Mutoy-choi/Tryondiffusion

Tryondiffusion

Introduction

This repository is an attempt to implement the Tryondiffusion model. For more details, visit the official Tryondiffusion website.

Environment

The code was developed and tested on the following environment:

• Operating System: Windows server 2019

• Python Version: Python 3.10

• GPU : NVIDIA Tesla T4 16GB

• CUDA 11.8

Getting Started

Clone the Repository

To get started with training examples, first clone this repository by running the following command in your terminal:

git clone https://github.com/Mutoy-choi/Tryondiffusion


cd Tryondiffusion

This will clone the repository and navigate you into the project directory.

Set Up Virtual Environment

python -m venv venv

.\venv\Scripts\activate

These commands create and activate a virtual environment named venv. This isolates the project dependencies, making it easier to manage.

Install Dependencies

pip install -r requirements.txt

Run the Example Training code

python one_shot_test_ParallelUnet.py

This file allows you to know if the model is working well or not using example data for ParallelUnet(From 128x128 to 256*256)

Further upload

• update preprocessing AI-Hub data

https://aihub.or.kr/aihubdata/data/view.do?currMenu=115&topMenu=100&aihubDataSe=realm&dataSetSn=78

Deployment content and amount of data provided

Studio fashion video (model photo) 6,741,328 cases

Studio Fashion Video Model Key Points: 120,936 Cases

Studio Fashion Video Model Semantic: 120,936 cases

Fashion products and fashion video pair: 117,270 cases

Fashion product representative photos (product photos) 40,036 cases

Fashion product key points: 40,036 cases

Fashion Product Semantic: 40,036 cases