AI-Powered Virtual Fitting with Stable Diffusion

Overview

This project explores AI-powered virtual fitting, leveraging Stable Diffusion fine-tuning and LoRA models to realistically apply a photographed piece of clothing onto different individuals. By combining inpainting, LoRA training, and model fine-tuning, the system generates highly accurate and natural-looking virtual try-on results.

Key Features

• Fine-Tuned Stable Diffusion Models – Trained AI models to seamlessly apply a specific clothing piece onto different body types.
• LoRA-Based Training – Enhanced the base model with LoRAs for improved adaptability to various styles and textures.
• Inpainting for Clothing Application – Using masking and inpainting techniques to blend the clothing piece naturally onto subjects.
• Photorealistic Rendering – Ensuring that lighting, texture, and perspective match the original clothing item.

Process

1. Photography & Data Collection – Captured high-resolution images of the target clothing piece.
2. LoRA Training – Fine-tuned a Stable Diffusion model to learn the unique features of the garment.
3. Inpainting Pipeline – Used masked inpainting to overlay the clothing onto new subjects while preserving natural body structure.
4. Model Refinement – Iteratively trained and adjusted LoRAs to improve fabric realism and adaptability.
5. Final Output – Generated realistic virtual try-on images where individuals appear to be wearing the target clothing item.

Sample Results

Same piece of cloth on 2 different models :

The second model was pre-photographed and was wearing different clothing.

Future Improvements

• Expanding Dataset – Increasing the number of garments and testing with different clothing categories.
• Real-Time Virtual Try-On – Exploring integration with live webcam input for instant outfit previews.
• Refining Textures & Fabric Physics – Further improving how fabric folds, drapes, and reacts to body movements.