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README.md

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----
-license: other
-license_name: other
-license_link: https://github.com/TencentARC/TokLIP/blob/main/LICENSE
----
+---
+license: other
+license_name: other
+license_link: https://github.com/TencentARC/TokLIP/blob/main/LICENSE
+language:
+- en
+base_model:
+- google/siglip2-so400m-patch16-384
+- google/siglip2-so400m-patch16-256
+tags:
+- Tokenizer
+- CLIP
+- UnifiedMLLM
+---
+
+
+# TokLIP: Marry Visual Tokens to CLIP for Multimodal Comprehension and Generation
+
+
+<h5 align="left"> 
+
+[![arXiv](https://img.shields.io/badge/TokLIP-2505.05422-b31b1b.svg?logo=arXiv)](https://arxiv.org/abs/2505.05422)
+[![GitHub](https://img.shields.io/badge/GitHub-Code-green?logo=github)](https://github.com/TencentARC/VideoPainter)
+[![HuggingFace](https://img.shields.io/badge/🤗%20Model-Huggingface-yellow)](https://huggingface.co/TencentARC/TokLIP)
+[![License](https://img.shields.io/badge/⚖️%20Code%20License-Other-blue)](https://github.com/TencentARC/TokLIP/blob/main/LICENSE)
+ <br>
+
+</h5>
+
+
+Welcome to the official code repository for "[**TokLIP: Marry Visual Tokens to CLIP for Multimodal Comprehension and Generation**](https://arxiv.org/abs/2505.05422)".
+
+Your star means a lot for us to develop this project! ⭐⭐⭐
+
+
+## 📰 News
+* [2025/06/05] 🔥 We release the code and models!
+* [2025/05/09] 🚀 Our paper is available on arXiv!
+
+
+## 👀 Introduction
+
+<img src="./docs//TokLIP.png" alt="TokLIP" style="zoom:50%;" />
+
+- We introduce TokLIP, a visual tokenizer that enhances comprehension by **semanticizing** vector-quantized (VQ) tokens and **incorporating CLIP-level semantics** while enabling end-to-end multimodal autoregressive training with standard VQ tokens.
+
+- TokLIP integrates a low-level discrete VQ tokenizer with a ViT-based token encoder to capture high-level continuous semantics.
+
+- Unlike previous approaches (e.g., VILA-U) that *discretize high-level features*, TokLIP **disentangles training objectives for comprehension and generation**, allowing the direct application of advanced VQ tokenizers without the need for tailored quantization operations.
+
+
+## 🔧 Installation
+```bash
+conda create -n toklip python=3.10 -y
+conda activate toklip
+git clone https://github.com/TencentARC/TokLIP
+pip install --upgrade pip 
+pip install -r requirements.txt
+```
+
+
+## ⚙️ Usage
+
+### Model Weight
+
+|  Model   | Resolution | IN Top1 | COCO TR@1 | COCO IR@1 |                            Weight                            |
+| :------: | :--------: | :-----: | :-------: | :-------: | :----------------------------------------------------------: |
+| TokLIP-S |    256     |  76.4   |   64.06   |   48.46   | [🤗 TokLIP_S_256](https://huggingface.co/TencentARC/TokLIP/blob/main/TokLIP_S_256.pt) |
+| TokLIP-L |    384     |  80.0   |   68.00   |   52.87   | [🤗 TokLIP_L_384](https://huggingface.co/TencentARC/TokLIP/blob/main/TokLIP_L_384.pt) |
+
+We are current working on TokLIP-XL with 512x512 resolution and it will be released soon!
+
+### Evaluation
+
+Please first download the TokLIP model weights.
+
+We provide the evalution scripts for ImageNet classification  and MSCOCO Retrieval in `src\test_toklip_256.sh` and `src\test_toklip_384.sh`. 
+
+Please revise the `--pretrained`, `--imagenet-val`, and `--coco-dir` with your specific paths.
+
+### Inference
+
+We provide the inference example in `src/inference.py`. 
+
+```shell
+cd src
+python inference.py --model-config 'ViT-SO400M-16-SigLIP2-384-toklip' --pretrained 'YOUR_TOKLIP_PATH'
+```
+
+### Model Usage
+
+We provide `build_toklip_encoder` function in `src/create_toklip.py`, you could direct load TokLIP with `model`, `image_size`, and `model_path` parameters.
+
+
+## 🔜 TODOs
+- [ ] Release training codes.
+- [ ] Release TokLIP-XL with 512 resolution.
+
+
+## 📂 Contact
+If you have further questions, please open an issue or contact <[email protected]>.
+
+Discussions and potential collaborations are also welcome.
+
+
+## 🙏 Acknowledgement
+This repo is build upon the following projects:
+
+* [OpenCLIP](https://github.com/mlfoundations/open_clip)
+* [LlamaGen](https://github.com/FoundationVision/LlamaGen)
+* [DeCLIP](https://github.com/Sense-GVT/DeCLIP)
+
+We thank the authors for their codes.
+
+
+## 📝 Citation
+Please cite our work if you use our code or discuss our findings in your own research:
+```bibtex
+@article{lin2025toklip,
+  title={TokLIP: Marry Visual Tokens to CLIP for Multimodal Comprehension and Generation},
+  author={Lin, Haokun and Wang, Teng and Ge, Yixiao and Ge, Yuying and Lu, Zhichao and Wei, Ying and Zhang, Qingfu and Sun, Zhenan and Shan, Ying},
+  journal={arXiv preprint arXiv:2505.05422},
+  year={2025}
+}
+```