--- license: mit license_link: https://github.com/microsoft/VidTok/blob/main/LICENSE tags: - video-to-video - tokenization - video generation - world model - vae - fsq --- # VidTok A Family of Versatile and State-Of-The-Art Video Tokenizers radar VidTok is a cutting-edge family of video tokenizers that delivers state-of-the-art performance in both continuous and discrete tokenizations with various compression rates. VidTok incorporates several key advancements over existing approaches: * ⚡️ **Efficient Architecture**. Separate spatial and temporal sampling reduces computational complexity without sacrificing quality. * 🔥 **Advanced Quantization**. Finite Scalar Quantization (FSQ) addresses training instability and codebook collapse in discrete tokenization. * 💥 **Enhanced Training**. A two-stage strategy—pre-training on low-res videos and fine-tuning on high-res—boosts efficiency. Reduced frame rates improve motion dynamics representation. VidTok, trained on a large-scale video dataset, outperforms previous models across all metrics, including PSNR, SSIM, LPIPS, and FVD. Resources and technical documentation: + [GitHub](https://github.com/microsoft/VidTok) + [arXiv](https://arxiv.org/abs/2412.13061) + [paper](https://huggingface.co/papers/2412.17726) ## Model Performance The following table shows model performance evaluated on 30 test videos in [MCL_JCL](https://mcl.usc.edu/mcl-jcv-dataset/) dataset, with a sample fps of 30. The input size is `17x256x256` for causal models and `16x256x256` for non-causal models. `VCR` indicates the video compression ratio `TxHxW`. | Model | Regularizer | Causal | VCR | PSNR | SSIM | LPIPS | FVD | |------|------|------|------|------|------|------|------| | [vidtok_kl_causal_488_4chn](https://huggingface.co/microsoft/VidTok/blob/main/checkpoints/vidtok_kl_causal_488_4chn.ckpt) | KL-4chn | ✔️ | 4x8x8 | 29.64 | 0.852| 0.114| 194.2| | [vidtok_kl_causal_488_8chn](https://huggingface.co/microsoft/VidTok/blob/main/checkpoints/vidtok_kl_causal_488_8chn.ckpt) | KL-8chn | ✔️ |4x8x8 | 31.83 | 0.897| 0.083| 109.3| | [vidtok_kl_causal_488_16chn](https://huggingface.co/microsoft/VidTok/blob/main/checkpoints/vidtok_kl_causal_488_16chn.ckpt) | KL-16chn | ✔️ | 4x8x8 | 35.04 |0.942 |0.047 | 78.9| | [vidtok_kl_causal_41616_4chn](https://huggingface.co/microsoft/VidTok/blob/main/checkpoints/vidtok_kl_causal_41616_4chn.ckpt) | KL-4chn | ✔️ | 4x16x16 | 25.05 | 0.711| 0.228| 549.1| | | [vidtok_kl_noncausal_488_4chn](https://huggingface.co/microsoft/VidTok/blob/main/checkpoints/vidtok_kl_noncausal_488_4chn.ckpt) | KL-4chn | ✖️ | 4x8x8 | 30.60 | 0.876 | 0.098| 157.9| | [vidtok_kl_noncausal_41616_4chn](https://huggingface.co/microsoft/VidTok/blob/main/checkpoints/vidtok_kl_noncausal_41616_4chn.ckpt) | KL-4chn | ✖️ | 4x16x16 | 26.06 | 0.751 | 0.190|423.2 | | [vidtok_fsq_causal_488_262144](https://huggingface.co/microsoft/VidTok/blob/main/checkpoints/vidtok_fsq_causal_488_262144.ckpt) | FSQ-262,144 | ✔️ | 4x8x8 | 29.82 | 0.867 |0.106 | 160.1| | [vidtok_fsq_causal_488_32768](https://huggingface.co/microsoft/VidTok/blob/main/checkpoints/vidtok_fsq_causal_488_32768.ckpt) | FSQ-32,768 | ✔️ | 4x8x8 | 29.16 | 0.854 | 0.117| 196.9| | [vidtok_fsq_causal_488_4096](https://huggingface.co/microsoft/VidTok/blob/main/checkpoints/vidtok_fsq_causal_488_4096.ckpt) | FSQ-4096 | ✔️ | 4x8x8 | 28.36 | 0.832 | 0.133| 218.1| | [vidtok_fsq_causal_41616_262144](https://huggingface.co/microsoft/VidTok/blob/main/checkpoints/vidtok_fsq_causal_41616_262144.ckpt) | FSQ-262,144 | ✔️ | 4x16x16 | 25.38 | 0.738 |0.206 | 430.1| | [vidtok_fsq_noncausal_488_262144](https://huggingface.co/microsoft/VidTok/blob/main/checkpoints/vidtok_fsq_noncausal_488_262144.ckpt) | FSQ-262,144 | ✖️ | 4x8x8 | 30.78 | 0.889| 0.091| 132.1| | [vidtok_fsq_noncausal_41616_262144](https://huggingface.co/microsoft/VidTok/blob/main/checkpoints/vidtok_fsq_noncausal_41616_262144.ckpt) | FSQ-262,144 | ✖️ | 4x16x16 | 26.37 | 0.772| 0.171| 357.0| ## Training ### Training Data The training data of VidTok is divided into two sets based on video quality. 