--- pipeline_tag: text-to-video --- # One-Forcing: Towards Stable One-Step Autoregressive Video Generation One-Forcing enables stable **1-step autoregressive video generation** by augmenting DMD-based causal distillation with a shared noised-latent adversarial critic. It achieves state-of-the-art 1-step VBench performance and efficient framewise generation. [**Project Page**](https://aurora-edu.github.io/one-forcing/) | [**Code**](https://github.com/Aurora-edu/One-Forcing) | [**Paper**](https://huggingface.co/papers/2605.23458) ## Introduction Recent advances have substantially improved real-time interactive video generation in the autoregressive regime. One-Forcing addresses the quality degradation and latency issues in one-step settings by augmenting the DMD objective with an auxiliary GAN loss. Experiments show it establishes state-of-the-art performance among one-step causal video generation methods. ## Inference To use the model, follow the installation instructions in the [official repository](https://github.com/Aurora-edu/One-Forcing). You can run inference using the following commands: ```bash # Download the trained One-Forcing checkpoint hf download JiaqiFeng/OneForcing checkpoints/one_forcing.pt --local-dir . # Run the inference script bash scripts/infer.sh \ --checkpoint_path checkpoints/one_forcing.pt \ --prompt_path prompts/demos.txt \ --output_folder outputs ``` ## Citation ```bibtex @article{feng2026oneforcing, title={One-Forcing: Towards Stable One-Step Autoregressive Video Generation}, author={Feng, Jiaqi and Cui, Justin and Ban, Yuanhao and Hsieh, Cho-Jui}, journal={arXiv preprint arXiv:2605.23458}, year={2026}, eprint={2605.23458}, archivePrefix={arXiv}, primaryClass={cs.CV}, url={https://arxiv.org/abs/2605.23458} } ``` ## Acknowledgements This codebase builds on [Causal Forcing](https://github.com/thu-ml/Causal-Forcing), [Self Forcing](https://github.com/guandeh17/Self-Forcing), [CausVid](https://github.com/tianweiy/CausVid), and the [Wan](https://github.com/Wan-Video/Wan2.1) model family.