README.md

---
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.