AdapTok: Learning Adaptive and Temporally Causal Video Tokenization in a 1D Latent Space

Overview

We propose AdapTok, an adaptive temporal causal video tokenizer that can flexibly allocate tokens for different frames based on video content. AdapTok is equipped with a block-wise masking strategy that randomly drops tail tokens of each block during training, and a block causal scorer to predict the reconstruction quality of video frames using different numbers of tokens. During inference, an adaptive token allocation strategy based on integer linear programming is further proposed to adjust token usage given predicted scores. Such design allows for sample-wise, content-aware, and temporally dynamic token allocation under a controllable overall budget. Extensive experiments for video reconstruction and generation on UCF-101 and Kinetics-600 demonstrate the effectiveness of our approach. Without additional image data, AdapTok consistently improves reconstruction quality and generation performance under different token budgets, allowing for more scalable and token-efficient generative video modeling.

Get Started

• Installation

conda create -n adaptok python=3.10
conda activate adaptok
pip install torch==2.5.1 torchvision==0.20.1 torchaudio==2.5.1 --index-url https://download.pytorch.org/whl/cu121
pip install -r requirements.txt

• Dataset

Prepare the datasets following data/README.md.

Pretrained Models

We provide pretrained models for AdapTok, AdapTok-Scorer, AdapTok-AR, and AdapTok-FP.

Model	FVD	🤗 HuggingFace
AdapTok	--	yeahhhh326/AdapTok
AdapTok-Scorer	28/36	yeahhhh326/AdapTok-Scorer
AdapTok-AR	67 (AR)	yeahhhh326/AdapTok-AR
AdapTok-FP	11 (FP)	yeahhhh326/AdapTok-FP

Please refer to the Evaluation section for details on how to use these models.

Training

Training AdapTok

bash scripts/train_adaptok.sh

To enable multi-nodes training, you need to config MASTER_ADDR, NNODES, etc, in the script.

Training AdapTok-Scorer

bash scripts/train_adaptok_scorer.sh

Training AdapTok-AR on UCF101 dataset

• prepare ar annotations

bash scripts/prepare_annotations_for_ar.sh

• train AdapTok-AR

bash scripts/train_adaptok_ar.sh

Training AdapTok-FP (frame prediction) on Kinetics-600 dataset

• prepare ar-fp annotations

bash scripts/prepare_annotations_for_fp.sh

• post-process annotations

bash scripts/prepare_annotations_for_fp_post_processing.sh

• train AdapTok-FP

bash scripts/train_adaptok_ar_fp.sh

Evaluation

Adaptive Reconstruction

bash scripts/eval_adaptok.sh

UCF-101 Class-conditional Generation

bash scripts/eval_adaptok_ar.sh

Kinetics-600 Frame Prediction

bash scripts/eval_adaptok_ar_fp.sh

Acknowledgement

Our code is built upon LARP. Thanks for their great work.

Citation

If you find this code useful in your research, please consider citing:

@misc{li2025learningadaptivetemporallycausal,
  title={Learning Adaptive and Temporally Causal Video Tokenization in a 1D Latent Space},
  author={Li, Yan and Tian, Changyao and Xia, Renqiu and Liao, Ning and Guo, Weiwei and Yan, Junchi and Li, Hongsheng and Dai, Jifeng and Li, Hao and Yang, Xue},
  year={2025},
  eprint={2505.17011},
  archivePrefix={arXiv},
  primaryClass={cs.CV},
  url={https://arxiv.org/abs/2505.17011},
}