Wan2.1 is a foundational open-source large-scale video generative model developed by the Wan team, providing high-quality video generation from text and images. It employs advanced diffusion-based architectures to produce coherent, temporally consistent videos with realistic motion and visual fidelity. Wan2.1 focuses on efficient video synthesis while maintaining rich semantic and aesthetic detail, enabling applications in content creation, entertainment, and research. The model supports text-to-video and image-to-video generation tasks with flexible resolution options suitable for various GPU hardware configurations. Wan2.1’s architecture balances generation quality and inference cost, paving the way for later improvements seen in Wan2.2 such as Mixture-of-Experts and enhanced aesthetics. It was trained on large-scale video and image datasets, providing generalization across diverse scenes and motion patterns.
Features
- Diffusion-based architecture optimized for video generation from text and images
- Supports text-to-video and image-to-video generation at multiple resolutions
- Generates coherent, temporally consistent videos with realistic motion
- Trained on large-scale diverse video datasets for strong generalization
- Balances high-quality synthesis with efficient inference for practical use
- Serves as the foundation for subsequent Wan2.2 improvements including MoE design
- Open-source with accessible inference code and checkpoints
- Enables a wide range of applications including creative content generation and research
License
Apache License V2.0Follow Wan2.1
User Reviews
-
Very solid open source AI video generation model