What Is Inpainting?

Inpainting is the process of editing a specific region of an existing image while leaving the rest of the image unchanged.

You supply the original image, draw a mask over the area you want to change, and write a prompt describing what should fill that region. The model generates new content that fits the masked area and blends with the surrounding pixels. Everything outside the mask stays exactly as it was. The result looks like the image was always that way.

How inpainting works

You start with a source image and a mask. The mask is typically a white-on-black shape that marks the region to replace. The AI model receives three inputs: the original image, the mask, and a text prompt.

During generation, the model treats the unmasked pixels as hard constraints. It has to match their colors, lighting, shadows, and texture at the boundary. Inside the mask, it generates freely, guided by your prompt. The denoising process runs only on the masked region, conditioned on the context the unmasked area provides.

The quality of the blend depends on how well the model was trained on inpainting tasks and how precisely the mask covers the intended region. Sloppy masking leaves visible seams. A tight mask with a clear prompt produces a result that reads as natural.

When you use inpainting

Remove an object. You masked the unwanted element and prompt with the background material that should replace it: "stone wall, same texture and lighting as surroundings." The model fills in what was behind the object.

Change wardrobe or product details. You have a lifestyle photo and want to swap a shirt color or replace a bag with a different model. Mask the item, describe the replacement. The lighting and pose stay; only the selected piece changes.

Fix a generation artifact. An image came out nearly perfect except for a garbled hand or a smeared logo. Mask the problem area and re-prompt it specifically. You keep the rest of the composition without running the full generation again.

Swap backgrounds within a tight crop. If the subject is complex to cut out cleanly, mask the background region instead and replace it with a new environment while the subject stays locked.

Examples

Flux Kontext in 8frame. Flux Kontext's edit mode accepts a source image and a text instruction describing the change. For precise inpainting, use 8frame's mask brush to isolate the target region before passing it to Kontext. A prompt like "replace the red jacket with a navy trench coat, same fabric weight and lighting" produces a clean swap that respects the original shadows and body position. Kontext is particularly strong at preserving fine edge detail at the mask boundary, so it handles hair and fabric fray without obvious artifacts.

Nano Banana Pro edit mode in 8frame. Nano Banana Pro is well-suited for product and commercial work. Mask a background element (a distracting prop, a visible seam) and prompt for the replacement material. Because Nano Banana Pro was trained heavily on studio-style imagery, it reads context well and fills neutral backgrounds, surfaces, and product details cleanly. For wardrobe swaps on model shots, the workflow is: generate the base image, open it in the canvas, mask the garment, describe the new piece, generate. One pass is usually enough.

Related concepts

• Flux Kontext Prompts for Editing Existing Images covers how to write effective edit prompts for Kontext specifically, including the phrasing patterns that produce accurate regional edits versus full-image restyle.
• Nano Banana vs Seedream vs Flux compares how each model handles edit and inpainting tasks side by side, which is useful when deciding which model to reach for on a given type of image fix.

---

Ready to try it? Open the 8frame canvas, upload your image, mask the region you want to change, and run it through Flux Kontext or Nano Banana Pro.