| # Motion-Aware Caching for Efficient Autoregressive Video Generation |
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| **Official implementation of MotionCache** |
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| **[Paper](https://arxiv.org/abs/2605.01725)** | **[Code](https://github.com/ywlq/MotionCache)** |
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| MotionCache is a training-free, motion-aware caching framework for efficient autoregressive video generation. |
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| ## Table of Contents |
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| - [Overview](#overview) |
| - [Method](#method) |
| - [Main Results](#main-results) |
| - [Quick Start](#quick-start) |
| - [Citation](#citation) |
| - [Acknowledgments](#acknowledgments) |
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| --- |
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| ## Overview |
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| Autoregressive video generation can synthesize long videos by generating chunks sequentially, but each chunk still requires expensive iterative denoising. Existing cache-reuse methods usually make coarse chunk-level reuse decisions, which can miss fine-grained motion differences inside a video: highly dynamic tokens need more updates, while static tokens can be reused more aggressively. |
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| MotionCache addresses this with motion-aware token-level caching. It uses inter-frame differences as a lightweight proxy for motion importance, then applies motion-weighted reuse so different tokens can receive different update frequencies during generation. |
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| ## Method |
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| MotionCache follows a coarse-to-fine caching strategy: |
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| - **Warm-up for semantic coherence**: early denoising steps are computed normally to establish stable video content. |
| - **Motion-aware token weighting**: inter-frame differences estimate which spatial tokens are more motion-sensitive. |
| - **Token-wise cache reuse**: static or low-motion regions reuse cached activations more aggressively, while high-motion regions are recomputed more often to reduce error accumulation. |
| - **Model-agnostic integration**: the method is implemented on both [MAGI-1](MotionCache4MAGI-1/) and [SkyReels-V2](MotionCache4SkyReels-V2/) without retraining. |
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| For full derivations and ablations, please refer to the paper. |
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| --- |
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| ## Main Results |
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| MotionCache improves the speed-quality tradeoff on both SkyReels-V2 and MAGI-1. The table below reports the main VBench results from the paper. |
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| ### SkyReels-V2 |
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| | Method | PFLOPs | Speedup | Latency (s) | VBench | PSNR | SSIM | LPIPS | |
| |:--|--:|--:|--:|--:|--:|--:|--:| |
| | Vanilla | 113 | 1.00x | 1540 | 83.84% | - | - | - | |
| | TeaCache-slow | 58 | 1.89x | 814 | 82.67% | 21.96 | 0.7501 | 0.1472 | |
| | TeaCache-fast | 49 | 2.20x | 686 | 80.06% | 18.39 | 0.6121 | 0.3063 | |
| | FlowCache-slow | 31 | 6.26x | 246 | 82.70% | 21.83 | 0.8733 | 0.1417 | |
| | FlowCache-fast | 27 | 7.19x | 214 | 82.38% | 21.17 | 0.8697 | 0.1634 | |
| | **MotionCache-slow** | **30** | **6.28x** | **245** | **82.84%** | **23.46** | **0.9093** | **0.0875** | |
| | **MotionCache-fast** | **26** | **7.26x** | **212** | **82.75%** | **21.78** | **0.8723** | **0.1478** | |
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| ### MAGI-1 |
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| | Method | PFLOPs | Speedup | Latency (s) | VBench | PSNR | SSIM | LPIPS | |
| |:--|--:|--:|--:|--:|--:|--:|--:| |
| | Vanilla | 139 | 1.00x | 1520 | 77.26% | - | - | - | |
| | TeaCache-slow | 129 | 1.14x | 1339 | 76.64% | 14.74 | 0.4132 | 0.6189 | |
| | TeaCache-fast | 101 | 1.41x | 1075 | 68.81% | 11.98 | 0.2632 | 0.7670 | |
| | FlowCache-slow | 104 | 1.39x | 1094 | 77.08% | 18.16 | 0.6486 | 0.3451 | |
| | FlowCache-fast | 78 | 1.94x | 782 | 73.42% | 14.92 | 0.3998 | 0.6088 | |
| | **MotionCache-slow** | **100** | **1.64x** | **925** | **77.25%** | **19.71** | **0.7231** | **0.2510** | |
| | **MotionCache-fast** | **64** | **2.07x** | **733** | **74.59%** | **17.70** | **0.5600** | **0.4861** | |
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| --- |
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| ## Quick Start |
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| Environment preparation follows the corresponding base projects and the FlowCache setup style. After dependencies and model checkpoints are ready, run MotionCache with the provided scripts. |
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| ### MAGI-1 VBench |
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| ```bash |
| cd MotionCache4MAGI-1 |
| bash scripts/motioncache.sh |
| ``` |
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| The script uses [MotionCache4MAGI-1/addconfig/config.yaml](MotionCache4MAGI-1/addconfig/config.yaml) as the MotionCache configuration file. |
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| ### SkyReels-V2 VBench |
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| ```bash |
| cd MotionCache4SkyReels-V2 |
| bash run_vbench.sh |
| ``` |
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| Please update model paths, VBench prompt paths, GPU IDs, and output directories in the scripts before running. |
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| --- |
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| ## Citation |
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| If you find MotionCache useful for your research, please cite: |
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| ```bibtex |
| @misc{xu2026motionawarecachingefficientautoregressive, |
| title={Motion-Aware Caching for Efficient Autoregressive Video Generation}, |
| author={Jing Xu and Yuexiao Ma and Xuzhe Zheng and Xing Wang and Shiwei Liu and Chenqian Yan and Xiawu Zheng and Rongrong Ji and Fei Chao and Songwei Liu}, |
| year={2026}, |
| eprint={2605.01725}, |
| archivePrefix={arXiv}, |
| primaryClass={cs.CV}, |
| url={https://arxiv.org/abs/2605.01725} |
| } |
| ``` |
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| --- |
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| ## Acknowledgments |
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| This repository builds on the following projects: |
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| - [MAGI-1](https://github.com/SandAI-org/MAGI-1) |
| - [SkyReels-V2](https://github.com/SkyworkAI/SkyReels-V2) |
| - [TeaCache](https://github.com/ali-vilab/TeaCache) |
| - [FlowCache](https://github.com/ByteDance-Seed/FlowCache) |
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