Best AI Tools for Inpainting and Outpainting Images (2026)

Last month I had a client photo — a perfect portrait shot, but the composition was too tight. The subject's shoulder was cut off on the left side and there wasn't enough headroom. In the old world, that shot was either fixed in reshoot or abandoned. I ran it through three different AI outpainting tools in about 20 minutes and had a natural-looking extended composition that nobody could identify as AI-generated.

That's what good inpainting and outpainting does now. It's not magic and it doesn't always work — I'll show you exactly where each tool fails — but when it works, it saves hours of manual retouching or expensive reshoots.

The distinction between these two operations matters for understanding which tool to reach for. Inpainting is surgery: you're replacing or healing a specific area inside the image. Outpainting is expansion: you're extending the canvas beyond the original frame. Different problems, and while many tools do both, they often excel at one more than the other.

Inpainting vs Outpainting Explained

Inpainting involves selecting (masking) an area within an image and having the AI fill that area with generated content that matches the surrounding scene. Use cases include:

• Removing unwanted objects (people in the background, logos, distractions)
• Fixing damage or defects in old photos
• Replacing elements (change a blue sky to dramatic sunset)
• Cleaning up artifacts from other AI generations

Outpainting extends the image beyond its original borders. The AI analyzes the existing scene's content, style, and lighting to generate plausible continuation. Use cases include:

• Fixing tight crops or poor composition
• Converting portrait-orientation images to landscape for specific platforms
• Creating wider establishing shots from close crops
• Generating context around product photos

Both operations run on diffusion model pipelines, and most tools that support one support the other. Quality varies dramatically between tools and between different types of source images.

Tool Comparison: Inpainting and Outpainting

I ran each tool through identical test cases: a landscape photo with object removal, a portrait with background extension, and a product image requiring aspect ratio conversion.

According to Adobe's 2025 Creative Trends report, Generative Fill was used in over 9 billion image edits within the first year of its release — making it the fastest adopted feature in Photoshop's history. That adoption rate reflects how fundamental these capabilities have become.

Real Use Cases and Results

Object Removal (Inpainting)

Removing a person from a beach photo is the classic test. Photoshop Generative Fill handles this best when the background has clear patterns (waves, sand, sky) that the model can extrapolate. The mask precision and edge blending are best-in-class here because Adobe has had the most refinement time on this specific use case.

Stable Diffusion with the built-in inpainting model produces comparable results with the right settings, particularly the "inpaint only" masking mode that blends more naturally. The advantage is no credit limits and more control over the generation parameters.

DALL-E's inpainting via the API or ChatGPT interface is surprisingly capable for this task, though it tends to overfill — generating slightly too much content that doesn't quite match the photographic style of the source.

For more context on how these tools work in broader photo editing workflows, the Adobe Firefly review covers the full range of Firefly's capabilities beyond just inpainting.

Background Extension (Outpainting)

This is where the quality gap between tools becomes most visible. Background extension requires the model to understand the depth, lighting direction, perspective lines, and content style of the original image and continue them convincingly.

Stable Diffusion excels here specifically because you can control how much influence the original image has on the generation (the denoising strength parameter). For photographic outpainting, running at 65–75% denoising strength produces extensions that blend naturally at the seam.

Adobe Firefly's expand-by-dragging interface makes outpainting very accessible, and it handles sky extensions and room expansions particularly well. The commercial licensing safety is a real advantage if you're doing this work for clients.

Canva's AI outpainting is the weakest on this list. It works for simple, low-stakes content like social media backgrounds, but the seam quality and consistency of complex scenes is noticeably lower than the other options.

Product Photo Expansion

This is one of my favorite use cases. An e-commerce client has product photos shot at 4:3 but needs 16:9 for banner ads. Instead of reshooting, outpainting can extend the background while keeping the product itself untouched.

Photoshop Generative Fill does this better than any other tool when the product is shot against a simple background (white, colored, gradient). The "Generative Expand" feature specifically is optimized for this workflow and handles subtle gradients and shadow extension with impressive accuracy.

