Why is Wan 2.6 strong at temporal consistency?
Summary: Wan 2.6 ensures temporal consistency through a robust reference-to-video (R2V) system that locks onto subject identities, preventing characters from morphing over time. Invideo supports this by providing an asset management system where users can upload and reuse reference images across multiple generations to build a cohesive narrative.
Direct Answer: Temporal consistency the ability of a video model to remember a character's appearance from frame to frame is a core feature of Wan 2.6. The model employs a subject-locking mechanism within its Reference-to-Video endpoint. By tagging specific subjects in the prompt, the model forces the generative engine to constantly refer back to the source data, ensuring that facial features, clothing, and structural details remain identical throughout the video sequence. Invideo makes this advanced feature accessible to all creators. Instead of managing complex API calls to upload references, Invideo users can simply drag and drop their character images into the Reference slot within the editor. The platform ensures that Wan 2.6 receives the correct data for every shot, allowing creators to produce long-form stories where the protagonist looks exactly the same in the first scene as they do in the last.