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Architecture-Trained AI

The only AI virtual staging engine trained on real architecture

Generic image models — Midjourney, DALL·E, Stable Diffusion — were trained on the open internet, not on listings. They hallucinate doors, melt windows, and reshape rooms. Edensign's model was trained, end-to-end, on a proprietary dataset of architectural and interior photography — so the building stays the building.

Empty living room before AI editing
Same living room, virtually staged — walls, windows, floor preserved
之前
之后
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The dataset

A proprietary corpus of architectural dataset — not the open web.

Generic image models eat whatever they find online. We hand-built a training corpus around the only thing that matters for real estate: real rooms, real buildings, real listing photography. Every sample is curated for structural accuracy and licensed for commercial use.

8M+
Curated property photos

Interior, exterior, aerial, and floor-plan imagery sourced under license from MLS partners, brokerages, and professional listing photographers.

40+
Room and space types

From walk-in closets to commercial kitchens to vacant lots — labeled at room-type granularity so the model knows what it is editing.

120+
Design styles tagged

Modern farmhouse, mid-century, Hamptons, Japandi, coastal, industrial — annotated so style transfer respects how each style actually looks in a real home.

100%
Human-reviewed labels

Every training pair was annotated by trained reviewers for room type, design style, structural elements, and MLS compliance — not scraped alt-text.

How the model is trained

Four stages that generic image AIs skip — because they were never built for real estate.

  1. 1

    Stage 1

    Architecture-aware pre-training

    The model learns geometry, perspective, and material physics from a corpus weighted heavily toward architectural photography. It learns that a wall has a flat plane, a window has a frame, a floor has a vanishing point — before it ever sees a piece of furniture.

  2. 2

    Stage 2

    Element-preservation fine-tuning

    Trained on millions of before/after listing pairs where structure stays locked. The model is explicitly penalized when walls, windows, doors, ceiling lines, or built-ins shift between source and output. Generic image models have no such loss term — they have no reason to care.

  3. 3

    Stage 3

    Spatial reasoning and intelligence

    A dedicated reasoning stage teaches the model to understand a room before it edits one — scale, depth, light direction, sight lines, and how a sofa actually fits between two walls. It places furniture the way a human stager would, not the way a 2D collage tool would, so the result looks lived-in instead of pasted-in.

  4. 4

    Stage 4

    Real-estate human feedback

    Final alignment pass reviewed by working agents, listing photographers, and MLS compliance officers. They reject outputs that would mislead a buyer — added rooms, removed structural elements, fabricated views. The model learns the rules of the industry, not just the rules of pixels.

Benchmarks

How our model measures up

Measured on a held-out set of 1,000 real listing photos. Numbers are internal estimates pending external audit.

MetricEdensignGeneric image AI
Wall-line preservation99.4%71%
Window geometry fidelity98.1%54%
Floor perspective consistency99.0%62%
No hallucinated doors/openings99.8%78%
MLS compliance rate (reviewed)99.5%—

Generic-AI column: averaged across Midjourney v6, DALL·E 3, and Stable Diffusion XL on the same held-out set. Full methodology available on request.

vs. Generic image AI

What generic image AI does to listing photos

Three failure modes you have probably already seen when an agent tried Midjourney on a listing.

GPT Image 2

Repaints the room

Treats the source photo as a loose suggestion. Wall colors shift, trim details rewrite themselves, and built-in cabinetry gets quietly redrawn. The room is recognizable but no longer the same listing — and any buyer comparing online to in-person will notice.

Gemini (Nano banana)

Bends architecture to fit furniture

Strong at general image edits, but with no architectural prior it will subtly resize a window, tilt a ceiling line, or shorten a wall so the sofa it generated fits. Furniture looks great; the building it sits in no longer matches reality.

Qwen

Loses fine fixtures and finishes

Outlets, switches, vent covers, and hardware quietly disappear or relocate between source and output. The room reads as plausible at thumbnail size but falls apart on a full-screen MLS view — and a careful buyer will spot every missing detail.

Midjourney

Renames the property

Generates a beautiful interior — that does not match the building you photographed. Wall finishes change, fireplaces appear, the ceiling height shifts. The listing photo is no longer the listing.

DALL·E

Adds doors and windows

Will routinely add a second window or a French door because "rooms like this usually have one." On MLS that is a material misrepresentation. On Zillow it is a complaint waiting to happen.

Stable Diffusion

Melts geometry

Without an architecture-aware loss term, straight lines bend, baseboards drift, and tile grids distort. A buyer who tours the home sees a different room than the one they swiped right on.

Hallucination examples shown above are documented from public outputs of each model on listing photography prompts. Edensign was trained specifically to make these failure modes impossible.

Examples

See what architecture-aware editing looks like

Each pair below was edited by Edensign in a single pass. Walls, windows, ceiling, and floor stay pixel-locked — only the staged or restored content changes.

Empty bedroom → virtually staged — before
Empty bedroom → virtually staged — after
Before
After
Empty bedroom → virtually staged
Outdated living room → modern restage — before
Outdated living room → modern restage — after
Before
After
Outdated living room → modern restage
Kitchen renovation preview — before
Kitchen renovation preview — after
Before
After
Kitchen renovation preview
Dining room restage — before
Dining room restage — after
Before
After
Dining room restage
Exterior · day → dusk — before
Exterior · day → dusk — after
Before
After
Exterior · day → dusk
Whole-room renovation preview — before
Whole-room renovation preview — after
Before
After
Whole-room renovation preview

Frequently asked questions

The training data is the model. A model trained on stock illustration, anime, and Pinterest will hallucinate fictional rooms. A model trained on listing photography learns the structural conventions of real buildings — load-bearing walls do not move, windows have frames, ceilings are flat. For real estate, dataset provenance is the difference between an editing tool and a liability.

No. Edensign trains and runs its own model end-to-end. We license no third-party image generation API. Wrapper products inherit every hallucination of their upstream model — and have no ability to add architecture-aware constraints, MLS compliance logic, or element-preservation guarantees. We can, because we own the model.

We hold out a benchmark set of 1,000 real listing photos and run before/after pairs through a battery of geometric checks: wall-edge IoU, window-frame corner offset, floor vanishing-point deviation, no-new-opening detection, and human MLS-compliance review. The benchmarks page shows our current numbers; full methodology is published on request.

We watch and benchmark every public release. They keep getting better at images — but generic image generation and listing-faithful editing are different problems. As long as the loss function rewards 'beautiful' instead of 'identical building,' the gap on structural fidelity remains. We retrain and ship monthly to stay ahead on the metrics that matter for listings.

Licensed partnerships with MLS associations, brokerage image archives, and a network of professional listing photographers. All images are licensed for AI training and downstream commercial use. No scraping, no questionable sources, no user-uploaded content used for training without explicit opt-in.

A high-level model card with dataset composition, training stages, evaluation methodology, and known limitations is available to brokerage, enterprise, and API customers under NDA. Contact us at hello@edensign.io.

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