AI Gallery Wall Planner: How GalleryPlanner Uses AI (And Where It Doesn't)

"AI gallery wall planner" is a search that turns up a wildly different mix of tools — some genuine, some hype, some doing something completely different from what you probably mean. Before diving into what GalleryPlanner does, it's worth being honest about what someone might actually want when they search this phrase.

Three Things People Mean By "AI Gallery Wall Planner"

1. AI that picks which photo goes in which frame. A real problem worth solving: you have 30 photos and 8 frames; which combinations actually look good? GalleryPlanner does this with Smart Fill, using a real on-device AI model for face detection plus a scoring engine for aspect ratio, resolution, and composition.

2. AI that arranges frames on the wall. Not a real AI problem. Packing rectangles into a defined area while respecting spacing and margins is a classical optimization problem — solved well by deterministic and stochastic algorithms decades ago. GalleryPlanner's Auto Layout uses six of them (Masonry, Center Out, Skyline, Grid, Organic, Tidy Up). They're faster, more predictable, and produce better-looking results than any LLM would.

3. AI that generates a finished wall image from a prompt. That's what AI image generators do — synthesize a stock-looking room from a text prompt. GalleryPlanner intentionally isn't this. We don't invent images; we plan with the actual photos and frames you have, against the actual wall you measured.

GalleryPlanner solves #1 with genuine AI, #2 with deliberate non-AI math, and isn't trying to be #3. Each choice is on purpose.

What's Genuinely AI in GalleryPlanner

Face Detection

When you use Smart Fill, GalleryPlanner runs a real neural network — Google's MediaPipe BlazeFace, running in your browser — to find faces in your photos. It tells the rest of the system how many faces are in each photo, where they are, and how much of the frame they take up.

Why does this matter? Because cropping a portrait so a face touches the mat looks wrong. With face information, Smart Fill biases photo-to-frame assignments toward placements that preserve face composition. A portrait goes in a vertical frame. A two-person photo gets enough space around both heads. A group shot doesn't get squeezed.

The model runs locally. It never sees the internet.

Smart Fill — AI-Assisted Photo Assignment

Smart Fill is the feature that decides which photo goes in which frame. You give it photos, you give it frames, click Generate Fill Options, and it returns ranked photo-to-frame assignment options.

Under the hood, every possible photo-frame combination gets scored across four pillars:

• Aspect-ratio fit — does the photo crop cleanly into the frame's opening?
• Resolution — is the photo high enough resolution to look good at the frame's print size?
• Orientation — portrait, landscape, or square?
• Face composition — using the face-detection step above

A search engine then explores the combinatorial space to find high-scoring assignments. You see multiple ranked options to compare and pick from.

Is the entire engine AI? No. The scoring is rule-based with carefully chosen weights, and the search is a smart combinatorial optimizer — not a learned model. But it has one genuine ML step (face detection) feeding into algorithmic reasoning. Honest framing: this is AI-assisted, not "pure AI."

Whole-Wall Balance (Pro)

Once Smart Fill has assignment candidates, Whole-Wall Balance (Pro) runs a second pass scoring how well each candidate distributes brightness, warmth, face area, and visual weight across the full wall. Heavy frames not all on one side. Warm tones balanced against cool. Brighter photos spread out, not clustered.

It's algorithmic — tuned coefficients applied spatially, no learned weights — but it's the kind of composition reasoning a thoughtful designer would do by eye. The math version frees you to focus on the photos themselves.

What's Algorithms (Not AI) — and Why That's a Feature

Auto Layout, the feature that arranges your frames on the wall, is intentionally not AI. Here's why.

Packing rectangles into a defined area while respecting spacing, margin, and constraints is classical combinatorial optimization. Decades of computer-science work has produced fast, well-understood algorithms that solve it well — and they keep solving it well, deterministically, in milliseconds. Asking an LLM to arrange your frames would be slower, less predictable, and add zero value over those algorithms. It would also waste compute on a problem that doesn't need a neural network.

A few other GalleryPlanner features sometimes get casually labeled "AI" but aren't:

• Auto Level (Pro) — one-click tonal correction. Histogram-stretch image processing. Same approach a 20-year-old Photoshop action would use. Useful, but not AI.
• Color harmony scoring inside Smart Fill — a heuristic based on dominant-color extraction. Algorithmic, not learned. The internal documentation calls it "a simplified approximation, not a perceptually accurate color model."

Honest labeling matters. AI is a tool, not the answer to every problem. Where it genuinely helps — recognizing faces, evaluating photo composition — we use it. Where deterministic math does the job better, we use math. That deliberate split is part of why GalleryPlanner is fast, predictable, and runs entirely in your browser.

Why Local AI Matters

A lot of "AI" tools — gallery-wall or otherwise — upload your photos to a server to run their models. That has real consequences:

• Privacy. Family photos, client work, personal images all leave your device. You're trusting the vendor's terms of service and data handling.
• Speed. Network roundtrips add latency. You wait.
• Offline. No internet, no AI.
• Vendor risk. Someone has to pay for the inference. The vendor recoups it somehow — subscriptions, ads, or using your data to train future models.

GalleryPlanner's AI runs in your browser. The face-detection model is bundled with the app and executes locally via WebAssembly. The Smart Fill optimizer runs in a background worker thread. None of your photos ever transit to a server during inference, and you can verify it — there's no network activity around the AI step at all.

Practically: your family photos, your wedding photographs, your client work — none of it gets transmitted, stored on a server, or used to train someone else's model.

How the AI Features Fit Into a Workflow

A natural sequence:

1. Build your wall in Wall Properties with the real dimensions.
2. Add the frames you'll be using at actual sizes.
3. Run Auto Layout to place the frames on the wall. (Not AI — algorithms.)
4. Upload photos to the library.
5. Run Smart Fill to assign photos to frames. (Real AI + scoring.)
6. With Pro, Whole-Wall Balance runs a composition-scoring pass automatically and promotes better-balanced solutions.
7. Pick the option that reads best, then fine-tune individual photos as needed.

You can also right-click any single frame and use Auto-Fill for one-shot suggestions on just that frame.

What This AI Isn't

Just to be explicit about what GalleryPlanner doesn't try to do:

• Not generative. No "make my gallery wall feel cozy" prompts. GalleryPlanner doesn't invent images.
• Not a room mockup. It plans against measured walls, not photographs of rooms (though you can add a photograph of your wall as a custom Wall Texture in Pro for visual context).
• Not prompt-driven. You drive frame selection and placement; AI assists with photo composition.

Try It on Your Own Photos

The honest answer to "is GalleryPlanner an AI gallery wall planner?" is: yes for the parts where AI actually helps, deliberately no for the parts where it doesn't, and built so your photos never leave your machine either way.

Open GalleryPlanner, drop in a stack of photos, and run Smart Fill. The core AI features are free to try; Whole-Wall Balance is behind Pro.

Open GalleryPlanner →