Inpainting
Inpainting is an image editing technique in which a generative AI model regenerates a selected region of an existing image while leaving the rest untouched. Users typically mask an area—such as a face, background, or object—and the model fills that region based on a text prompt or contextual inference from the surrounding pixels. This differs from full image generation, which creates an entire scene from scratch, and from outpainting, which extends an image beyond its original borders.
On AI adult-content platforms, inpainting is commonly used to modify specific parts of a generated or uploaded image, such as altering clothing, adjusting body features, or replacing backgrounds. This capability raises particular technical and policy considerations: inpainting tools that accept uploaded photographs can be used to edit real images of identifiable people, which intersects with consent and likeness-rights concerns distinct from those involved in fully synthetic generation. Platforms differ in whether they restrict inpainting to AI-generated base images only, or permit user-uploaded photos as a starting point, and in what verification or consent mechanisms, if any, apply to uploaded material.
For users, the relevant practical factors include processing cost (inpainting operations may be billed separately from full generations), resolution or mask-size limitations, and the platform's stated policy on using inpainting with real photographs. Because inpainting can be used to alter identifiable imagery, platforms' documented restrictions on this specific feature are a meaningful data point when assessing how a service manages consent and misuse risks, separate from its general content-generation rules.
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