Raos Virtual Try-On Model: Open-source Virtual Try-on System - Freely Experience Realistic Try-on Effects

Raos Virtual Try On Model

Developed by gouthaml

A virtual try-on system built on the Stable Diffusion framework, integrating DreamBooth training, EfficientNetB3 feature extraction, and OpenPose pose detection technologies

Image Generation Open Source License:Openrail #Virtual Try-On #Pose Awareness #Fashion Retail

Downloads 258

Release Time : 5/18/2023

Model Overview

This model provides highly realistic virtual clothing try-on experiences through deep learning technology, primarily applied in the fashion retail sector

Model Features

Multi-Technology Integration

Combines four core technologies: Stable Diffusion, DreamBooth, EfficientNetB3, and OpenPose

High-Realism Rendering

Generates realistic virtual try-on effects through the Stable Diffusion framework

Real-Time Pose Adaptation

Utilizes OpenPose for human keypoint detection to ensure precise clothing alignment with user poses

Deep Feature Extraction

Employs the EfficientNetB3 CNN model for efficient feature extraction

Model Capabilities

Image Generation

Human Pose Detection

Clothing Feature Extraction

Virtual Try-On Rendering

Use Cases

Fashion Retail

Online Virtual Fitting Room

Provides real-time virtual try-on functionality for e-commerce platforms

Enhances user shopping experience and conversion rates

Apparel Design Preview

Designers can quickly visualize design drafts on different body types

Accelerates the design iteration process

🚀 DeepVTO or Raos_virtual_try_on_model

This model combines stable-diffusion, DreamBooth, and other advanced techniques to offer users a realistic and visually appealing virtual try-on experience.

✨ Features

Advanced Techniques Integration: The model integrates stable-diffusion, DreamBooth, feature extraction using the EfficientNetB3 CNN model, and OpenPose for estimating person keypoints.
High - Quality Experience: Based on stable diffusion and vector embeddings, it provides a high - quality virtual try - on system.
Industry Potential: Demonstrates the potential of deep learning in the fashion retail industry, enhancing the online shopping experience.

📦 Installation

The document doesn't provide specific installation steps, so this section is skipped.

💻 Usage Examples

The document doesn't contain code examples, so this section is skipped.

📚 Documentation

Model Overview

The DeepVTO model is hosted on the Hugging Face Model Hub at (https://huggingface.co/gouthaml/raos-virtual-try-on-model). It combines advanced deep learning techniques and architectures to create a virtual try - on system.

Technical Details

This model leverages stable diffusion and vector embeddings. It is trained using the DreamBooth model, a stable - diffusion model. Feature extraction is performed using the EfficientNetB3 CNN model, and OpenPose is used for estimating person keypoints.

Requirements

Hardware: A GPU A100 and high RAM are required for optimal performance.
Software: PyTorch, stable - diffusion - v1 - 5, Python 3.0, U - Net Architecture, Dreambooth, OpenPose, and the EfficientNetB3 pre - trained CNN model are needed.

Sample Pictures

Here are some sample pictures of the virtual try - on concept:

🔧 Technical Details

The model combines multiple advanced deep learning techniques. Stable diffusion and vector embeddings form the basis of the system. The DreamBooth model is used for training, the EfficientNetB3 CNN model for feature extraction, and OpenPose for estimating person keypoints. These techniques work together to provide a realistic virtual try - on experience.

📄 License

Property	Details
License	creativeml - openrail - m
Tags	text - to - image, stable - diffusion

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