Open-source virtual dress try-on model - Free experience of realistic online dressing effects

Virtual Dress Try On

Developed by ares1123

A virtual try-on model based on stable diffusion and multiple deep learning technologies, providing realistic online fitting experiences

Image Generation Open Source License:Openrail #High-fidelity Virtual Try-On #Multimodal Feature Fusion #Fashion Retail AI

Downloads 121

Release Time : 1/18/2024

Model Overview

This model combines stable diffusion, DreamBooth, EfficientNetB3 feature extraction, and OpenPose technologies to deliver high-quality virtual try-on solutions for the fashion retail industry

Model Features

Multi-Technology Integration

Combines stable diffusion, DreamBooth, EfficientNetB3 CNN, and OpenPose technologies to achieve high-precision virtual try-on

Realistic Effects

Generates visually appealing try-on effects through vector embedding technology

Real-time Keypoint Detection

Utilizes OpenPose for real-time detection of human body, hand, face, and foot keypoints

Model Capabilities

Image Generation

Human Pose Estimation

Clothing Feature Extraction

Virtual Try-On Rendering

Use Cases

Fashion Retail

Online Virtual Fitting Room

Provides realistic virtual try-on experiences for e-commerce platforms

Enhances user engagement and purchase conversion rates

Personalized Clothing Recommendations

Displays personalized clothing based on user body shape and posture

Improves the personalization level of shopping experiences

🚀 Virtual Try-On (VTO) Model

This VTO model is a cutting - edge solution that combines advanced deep learning techniques to offer users a realistic and visually appealing virtual try - on experience. It leverages stable diffusion, DreamBooth, feature extraction, and person keypoint estimation to create a high - quality virtual try - on system.

✨ Features

Advanced Techniques: Utilizes stable - diffusion, DreamBooth, EfficientNetB3 CNN model for feature extraction, and OpenPose for estimating person keypoints.
High - Quality Experience: Based on stable diffusion and vector embeddings principles to provide a realistic virtual try - on.
Industry Potential: Demonstrates the potential of deep learning in the fashion retail industry, enhancing the online shopping experience.

📚 Documentation

Model Hosting

The VTO model is hosted on the Hugging Face Model Hub. https://huggingface.co/ares1123/virtual-dress-try-on

Technical Approach

This model leverages a combination of advanced deep learning techniques and architectures. Stable - diffusion and DreamBooth are used for generating high - quality virtual try - on results. Feature extraction is performed using the EfficientNetB3 CNN model, which helps in capturing important characteristics of the input images. OpenPose, a real - time multi - person system to jointly detect human body, hand, facial, and foot keypoints, is used for estimating person keypoints, making the virtual try - on more accurate and realistic.

Hardware and Software Requirements

The model requires specific hardware and software for optimal performance:

Property	Details
Hardware	GPU A100, High RAM
Software	PyTorch, stable - diffusion - v1 - 5, Python 3.0, U - Net Architecture, DreamBooth, OpenPose, EfficientNetB3 pre - trained CNN model

Model Significance

The VTO model showcases the potential of deep learning in the fashion retail industry. It enhances the online shopping experience by making it more interactive and personalized. This model also provides a foundation for future research and development in the field of virtual try - on systems, addressing challenges such as the accuracy of virtual representations and the scalability of the system.