| --- |
| 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 |
|
|
| <h5 align="center"> |
|
|
| [](https://arxiv.org/abs/2505.05422) |
| [](https://github.com/TencentARC/TokLIP) |
| [](https://huggingface.co/TencentARC/TokLIP) |
| [](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 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 |
|
|
| <img src="https://raw.githubusercontent.com/TencentARC/TokLIP/main/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. |
|
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| - 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. |
|
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| 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 <haokun.lin@cripac.ia.ac.cn>. |
|
|
| 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} |
| } |
| ``` |
