Model reference · open weights

DFoT

Available as managed deployment Video kiwhansong · community Image→video 1 variants 1k dl/mo

DFoT is an open-weight video model from kiwhansong. AxForge deploys and operates it for you on dedicated EU-owned hardware — with the licence handled where one is required.

Available as managed deployment — configured and operated for you on dedicated EU hardware, quoted per deployment.

What it is

Released bykiwhansong
TypeVideo models
TaskImage→video
Released2025-02-07
Popularity1k downloads / month
LicenceOpen weights

About

What DFoT is

· · · · · *Equal contribution 1MIT 2CMU 3Harvard

This is the official model hub for the paper History-guided Video Diffusion. We introduce the Diffusion Forcing Tranformer (DFoT), a novel video diffusion model that designed to generate videos conditioned on an arbitrary number of context frames. Additionally, we present History Guidance (HG), a family of guidance methods uniquely enabled by DFoT. These methods significantly enhance video generation quality, temporal consistency, and motion dynamics, while also unlocking new capabilities such as compositional video generation and the stable rollout of extremely long videos.

Read the full model card

🤗 Try generating videos with DFoT!

We provide an interactive demo on HuggingFace Spaces, where you can generate videos with DFoT and History Guidance. On the RealEstate10K dataset, you can generate:

  • Any Number of Images → Short 2-second Video
  • Single Image → Long 10-second Video
  • Single Image → Endless Navigation Video (like the teaser above!)

Please check it out and have fun generating videos with DFoT!

🚀 Usage

All pretrained models can be automatically loaded from our GitHub codebase. Please visit our repository for further instructions!

📌 Citation

If our work is useful for your research, please consider citing our paper:

@misc{song2025historyguidedvideodiffusion,
  title={History-Guided Video Diffusion},
  author={Kiwhan Song and Boyuan Chen and Max Simchowitz and Yilun Du and Russ Tedrake and Vincent Sitzmann},
  year={2025},
  eprint={2502.06764},
  archivePrefix={arXiv},
  primaryClass={cs.LG},
  url={https://arxiv.org/abs/2502.06764},
}

From the published model card. Full card on the HuggingFace links in the sidebar.

How it works

How video models work

Prompt / imagestart pointTemporal diffusionframes over timeVideoMP4 clipA video model generates a sequence of coherent frames from your prompt or a starting image.

Using it via the API

Call it like any OpenAI endpoint

Once AxForge deploys dfot for you, it answers on the OpenAI-compatible API — the same base URL and keys as every other model. (dfot below is illustrative; you get the exact model name on deployment.)

$ curl -sS https://api.axforge.ai/v1/videos/generations \
  -H "Authorization: Bearer $AXFORGE_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{"model":"dfot","prompt":"a drone shot over a forest"}'

Create an account — your API key is available in the console. 3M free tokens every 30 days with every new account.

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