In text-to-video (T2V) applications, Wan2.2 generates high-resolution, semantically consistent video clips directly from natural language prompts. The model leverages a Mixture of Experts (MoE) architecture and a highly compressed VAE, alongside multi-phase sampling and deep text-visual alignment, to ensure strong correspondence between video content and input text.
Wan2.2 demonstrates robust understanding of complex actions, scenes, and aesthetic demands, delivering visually varied and richly detailed output from both simple and extended narrative prompts. With fast generation speed and low resource requirements, it's well suited for AIGC video creation, storyboarding, and creative advertising.