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Wan2.2-S2V - A multimodal video generation model open-sourced by Alibaba Tongyi

Wan2.2-S2V is an open-source multimodal video generation model that can generate cinematic digital human videos with a duration of up to minutes, requiring only a single still image and a piece of audio. It also supports various image types and aspect ratios.

What is Wan2.2-S2V?

Wan2.2-S2V is an open-source multimodal video generation model that can generate cinematic-quality digital human videos from just a single still image and audio clip. Videos can be up to minutes long and support various image types and aspect ratios. Users can control the video feed by inputting text prompts, enriching the visuals. The model integrates multiple innovative technologies to achieve audio-driven video generation in complex scenes, supporting long video generation and multi-resolution training and inference. The model has wide applications in digital human live streaming, film and television production, and AI education.

Main functions of Wan2.2-S2V

  • Video generationIt can generate high-quality digital human videos with a duration of up to minutes, using only a still image and an audio clip.
  • Supports multiple image typesThe model can drive various types of images, including real people, cartoons, animals, and digital humans, and supports any aspect ratio such as portrait, half-body, and full-body.
  • Text controlBy inputting text prompts, you can control the video feed, making the movement of the main subject and background changes more varied.
  • Long video generationHierarchical frame compression technology is used to achieve stable long video generation results.
  • Multi-resolution supportIt supports video generation needs in different resolution scenarios, meeting diverse application needs.

Technical Principles of Wan2.2-S2V

  • Multimodal fusionBased on the generalized universal video generation model, it integrates text-guided global motion control and audio-driven fine-grained local motion.
  • AdaIN and CrossAttentionTwo control mechanisms, AdaIN (Adaptive Instance Normalization) and CrossAttention, are introduced to enable audio-driven video generation in complex scenes.
  • Hierarchical frame compressionBased on hierarchical frame compression technology, the length of historical reference frames is extended from several frames to 73 frames, achieving stable long video generation results.
  • Hybrid Parallel TrainingWe constructed an audio and video dataset of over 600,000 segments and performed fully parameterized training through hybrid parallel training to improve model performance.
  • Multi-resolution training and inferenceIt supports video generation needs in different resolution scenarios, meeting diverse application scenarios.

Project address of Wan2.2-S2V

  • Project official website：General Meaning and Myriad Phenomena
  • HuggingFace model libraryhttps://huggingface.co/Wan-AI/Wan2.2-S2V-14B

How to use Wan2.2-S2V

  • Running open source code
    • Get codeAccess the HuggingFace model library.
    • Install dependenciesInstall the required dependency libraries according to the project documentation.
    • Prepare input dataPrepare a still image and an audio clip, along with an optional text prompt.
    • Run codeFollow the instructions in the documentation to run the code and generate the video.
  • Tongyi Wanxiang Official Website Experience
    • Visit the official websiteVisit the official website of Tongyi Wanxiang.
    • Upload input dataUpload a still image and an audio clip, and enter a text prompt.
    • Generate videoClick the "Generate" button and wait for the video to be generated and downloaded.

Application scenarios of Wan2.2-S2V

  • Digital Human Live StreamingBy rapidly generating high-quality digital human videos, we can enhance the richness and interactivity of live streaming content and reduce live streaming costs.
  • Film and television productionIt provides the film and television industry with efficient and low-cost digital human performance generation solutions, saving shooting time and costs.
  • AI EducationGenerate personalized teaching videos to make educational content more vivid and interesting, and improve students' learning interest and effectiveness.
  • Social media content creationIt helps creators quickly generate engaging video content and increase the activity and influence of their social media accounts.
  • Virtual Customer ServiceCreate a natural and fluid virtual customer service avatar to improve customer service efficiency and user experience.
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