The Canva AI vs Adobe Firefly comparison covers how these two tools stack up for professional use cases like this in more detail.

Working With Stable Diffusion for Inpainting

If you want the maximum quality ceiling without ongoing costs, Stable Diffusion with ControlNet is the answer. The setup is more involved, but the control you get is unmatched.

The key settings for quality inpainting in Automatic1111:

• Inpaint masked / Original: Use "Inpaint masked" to change selected areas, "Inpaint not masked" to recontextualize everything else
• Masked content: "Fill" works well for replacing objects; "Original" preserves more texture at mask edges
• Denoising strength: 0.75–0.85 for significant changes; 0.4–0.6 for subtle blending
• Soft inpainting: Enable this for natural feathered edges — critical for photographic work

For outpainting specifically, the "Poor man's outpainting" script in Automatic1111 is built-in and effective. For complex scenes, using ControlNet's tile model during outpainting dramatically improves consistency with the source image.

The best AI image generators 2026 overview includes Stable Diffusion in the broader context of the current tool landscape.

DALL-E Inpainting: When It Makes Sense

DALL-E 3's inpainting (accessible via the ChatGPT interface or API) has one specific advantage over all others: it understands natural language descriptions of what you want to happen in the masked area. You can write "remove the person and replace with wooden dock continuing the lake scene" and it actually follows that instruction.

This text-understanding capability makes DALL-E the best choice when you're doing creative replacement rather than removal — swapping one object for something specific, adding elements to a scene based on a description, or making interpretive changes to a photo.

The Midjourney vs DALL-E 3 comparison covers how these two approaches to AI-guided image creation differ at a fundamental level.

Free access to DALL-E inpainting is available through Bing Image Creator with daily free credits. The Bing Image Creator guide walks through the current free access options.

Tips for Getting Clean Results

Match your mask to the content. Rough masks work for solid object removal; tight, precise masks are essential for hair, fine edges, and transparent objects.

Work at full resolution. Both inpainting and outpainting degrade faster at low resolutions. If your source image is small, upscale it first.

Use feathering on your masks. Hard-edged masks produce visible seams. Most tools have a feather/blur option for masks — use 5–15 pixels for photographic work.

Generate multiple variations. Never accept the first result. Run 4–6 variations of any inpainting or outpainting job and pick the best. Good tools let you iterate quickly.

Extend in stages for large outpaints. If you're expanding an image significantly (more than 50% of original size), do it in two passes. Extend 25% each time rather than trying to do 50% at once — the quality degrades significantly with very large single expansions.

For exploring the full range of what these AI editing tools can do, the Ideogram AI review covers a tool with some unique approaches to image editing alongside generation.

When AI Inpainting Fails

These tools aren't reliable for everything. Complex architectural details with repeating patterns (brick walls, tile floors, ornate moldings) often produce obvious artifacts in filled areas. Human faces near the mask edge get distorted. Reflections and shadows don't extend correctly in most outpainting scenarios.

If you're working on high-stakes commercial images where quality must be perfect, use AI tools for rough work and budget manual retouching time for final polish. AI inpainting as a first pass that you refine manually is more realistic than AI inpainting as a complete solution for complex edits.

Conclusion

For most photo editors, the best starting point is whichever tool you already use. If you're in Photoshop's ecosystem, Generative Fill is genuinely excellent and worth the subscription cost. If you want no ongoing cost, Stable Diffusion gives you the highest ceiling with the most control.

Adobe Firefly is the right choice if commercial safety matters — its training on licensed content means you can use the outputs in client work without worrying about copyright complications.

Start with one specific use case you face regularly — background removal, crop extension, or object replacement — and practice that one workflow with one tool before expanding. The learning curve for good results is real but short. A few hours of practice separates mediocre AI editing from results that look genuinely professional.

The prompt engineering guide will help you get better results from any of the text-guided tools in this comparison.