Update README.md

README.md

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-license: apache-2.0
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+<div align="center">
+<h1>WinTok: A Win-Win Hybrid Tokenizer via Decomposing Visual Understanding and Generation with Transferable Tokens</h1>
+
+[![arXiv](https://img.shields.io/badge/arXiv-2508.05599-b31b1b.svg)](https://arxiv.org/abs/2605.18115)
+[![Github](https://img.shields.io/badge/Github-WeTok-blue)](https://github.com/markywg/WinTok)
+[![Hugging Face Model](https://img.shields.io/badge/%F0%9F%A4%97%20Hugging%20Face-Models-yellow)](https://huggingface.co/markyw/WinTok/tree/main)
+</div>
+
+This project introduces **WinTok**, a concise hybrid visual tokenizer designed to resolve the long-standing conflict between visual understanding and generation. By decoupling semantic and pixel tokens with an asymmetric distillation mechanism, WinTok achieves a win-win across reconstruction, understanding, and generation, surpassing strong baselines with substantially less training data. <br><br>
+
+> <a href="https://arxiv.org/abs/2605.18115">WinTok: A Win-Win Hybrid Tokenizer via Decomposing Visual Understanding and Generation with Transferable Tokens</a><br>
+> [Yiwei Guo](https://scholar.google.com/citations?user=HCAyeJIAAAAJ&hl=zh-CN&oi=ao), [Shaobin Zhuang](https://scholar.google.com/citations?user=PGaDirMAAAAJ&hl=zh-CN&oi=ao), Canmiao Fu, [Zhipeng Huang](https://scholar.google.com/citations?user=_fnuIHUAAAAJ&hl=zh-CN&oi=ao), [Chen Li](https://scholar.google.com/citations?hl=zh-CN&user=WDJL3gYAAAAJ), Jing LYU, [Yali Wang](https://scholar.google.com/citations?hl=zh-CN&user=hD948dkAAAAJ)<br>
+> Shenzhen Institutes of Advanced Technology (Chinese Academy of Sciences), WeChat Vision (Tencent Inc.), Shanghai Jiao Tong University<br>
+> ```
+> @article{guo2026wintok,
+>   title={WinTok: A Win-Win Hybrid Tokenizer via Decomposing Visual Understanding and Generation with Transferable Tokens},
+>   author={Guo, Yiwei and Zhuang, Shaobin and Huang, Zhipeng and Fu, Canmiao and Li, Chen and LYU, Jing and Wang, Yali},
+>   journal={arXiv preprint arXiv:2605.18115},
+>   year={2026}
+> }
+> ```
+
+<p align="center">
+  <img src="./assets/visualization.jpg" width="90%">
+  <br>
+  <em>WinTok achieves superior performance on downstream applications, surpassing previous unified tokenizers, with a more flexible hybrid encoding mechanism.</em>
+</p>
+
+## 📰 News
+* **[2026.05.19]** 🚀 🚀 🚀 We are excited to release **WinTok**, a unified visual tokenizer featuring our novel **hybrid encoding** and **asymmetric distillation**. Code and model are now available!
+
+## 📖 Implementations
+
+### 🛠️ Installation
+- **Dependencies**: 
+```
+bash env.sh
+```
+
+### Evaluation
+
+- **Evaluation on ImageNet 50K Validation Set**
+
+The dataset should be organized as follows:
+```
+imagenet
+└── val/
+    ├── ...
+```
+
+Run the 256×256 resolution evaluation script, change the corresponding path:
+```
+bash scripts/eval_tokenizer/eval_metrics_ddp.sh
+```
+
+- **Evaluation on MS-COCO Val2017**
+
+The dataset should be organized as follows:
+```
+MSCOCO2017
+└── val2017/
+    ├── ...
+```
+
+Run the 256×256 resolution evaluation script, change the corresponding path:
+```
+bash scripts/eval_tokenizer/eval_metrics_ddp.sh
+```
+
+
+### Inference
+
+Simply test the effect of model reconstruction:
+```
+python recon.py --ckpt_path path_to_ckpt
+```