Zum Inhalt springen
ModelAufgelistetExtern
Browse files

OneForcing

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...

ArtOtherVersionRevision 09c0204CBewertung

Modellquelle

Art
Other
Dateigröße
5,29–16,0 GB
Version
vRevision 09c0204
Aktualisiert
4. Okt. 2026
Downloads
0
Quelle
Hugging Face

Quellenbeschreibung

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 | Code | Paper

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. You can run inference using the following commands:

# 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

@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, Self Forcing, CausVid, and the Wan model family.

Quellen

1 Quelle
Hugging FacePrimärVersion 09c02046ca08c12e8a6a048276a939c749767540
huggingface.co/JiaqiFeng/OneForcing/tree/09c02046ca08c12e8a6a048276a939c749767540

Verifiziert 24. Aug.

Download files

Modellartefakte

2 Artefakte

checkpoints/framewise/causal_ode.pt

pt · 5,29 GB · SHA-256 58bd2f6c3b75…79bf · Hugging Face

Download file

checkpoints/one_forcing.pt

pt · 16,0 GB · SHA-256 2214c9c37d0d…93d5 · Hugging Face

Download file

Quellenauszüge

2 Auszüge

--- 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.

Source context: 0 downloads · 2 likes · Pipeline text-to-video · Repo JiaqiFeng/OneForcing