LSD-VTON

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An e-commerce website that has a set of clothes to do try-on using test dataset, and also provides the user to do try-on given they personal and cloth images.

LSD-VTON 최신버젼 다운로드

최종 버전 다운로드 (.zip)

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# **Project Name**
**LSD-VTON** for **L**ocal Flow Global Parsing Warping integrated with **S**table **D**iffusion Virtual Try-on

# **Developed and maintained By (Order Does not matter):**

[Mohamed Walid](https://github.com/MohamedWalidxX), [Karim Metwally](https://github.com/KarimMetwaly19), [Mohamed Mostafa](https://github.com/pmohmmed), [Mohamed Hesham](https://github.com/mohamedhesham840), [Omar Ehab](https://github.com/OmarEhab162), and [Mohamed Ahmed](https://github.com/SEmohamedAhmed)

## **Introduction**
The virtual tryon system is based on GP-VTON and LaDi-VTON

We exploit the GP-VTON power in warping(LFGP) and the LaDi-VTON power in generation(Stable diffusion)

Dataset: VITON-HD

## **Project Objective**
- Create an e-commerce application that offers virtual try-on feature.
- Deal with challenging inputs:
  - Complex poses
  - Complex garments
  - Clothes style: tucked-in or tucked-out
- Train the developed warping and generative models.
- Evaluate the performance of the developed virtual try-on model against benchmarks.

**LSD-VTON System Architecture**
<div style="width: 100%; text-align: center;margin:auto">
<img style="width:100%" src="LSD-VTON System Architecture.jpg">
</div>

**Used Technologies/Apps:**
- Python
- FastAPI
- Kaggle API
- SQL Server
- HTML, CSS, JS
- Kaggle (P100 GPU 16 GB VRAM, 30 GB RAM)
- Pycharm IDE

## **Demo video**
[Click Here for the demo](https://drive.google.com/file/d/1UZuarGgWUsnE3_fvW8du39U0Rbuy5FyX/view?usp=sharing)

## **Mody Test**
In this section we will introduce our work (no code here) to test the
**GP-VTON**, **LaDi-VTON** and **LSD-VTON** architectures but using new person/cloth images (which not included in the original dataset) i.e we will try-on our personal image and our clothes

I called it **Mody Test**!

For **Mody Test** (custom input) we only have (our inputs)
- Person image
- Cloth image

and in **Mody Test**
we do a preprocessing task which includes complicated tasks which are: 
- Person parse (Human parse)
- Person keypoints (skeleton)
- Person densepose
- Cloth parse
- Cloth binary mask

<strong>Here is a spoiler image for our work (Mody Test)</strong>

## **Citation**

```bibtex
@inproceedings{xie2023gpvton,
  title     = {GP-VTON: Towards General Purpose Virtual Try-on via Collaborative Local-Flow Global-Parsing Learning},
  author    = {Zhenyu, Xie and Zaiyu, Huang and Xin, Dong and Fuwei, Zhao and Haoye, Dong and Xijin, Zhang and Feida, Zhu and Xiaodan, Liang},
  booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
  month     = {June},
  year      = {2023},
}
```

```bibtex
@inproceedings{morelli2023ladi,
  title={{LaDI-VTON: Latent Diffusion Textual-Inversion Enhanced Virtual Try-On}},
  author={Morelli, Davide and Baldrati, Alberto and Cartella, Giuseppe and Cornia, Marcella and Bertini, Marco and Cucchiara, Rita},
  booktitle={Proceedings of the ACM International Conference on Multimedia},
  year={2023}
}
```

## **Acknowledgments**
Thanks for all authors of 
- [GP-VTON](https://github.com/xiezhy6/GP-VTON)
- [LaDi-VTON](https://github.com/miccunifi/ladi-vton/tree/master)

## **License**
The use of this code is RESTRICTED to non-commercial research and educational purposes.

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