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LatestQwen · AlibabaWan2.2-I2V-A14B
Qwen · AlibabaImage → Video

Qwen Releases Wan2.2, a 14B Image-to-Video Model

The new 14-billion parameter model from Alibaba's AI team uses a Mixture-of-Experts design and is available under the permissive Apache 2.0 license.

Jul 24, 2025
NotableApache 2.0
Wan2.2-I2V-A14B

Alibaba's Qwen team has introduced Wan2.2-I2V-A14B, a new open-source model designed for generating short video clips from a single static image. This release adds a powerful new tool to the rapidly expanding field of generative video.

The model features a 14-billion parameter architecture built on a Mixture-of-Experts (MoE) framework. This design allows for more efficient computation by selectively activating parts of the network during inference, a technique that can offer the performance of a much larger dense model while managing computational costs.

Why It Matters

A key aspect of this release is its Apache 2.0 license. Unlike many generative models limited to non-commercial or research-only use, Wan2.2 is available for a wide range of applications, including commercial products. This permissive licensing is a significant contribution to the open-source creative AI ecosystem, enabling developers and businesses to innovate on the technology freely.

The release of Wan2.2 adds another strong contender to the competitive landscape of open video generation. Researchers and developers can access the model and its components directly from the Hugging Face Hub.

Sources

  • Wan-AI/Wan2.2-I2V-A14B

    Hugging Face

    Visit
OlderQwen Releases 480B Open-Source Model for Code AgentsQwen · Alibaba · Code · last yearNewerQwen Unveils Wan2.2, a 14B Open Text-to-Video ModelQwen · Alibaba · Text → Video · last year

Get the model

Hugging Face

Specs

Parameters14B · MoE
Resolution480P, 720P
LicenseAPACHE-2.0
Downloads8.5K
Likes936

Can you run it?

Runs on a laptop — about 15 GB at FP8.

  • BF16 (as published)

    32 GB GPU (RTX 5090) · 48 GB Mac

    29 GB
  • FP8

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

    15 GB
Your machine
  • Mixture-of-experts: every expert must be loaded, so memory follows total parameters, not the active slice.
  • Precision not published; assuming 16-bit (BF16) weights.
  • Parameter count is an editorial estimate — no weights metadata on Hugging Face.
  • Covers the published checkpoint; text encoders and VAE loaded alongside may add a few GB (many runtimes offload them to CPU).

Estimated from the parameter count. 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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