WinTok: A Win-Win Hybrid Tokenizer via Decomposing Visual Understanding and Generation with Transferable Tokens
[](https://arxiv.org/abs/2605.18115)
[](https://github.com/markywg/WinTok)
[](https://huggingface.co/markyw/WinTok/tree/main)
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.
> WinTok: A Win-Win Hybrid Tokenizer via Decomposing Visual Understanding and Generation with Transferable Tokens
> [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)
> Shenzhen Institutes of Advanced Technology (Chinese Academy of Sciences), WeChat Vision (Tencent Inc.), Shanghai Jiao Tong University
> ```
> @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}
> }
> ```
WinTok achieves superior performance on downstream applications, surpassing previous unified tokenizers, with a more flexible hybrid encoding mechanism.
## 📰 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
```