README.md

---
license: other
language:
- en
base_model:
- Wan-AI/Wan2.1-T2V-14B-Diffusers
pipeline_tag: text-to-video
tags:
- Any-Step
- Text-to-Video
- Image-to-Video
- Video-to-Video
---

# AnyFlow

<p align="center">
    🖥️ <a href="https://github.com/NVlabs/AnyFlow">GitHub</a> &nbsp;&nbsp; ｜ &nbsp;&nbsp; 🤗 <a href="https://huggingface.co/collections/nvidia/anyflow">Hugging Face</a> &nbsp;&nbsp; ｜ &nbsp;&nbsp; 📑 <a href="https://arxiv.org/">Paper</a> &nbsp;&nbsp; ｜ &nbsp;&nbsp; 🌐 <a href="https://nvlabs.github.io/AnyFlow">Website</a>
<br>
</p>

-----

**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**](https://huggingface.co/Wan-AI/Wan2.1-T2V-14B-Diffusers) text-to-video backbone.

## Video Demos

<div align="center">
    <video width="80%" autoplay loop muted playsinline controls>
        <source src="https://nvlabs.github.io/AnyFlow/assets/videos/demo_video.m4v" type="video/mp4">
        Your browser does not support the video tag.
    </video>
</div>

## 🔥 Latest News!!

* May 4, 2026: 👋 We've released the codebase and weights of AnyFlow.

## Quickstart

### Setup Environment

**1️⃣ Create Conda Environment**

```bash
conda create -n far python=3.10
conda activate far
```

**2️⃣ Install PyTorch and Dependencies**

```bash
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](https://huggingface.co/nvidia/AnyFlow-FAR-Wan2.1-1.3B-Diffusers) |
| `AnyFlow-FAR-Wan2.1-14B-Diffusers` | T2V, I2V, V2V | 480P | 🤗 [Hugging Face](https://huggingface.co/nvidia/AnyFlow-FAR-Wan2.1-14B-Diffusers) |
| `AnyFlow-Wan2.1-T2V-14B-Diffusers` | T2V | 480P | 🤗 [Hugging Face](https://huggingface.co/nvidia/AnyFlow-Wan2.1-T2V-14B-Diffusers) |
| `AnyFlow-Wan2.1-T2V-1.3B-Diffusers` | T2V | 480P | 🤗 [Hugging Face](https://huggingface.co/nvidia/AnyFlow-Wan2.1-T2V-1.3B-Diffusers) |

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

```python
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

```python
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

```python
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.

```bibtex
@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](https://github.com/huggingface/diffusers). We also refer to implementations from [FAR](https://github.com/showlab/FAR), [Self-Forcing](https://github.com/guandeh17/Self-Forcing), and [TiM](https://github.com/WZDTHU/TiM). We thank the authors for open-sourcing their work.