---
title: "Wan: Open and Advanced Large-Scale Video Generative Models"
description: "The Alibaba Wan Team presents Wan, an openly released family of video foundation models. 129 references, cited by 246 papers here."
authors: ["Team Wan", "Ang Wang", "Baole Ai", "Bin Wen", "Chaojie Mao", "Chen-Wei Xie", "Di Chen", "Feiwu Yu", "Haiming Zhao", "Jianxiao Yang", "Jianyuan Zeng", "Jiayu Wang", "Jingfeng Zhang", "Jingren Zhou", "Jinkai Wang", "Jixuan Chen", "Kai Zhu", "Kang Zhao", "Keyu Yan", "Lianghua Huang", "Mengyang Feng", "Ningyi Zhang", "Pandeng Li", "Pingyu Wu", "Ruihang Chu", "Ruili Feng", "Shiwei Zhang", "Siyang Sun", "Tao Fang", "Tianxing Wang", "Tianyi Gui", "Tingyu Weng", "Tong Shen", "Wei Lin", "Wei Wang", "Wei Wang", "Wenmeng Zhou", "Wente Wang", "Wenting Shen", "Wenyuan Yu", "Xianzhong Shi", "Xiaoming Huang", "Xin Xu", "Yan Kou", "Yangyu Lv", "Yifei Li", "Yijing Liu", "Yiming Wang", "Yingya Zhang", "Yitong Huang", "Yong Li", "You Wu", "Yu Liu", "Yulin Pan", "Yun Zheng", "Yuntao Hong", "Yupeng Shi", "Yutong Feng", "Zeyinzi Jiang", "Zhen Han", "Zhi-Fan Wu", "Ziyu Liu"]
year: 2025
venue: "arXiv:2503.20314"
source: "https://arxiv.org/abs/2503.20314"
doi: "10.48550/arxiv.2503.20314"
arxiv: "2503.20314"
licence: "arXiv — non-exclusive license to distribute"
licence_url: "http://arxiv.org/licenses/nonexclusive-distrib/1.0/"
summary_licence: "Elamur Cosmic License"
canonical: "https://research.elamur.ai/free/en/papers/wan-2025-wan-open-advanced-large-scale/"
---
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# Wan: Open and Advanced Large-Scale Video Generative Models

**Authors:** Team Wan, Ang Wang, Baole Ai, Bin Wen, Chaojie Mao, Chen-Wei Xie, Di Chen, Feiwu Yu, Haiming Zhao, Jianxiao Yang, Jianyuan Zeng, [Jiayu Wang](https://md.research.elamur.ai/free/en/authors/jiayu-wang/), Jingfeng Zhang, [Jingren Zhou](https://md.research.elamur.ai/free/en/authors/jingren-zhou/), Jinkai Wang, Jixuan Chen, Kai Zhu, Kang Zhao, Keyu Yan, Lianghua Huang, Mengyang Feng, Ningyi Zhang, Pandeng Li, Pingyu Wu, Ruihang Chu, Ruili Feng, Shiwei Zhang, Siyang Sun, Tao Fang, Tianxing Wang, Tianyi Gui, Tingyu Weng, Tong Shen, [Wei Lin](https://md.research.elamur.ai/free/en/authors/wei-lin/), [Wei Wang](https://md.research.elamur.ai/free/en/authors/wei-wang/), [Wei Wang](https://md.research.elamur.ai/free/en/authors/wei-wang/), Wenmeng Zhou, Wente Wang, Wenting Shen, Wenyuan Yu, Xianzhong Shi, Xiaoming Huang, [Xin Xu](https://md.research.elamur.ai/free/en/authors/xin-xu/), Yan Kou, Yangyu Lv, [Yifei Li](https://md.research.elamur.ai/free/en/authors/yifei-li/), Yijing Liu, Yiming Wang, Yingya Zhang, Yitong Huang, [Yong Li](https://md.research.elamur.ai/free/en/authors/yong-li/), You Wu, [Yu Liu](https://md.research.elamur.ai/free/en/authors/yu-liu/), Yulin Pan, Yun Zheng, Yuntao Hong, Yupeng Shi, Yutong Feng, Zeyinzi Jiang, Zhen Han, Zhi-Fan Wu and Ziyu Liu · **Year:** 2025 · **Venue:** arXiv:2503.20314 · **Licence:** [arXiv — non-exclusive license to distribute](http://arxiv.org/licenses/nonexclusive-distrib/1.0/)

