Master Virtual Try-On: Implementing VITON-HD with PyTorch for High-Resolution Results
Want to see if that new jacket really suits you? VITON-HD lets you virtually try on clothes with amazing realism. This article delves into using the official PyTorch implementation of VITON-HD, the groundbreaking "High-Resolution Virtual Try-On via Misalignment-Aware Normalization" model presented at CVPR 2021. We'll cover installation, setup, and how to generate your own high-definition virtual try-on images.
What is VITON-HD and Why Should You Care?
Traditional virtual try-on methods struggle with image resolution and accurate clothing alignment. VITON-HD tackles these problems head-on, delivering stunning 1024x768 virtual try-on images. Imagine seeing exactly how clothes will look on you before even ordering them! This is made possible through:
- Misalignment-Aware Normalization: Handles discrepancies between clothing and body, reducing artifacts.
- High-Quality Body Part Generation: Creates realistic and detailed results, resolving texture sharpness.
- ALIAS Normalization and Generator: Allowing the preservation of details in high resolution images.
Getting Started: Installation and Setup of VITON-HD
Ready to dive in? Here's how to get VITON-HD up and running on your machine.
-
Clone the Repository: Start by cloning the VITON-HD GitHub repository.
git clone https://github.com/shadow2496/VITON-HD.git cd ./VITON-HD/ -
Install Dependencies: Set up your environment with the necessary packages.
conda create -y -n [ENV] python=3.8 conda activate [ENV] conda install -y pytorch=[>=1.6.0] torchvision cudatoolkit=[>=9.2] -c pytorch pip install opencv-python torchgeometry
Datasets and Pre-trained Networks to Start
To get the best results from VITON-HD, you'll need a suitable dataset and pre-trained networks.
- Download the Dataset: The original authors provide a preprocessed dataset (1024x768 images) for research purposes. You can find it on the VITON-HD DropBox. It contains over 11,000 training pairs and 2,000 test pairs of frontal-view women and top clothing images.
- Get Pre-trained Networks: Download the pre-trained networks (
*.pklfiles) and sample images from the VITON-HD Google Drive folder. Unzip the files into the./checkpoints/and./datasets/directories, respectively.
Running VITON-HD: Generating Your Virtual Try-On Image
With the software set up and the data in place, you're ready to create virtual try-on images.
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Execute the Test Script: Use the provided
test.pyscript to generate the images. Specify your GPU ID and a name for the run.CUDA_VISIBLE_DEVICES=[GPU_ID] python test.py --name [NAME] -
Find Your Results: The generated images are saved in the
./results/directory. You can customize the output location using the--save_dirargument. -
Experiment with Different Outfits: Edit the
./datasets/test_pairs.txtfile to use different clothing and person combinations.
Mastering VITON-HD: Tips for Optimal Results
- High-Quality Input Images: The better the quality of your person and clothing images, the better the final result.
- Experiment with Parameters: Tweak the parameters in
test.pyto fine-tune the try-on process. - Explore Follow-Up Research: Enhance your results even more with follow-up research from the same team, such as HR-VITON, available here.
License and Citation: Giving Credit Where It's Due
VITON-HD is available under the Creative Commons BY-NC 4.0 license. You're free to use, share, and modify it for non-commercial purposes. If you use VITON-HD in your research, please cite the original paper:
@inproceedings{choi2021viton,
title={VITON-HD: High-Resolution Virtual Try-On via Misalignment-Aware Normalization},
author={Choi, Seunghwan and Park, Sunghyun and Lee, Minsoo and Choo, Jaegul},
booktitle={Proc. of the IEEE conference on computer vision and pattern recognition (CVPR)},
year={2021}
}
The Future of Fashion is Here: Virtual Try-On with VITON-HD
VITON-HD represents a huge leap in virtual try-on technology. By following this guide, you can start experimenting with this innovative tool and experience the future of fashion firsthand. High resolution virtual try on is now within reach.
