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Wan 2.1

Project page arXiv paper Official code

Wan2.1 is a bidirectional video generation model, supporting both text-to-video (T2V) and image-to-video (I2V) tasks.

Generated via FlashDreams with the prompt: "two cats dancing together in a circle in the rain, in a rainforest"

Quick Start

T2V

uv sync --package flashdreams-wan21 --inexact
uv run --no-sync flashdreams-run-v2 \
  t2v-wan21-t2v-1.3b-480p \
  --output-path artifacts/wan21.mp4 --timeout unbound -- --prompt "A cat surfing"
T2V Presets Demo Arguments

Minimal T2V

uv sync --package flashdreams-wan21 --inexact
uv run --no-sync flashdreams-run-v2 minimal-t2v-wan21 --mode native-window -- --default-prompt "A cat surfing"
Minimal T2V Presets Demo Arguments

Demo Presets

T2V

Preset Description
t2v-wan21-t2v-1.3b-480p Wan 2.1 T2V 1.3B at 480p with a single bidirectional block.

Minimal T2V

Preset Description
minimal-t2v-wan21 Minimal T2V app with Wan 2.1 T2V 1.3B at 480p, generating one block per prompt.

Developer Details

Integration Source Pipeline configurations
  • Minimum VRAM: about 46 GB.
  • PyTorch: 2.9 or newer.

Pipeline configurations

Method Description
wan21-t2v-1.3b-480p Wan 2.1 T2V 1.3B at 480p (single AR step, prompt-only).
wan21-i2v-14b-480p Wan 2.1 I2V 14B at 480p (single AR step, prompt + first-frame).

Performance (Outdated)

Here is the profiling benchmark on DiT per-step runtime for FlashDreams Wan2.1 compared to the official Wan2.1 implementation and the FastVideo baseline under matched settings.

This chart shows per-diffusion-step DiT runtime in milliseconds with CFG at 480p (81 frames) on a single GPU. For an apples-to-apples comparison, all implementations are forced to use cuDNN attention backend under matched runtime settings. For the official Wan2.1 implementation, see this instruction. For the FastVideo baseline, see this instruction.

Samples

prompt: "Summer beach vacation style, a white cat wearing sunglasses sits on a surfboard. The fluffy-furred feline gazes directly at the camera with a relaxed expression. Blurred beach scenery forms the background featuring crystal-clear waters, distant green hills, and a blue sky dotted with white clouds. The cat assumes a naturally relaxed posture, as if savoring the sea breeze and warm sunlight. A close-up shot highlights the feline's intricate details and the refreshing atmosphere of the seaside."
image: https://raw.githubusercontent.com/Wan-Video/Wan2.1/main/examples/i2v_input.JPG

Citation

If you use Wan2.1, please cite the original work:

@article{wan2025,
      title={Wan: Open and Advanced Large-Scale Video Generative Models},
      author={Team Wan and Ang Wang and Baole Ai and Bin Wen and Chaojie Mao and Chen-Wei Xie and Di Chen and Feiwu Yu and Haiming Zhao and Jianxiao Yang and Jianyuan Zeng and Jiayu Wang and Jingfeng Zhang and Jingren Zhou and Jinkai Wang and Jixuan Chen and Kai Zhu and Kang Zhao and Keyu Yan and Lianghua Huang and Mengyang Feng and Ningyi Zhang and Pandeng Li and Pingyu Wu and Ruihang Chu and Ruili Feng and Shiwei Zhang and Siyang Sun and Tao Fang and Tianxing Wang and Tianyi Gui and Tingyu Weng and Tong Shen and Wei Lin and Wei Wang and Wei Wang and Wenmeng Zhou and Wente Wang and Wenting Shen and Wenyuan Yu and Xianzhong Shi and Xiaoming Huang and Xin Xu and Yan Kou and Yangyu Lv and Yifei Li and Yijing Liu and Yiming Wang and Yingya Zhang and Yitong Huang and Yong Li and You Wu and Yu Liu and Yulin Pan and Yun Zheng and Yuntao Hong and Yupeng Shi and Yutong Feng and Zeyinzi Jiang and Zhen Han and Zhi-Fan Wu and Ziyu Liu},
      journal={arXiv preprint arXiv:2503.20314},
      year={2025}
}