Wan2.2-Animate-2 is a novel end-to-end character animation framework
Wan-Animate-2 is a novel end-to-end character animation framework that directly consumes driving videos in a redesigned Diffusion Transformer, which achieves high-fidelity motion generation and strong identity preservation by eliminating intermediate motion extractors.
Wan-Animate-2 was developed by Wan-AI team.
This model is ready for commercial or non-commercial use.
This model is not owned or developed by NVIDIA. This model have been developed and built to a third-party’s requirements for this application and use case; see links to:
GOVERNING TERMS: The trial service is governed by the NVIDIA API Trial Terms of Service; and use of this model is governed by the NVIDIA Open Model License. Additional Information: Apache 2.0 license.
You are responsible for ensuring that your use of NVIDIA provided models complies with all applicable laws.
Global
Architecture Type: Transformer
Network Architecture: Mixture of Experts (MoE)
Number of model parameters: 14B (1.4*10^10)
[Text, Image, Video]
Output Type(s): Video Output Format: Tensor Output Parameters: Three-Dimensional (3D)
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.
Runtime Engine(s):
Supported Hardware Microarchitecture Compatibility:
Supported Operating System(s):
The integration of foundation and fine-tuned models into AI systems requires additional testing using use-case-specific data to ensure safe and effective deployment. Following the V-model methodology, iterative testing and validation at both unit and system levels are essential to mitigate risks, meet technical and functional requirements, and ensure compliance with safety and ethical standards before deployment.
Data Modality:
Image Training Data Size: Undisclosed
Text Training Data Size: Undisclosed
Video Training Data Size: Undisclosed
Data Collection Method by dataset: Undisclosed
Labeling Method by dataset: Undisclosed
Properties: Undisclosed
Data Collection Method by dataset: Undisclosed
Labeling Method by dataset: Undisclosed
Properties: Undisclosed
Benchmark Score: Undisclosed
Data Collection Method by dataset: Undisclosed
Labeling Method by dataset: Undisclosed
Properties: Undisclosed
This model can generate synthetic videos 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.
Acceleration Engine: SGLang Diffusion
Test Hardware:
H100 SXM
H200 SXM
B200 SXM
H20
GH200
GB200 NVL
RTX 6000 Blackwell SV
RTX 6000 Blackwell WS
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. 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.
Deploying and integrating the NIM is straightforward thanks to our industry standard APIs. Visit the Visual Generative AI NIM page for release documentation, deployment guides and more.
Get access to community knowledge base articles and support cases (https://forums.developer.nvidia.com/)