KO | EN

GitLite

Minimalist Open Source Repository Search Engine

gitlite — search

#typescript #ai-agents #cli #open-source #ai #claude-code #codex #developer-tools #react #windows #deepseek-harness #ai-agent

TPD

★ 154 Open GitHub ↗

This is the official repository for the paper "Texture-Preserving Diffusion Models for High-Fidelity Virtual Try-On". CVPR 2024

// repository documentation

Was this content helpful?

★ 0 (0 ratings)

# [CVPR2024] TPD

This repository is the official implementation of [TPD](https://arxiv.org/abs/2404.01089)

> **Texture-Preserving Diffusion Models for High-Fidelity Virtual Try-On**
>
> Xu Yang, Changxing Ding, Zhibin Hong, Junhao Huang, Jin Tao, Xiangmin Xu

![teaser](https://raw.githubusercontent.com/MPI-Lab/TPD/main/assets/Teaser.jpg)&nbsp;

## TODO List

- [x] Release inference code
- [x] Release model weights
- [x] Release training code
- [x] Release evaluation code

## Environments

```
conda env create -f environment.yml
conda activate TPD
```

## Data Preparation

### Weights

Download the pretrained [checkpoint](https://drive.google.com/file/d/1twsjZ0kQkyFdfLcw8EYmvQmsRIgqnI3o/view?usp=sharing) and save it in the checkpoints folder like:

```
checkpoints
|-- release
	|-- TPD_240epochs.ckpt
```

### Datasets

Download the VITON-HD dataset from [here](https://github.com/shadow2496/VITON-HD).

You should copy the test folder for validation and the dataset structure should be like:

## Inference

Refer to [commands/inference.sh](./commands/inference.sh)

## Training

### Prepare

We utilize the pretrained Paint-by-Example as initialization, and increase it's first conv-layer from 9 to 18 channels (zero initiated).  Please download the [pretrained model](https://github.com/Fantasy-Studio/Paint-by-Example) first and save it in the checkpoints folder. Then run [utils/rm_clip_and_add_channels.py](./utils/rm_clip_and_add_channels.py) to add input channels of the first conv-layer and remove CLIP module. The final checkpoints folder structure is like:

```
checkpoints
|-- original
	|-- model.ckpt
	|-- mode_prepared.ckpt	
```

### Commands

Refer to [commands/train.sh](./commands/train.sh)

## Evaluation

### Prepare

LPIPS: https://github.com/richzhang/PerceptualSimilarity

FID: https://github.com/mseitzer/pytorch-fid

Run [utils/generate_GT.py](./utils/generate_GT.py) to generate GT images with 384*512 resolution

### Commands

Refer to  [calculate_metrics/calculate_metrics.sh](./calculate_metrics/calculate_metrics.sh)

## Acknowledgements

Thanks to [Paint-by-Example](https://github.com/Fantasy-Studio/Paint-by-Example), our code is heavily borrowed from it.

## Copyright Notice
This open-source project is for non-commercial use only. Commercial use requires explicit authorization. For commercial licensing, contact: <chxding@scut.edu.cn>

## Citation

```
@InProceedings{Yang_2024_CVPR,
    author    = {Yang, Xu and Ding, Changxing and Hong, Zhibin and Huang, Junhao and Tao, Jin and Xu, Xiangmin},
    title     = {Texture-Preserving Diffusion Models for High-Fidelity Virtual Try-On},
    booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
    month     = {June},
    year      = {2024},
    pages     = {7017-7026}
}
```

TPD

Explore Similar Repositories

Was this content helpful?

Recent Feedback

Download README