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LatestQwen · Alibaba2.2
Qwen · AlibabaImage → Video

Alibaba Releases Wan2.2, a 14B MoE Video Model

The new open-source diffusion model from the team behind Qwen uses a Mixture-of-Experts architecture to animate still images.

Jul 28, 2025
Major releaseApache 2.0
Wan2.2-I2V-A14B

Alibaba's Qwen team has introduced a new contender in the open-source video generation space with the release of Wan2.2-I2V-A14B. This 14-billion parameter diffusion model is designed to animate still images, turning them into short video clips. It's available under a permissive Apache 2.0 license, encouraging broad adoption and experimentation.

What sets Wan2.2 apart is its use of a Mixture-of-Experts (MoE) architecture. While MoE has become a popular technique for efficiently scaling large language models, its application in video diffusion is less common. This approach allows the model to selectively activate parts of its network for a given task, potentially offering a more computationally efficient path to high-quality video generation at a large scale.

The model has been released in the popular Diffusers format, making it readily accessible for developers and researchers. This simplifies integration into existing pipelines and encourages experimentation within the community. The full model weights and usage instructions are available on the project's Hugging Face repository.

The arrival of Wan2.2 signifies a notable architectural experiment from a major AI lab. As the community pushes the boundaries of open video generation, exploring techniques like MoE could prove crucial for creating more powerful and efficient models that can compete with leading closed-source alternatives.

Sources

  • Wan-AI/Wan2.2-I2V-A14B-Diffusers

    Hugging Face

    Visit
OlderTencent Releases Wan2.2, a 14B MoE Video ModelTencent · Text → Video · last yearNewerSkywork Releases UniPic, a Unified 1.5B Vision ModelSkywork · Any-to-Any · last year

Get the model

Hugging Face

Specs

Parameters14B · MoE
Resolution480P & 720P
Size57.2 GB
PrecisionFP32
ArchitectureWanImageToVideoPipeline
LicenseAPACHE-2.0
Downloads8.5K
Likes936

Can you run it?

Runs on a laptop — about 15.3 GB at FP8.

  • FP32 (as published)

    80 GB GPU (A100 / H100) · 128 GB Mac Studio

    58.2 GB
  • 16-bit

    32 GB GPU (RTX 5090) · 48 GB Mac

    29.6 GB
  • FP8

    16 GB GPU (RTX 4080 / 5070 Ti) · 24 GB Mac

    15.3 GB
Your machine
  • Mixture-of-experts: every expert must be loaded, so memory follows total parameters, not the active slice.
  • Covers the published checkpoint; text encoders and VAE loaded alongside may add a few GB (many runtimes offload them to CPU).

Based on Hugging Face weights metadata. Assumes an 8K context and ~1 GB runtime overhead; actual needs vary. How we estimate


Modalities

Image → Video
2 versions — view changelog

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