AI + REAL ESTATE

AI Property Visualization

Stable Diffusion + ControlNet for Interior Design at Scale

We built Proptifi — a generative AI platform that lets property developers and buyers visualize interior redesigns in seconds. Upload a room photo, choose a style, and receive photorealistic AI renders — powered by Stable Diffusion and ControlNet. This is one of our flagship AI engineering projects.

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Project Snapshot

ClientProptifi
DomainPropTech / Generative AI
StackAngular, Python, Stable Diffusion, ControlNet
Live URLproptifi.com
Year2024 – 2025

The Problem We Solved

Real estate developers spend ₹5–15 lakh on 3D renders for an apartment project before a single unit is sold. Buyers still walk through empty shells and struggle to imagine what the finished space will look like. Interior designers charge for revisions on early-stage concept work.

The Proptifi founders had a sharp insight: Stable Diffusion had reached a quality threshold where AI-generated room redesigns were indistinguishable from commissioned renders — but accessing it required GPU expertise most product teams don't have.

They needed a product that a non-technical property agent could use: upload a photo, pick a style from a curated menu (Scandinavian / Industrial / Luxury Modern / Minimalist), and receive 4 photorealistic variants in under 60 seconds.

Our Approach

Two hard problems: structural consistency + production-grade throughput at low cost.

1. ControlNet for structure preservation

Vanilla Stable Diffusion ignores room geometry — it hallucinates windows in walls and moves doorways. We used ControlNet with a depth map preprocessor to lock the structural skeleton of the original room, allowing the diffusion model to change only surfaces (walls, floor, furniture) while keeping architectural lines intact.

2. Style-specific prompt engineering

We built a curated prompt library for each interior style, fine-tuned against a validation set of professional renders. Each style has a positive prompt, a negative prompt, and a ControlNet weight schedule calibrated to balance style strength against structural fidelity. The result: consistent, on-brand outputs without per-user prompt knowledge.

3. Cost-optimised GPU pipeline

We designed the inference pipeline on spot GPU instances (RunPod) with a queue-based job system. A generation job costs under ₹2 per render at 512×512 upscaled to 1024×1024. The Angular frontend polls for completion and renders results progressively — users see partial results in ~20 seconds rather than waiting 60 seconds for a full set.

Technology Stack

FrontendAngular 17, TypeScript, RxJS
Backend APIPython (FastAPI), RESTful JSON
AI ModelsStable Diffusion XL, ControlNet (depth), ESRGAN upscaler
GPU InferenceRunPod spot instances, CUDA 12, VRAM-optimised batching
Image ProcessingOpenCV (depth map extraction), Pillow, ComfyUI pipeline
StorageAWS S3 (render storage), CloudFront CDN
DatabasePostgreSQL (job queue + user history)

What Was Delivered

< 60s

4 AI renders delivered

₹2

Cost per render at scale

4 styles

Curated interior design modes

AI Product Development — Common Questions

Yes. The core pattern — image-conditioned generation with structure preservation — applies to fashion, e-commerce product staging, architecture, and landscaping. Contact us with your use case; we'll assess feasibility and likely model choices within a few days.

Not at the start. We typically begin with spot GPU instances (RunPod, Lambda Labs, or Replicate) to keep upfront cost near zero, then help you evaluate whether dedicated GPUs make economic sense at your usage volume. For most early-stage products, serverless inference is the right answer.

It depends on the task. For photorealistic image generation with structural control, Stable Diffusion + ControlNet is the right choice. For text tasks, document analysis, or conversational features, we use Gemini API (our preferred LLM integration — see our AI Integration service). We pick the right model for the job, not the most-hyped one.