AI Image Editing Explained: Generative Fill, Inpainting, and More

Two Techniques, One Underlying Idea

Nearly everything under the umbrella of "AI photo editing" today comes down to two related techniques: inpainting, which fills or replaces content inside an image's existing borders, and outpainting, which generates new content beyond the image's original edges. Both rely on the same underlying diffusion technology used to generate images from scratch, just pointed at a specific region rather than an entire blank canvas. The AI looks at the surrounding colors, textures, lighting, and composition, and generates new pixels that blend into that context rather than looking pasted on.

What Inpainting Actually Does

Inpainting starts with you selecting, or "masking," a region of an existing image — a person you want removed, a blemish, a stray object in the background. The AI model then analyzes everything around that masked area and generates new content to fill it, guided either automatically by context alone or by a text prompt describing what should appear there instead.

What Outpainting Adds to the Picture

Outpainting works the opposite direction from inpainting: instead of filling a gap inside the image, it generates entirely new content past the original edges, extending the scene outward. The AI examines the composition, lighting, and perspective at the boundary and continues it convincingly outward, rather than simply stretching or blurring the existing pixels the way a basic crop tool would.

This is especially useful for changing aspect ratio without cropping anything important out — turning a square Instagram photo into a widescreen YouTube thumbnail, for instance, by generating new scenery on either side rather than cutting off the subject. It's also used to add headroom to a tightly cropped portrait, build out a panoramic view from a standard photo, or add bleed area around a design for print-on-demand products where the file needs extra margin that isn't part of the original composition.

How the AI Understands What Belongs There

Both inpainting and outpainting use the same diffusion-based process behind text-to-image generation, just applied selectively. Instead of starting from pure random noise across the whole canvas, the model treats the masked region (or the space just past the image's edge, for outpainting) as the area to fill with noise, while keeping the rest of the image untouched as a fixed reference. It then runs the same iterative denoising process used in ordinary image generation, but constantly checks its output against the surrounding pixels for consistency in color, lighting direction, texture, and perspective, so the new content doesn't look like a visibly different image spliced in.

This is why mask size matters so much for quality. A very tight mask around just the exact pixels you want changed gives the model little surrounding context to match against, which can produce visible seams or mismatched lighting at the boundary. A slightly generous mask gives the model more of the surrounding scene to reference, which usually produces a cleaner, better-blended result, even though it technically means the AI is regenerating a larger area than strictly necessary.

Where This Actually Gets Used

Beyond obvious photo cleanup, these techniques have become a standard part of several professional workflows. E-commerce teams use inpainting to swap product backgrounds without a physical reshoot, generating clean studio backdrops or lifestyle scenes around an existing product photo. Marketing teams rely on outpainting to repurpose a single hero image across platforms with wildly different aspect ratios — a square social post, a widescreen banner, a tall story format — without commissioning new photography for each one. Print shops and publishers use outpainting to add bleed margins around artwork that was cropped too tightly for physical printing.

On the creative side, concept artists and illustrators frequently use inpainting to fix specific problem areas in an otherwise-good AI-generated image, rather than regenerating the whole composition and losing everything that was already working. This targeted fixing is often faster and more controllable than repeatedly re-rolling a full generation and hoping for a better result. Restoration work on old or damaged photographs is another growing use case, where inpainting fills in torn, faded, or missing sections of a historical image using the surrounding undamaged areas as a guide for texture, tone, and detail.

Where "Generative Fill" Fits In

Generative fill is technically Adobe's product name for the inpainting and outpainting feature built into Photoshop, introduced in 2023 as one of the first mainstream implementations of this technology. Because Photoshop was the tool most people first encountered it through, "generative fill" has become something of a general-purpose term people use for any AI tool that fills a selected image area based on a prompt, even outside Adobe's ecosystem, similar to how "Photoshop" itself became a verb for image editing broadly.

The underlying technology isn't exclusive to Adobe. Stable Diffusion-based interfaces, Runway, and various dedicated inpainting-focused tools offer comparable capability, and several free options exist that don't require a Creative Cloud subscription. What differs between tools is mostly the underlying model, the quality of masking and blending controls, and how much fine-tuning you get over the output style.

This distinction is worth knowing if you're comparing tools, because pricing and feature depth vary considerably. Some free web-based tools cap resolution or watermark output unless you pay, while others are fully open with no signup required at all, often because they're built on open-weight models running on the provider's own infrastructure rather than a proprietary closed model. If cost matters more than having the single most polished interface, it's worth trying a couple of free options before assuming a paid subscription is necessary.

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