Improve model card for BitDance: Add metadata and tokenizer details

README.md

@@ -1,5 +1,10 @@
 ---
 license: apache-2.0
+pipeline_tag: image-feature-extraction
+tags:
+- image-generation
+- autoregressive
+- vision
 ---
 
 # BitDance: Scaling Autoregressive Generative Models with Binary Tokens
@@ -11,10 +16,10 @@ license: apache-2.0
       alt="Project Page"
     />
   </a>
-  <a href="https://arxiv.org/abs/2602.14041">
+  <a href="https://huggingface.co/papers/2602.14041">
     <img
-      src="https://img.shields.io/badge/arXiv paper-2602.14041-red?logo=arxiv&logoColor=red"
-      alt="BitDance Paper on arXiv"
+      src="https://img.shields.io/badge/Paper-arXiv-red?logo=arxiv&logoColor=red"
+      alt="BitDance Paper"
     />
   </a>
   <a href="https://github.com/shallowdream204/BitDance">
@@ -29,36 +34,37 @@ license: apache-2.0
         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>
 
+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).
 
-> [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.
+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.
 
-This repository hosts the **BitDance** tokenizer weights. For detailed instructions, please visit our [GitHub repository](https://github.com/shallowdream204/BitDance).
+## 🦄 Binary Visual Tokenizers
 
+We release three binary tokenizers with different downsampling ratios and vocabulary sizes. 
+
+| Vocabulary Size | Down Ratio | IN-256 PSNR | IN-256 SSIM  | Weight | Config | 
+|:---: |:---:|:---:|:---:|:---:|:---:|
+| $2^{32}$ | 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) |
+| $2^{128}$ | 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) |
+| $2^{256}$ | 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) |
+
+For detailed instructions and full generative model weights, 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},
+  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},
   journal = {arXiv preprint arXiv:2602.14041},
   year    = {2026}
 }