"> |
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<img |
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src="https://img.shields.io/badge/Project-Page-0A66C2?logo=chromewebstore&logoColor=0A66C2" |
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alt="Project Page" |
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/> |
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</a> |
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<a href="https://huggingface.co/papers/2602.14041"> |
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<img |
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src="https://img.shields.io/badge/Paper-arXiv-red?logo=arxiv&logoColor=red" |
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alt="BitDance Paper" |
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/> |
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</a> |
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<a href="https://github.com/shallowdream204/BitDance"> |
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<img |
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src="https://img.shields.io/badge/Github-Code-blue?logo=github&logoColor=white" |
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alt="BitDance GitHub" |
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/> |
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</a> |
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<a href="https://huggingface.co/collections/shallowdream204/bitdance"> |
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<img |
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src="https://img.shields.io/badge/Weights-BitDance-yellow?logo=huggingface&logoColor=yellow" |
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alt="BitDance Model" |
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/> |
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</a> |
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</p> |
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<p align="center"><img src="https://github.com/shallowdream204/BitDance/raw/main/assets/speed.webp" width=90%"></p> |
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This repository hosts the **binary visual tokenizer** weights for BitDance, as introduced in the paper [BitDance: Scaling Autoregressive Generative Models with Binary Tokens](https://huggingface.co/papers/2602.14041). |
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BitDance addresses challenges in discrete autoregressive modeling 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. |
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## 🦄 Binary Visual Tokenizers |
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We release three binary tokenizers with different downsampling ratios and vocabulary sizes. |
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| Vocabulary Size | Down Ratio | IN-256 PSNR | IN-256 SSIM | Weight | Config | |
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|:---: |:---:|:---:|:---:|:---:|:---:| |
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| $2^{32}$ | 16 | 24.90 | 0.72 |[ae_d16c32.safetensors](https://huggingface.co/shallowdream204/BitDance-Tokenizer/blob/main/ae_d16c32.safetensors) | [ae_d16c32_config.json](https://huggingface.co/shallowdream204/BitDance-Tokenizer/blob/main/ae_d16c32_config.json) | |
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| $2^{128}$ | 32 | 23.26 | 0.67 |[ae_d32c128.safetensors](https://huggingface.co/shallowdream204/BitDance-Tokenizer/blob/main/ae_d32c128.safetensors) | [ae_d32c128_config.json](https://huggingface.co/shallowdream204/BitDance-Tokenizer/blob/main/ae_d32c128_config.json) | |
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| $2^{256}$ | 32 | 25.29 | 0.74 |[ae_d32c256.safetensors](https://huggingface.co/shallowdream204/BitDance-Tokenizer/blob/main/ae_d32c256.safetensors) | [ae_d32c256_config.json](https://huggingface.co/shallowdream204/BitDance-Tokenizer/blob/main/ae_d32c256_config.json) | |
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For detailed instructions and full generative model weights, please visit our [GitHub repository](https://github.com/shallowdream204/BitDance). |
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## 🪪 License |
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BitDance is licensed under the Apache 2.0 license. |
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## 📖 Citation |
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If you find our work useful for your research, please consider citing our paper: |
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```bibtex |
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@article{ai2026bitdance, |
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title = {BitDance: Scaling Autoregressive Generative Models with Binary Tokens}, |
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author = {Ai, Yuang and Han, Jiaming and Zhuang, Shaobin and Hu, Xuefeng and {Mao, Weijia} and Hu, Xuefeng and Yang, Ziyan and Yang, Zhenheng and Huang, Huaibo and Yue, Xiangyu and Chen, Hao}, |
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journal = {arXiv preprint arXiv:2602.14041}, |
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year = {2026} |
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} |
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``` |
