| --- |
| license: mit |
| pipeline_tag: image-to-video |
| datasets: |
| - MIN-Lab/minWM-data |
| tags: |
| - Video |
| - WorldModels |
| - Stream |
| - Diffusion |
| --- |
| |
| # 🌍 minWM: The First Full-Stack Open-Source World Model Framework |
|
|
| > ***A full-stack framework and tutorial for newcomers, rather than a specific model.*** |
|
|
| **minWM** is our contribution to the world-model community: a **full-stack open-source framework** that walks you end-to-end through turning a bidirectional T2V foundation model into an action-conditioned video world model — with example data, runnable scripts, **Claude skills** capturing our hands-on experience, and **onboarding knowledge** for newcomers. We hope more researchers and developers join us in growing the community together. |
|
|
| ## Code: https://github.com/shengshu-ai/minWM |
|
|
| ## Citation |
|
|
| If you find this work useful, please cite: |
|
|
| ```bibtex |
| @article{zhu2026causal, |
| title={Causal Forcing: Autoregressive Diffusion Distillation Done Right for High-Quality Real-Time Interactive Video Generation}, |
| author={Zhu, Hongzhou and Zhao, Min and He, Guande heg and Su, Hang and Li, Chongxuan and Zhu, Jun}, |
| journal={arXiv preprint arXiv:2602.02214}, |
| year={2026} |
| } |
| |
| @article{zhao2026causal, |
| title={Causal Forcing++: Scalable Few-Step Autoregressive Diffusion Distillation for Real-Time Interactive Video Generation}, |
| author={Zhao, Min and Zhu, Hongzhou and Zheng, Kaiwen and Zhou, Zihan and Yan, Bokai and Li, Xinyuan and Yang, Xiao and Li, Chongxuan and Zhu, Jun}, |
| journal={arXiv preprint arXiv:2605.15141}, |
| year={2026} |
| } |
| ``` |
