TokLIP: Marry Visual Tokens to CLIP for Multimodal Comprehension and Generation

Welcome to the official code repository for "TokLIP: Marry Visual Tokens to CLIP for Multimodal Comprehension and Generation".

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

• 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

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
TokLIP-L	384	80.0	68.00	52.87	🤗 TokLIP_L_384

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.

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
• LlamaGen
• 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:

@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}
}