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README.md

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-license: apache-2.0
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+---
+license: apache-2.0
+---
+
+# BitDance: Scaling Autoregressive Generative Models with Binary Tokens
+
+<p align="center">
+  <a href="TBD">
+    <img
+      src="https://img.shields.io/badge/Project-Page-0A66C2?logo=chromewebstore&logoColor=0A66C2"
+      alt="Project Page"
+    />
+  </a>
+  <a href="TBD">
+    <img
+      src="https://img.shields.io/badge/arXiv paper-TBD-red?logo=arxiv&logoColor=red"
+      alt="BitDance Paper on arXiv"
+    />
+  </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/HF Space-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*](https://shallowdream204.github.io/), [Jiaming Han*](https://csuhan.com/), [Shaobin Zhuang*](https://scholar.google.com/citations?user=PGaDirMAAAAJ), [Weijia Mao](https://scholar.google.com/citations?user=S7bGBmkyNtEC), [Xuefeng Hu](https://xuefenghu.me/), [Ziyan Yang](https://ziyanyang.github.io/), [Zhenheng Yang](https://zhenheny.github.io/), [Huaibo Huang†](https://hhb072.github.io/), [Xiangyu Yue†](https://xyue.io/), [Hao Chen*†‡](https://haochen-rye.github.io/)
+>
+> <sup>*</sup> Equal Contribution&nbsp;&nbsp;<sup>†</sup> Corresponding Author&nbsp;&nbsp;<sup>‡</sup> 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 ImageNet Generation. For detailed instructions, please visit our [GitHub Repository](https://github.com/shallowdream204/BitDance).
+
+
+## 🪪 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:
+```bibtex
+@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 = {TBD},
+  year    = {2026}
+}
+```