**Links:** [Publisher's page](https://arxiv.org/abs/2503.20314) · [PDF](https://arxiv.org/pdf/2503.20314) · [DOI 10.48550/arxiv.2503.20314](https://doi.org/10.48550/arxiv.2503.20314) · [arXiv 2503.20314](https://arxiv.org/abs/2503.20314) · [OpenAlex](https://openalex.org/W4409092337)

**Citations:** 14 citations (OpenAlex, 2026-10-14) · [246](https://md.research.elamur.ai/free/en/papers/wan-2025-wan-open-advanced-large-scale/cited-by/) in our library, of the 149,439 papers whose bibliographies we read

## Summary
Open video generation models still trail commercial systems in quality. They are mostly limited to plain text-to-video and often need too much compute for small teams. The Alibaba Wan Team presents Wan, an openly released family of video foundation models. It builds on Diffusion Transformers trained with Flow Matching, injects text through cross-attention and applies full attention across space and time to model complex motion. Supporting pieces include a new spatio-temporal VAE, a large data curation and dense captioning pipeline, scalable pre-training, acceleration techniques and automated evaluation metrics. The flagship 14B model was trained on billions of images and videos, around O(1) trillion tokens. It shows scaling gains with both data and model size and beats open and commercial competitors on internal and external benchmarks and in human preference tests. A 1.3B variant runs on consumer GPUs. The whole suite, with code, is open-sourced, which gives researchers and practitioners a strong, reproducible base for video generation work.

## Key points
- There are two model sizes, 14B for quality and 1.3B for efficiency. Both cover T2V and I2V at 480p, and the 1.3B model needs only 8.19 GB VRAM.
- The model family supports up to eight tasks. These include image-to-video, instruction-guided video editing (V2V), zero-shot personalization, camera motion control, real-time/streaming generation and audio generation.
- The authors say Wan is the first model able to render visual text in both Chinese and English inside generated videos.
- Evaluation combines an automated Wan-Bench score with human preference win, draw and loss rates. Comparisons include Sora, Runway, Mochi, HunyuanVideo and several anonymized Chinese commercial systems (CN-TopA/B/C).
- Code and weights are public at github.com/Wan-Video/Wan2.1. The report also describes the data pipeline, the VAE, training strategies, inference speedups (diffusion cache, quantization, parallelism) and consistency model distillation.

## Interesting ideas
- **Paired model sizes:** Releasing a large model for quality alongside a small one for constrained hardware widens adoption. This would suit resource-limited Ukrainian NLP projects that want a strong reference model and a deployable one.
- **Dense captioning of training data:** The authors build a dedicated captioner that writes rich descriptions of training videos, which strengthens text–visual alignment. The same approach could generate detailed annotations for any weakly labelled multimodal corpus.
- **Two-sided evaluation:** Automated benchmark scores are reported together with human win/draw/loss rates against named competitors. This gives both a scalable signal and a check that the metric matches real preference, a pattern that transfers well to evaluating generation in other domains.
- **Text rendering as a capability:** The paper treats in-video text in specific scripts (Chinese, English) as an explicit, testable skill. A similar check could be designed for Cyrillic or Ukrainian text rendering in generative models.
- **Openness beyond weights:** The authors document the full training and data pipeline, not only the final checkpoint. That makes it easier for others to build specialized models, and it is a good norm for shared research resources.

*Our summary of the paper, in our own words — not its text. The work belongs to its authors; read it at the links above.*

📚 Shelves (2): [🖥️ Computer Science](https://md.research.elamur.ai/free/en/shelves/computer-science/) · 18,614 works · [👁️ Computer Vision and Pattern Recognition](https://md.research.elamur.ai/free/en/shelves/computer-science/computer-vision-and-pattern-recognition/) · 3,486 works

