Wan Videov2.7Version updatev2.7Apr 1, 2026More precise identity control points to stronger facial variation and better character consistency across generations.
Palette-driven prompting with up to 8 hex codes suggests finer aesthetic control for brand colors and art direction.
Stronger text rendering implies better handling of posters, charts, formulas, and other dense layout-heavy outputs.
Multi-image composition indicates improved continuity when generating grouped visuals or related asset sets.
Reference-aware generation plus localized editing points to a more controllable image workflow, where subjects stay stable and changes can be applied without regenerating the whole scene.
Overview
Wan 2.2, developed by Alibaba, is an AI-driven video generation model housed within an expansive creative platform known as Wan. The underlying objective of Wan 2.2 is to facilitate easier access to creative work by leveraging the capabilities of Artificial Intelligence.
Features incorporated in Wan 2.2 are designed with the intention to transform and streamline different facets of creative work. These features span across text-to-image, image-to-image, text-to-video, and image-to-video conversion capabilities, as well as image editing tools.
Each of these automated conversion routines offers users innovative ways to produce visual content starting from varied formats, thereby simplifying the generation process and unlocking new creative possibilities.
Particularly, the model's ability to convert user input such as text or images into videos introduces a new dimension to content creation. The image editing tools further enhance the creative scope of the model, allowing users to apply transformations and modifications to their visual compositions.
Ultimately, Wan 2.2 is a comprehensive tool for users to bring their creative concepts to fruition.
Supported features
Releases
Palette-driven prompting with up to 8 hex codes suggests finer aesthetic control for brand colors and art direction.
Stronger text rendering implies better handling of posters, charts, formulas, and other dense layout-heavy outputs.
Multi-image composition indicates improved continuity when generating grouped visuals or related asset sets.
Reference-aware generation plus localized editing points to a more controllable image workflow, where subjects stay stable and changes can be applied without regenerating the whole scene.
Top alternatives
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