Wan 2.1 FLF2V

Wan 2.1 FLF2V is an open-source video generation model developed by the Wanxiang team and is the latest open-source model in the Wan AI ecosystem. With a parameter size of 14 billion, it can generate a 720p high-definition video that seamlessly connects the first and last frames based on user-specified start and end images. This technology has brought higher controllability and customization to the field of video creation.

Core Functions and Features

  • First-and-Last-Frame Generation: The core function of Wan 2.1 FLF2V is to generate a naturally transitioning video based on the first and last frame images provided by the user.
  • High-Resolution Support: The model can generate 720p high-definition videos, ensuring high-quality and clear visual effects.
  • Controllability and Customization: Users can guide the video generation process through input prompts, achieving more complex video effects, such as special effects changes and scene transitions.
  • Smoothness and Naturalness: When generating videos, Wan 2.1 FLF2V ensures coordinated and natural movements, making the video content look smooth and realistic.
  • Open Source and Ease of Use: The model has been open-sourced on multiple platforms. Users can download the model for local deployment or secondary development on Github and HuggingFace. In addition, users can also directly experience the model for free on the Wanxiang official website.
Wan 2.1 FLF2V Model Architecture

Technical Details

  • Model Architecture: Wan 2.1 FLF2V is based on the existing Wan2.1 text-to-video foundation model architecture, with additional conditional control mechanisms introduced to achieve smooth and precise first-and-last-frame transitions.
  • Training Strategy: The team has constructed specialized training data for the first-and-last-frame mode. Parallel strategies have been adopted for the text and video encoding modules and the diffusion transformer module, which have improved model training and generation efficiency and ensured the model's capability for high-resolution video generation.
  • Inference Optimization: To support high-definition video inference with limited memory resources, the Wan FLF2V model has adopted model splitting and sequence parallel strategies. These strategies significantly reduce inference time while ensuring lossless inference results.

Application Scenarios

  • Video Creation: Wan 2.1 FLF2V can be used to generate various creative videos, such as special effects changes, scene transitions, and time-lapse photography.
  • Advertising and Marketing: The model can provide customized video content for the advertising and marketing industry, helping brands better showcase their products or services.
  • Education and Training: In the education field, Wan 2.1 FLF2V can generate vivid teaching videos to enhance the learning experience.
  • Entertainment and Media: The model can be used to produce short dramas, animations, and other entertainment content, providing creators with more creative space.

Advantages and Limitations

Advantages

  • High-quality video generation capability, supporting 720p high-definition output.
  • High controllability, enabling users to achieve complex video effects through prompts.
  • Open source and easy to use, suitable for individual developers and enterprise users.
  • Support for multiple platforms, facilitating local deployment and secondary development for users.

Limitations

  • The current model size of 14 billion parameters may require higher computing resources to run.
  • The length and complexity of video generation may be subject to certain limitations and require further optimization.