Online fashion retailers face a critical challenge: customers cannot physically try on clothing before purchase, leading to high return rates (30-40% industry average) and lower conversion rates. Traditional product photography shows individual items in isolation, making it difficult for customers to visualize how different combinations of accessories like hats, boots, scarves, and jackets will look when worn together. Generic AI virtual try-on tools often fail to maintain visual consistency or preserve exact product details.
We developed a single-page web application (SPA) that functions as a real-time virtual stylist, allowing customers to select specific clothing items and accessories, then instantly see photorealistic visualizations of the complete outfit on a model using a reference-anchored workflow powered by generative AI.
Built with React/Next.js frontend and Python FastAPI backend. Generative AI models (Stable Diffusion variants: Nano Banana, Veo3, Kling 2.6) hosted on AWS EC2/S3 with Docker containerization.

Instant studio-quality images of complete outfits with detail-accurate rendering of every product element.
Conversion Rate Increase
Customers confident purchasing complete outfitsReturn Rate Reduction
Accurate visualization reduces expectation mismatchesInstant Results
Near-instant AI synthesis for unlimited combinationsPhotorealistic Output
AI matches professional fashion photographyFrontend SPA framework
Backend AI request processing
Stable Diffusion variants (Nano Banana, Veo3, Kling 2.6)
Image processing and segmentation
Model hosting and asset storage
Containerization and deployment