Inpainting Prompting
Selectively edit, replace, or restore specific regions within existing images using text-guided AI — while the surrounding context remains perfectly preserved.
Introduced: Inpainting as a concept predates modern AI by over a decade — Adobe Photoshop introduced Content-Aware Fill in 2010, using traditional algorithms to fill selected regions with plausible textures. However, AI-powered text-guided inpainting became practical in 2022 with the release of Stable Diffusion inpainting models and the RunwayML editor. The technique allows users to mask specific image regions and describe what should replace them through natural language prompts, while the diffusion model maintains visual coherence with the surrounding unmasked areas. Unlike earlier algorithmic approaches, text-guided inpainting can generate entirely new objects, scenes, or textures within the masked region — not merely clone nearby pixels.
Modern Status: Inpainting is now a standard feature in most major image editing tools, including Adobe Photoshop Generative Fill, the DALL-E editor in ChatGPT, Midjourney’s vary-region feature, and open-source tools built on Stable Diffusion. The technique has evolved from a specialized research capability into a core editing workflow used daily by photographers, designers, and content creators. Advances in model architecture continue to improve edge blending, context awareness, and prompt adherence — making inpainting one of the most practically useful applications of generative AI.
Regenerate What You Select, Preserve Everything Else
Inpainting works by selectively regenerating masked portions of an image while conditioning on both the surrounding visual context and a text prompt. The core insight is that the masked region must blend seamlessly with its neighbors in terms of lighting, perspective, color temperature, and artistic style. The model does not simply paste new content into a hole — it synthesizes pixels that belong to the same visual world as the unmasked area.
Effective inpainting prompts describe not just what to generate, but how it should harmonize with the existing image. A prompt that says “a red barn” will produce very different results depending on whether the surrounding image shows a sunny meadow or a snowy hillside. The best inpainting prompts reference the lighting conditions, material textures, and spatial relationships already present in the original image.
Think of inpainting as giving a painter a canvas where most of the painting is already complete — they must fill in the blank area so convincingly that no viewer can tell where the original ends and the new work begins.
The quality of an inpainting result depends heavily on how well the generated content matches its surroundings. The model analyzes the unmasked pixels to infer lighting direction, camera angle, depth of field, color palette, and artistic style — then generates content that continues those visual properties into the masked region. This is why vague prompts often succeed for simple removals (the model can extrapolate context from neighbors) but specific prompts are essential when adding new objects that must integrate convincingly.
The Inpainting Process
Four stages from mask selection to seamless result
Select the Region
Mask the area to be edited using a brush, lasso, or selection tool. The mask defines which pixels the model will regenerate and which it will preserve. Precise masking produces cleaner results — a tight mask around an object yields sharper edges, while a loose mask gives the model more creative freedom for blending.
Use a brush tool to paint over an unwanted power line crossing a landscape photograph. The mask should cover the line plus a few pixels on each side for smooth blending.
Describe the Replacement
Write a text prompt describing what should appear in the masked area. The prompt should account for the existing image context — mention lighting, style, and spatial relationships that the new content must match. For removal tasks, describe the background that should fill the space. For additions, describe the new element and how it integrates with its surroundings.
“Clear blue sky with soft wispy clouds, matching the warm afternoon lighting of the surrounding landscape” — replaces the masked power line with contextually appropriate sky.
Set Context Parameters
Configure how much the model should reference surrounding pixels versus following the text prompt. Key parameters include denoising strength (how much the masked area changes from its original content), mask blur (softness of the transition between masked and unmasked areas), and padding (how many surrounding pixels the model can see for context). Lower denoising preserves more of the original; higher values give the prompt more influence.
Denoising strength of 0.75 with mask blur of 8 pixels — enough to fully replace the content while maintaining a soft transition at the edges.
Refine Results
Evaluate the output for blending quality and adjust as needed. Common refinements include tightening or expanding the mask edges, adding more specific prompt details about texture or lighting, adjusting denoising strength, or running multiple generations to find the best result. Iterative refinement is expected — most professional workflows involve two to four passes before achieving a seamless edit.
First pass shows a slight color temperature mismatch. Adding “warm golden-hour tones” to the prompt and increasing mask blur to 12 pixels produces a seamless blend on the second pass.
See the Difference
How text-guided inpainting outperforms manual editing
Manual Editing
Manually remove an unwanted bench from a park photograph using clone stamp and healing brush tools. The editor samples nearby grass and path textures, painting over the bench pixel by pixel.
Visible seams where cloned textures meet the original. Repeating grass patterns create an obvious “stamped” appearance. Color mismatch along shadow edges where the bench used to cast shade. Requires 15-30 minutes of skilled manual work.
AI Inpainting
“Grassy meadow matching the surrounding landscape, soft afternoon lighting with dappled tree shadows continuing naturally across the path”
Seamless removal with naturally varied grass textures. Shadows continue logically from nearby trees. Color temperature matches the warm afternoon light of the original. The path curves naturally through the space where the bench was. Completed in under 10 seconds.
