Overview
How might we design in-painting so people can trust and act on AI output?
When this pattern fits
- Perfect for AI image editing tools, design software, and creative applications where users need precise control over specific image regions.
When to skip or lighten it
- The whole image needs a new concept, not a local fix.
- Mask tools would confuse casual users on mobile.
- Output must be pixel-identical outside the edit for compliance.
States
State model coming soon
Key UX elements
Key UX elements coming soon
Anti-patterns to avoid
No visible mask preview before generation.
Seams or color shifts at mask edges with no refine pass.
Regenerating the entire image when only a patch changed.
Hiding which pixels were model-generated.
How products use it
| Product | Implementation |
|---|---|
| Adobe Firefly | Generative fill inside selections with non-destructive layers. |
| DALL-E 3 | Edit region flows in ChatGPT image tools. |
| Stable Diffusion | Inpaint nodes and mask brushes in WebUI and ComfyUI. |
| Midjourney | Vary region and inpaint-style refinement on upscaled images. |
Implementation
Copy this prompt to generate a production-ready implementation in Cursor, Claude Code, Lovable, or any AI coding agent.
Generate a production-ready implementation of the "In-painting" AI interface design pattern.
Pattern Definition:Frequently asked questions
What is inpainting in AI image UX?
Users select an area, describe the change, and the model fills only that region while blending with surrounding pixels.
How does inpainting differ from full regeneration?
Full regeneration replaces the image. Inpainting preserves everything outside the mask for iterative local edits.
What mask UX matters most?
Brush size, feather, undo, and a clear overlay so users know exactly what will change.
When pair with object removal?
Object removal is inpainting with an empty prompt or “remove” intent. Same mask UX, different default action.