1. Training Set 1 consists of approximately 400K of low-resolution videos (e.g., 480p). The videos are natural videos with diverse lightning, motions, and scenarios. 2. Training Set 2 includes approximately 10K of high-resolution videos (e.g., 1080p). The videos are natural videos with diverse lightning, motions, and scenarios. ### Training Procedure Please refer to the [paper](https://arxiv.org/pdf/2412.13061) and [code](https://github.com/microsoft/VidTok) for detailed training instructions. ## Evaluation Please refer to the [paper](https://arxiv.org/pdf/2412.13061) and [code](https://github.com/microsoft/VidTok) for detailed evaluation instructions. ## Intended Uses We are sharing our model with the research community to foster further research in this area: * Training your own video tokenizers for research purpose. * Video tokenization with various compression rates. ## Downstream Uses Our model is designed to accelerate research on video-centric research, for use as a building block for the following applications: * Video generation on the continuous / discrete latent tokens. * World modelling on the continuous / discrete latent tokens. * Generative games on the continuous / discrete latent tokens. * Video understanding from the latent tokens. ## Out-of-scope Uses Our models are not specifically designed or evaluated for all downstream purposes. Developers should consider common limitations of video tokenizers (e.g., performance degradation on out-of-domain data) as they select use cases, and evaluate and mitigate for privacy, safety, and fairness before using within a specific downstream use case, particularly for high-risk scenarios. Developers should be aware of and adhere to applicable laws or regulations (including privacy, trade compliance laws, etc.) that are relevant to their use case. ## Risks and Limitations Some of the limitations of this model to be aware of include: * VidTok may lose detailed information on the reconstructed content. * VidTok inherits any biases, errors, or omissions characteristic of its training data. * VidTok was developed for research and experimental purposes. Further testing and validation are needed before considering its application in commercial or real-world scenarios. ## Recommendations Some recommendations for alleviating potential limitations include: * Lower compression rate provides higher reconstruction quality. * For domain-specific video tokenization, it is suggested to fine-tune the model on the domain-specific videos. ## License The model is released under the [MIT license](https://github.com/microsoft/VidTok/blob/main/LICENSE). ## Contact We welcome feedback and collaboration from our audience. If you have suggestions, questions, or observe unexpected/offensive behavior in our technology, please contact us at tianyuhe@microsoft.com. ## Contributing This project welcomes contributions and suggestions. Most contributions require you to agree to a Contributor License Agreement (CLA) declaring that you have the right to, and actually do, grant us the rights to use your contribution. 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