Inpainting in Action
Three common inpainting workflows with prompt strategies
A portrait photograph has an unwanted trash can visible in the background behind the subject. The background shows a brick wall with climbing ivy and warm directional sunlight from the left side.
“Continuation of weathered red brick wall with green ivy climbing upward, warm sunlight casting soft shadows from upper left, matching the depth of field blur of the surrounding background”
The prompt references specific visual properties already present in the image — the brick texture, ivy, lighting direction, and background blur level. Rather than simply saying “remove the trash can,” it tells the model exactly what should fill the space, ensuring the result is indistinguishable from the original background.
A product photograph of a ceramic vase was shot on a cluttered desk. The client needs the product displayed against a clean, professional studio background for an e-commerce listing.
“Seamless light grey studio backdrop with subtle gradient, soft even lighting matching the existing illumination on the product, clean shadow beneath the vase falling slightly to the right”
The prompt specifies a studio environment that matches the product’s existing lighting. By describing the shadow direction and gradient, it ensures the vase looks naturally placed rather than floating. The mask covers everything except the product, and the denoising strength is set high enough to fully replace the cluttered background while preserving the vase’s edges.
A scanned historical photograph from the 1940s has a large tear running diagonally across the upper right corner, destroying part of the sky and a building roofline. The image is in black and white with visible film grain.
“Continuation of overcast sky with period-appropriate clouds, completing the building roofline with matching 1940s architectural details, black and white photograph with natural film grain texture”
Restoration inpainting must match not just the scene content but the photographic medium itself. The prompt specifies the era, the monochrome format, and the film grain texture. By mentioning “continuation” and “completing,” it signals to the model that the goal is to seamlessly extend existing structures rather than generate new ones. The mask covers only the torn region, giving the model maximum context from the undamaged areas.
When to Use Inpainting
Best for targeted edits that must blend with existing imagery
Perfect For
Changing specific elements within a photograph while keeping the rest of the image untouched — replacing a sign, altering clothing color, or modifying a facial expression.
Eliminating unwanted elements from images — removing photobombers, power lines, blemishes, or distracting background objects — and filling with contextually appropriate content.
Swapping the background of a product shot, portrait, or scene while keeping the foreground subject perfectly preserved with natural edge transitions.
Repairing damaged, torn, scratched, or faded regions of photographs — particularly valuable for historical photo restoration and archival work.
Skip It When
When you want to transform the whole image rather than a specific region, use full image-to-image generation or text-to-image generation instead of masking the entire canvas.
When edits must be exact to the pixel — such as precise text replacement, technical diagram corrections, or UI mockup modifications — traditional vector or raster editing tools remain more reliable.
When the masked area exceeds roughly 60-70% of the image, the model has insufficient context to maintain coherence. At that scale, full image generation with a reference image produces better results.
Use Cases
Where inpainting delivers the most practical value
Photo Retouching
Remove blemishes, distracting objects, or unwanted reflections from photographs. Professional photographers use inpainting to clean up event photos, portraits, and editorial images in a fraction of the time traditional retouching requires.
Historical Image Restoration
Repair torn, scratched, water-damaged, or faded regions of archival photographs. Museums and archives use inpainting to restore historical images while preserving the period-appropriate photographic style and grain texture.
Real Estate Virtual Staging
Replace empty rooms or outdated furniture in property photographs with virtually staged interiors. Agents mask specific areas and prompt for furniture, decor, and finishes that match the room’s lighting and architectural style.
Product Photography Cleanup
Remove imperfections from product shots — dust, reflections, label damage, or background clutter — and replace with clean studio-quality fills. Particularly valuable for e-commerce where image quality directly affects conversion rates.
Content Moderation
Automatically redact or replace sensitive, inappropriate, or personally identifiable content within images. Platforms use inpainting to remove offensive graffiti, obscure license plates, or replace inappropriate content with neutral fills.
Creative Compositing
Add new elements to existing scenes — placing a character into a landscape, adding atmospheric effects, or compositing multiple visual elements. Artists use inpainting to seamlessly integrate generated content into hand-crafted or photographed scenes.
Where Inpainting Fits
From algorithmic fill to intelligent scene understanding
For simple removals (erasing a blemish, removing a wire), a minimal prompt or even an empty prompt often suffices — the model can infer what belongs from context alone. But as the edit grows more complex (adding a new object, changing a material, restoring missing architecture), prompt specificity becomes critical. The more the generated content must differ from what the context alone would suggest, the more detailed your prompt needs to be about lighting, perspective, material, and style.
Related Techniques
Explore complementary image generation techniques
Edit Images Intelligently
Apply inpainting techniques to your own image editing workflows or explore more AI image frameworks with our tools.