AnyFlow
AnyFlow: Any-Step Video Diffusion Model with On-Policy Flow Map Distillation
In this repository, we present AnyFlow, the first any-step video diffusion framework built on flow maps. AnyFlow offers these key features:
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⚡ Any-Step Generation: Unlike traditional distilled models tied to fixed step budgets, AnyFlow enables a single model to adapt to arbitrary inference budgets. It achieves high-quality few-step generation while providing stable improvements as more sampling steps are added.
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🔀 Multiple Architectures: AnyFlow supports any-step distillation for both causal and bidirectional video diffusion models.
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🎬 Multiple Tasks: AnyFlow supports Text-to-Video, Image-to-Video, and Video-to-Video generation within one causal video diffusion model.
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📈 Scalable Performance: AnyFlow is validated from 1.3B up to 14B parameters.
This directory contains AnyFlow-Wan2.1-T2V-14B-Diffusers (a 14B bidirectional video diffusion model) in Hugging Face Diffusers format, derived from the Wan2.1-T2V-14B-Diffusers text-to-video backbone.
Video Demos
🔥 Latest News!!
- May 4, 2026: 👋 We've released the codebase and weights of AnyFlow.
Quickstart
Setup Environment
1️⃣ Create Conda Environment
conda create -n far python=3.10
conda activate far
2️⃣ Install PyTorch and Dependencies
pip install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/cu128
pip install -r requirements.txt --no-build-isolation
Model Download
| Model | Tasks | Resolution | Download Link |
|---|---|---|---|
AnyFlow-FAR-Wan2.1-1.3B-Diffusers | T2V, I2V, V2V | 480P | 🤗 Hugging Face |
AnyFlow-FAR-Wan2.1-14B-Diffusers | T2V, I2V, V2V | 480P | 🤗 Hugging Face |
AnyFlow-Wan2.1-T2V-14B-Diffusers | T2V | 480P | 🤗 Hugging Face |
AnyFlow-Wan2.1-T2V-1.3B-Diffusers | T2V | 480P | 🤗 Hugging Face |
Download models using 🤗 hf download:
pip install "huggingface_hub[cli]"
hf download nvidia/AnyFlow-FAR-Wan2.1-1.3B-Diffusers --repo-type model --local-dir experiments/pretrained_models/AnyFlow-FAR-Wan2.1-1.3B-Diffusers
Run Text-to-Video Generation with Diffusers
import torch
from diffusers.utils import export_to_video
from far.pipelines.pipeline_wan_anyflow import WanAnyFlowPipeline
model_id = "nvidia/AnyFlow-Wan2.1-T2V-14B-Diffusers"
pipeline = WanAnyFlowPipeline.from_pretrained(model_path).to('cuda', dtype=torch.bfloat16)
prompt = "CG game concept digital art, a majestic elephant with a vibrant tusk and sleek fur running swiftly towards a herd of its kind."
video = pipeline(
prompt=prompt,
height=480,
width=832,
num_frames=81,
num_inference_steps=4,
generator=torch.Generator('cuda').manual_seed(0)
).frames[0]
export_to_video(output, "output.mp4", fps=16)
License
This model is released under the NVIDIA One-Way Noncommercial License (NSCLv1).
Under the NVIDIA One-Way Noncommercial License (NSCLv1), NVIDIA confirms:
- Models are not for commercial use.
- NVIDIA does not claim ownership to any outputs generated using the Models or Derivative Models.
Citation
If you find our work helpful, please cite us.
@article{gu2026anyflow,
title={AnyFlow: Any-Step Video Diffusion Model with On-Policy Flow Map Distillation},
author={Gu, Yuchao and Fang, Guian and Jiang, Yuxin and Mao, Weijia and Han, Song and Cai, Han and Shou, Mike Zheng},
journal={arXiv preprint arXiv:2605.13724},
year={2026}
}
@article{gu2025long,
title={Long-Context Autoregressive Video Modeling with Next-Frame Prediction},
author={Gu, Yuchao and Mao, weijia and Shou, Mike Zheng},
journal={arXiv preprint arXiv:2503.19325},
year={2025}
}
Acknowledgements
This codebase is built on Diffusers. We also refer to implementations from FAR, Self-Forcing, and TiM. We thank the authors for open-sourcing their work.