--- base_model: - google/siglip2-so400m-patch16-384 - google/siglip2-so400m-patch16-256 language: - en license: other license_name: other license_link: https://github.com/TencentARC/TokLIP/blob/main/LICENSE pipeline_tag: image-text-to-text tags: - Tokenizer - CLIP - UnifiedMLLM --- # TokLIP: Marry Visual Tokens to CLIP for Multimodal Comprehension and Generation
[![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/TokLIP) [![HuggingFace](https://img.shields.io/badge/%F0%9F%A4%97%20Model-Huggingface-yellow)](https://huggingface.co/TencentARC/TokLIP) [![License](https://img.shields.io/badge/%E2%9A%96%EF%B8%8F%20Code%20License-Other-blue)](https://github.com/TencentARC/TokLIP/blob/main/LICENSE)
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 to us in developing this project! ⭐⭐⭐ ## 📰 News * [2025/08/18] 🚀 Check our latest results on arXiv ([PDF](https://arxiv.org/pdf/2505.05422))! * [2025/08/18] 🔥 We release TokLIP XL with 512 resolution [🤗 TokLIP_XL_512](https://huggingface.co/TencentARC/TokLIP/blob/main/TokLIP_XL_512.pt)! * [2025/08/05] 🔥 We release the training code! * [2025/06/05] 🔥 We release the code and models! * [2025/05/09] 🚀 Our paper is available on arXiv! ## 👀 Introduction TokLIP - 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 | VQGAN | IN Top1 | COCO TR@1 | COCO IR@1 | Weight | | :-------: | :--------: | :----------------------------------------------------------: | :-----: | :-------: | :-------: | :----------------------------------------------------------: | | TokLIP-S | 256 | [LlamaGen](https://huggingface.co/peizesun/llamagen_t2i/blob/main/vq_ds16_t2i.pt) | 76.4 | 64.06 | 48.46 | [🤗 TokLIP_S_256](https://huggingface.co/TencentARC/TokLIP/blob/main/TokLIP_S_256.pt) | | TokLIP-L | 384 | [LlamaGen](https://huggingface.co/peizesun/llamagen_t2i/blob/main/vq_ds16_t2i.pt) | 80.0 | 68.00 | 52.87 | [🤗 TokLIP_L_384](https://huggingface.co/TencentARC/TokLIP/blob/main/TokLIP_L_384.pt) | | TokLIP-XL | 512 | [IBQ](https://huggingface.co/TencentARC/IBQ-Tokenizer-262144/blob/main/imagenet256_262144.ckpt) | 80.8 | 69.40 | 53.77 | [🤗 TokLIP_XL_512](https://huggingface.co/TencentARC/TokLIP/blob/main/TokLIP_XL_512.pt) | ### Training 1. Please refer to [img2dataset](https://github.com/rom1504/img2dataset) to prepare the WebDataset required for training. You may choose datasets such as **CC3M**, **CC12M**, or **LAION**. 2. Prepare the teacher models using `src/covert.py`: ```bash cd src TIMM_MODEL='original' python covert.py --model_name 'ViT-SO400M-16-SigLIP2-256' --save_path './model/siglip2-so400m-vit-l16-256.pt' TIMM_MODEL='original' python covert.py --model_name 'ViT-SO400M-16-SigLIP2-384' --save_path './model/siglip2-so400m-vit-l16-384.pt' ``` 3. Train TokLIP using the scripts `src\train_toklip_256.sh` and `src\train_toklip_384.sh`. You need to set `--train-data` and `--train-num-samples` arguments accordingly. ### Evaluation Please first download the TokLIP model weights. We provide the evaluation scripts for ImageNet classification and MSCOCO Retrieval in `src\test_toklip_256.sh`, `src\test_toklip_384.sh`, and `src\test_toklip_512.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 directly load TokLIP with `model`, `image_size`, and `model_path` parameters. ## 🔜 TODOs - [x] Release training codes. - [x] Release TokLIP-XL with 512 resolution. ## 📂 Contact If you have further questions, please open an issue or contact . Discussions and potential collaborations are also welcome. ## 🙏 Acknowledgement This repo is built upon the following projects: * [OpenCLIP](https://github.com/mlfoundations/open_clip) * [LlamaGen](https://github.com/FoundationVision/LlamaGen) * [DeCLIP](https://github.com/Sense-GVT/DeCLIP) * [SEED-Voken](https://github.com/TencentARC/SEED-Voken) 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} } ```