Generating an image is only the first step. The techniques that turn a decent AI-generated image into a polished asset are upscaling (increasing resolution with added detail), inpainting (editing specific regions), and outpainting (extending the image beyond its original canvas). Together these three techniques cover almost all the post-generation work that separates "AI output" from "professional image." This guide explains what each is, how to use them well, which tools excel at each, and the workflow patterns that chain them together for finished results.
The problem each technique solves
Three distinct problems in AI image work.
Upscaling. AI models typically generate at 1024x1024 or similar resolution. For print, large displays, or detailed work, you need higher resolution. Upscaling increases resolution, ideally adding detail rather than just interpolating pixels.
Inpainting. Generated images often have specific problems — a wrong hand, an awkward expression, unwanted text, a weird background element. Inpainting lets you regenerate just the problem region while keeping everything else unchanged.
Outpainting. A generated image's canvas is fixed at generation time. If you need more canvas — for a different aspect ratio, or because the composition is too tight — outpainting extends the image coherently into new space.
Professional AI image work almost always uses all three, sequentially or in combination, to turn initial generations into finished assets.
Upscaling: adding detail vs interpolating
Traditional image upscaling (bilinear, bicubic) just interpolates existing pixels. The resulting image is larger but not more detailed; it actually looks slightly blurry because no new information was added.
AI upscaling is different. It uses a model trained on image pairs (low-res / high-res) to hallucinate plausible detail as it upscales. Faces gain fine features, textures gain grain, edges sharpen. The result is a larger image that looks genuinely higher-detail, not just enlarged.
Several upscaling models dominate in 2026.
Real-ESRGAN. The open-source workhorse. Decent quality, widely integrated, free. Still the default in many automated pipelines.
Topaz Gigapixel AI. Commercial product, particularly strong for photography. Standard in professional workflows.
Magnific AI. A service built specifically for AI-image upscaling with emphasis on artistic consistency. Adds detail aggressively.
Clarity Upscaler. Another AI-image-focused service with strong detail preservation.
Midjourney's built-in upscale. Integrated into the Midjourney workflow; produces images ready for most digital uses.
Stable Diffusion tiled upscaling. Using a process called "tiled diffusion," you can upscale images to very high resolutions (4K, 8K) using the diffusion model itself. Most flexible; requires technical setup.
Upscaling workflow patterns
A few patterns for different upscaling needs.
Simple 2x upscale for digital use. Midjourney's built-in upscale or Real-ESRGAN via API. One-step, zero-config. Produces images suitable for web, social media, screens.
4x upscale for print. Topaz Gigapixel or Magnific. Better detail preservation at larger scales. Takes minutes per image but produces print-ready output.
Extreme upscale (8x or more) for billboards or large prints. Tiled Stable Diffusion upscaling. Requires patience but produces images at effectively arbitrary resolution with coherent detail.
Selective detail upscaling. Sometimes you want specific regions (a face, a detail) upscaled more than the rest. Tile-based upscaling or separate-upscale-then-composite approaches.
For most work, Midjourney's built-in upscale or a single-pass through Real-ESRGAN is sufficient. Advanced upscaling is for specific high-bar work.
Inpainting: surgical edits
Inpainting is AI generation restricted to a specific mask. You select a region, write a prompt, and the model regenerates just that region — seamlessly blending with the surrounding image.
Typical inpainting uses. Fix hands or faces that came out wrong. Remove unwanted elements (a watermark, a stray object, an awkward background detail). Replace a specific element (change a shirt colour, swap out a background element). Add elements (put a prop in a character's hand, add a sign to a scene).
Inpainting is where a rough generated image becomes a polished final image. It turns "the composition is right but three things are wrong" into "the composition is right and everything works."
Every major tool supports inpainting. Midjourney has Vary Region. Stable Diffusion has full-featured inpainting in Automatic1111, ComfyUI, and similar tools. Flux has inpainting via Flux Inpaint. DALL-E and Imagen support inpainting through their respective UIs.
Inpainting technique
Getting good inpainting results takes practice.
Mask carefully. A mask that is too tight leaves visible seams; a mask that is too loose changes unrelated parts. Good masks follow the edges of the thing you want to replace with a small feather.
Write focused prompts. The inpainting prompt should describe what you want in the masked area, with context about what surrounds it. "A natural-looking hand holding a coffee cup" is better than "fix this hand."
Adjust denoising strength. In Stable Diffusion-based tools, the denoising strength parameter controls how much the original pixels influence the output. Low strength preserves original; high strength regenerates aggressively. Most inpainting wants values between 0.5 and 0.8.
Iterate on the mask. If the inpainting does not look right, often the fix is a better mask — redraw, re-inpaint.
Consider context. The inpainting model only sees the masked region plus a bit of context. If you need to maintain consistency with distant parts of the image (same character features across a full-body shot), additional conditioning (IPAdapter, ControlNet) may be needed.
