Same Character, Different Face? Fix GPT Image 2.6 Drift Across Scenes

A consistent-character workflow for GPT Image 2.6 centers on creating and preserving a single approved character anchor, then uploading that original image for every new scene while repeating a concise set of visual identity traits in each prompt. Using Mara as an example, the guide distinguishes fixed identity features such as facial shape, eyes, freckles, hair, signature curl, proportions, outfit, and watercolor style from variable elements such as location, action, pose, weather, and lighting. It recommends generating an uncropped full-body anchor with a clear face, producing each scene independently from that anchor rather than chaining prior scene outputs, and reviewing images side by side at both full and final display size. When drift occurs, such as altered facial features, hair, clothing, or style, creators should use narrowly targeted edits that preserve the rest of the composition, while recognizing that reference images guide but do not guarantee exact replication. The process includes a five-point review of face, hair, proportions, outfit, and rendering style, along with documentation of prompts, settings, costs, and approved files to support repeatable production across a larger storyboard or picture-book series.

No tracked trend matches for this post yet.

Use this post, company, and trend context to find content marketing opportunities, perform competitive analysis, or address product feature gaps via the Plushcap MCP server or the Plushcap API.