Wan2.2 introduces a Mixture-of-Experts (MoE) architecture into video diffusion models. By separating the denoising process cross timesteps with specialized powerful expert models.
Wan2.2 Container Overview
Description:
This NIM container houses the Wan2.2 models, which introduces a Mixture-of-Experts (MoE) architecture into video diffusion models. By separating the denoising process cross timesteps with specialized powerful expert models, this enlarges the overall model capacity while maintaining the same computational cost. Wan2.2 also incorporates meticulously curated aesthetic data, complete with detailed labels for lighting, composition, contrast, color tone, and more. This allows for more precise and controllable cinematic style generation, facilitating the creation of videos with customizable aesthetic preferences.
The container components are ready for commercial or non-commercial use.
Third-Party Community Consideration:
These models are not owned or developed by NVIDIA. These models have been developed and built to a third-party’s requirements for this application and use case; see links to:
License/Terms of Use:
GOVERNING TERMS: The use of the NIM container is governed by the NVIDIA Software and Model Evaluation License. The use of the models is governed by the NVIDIA Open Model Agreement. Additional Information: Apache 2.0 license.
You are responsible for ensuring that your use of NVIDIA provided models complies with all applicable laws.
Deployment Geography:
Global
Release Date:
- HuggingFace: Wan2.2-T2V-A14B variant July 24, 2025 via Link
- HuggingFace: Wan2.2-I2V-A14B variant July 24, 2025 via Link
Wan 2.2:
The Wan2.2 Container includes the following models:
| Model Name & Link | Use Case | How to Pull the Model |
|---|---|---|
| Wan2.2-T2V-A14B | Performs text to video generation | Automatic |
| Wan2.2-I2V-A14B | Performs image to video generation | Automatic |
Deployment Details:
Our AI models are designed and/or optimized to run on NVIDIA GPU-accelerated systems. By leveraging NVIDIA’s hardware (e.g. GPU cores) and software frameworks (e.g., CUDA libraries), the model achieves faster training and inference times compared to CPU-only solutions.
Reference:
Key Considerations:
The models embedded in this container can generate synthetic images and may produce content that is inaccurate, offensive, or otherwise inappropriate. Users should implement robust safety guardrails — including content filtering, abuse monitoring, and access controls— to reduce the risk of harmful outputs. Users are responsible for ensuring that their use of the model complies with all applicable laws and regulations, and for regularly reviewing and updating their guardrails as risks evolve.
For more information about the implementation of Cosmos pre and post guardrails to improve model safety, please see the Cosmos-1.0 Guardrail Model.
Security Common Vulnerabilities and Exposures (CVEs)
Please review the Security Scanning tab on NGC to view the latest security scan results. For certain open-source vulnerabilities listed in the scan results, NVIDIA provides a response in the form of a Vulnerability Exploitability eXchange (VEX) document. The VEX information can be reviewed and downloaded from the Security Scanning tab.
Ethical Considerations:
NVIDIA believes Trustworthy AI is a shared responsibility and we have established policies and practices to enable development for a wide array of AI applications. When downloaded or used in accordance with our terms of service, developers should work with their internal developer team to ensure these software components meet requirements for the relevant industry and use case and address unforeseen product misuse.
Please make sure you have proper rights and permissions for all input image and video content; if image or video includes people, personal health information, or intellectual property, the image or video generated will not blur or maintain proportions of image subjects included.
Users are responsible for model inputs and outputs. Users are responsible for ensuring safe integration of this model, including implementing guardrails as well as other safety mechanisms, prior to deployment.
Please report model quality, risk, security vulnerabilities or NVIDIA AI Concerns here.
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