Add model card for One-Forcing

README.md

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+---
+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.