AI Fill

AI Fill is the umbrella term for letting a model generate pixels to fill a gap in an image, whether the gap was left by removing an object or created intentionally to add something new. It covers two closely related jobs: reconstructive fill (rebuild what was behind a removed object, using surrounding texture, color, and structure as reference) and generative fill (create new content from a prompt or context, such as extending a background). The two share the same underlying capability — a diffusion model predicting plausible pixels conditioned on the visible image — and differ mainly in intent. In Magic Eraser, AI Fill is what runs after you brush over an object: the model reconstructs the area so the removal reads as if the object was never there, matching lighting direction, perspective, and texture at the seam. The quality of an AI Fill depends on how much usable context surrounds the region — a small gap over consistent texture (sky, wall, grass) fills perfectly, while a large gap over complex unique detail gives the model less to infer from. AI Fill replaced the older clone-stamp and patch workflows, which copied existing pixels rather than understanding the scene, and is the core technology behind one-tap object removal.