Outpainting: extending the canvas
Outpainting is conceptually the reverse of inpainting. Instead of regenerating a region inside the image, you extend beyond the existing edges, letting the AI generate what plausibly continues the scene.
Common uses. Change aspect ratio (a 1:1 image extended to 16:9 for a banner). Loosen a too-tight composition (more room around the subject). Add environmental context (reveal more of the scene around a portrait). Pan across a scene (generate a wider view).
Outpainting is particularly useful when your initial generation nailed the subject but missed the framing. Instead of regenerating the whole image with a different aspect ratio (which changes everything), outpaint to add the needed space while keeping the core composition.
Midjourney's Zoom Out and Pan features handle outpainting well. Stable Diffusion and Flux support outpainting through the same inpainting tools (essentially inpainting a transparent canvas extension). DALL-E supports outpainting via ChatGPT's editor.
Outpainting technique
Good outpainting requires care about continuity.
Extend gradually. Large outpainting extensions (doubling the canvas) often produce disconnected results. Extending by 25-50% at a time and iterating produces more coherent extensions.
Prompt with scene context. The outpainting prompt should describe what should appear in the new area, consistent with the existing scene. "Continuing the forest scene into a meadow with wildflowers in soft morning light."
Respect existing lighting. Outpainted regions should match the lighting of the original. Explicit lighting descriptions in the prompt help.
Iterate on the edges. The seam between original and outpainted region is where continuity breaks first. If the seam is visible, mask and re-inpaint a strip across it to blend.
Set expectations. Outpainting works best for extending natural environments and open compositions. Outpainting tight product shots or architectural images often produces worse results than generating wider from scratch.
Workflow: combining all three
A complete post-generation workflow often uses all three in sequence.
Step 1: generate the initial image. Get the composition, subject, and style roughly right. Accept imperfections that can be fixed.
Step 2: inpaint specific problems. Fix hands, faces, unwanted elements, clothing issues. Each inpainting pass targets one specific problem.
Step 3: outpaint if needed. Adjust composition by extending the canvas. Typically one or two outpaint passes for a meaningful adjustment.
Step 4: upscale. Final resolution pass using your preferred upscaler.
Step 5: final polish. Any adjustments that AI tools cannot produce — colour grading, logo addition, text overlay, compositing.
This five-step workflow takes 30-60 minutes per hero image. The result is output that can legitimately compete with traditional creative production. Skipping any step leaves artefacts of the AI process.
Inpainting with identity preservation
A specific inpainting challenge: editing a face or character while preserving identity. If you change an expression, the person should still look like themselves. Generic inpainting often produces a new face that does not match.
Techniques that help. Use IPAdapter FaceID alongside inpainting to condition on the target face. Narrow the inpaint mask to just the region you want to change (eyes, mouth) rather than the whole face. Use a lower denoising strength to preserve more of the original.
For production work, dedicated face-swap and expression-editing tools (Face Fusion, specific Stable Diffusion workflows) offer more reliable identity preservation than generic inpainting. These are worth learning for any character-heavy project.
Outpainting workflow for different aspect ratios
A common use of outpainting: converting an image to a different aspect ratio while preserving the subject. The workflow.
Start with the original image generated at whatever aspect ratio you initially chose. Often the composition is right but you need a different shape for a specific placement.
Extend the canvas in the direction needed. For a 1:1 image that needs to become 16:9, outpaint left and right equally. For a portrait becoming landscape, outpaint horizontally.
Prompt specifically about what should fill the new area. "Continuing the sunset sky on both sides, extending the beach environment" is better than leaving it to default.
Outpaint iteratively in modest increments. One 100% extension usually produces artefacts; two or three 25-30% extensions maintain coherence.
Sanity-check the result. Outpainting sometimes changes the apparent lighting or style subtly; compare side-by-side with the original to spot drift.
Tiled upscaling for extreme resolutions
For images intended for large prints, billboards, or high-resolution digital displays, standard 2x or 4x upscaling is not enough. You may need 8x, 16x, or beyond.
Tiled upscaling is the technique that makes this possible. The image is broken into overlapping tiles; each tile is upscaled with added detail via a diffusion model; tiles are stitched back together with seamless edges.
Stable Diffusion with tiled diffusion extensions (Ultimate SD Upscale, Tiled Diffusion) produces astonishing results at very high resolutions. Processing takes longer — a 1024x1024 image upscaled to 8192x8192 might take 10-20 minutes on a decent GPU — but the quality is print-ready.
For professional print work, tiled upscaling has effectively replaced traditional large-format upscaling in many workflows. The resulting detail at arbitrary scale is unprecedented.
Specialised tools worth knowing
Beyond the general-purpose tools, a few specialised services.
Magnific Upscaler. Specifically designed for AI images with aggressive detail enhancement. Often produces images that look more realistic than the input.
