Update README.md

README.md

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-license: apache-2.0
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+<div align="center">
+<h1>🚀 WeTok: Powerful Discrete Tokenization for High-Fidelity Visual Reconstruction</h1>
+
+[![arXiv](https://img.shields.io/badge/arXiv-2505.12489-b31b1b.svg)](https://arxiv.org/abs/2505.12489)
+[![Github](https://img.shields.io/badge/Github-WeTok-blue)](https://github.com/zhuangshaobin/WeTok.github)
+[![Hugging Face Model](https://img.shields.io/badge/%F0%9F%A4%97%20Hugging%20Face-Models-yellow)](https://huggingface.co/GrayShine/WeTok)
+
+</div>
+
+This project introduces **WeTok**, a powerful discrete visual tokenizer designed to resolve the long-standing conflict between compression efficiency and reconstruction fidelity. WeTok achieves state-of-the-art reconstruction quality, surpassing previous leading discrete and continuous tokenizers. <br><br>
+
+> <a href="https://github.com/zhuangshaobin/WeTok">WeTok: Powerful Discrete Tokenization for High-Fidelity Visual Reconstruction</a><br>
+> [Shaobin Zhuang](https://scholar.google.com/citations?user=PGaDirMAAAAJ&hl=zh-CN&oi=ao), [Yiwei Guo](https://scholar.google.com/citations?user=HCAyeJIAAAAJ&hl=zh-CN&oi=ao), Canmiao Fu, Zhipeng Huang, [Zeyue Tian](https://scholar.google.com/citations?user=dghq4MQAAAAJ&hl=zh-CN&oi=ao), [Ying Zhang](https://scholar.google.com/citations?user=R_psgxkAAAAJ&hl=zh-CN&oi=ao), Chen Li, [Yali Wang](https://scholar.google.com/citations?hl=zh-CN&user=hD948dkAAAAJ)<br>
+> Shanghai Jiao Tong University, WeChat Vision (Tencent Inc.), Shenzhen Institutes of Advanced Technology (Chinese Academy of Sciences), Hong Kong University of Science and Technology, Shanghai AI Laboratory<br>
+> <a href="./docs/WeTok.md">📚WeTok.md</a>
+> ```
+> @article{zhuang2026wetok,
+>   title={WeTok: Powerful Discrete Tokenization for High-Fidelity Visual Reconstruction},
+>   author={Zhuang, Shaobin and Guo, Yiwei and Fu, Canmiao and Huang, Zhipeng and Tian, Zeyue and Zhang, Ying and Li, Chen and Wang, Yali},
+>   journal={arXiv preprint arXiv:2409.04410},
+>   year={2025}
+> }
+> ```
+
+<p align="center">
+  <img src="./assets/teaser.png" width="90%">
+  <br>
+  <em>WeTok achieves a new state-of-the-art in reconstruction fidelity, surpassing both discrete and continuous tokenizers, while offering high compression ratios.</em>
+</p>
+
+## 📰 News
+* **[2025.08.05]**:fire::fire::fire: We release a series of WeTok models, achieving a record-low zero-shot rFID of **0.12** on ImageNet, surpassing top continuous tokenizers like FLUX-VAE and SD-VAE 3.5.
+* **[2025.08.05]** We are excited to release **WeTok**, a powerful discrete tokenizer featuring our novel **Grouped Lookup-Free Quantization (GFQ)** and a **generative decoder**. Code and pretrained models 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:
+```
+bash scripts/evaluation/imagenet_evaluation_256_dist.sh
+```
+
+Run the original resolution evaluation script:
+```
+bash scripts/evaluation/imagenet_evaluation_original_dist.sh
+```
+
+- **Evaluation on MS-COCO Val2017**
+
+The dataset should be organized as follows:
+```
+MSCOCO2017
+└── val2017/
+    ├── ...
+```
+
+Run the evaluation script:
+```
+bash scripts/evaluation/mscocoval_evaluation_256_dist.sh
+```
+
+Run the original resolution evaluation script:
+```
+bash scripts/evaluation/mscoco_evaluation_original_dist.sh
+```
+
+
+### Inference
+
+Simply test the effect of each model reconstruction:
+```
+bash scripts/inference/reconstruct_image.sh
+```
+
+<p align="center">
+  <img src="./assets/compare.png" width="90%">
+  <br>
+  <em>Qualitative comparison of 512 × 512 image reconstruction on TokBench.</em>
+</p>
+
+<p align="center">
+  <img src="./assets/gen.png" width="90%">
+  <br>
+  <em>WeTok-AR-XL generated samples at 256 × 256 resolution.</em>
+</p>
+
+
+
+## ❤️ Acknowledgement
+Our work builds upon the foundations laid by many excellent projects in the field. We would like to thank the authors of [Open-MAGVIT2](https://arxiv.org/abs/2409.04410). We also drew inspiration from the methodologies presented in [LFQ](https://arxiv.org/abs/2310.05737), [BSQ](https://arxiv.org/abs/2406.07548). We are grateful for their contributions to the community.