🏥 Production ML

Virtual Try-On POC (GenAI)

Stable Diffusion-based virtual try-on POC for major enterprise retail brand's digital commerce platform

Overview

Developed Stable Diffusion-based Virtual Try-On POC for major enterprise retail brand, enabling AI-generated visualization of apparel on user images for their digital commerce platform.

Key Features

AI-Powered Apparel Visualization

Stable Diffusion-based image generation
Realistic garment rendering on user-provided images
Preserves user appearance while swapping clothing
Production-quality output for e-commerce applications

Attribute Preservation

Implemented advanced image conditioning workflows
Preserves garment attributes during generation:
- Style and design patterns
- Texture and fabric appearance
- Color accuracy and variations
90%+ attribute preservation accuracy

Performance Optimization

Optimized inference pipelines for latency vs. quality trade-offs
Near real-time preview generation: <3 seconds per image
Balanced quality and speed for production use
Efficient model loading and caching strategies

Technical Implementation

Generative AI Pipeline

Stable Diffusion as core generative model
Custom conditioning mechanisms for garment attributes
Prompt engineering workflows for consistent results
Image-to-image generation with controlled variations

Computer Vision Integration

Image preprocessing and segmentation
Garment detection and extraction
Pose estimation for realistic placement
Post-processing for output refinement

Production Considerations

Designed for integration with digital commerce platform
Scalable architecture for high-volume requests
Quality assurance mechanisms
User experience optimization

Technical Highlights

Prompt Engineering

Developed specialized prompts for apparel visualization
Attribute-preserving conditioning strategies
Consistent style transfer across garment types
Iterative prompt refinement for quality

Latency Optimization

Inference pipeline tuning for speed
Model optimization techniques
Caching strategies for common requests
Trade-off analysis: quality vs. response time

Image Quality

High-fidelity garment rendering
Realistic lighting and shadow integration
Natural pose and fit appearance
Production-ready visual output

Impact & Results

90%+ accuracy in preserving garment attributes
<3 seconds per generation (near real-time)
major enterprise retail brand client POC
Digital commerce platform integration potential
Generative AI + CV expertise demonstration