nvidia/AnyFlow-FAR-Wan2.1-14B-Diffusers

AnyFlow

🖥️ GitHub ｜ 🤗 Hugging Face ｜ 📑 Paper ｜ 🌐 Website

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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:

• ⚡ 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.

• 🔀 Multiple Architectures: AnyFlow supports any-step distillation for both causal and bidirectional video diffusion models.

• 🎬 Multiple Tasks: AnyFlow supports Text-to-Video, Image-to-Video, and Video-to-Video generation within one causal video diffusion model.

• 📈 Scalable Performance: AnyFlow is validated from 1.3B up to 14B parameters.

This directory contains AnyFlow-FAR-Wan2.1-14B-Diffusers (a 14B causal 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_far_wan_anyflow import FARWanAnyFlowPipeline

model_id = "nvidia/AnyFlow-FAR-Wan2.1-14B-Diffusers"
pipeline = FARWanAnyFlowPipeline.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)

Run Image-to-Video Generation with Diffusers

import torch
from diffusers.utils import export_to_video
from PIL import Image
from torchvision import transforms

from far.pipelines.pipeline_far_wan_anyflow import FARWanAnyFlowPipeline

model_id = "nvidia/AnyFlow-FAR-Wan2.1-14B-Diffusers"
pipeline = FARWanAnyFlowPipeline.from_pretrained(model_path).to('cuda', dtype=torch.bfloat16)

# load image
image_path = 'assets/example_image.jpg'
prompt = 'A towering, battle-scarred humanoid robot walking through the skeletal remains of a city ruin.'

image = Image.open(image_path).convert('RGB')
image = transforms.ToTensor()(transforms.Resize([480, 832])(image)).unsqueeze(0).unsqueeze(0)

video = pipeline(
prompt=prompt,
context_sequence={'raw': image},
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)

Run Video-to-Video Generation with Diffusers

import torch
from diffusers.utils import export_to_video
import decord
from torchvision import transforms

from far.pipelines.pipeline_far_wan_anyflow import FARWanAnyFlowPipeline

decord.bridge.set_bridge('torch')

model_id = "nvidia/AnyFlow-FAR-Wan2.1-14B-Diffusers"
pipeline = FARWanAnyFlowPipeline.from_pretrained(model_path).to('cuda', dtype=torch.bfloat16)

# load video
video_path = 'assets/example_video.mp4'
prompt = "A focused trail runner's powerful strides through a dense, sun-dappled forest."

video_reader = decord.VideoReader(video_path)
frame_idxs = select_frame_indices(len(video_reader), video_reader.get_avg_fps(), target_fps=16)[:num_cond_frames]
frames = video_reader.get_batch(frame_idxs)
frames = (frames / 255.0).float().permute(0, 3, 1, 2).contiguous()
frames = transforms.Resize([480, 832])(frames).unsqueeze(0)

video = pipeline(
prompt=prompt,
context_sequence={'raw': frames},
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](LICENSE.md)).

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…