## Cite

Cite as: Team Wan, Ang Wang, Baole Ai, Bin Wen, Chaojie Mao, Chen-Wei Xie, Di Chen, Feiwu Yu, Haiming Zhao, Jianxiao Yang, Jianyuan Zeng, Jiayu Wang, Jingfeng Zhang, Jingren Zhou, Jinkai Wang, Jixuan Chen, Kai Zhu, Kang Zhao, Keyu Yan, Lianghua Huang, Mengyang Feng, Ningyi Zhang, Pandeng Li, Pingyu Wu, Ruihang Chu, Ruili Feng, Shiwei Zhang, Siyang Sun, Tao Fang, Tianxing Wang, Tianyi Gui, Tingyu Weng, Tong Shen, Wei Lin, Wei Wang, Wei Wang, Wenmeng Zhou, Wente Wang, Wenting Shen, Wenyuan Yu, Xianzhong Shi, Xiaoming Huang, Xin Xu, Yan Kou, Yangyu Lv, Yifei Li, Yijing Liu, Yiming Wang, Yingya Zhang, Yitong Huang, Yong Li, You Wu, Yu Liu, Yulin Pan, Yun Zheng, Yuntao Hong, Yupeng Shi, Yutong Feng, Zeyinzi Jiang, Zhen Han, Zhi-Fan Wu and Ziyu Liu. 2025. Wan: Open and Advanced Large-Scale Video Generative Models. arXiv:2503.20314.

The paper itself, not this page. BibTeX from the library's record of the work.

```bibtex
@misc{wan2025wan,
  title = {{Wan: Open and Advanced Large-Scale Video Generative Models}},
  author = {Team Wan, Ang Wang, Baole Ai, Bin Wen, Chaojie Mao, Chen-Wei Xie, Di Chen, Feiwu Yu, Haiming Zhao, Jianxiao Yang, Jianyuan Zeng, Jiayu Wang, Jingfeng Zhang, Jingren Zhou, Jinkai Wang, Jixuan Chen, Kai Zhu, Kang Zhao, Keyu Yan, Lianghua Huang, Mengyang Feng, Ningyi Zhang, Pandeng Li, Pingyu Wu, Ruihang Chu, Ruili Feng, Shiwei Zhang, Siyang Sun, Tao Fang, Tianxing Wang, Tianyi Gui, Tingyu Weng, Tong Shen, Wei Lin, Wei Wang, Wei Wang, Wenmeng Zhou, Wente Wang, Wenting Shen, Wenyuan Yu, Xianzhong Shi, Xiaoming Huang, Xin Xu, Yan Kou, Yangyu Lv, Yifei Li, Yijing Liu, Yiming Wang, Yingya Zhang, Yitong Huang, Yong Li, You Wu, Yu Liu, Yulin Pan, Yun Zheng, Yuntao Hong, Yupeng Shi, Yutong Feng, Zeyinzi Jiang, Zhen Han, Zhi-Fan Wu and Ziyu Liu},
  year = {2025},
  doi = {10.48550/arxiv.2503.20314},
  eprint = {2503.20314},
  archivePrefix = {arXiv},
  url = {https://doi.org/10.48550/arxiv.2503.20314}
}
```

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124. Shiwei Zhang, Jiayu Wang, Yingya Zhang, Kang Zhao, Hangjie Yuan, Zhiwu Qin, Xiang Wang, Deli Zhao, and Jingren Zhou. I2vgen-xl: High-quality image-to-video synthesis via cascaded diffusion models. arXiv preprint arXiv:2311.04145, 2023c. — [arXiv:2311.04145](https://arxiv.org/abs/2311.04145) [find](https://scholar.google.com/scholar?q=Shiwei+Zhang%2C+Jiayu+Wang%2C+Yingya+Zhang%2C+Kang+Zhao%2C+Hangjie+Yuan%2C+Zhiwu+Qin%2C+Xiang+Wang%2C+Deli+Zhao%2C+and+Jingren+Zhou%2E+I2vgen%2Dxl%3A+High%2Dquality+image%2Dto%2Dvideo+synthesis+via+cascaded+diffusion+models%2E+arX)
125. Youcai Zhang, Xinyu Huang, Jinyu Ma, Zhaoyang Li, Zhaochuan Luo, Yanchun Xie, Yuzhuo Qin, Tong Luo, Yaqian Li, Shilong Liu, Yandong Guo, and Lei Zhang. Recognize Anything: A Strong Image Tagging Model. arXiv preprint arXiv:2306.03514, 2023d. — [arXiv:2306.03514](https://arxiv.org/abs/2306.03514) [find](https://scholar.google.com/scholar?q=Youcai+Zhang%2C+Xinyu+Huang%2C+Jinyu+Ma%2C+Zhaoyang+Li%2C+Zhaochuan+Luo%2C+Yanchun+Xie%2C+Yuzhuo+Qin%2C+Tong+Luo%2C+Yaqian+Li%2C+Shilong+Liu%2C+Yandong+Guo%2C+and+Lei+Zhang%2E+Recognize+Anything%3A+A+Strong+Image+Tagging+Model)
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*Updated 2 November 2026*