MODEL TYPE
How Does AI Image Inpainting Work?
Inpainting lets a diffusion model change one region of an image, removing an object, filling a gap, swapping what's in a selection, while leaving the rest of the picture exactly as it was. It works by using a mask to tell the model which pixels it's allowed to touch, then running the same denoising process as text-to-image generation only inside that boundary.
A mask draws the boundary between fixed and free
Inpainting starts with a mask: a black-and-white image the same size as the source, where white marks the region to regenerate and black marks the region to preserve exactly. The source image and the mask both feed into the model alongside the usual text prompt, which now only has to describe what should appear inside the masked area, not the whole scene.
During denoising, the model doesn't apply noise removal uniformly across the canvas. At every step, the known, unmasked pixels are held fixed (or reset back to their original latent values), and only the masked region is actually being denoised toward the prompt. That's the entire mechanism: the same reverse diffusion process behind text-to-image, constrained so it can only act where the mask allows.
Why the edited region blends in instead of looking pasted on
Fixing unmasked pixels exactly at every step, the most direct implementation, tends to leave a visible seam: the model has no way to adjust lighting or color right at the mask's edge to match the surrounding image, since those pixels are frozen. RePaint, one of the approaches that shaped how modern inpainting works, instead resamples: it lets the unmasked region also participate in a few extra denoising passes near the boundary, harmonizing it with the newly generated content instead of leaving it completely static.
This is why inpainting output usually keeps consistent lighting and color across the mask edge without an explicit blending step: the model is generating the masked content with the rest of the image as context at every denoising step, not painting new pixels into a hole and hoping they match.
Object removal is inpainting with an empty prompt
Removing an object works the same way as adding one: mask the object, then run generation with a prompt describing what should replace it, or a generic prompt like 'background' if the goal is just plausible continuation of the surroundings. The model isn't deleting anything; it's generating new, plausible content for the masked region conditioned on everything around it, which is why removing a person from in front of a wall generally produces a clean, continued wall rather than a visible gap.
FAQ
- Why does an inpainted region sometimes not match the photo's style?
- The model is generating the masked area from its own learned sense of plausible images, guided by the surrounding context and the prompt, not copying a style from the untouched region. A very specific or unusual source style (a particular film grain, a distinctive lighting setup) can be underrepresented in what the model learned, producing a fill that looks technically clean but stylistically off.
- Is inpainting the same as outpainting?
- They share the identical mechanism, mask what should change, generate the rest, and differ only in where the mask sits. Inpainting masks a region inside the existing image; outpainting masks new canvas added outside the image's original borders, extending the scene past its original edges.
- Does the mask need to be pixel-precise?
- Not usually. A mask that's slightly larger than the object being edited generally produces a cleaner result than one that hugs its edges exactly, because a tight mask can leave a thin halo of the original object's edge pixels behind, visible as a faint outline after generation.
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Last updated 2026-09-16