Leonardo AI's Photoshop-like tools. Browser-based editor combining multiple generation and editing tools in one UI.
Photoshop's Generative Fill. Photoshop's built-in AI inpainting and outpainting. Integrates with Adobe's Firefly model. Not the best quality but the tightest integration with Photoshop workflows.
Krea AI. Real-time AI image editing with immediate feedback. Good for quick exploration; less suited to precise high-bar work.
ClipDrop. From Stability AI. Suite of specialised tools including inpainting, outpainting, upscaling, background removal. Integrated workflow for common tasks.
Common mistakes with post-generation tools
Anti-patterns.
Accepting the first upscale. Upscaling can introduce artefacts. Always examine the output at 100% zoom before committing.
Over-inpainting. Fixing one thing often reveals another; tempting to keep fixing. Know when to stop. Three inpainting passes is usually a reasonable maximum.
Outpainting beyond coherence. Extending an image 3x usually fails. Two iterations of 50% extension usually work.
Skipping the final polish. AI tools take you 90% of the way; manual polish in Photoshop or similar is often what separates pro from amateur.
Not documenting the process. For team work, save intermediate versions. The original generation, the inpainted version, the outpainted version, the upscaled version — each is a snapshot you might want to revisit.
Comparing major upscalers in practice
A concrete comparison of how different upscalers handle the same AI-generated images.
Real-ESRGAN: fast, competent, free. Adds reasonable detail but sometimes smooths textures in ways that look slightly artificial. Still the workhorse for automated pipelines.
Topaz Gigapixel AI: strongest for photographs. Adds film-grain and texture that looks natural. Commercial pricing but standard in professional photo workflows.
Magnific AI: aggressive detail addition. Can sometimes add detail that was not implied by the source. Impressive for stylised AI images; occasionally too much for photos.
Clarity Upscaler: balanced between subtlety and detail enhancement. Good middle ground for AI images.
Tiled Stable Diffusion: most flexible, highest ceiling, most effort. For serious print work or extreme resolutions.
For most 2x-4x upscaling tasks, Real-ESRGAN is sufficient and free. For professional work, Topaz or Magnific justify their cost. For extreme scales, tiled Stable Diffusion is unique.
When AI tools are not the right answer
For some post-generation tasks, traditional tools beat AI.
Precise colour grading remains better in Photoshop, Lightroom, or dedicated grading tools. AI can suggest grades; applying them consistently across a series is still better done manually.
Compositing multiple elements — combining an AI character with an AI background and real logos — is faster in Photoshop than trying to generate the composite in one AI step.
Typography and layout for posters, book covers, or marketing materials is a design task. AI-generated text is unreliable; proper typography tools are the right choice.
Fine retouching of skin, small detail adjustments, or subtle tonal changes benefit from traditional tools. AI tends to overshoot or understretch these.
The common pattern: AI for generation and major edits, traditional tools for precision and typography.
Automation: batch processing with these techniques
For teams processing many images, the post-generation techniques can be automated. A few patterns.
Automated upscaling pipelines. After generation, every image runs through a standardised upscaling step. Tools like Replicate, Modal, and internal job queues handle this easily. Produces consistent quality across a library of generated images.
Selective inpainting via automated masks. For common issues (faces, hands), automated mask-generation plus inpainting can fix problems before human review. Not perfect but reduces the manual workload.
Aspect-ratio converters. When the same image is needed at multiple aspect ratios (one for Instagram feed, one for Stories, one for banner), automated outpainting pipelines produce all variants from one generation.
Building these pipelines takes engineering investment but pays off at scale. For teams producing hundreds or thousands of images per month, the automation cost is trivial compared to the time savings.
Quality benchmarking for your pipeline
How to evaluate whether your post-generation workflow is working.
Before/after comparisons. For each image, save the original generation alongside the final polished version. Side-by-side review reveals whether your tools are genuinely improving quality.
A/B testing across tools. Run the same images through different upscalers, different inpainting workflows. Compare outputs. Often one tool clearly produces better results for your specific domain.
Human evaluation on sample sets. Have non-AI-experts rate images on usability. This catches subtle issues that look fine to an experienced eye but feel "off" to viewers.
Downstream metrics. If your images go into products (ads, social posts, ecommerce listings), track performance. Better-polished images usually perform better; quality investments show up in conversion numbers.
Upscale to add resolution, inpaint to fix one part, outpaint to extend the scene. Three tools, three jobs — and a complete post-generation workflow that turns rough AI output into polished finished work.
The short version
Upscaling, inpainting, and outpainting are the three techniques that turn AI-generated images into finished assets. Upscaling adds resolution with detail. Inpainting fixes or changes specific regions. Outpainting extends the canvas. Each has mature tools across the major AI image ecosystems. A complete workflow uses all three, plus final polish in traditional tools. Learning these techniques well is the difference between sharing AI output that looks AI-generated and shipping work that looks professionally produced.