What if You Could Erase Anything from a Photo?
Imagine pointing at an unwanted object in a photo – a photobomber, a trash can, an ex – and having it simply disappear, replaced with a perfect continuation of the background. That's the magic of inpainting.
Once the exclusive domain of skilled Photoshop artists spending hours with clone stamps and healing brushes, AI has made inpainting accessible to everyone. But how does it actually work? And how can you get the best results?
Understanding Inpainting
Inpainting is the process of reconstructing missing or damaged parts of an image. The term comes from art restoration, where conservators "paint in" damaged areas of historical paintings to make them whole again.
In digital imaging, inpainting fills in selected regions with content that seamlessly blends with the surrounding image. The AI doesn't just blur or smudge – it generates new, contextually appropriate content.
The Two Key Components
- The Mask: A selection that tells the AI which part of the image to replace
- The Context: The surrounding image that informs what the replacement should look like
When you mask out a person standing on a beach, the AI looks at the sand, water, and sky around them and generates a seamless continuation of that scene.
How AI Inpainting Works
Traditional Inpainting (Pre-AI)
Classical algorithms used mathematical approaches:
- Diffusion-based: Spreads pixel values from edges inward, like ink bleeding on paper
- Patch-based: Finds similar patches elsewhere in the image and copies them
- Exemplar-based: Combines patch matching with priority-based filling
These work reasonably well for small areas and simple textures but fail spectacularly with large regions or complex content. They have no understanding of what's in the image.
AI-Powered Inpainting
Modern AI inpainting uses deep learning, typically based on one of these architectures:
Generative Adversarial Networks (GANs)
Two neural networks compete: a generator creates the inpainted content, while a discriminator tries to detect if it's fake. This adversarial process produces remarkably realistic results.
Diffusion Models
The same technology behind Stable Diffusion and DALL-E. The model learns to reverse a noise-adding process, effectively "denoising" the masked region into coherent content. Currently produces the most impressive results.
Transformer-Based Models
Use attention mechanisms to understand long-range dependencies in images. Excellent at maintaining consistency across the filled region.
The Magic of Context Understanding
What makes AI inpainting remarkable is semantic understanding. The AI doesn't just see pixels – it understands concepts:
- "This is a face, and faces have bilateral symmetry"
- "This is grass, and grass has a particular texture pattern"
- "This is a window on a building, and windows are usually rectangular"
This understanding allows the AI to generate content that's not just visually similar but conceptually correct.
Inpainting Applications
Object Removal
The most common use case. Remove unwanted elements:
- Photobombers from vacation photos
- Power lines from landscape shots
- Watermarks or logos
- Temporary objects (construction, vehicles)
- Ex-partners from group photos
Photo Restoration
Repair damaged photographs:
- Fill in torn or missing sections
- Remove scratches and dust marks
- Restore water-damaged areas
- Reconstruct faded or stained regions
Content-Aware Fill
Extend or modify compositions:
- Remove and replace backgrounds
- Extend images beyond their original boundaries
- Fill gaps when stitching panoramas
- Adjust composition by removing elements
Creative Editing
Transform images artistically:
- Replace objects with different ones
- Change clothing or accessories
- Modify environments and settings
- Create surreal compositions
Mastering the Mask
The quality of your inpainting depends heavily on your mask. Here's how to create effective masks:
Size Matters
Make your mask slightly larger than the object you're removing. This gives the AI more context and creates smoother blending. A mask that's too tight leaves visible edges.
Edge Quality
For best results:
- Soft edges: Feather the mask slightly for seamless blending
- Include shadows: Don't forget to mask shadows cast by the object
- Include reflections: Water, glass, and shiny surfaces need attention
Shape Strategy
The shape of your mask affects results:
- Simple shapes: Easier for the AI to fill convincingly
- Complex shapes: May require multiple passes or manual cleanup
- Avoid thin protrusions: These can confuse the AI
Tips for Better Results
1. Work in Stages
For complex removals, don't try to remove everything at once. Remove one element, let the AI fill it, then move to the next. Each step gives the AI more context for the next.
2. Use Prompts When Available
Many modern inpainting tools accept text prompts. Instead of just removing, tell the AI what you want:
- "Continue the brick wall texture"
- "Grass and wildflowers"
- "Clear blue sky"
3. Mind the Lighting
AI can struggle when the masked area crosses different lighting zones. If removing a person who's partly in shadow and partly in sunlight, consider doing it in two passes.
4. Consider Perspective
When removing objects from surfaces with perspective (floors, roads, buildings), the AI needs to maintain that perspective. Larger masks help here.
5. Clean Up Afterwards
AI inpainting isn't always perfect. After the main fill:
- Check for repeated patterns (AI sometimes gets stuck in loops)
- Look for color inconsistencies
- Verify edge blending
- Small manual touch-ups may be needed
Inpainting Limitations
What AI Struggles With
- Faces: Removing or replacing facial features is challenging – the AI may create uncanny results
- Text: Inpainting over text rarely produces readable replacement text
- Complex patterns: Intricate, regular patterns (like plaid or detailed architecture) may not align perfectly
- Unique objects: If you remove a landmark or specific item, the AI can't know what should replace it
- Large areas: The bigger the mask, the more the AI has to "imagine" – and the more room for error
The Context Problem
Inpainting can only work with available context. If you mask 80% of an image, the AI has very little to work with. Similarly, if the object you're removing is the only instance of something (the only person at a specific distance, the only tree of that species), the AI won't have reference material.
Inpainting vs. Other Techniques
Inpainting vs. Outpainting
Inpainting fills in masked regions within an existing image. Outpainting extends the image beyond its original boundaries. Both use similar AI technology but for different purposes.
Inpainting vs. Image-to-Image
Inpainting replaces specific masked areas while preserving everything else. Image-to-image transforms the entire image based on a prompt, affecting everything to some degree.
Inpainting vs. Background Removal
Background removal separates foreground from background (usually keeping the subject). Inpainting can remove the subject and fill in the background, or modify any part of the image.
The Ethics of Inpainting
With great power comes responsibility. AI inpainting raises important questions:
- Authenticity: Should edited photos be disclosed?
- Historical accuracy: Is it okay to "restore" photos in ways that alter history?
- Consent: Removing people from photos without their knowledge
- Deception: Creating fake evidence or misleading images
Use inpainting ethically. For professional work, many industries require disclosure of significant edits.
Getting Started with Inpainting
Tools to Try
- Pixelift: Easy-to-use interface with powerful AI inpainting
- Stable Diffusion + ControlNet: Maximum control for advanced users
- Adobe Photoshop: Content-Aware Fill with AI enhancement
- DALL-E: Inpainting through ChatGPT or the API
- Runway ML: Professional-grade tools with inpainting capabilities
Your First Inpainting Project
- Choose an image with a clearly removable element
- Create a mask slightly larger than the object
- Include any shadows or reflections in the mask
- Run the inpainting
- Review and refine if needed
Conclusion
AI inpainting has democratized a skill that once required years of practice. Whether you're cleaning up family photos, preparing professional images, or exploring creative possibilities, inpainting is an essential tool in the modern image editing toolkit.
The technology continues to improve rapidly. What requires careful masking and multiple attempts today may be seamless and automatic tomorrow. But understanding the fundamentals – masking, context, and limitations – will always help you get better results.