Awesome

[CVPR2024] TPD

This repository is the official implementation of TPD

Texture-Preserving Diffusion Models for High-Fidelity Virtual Try-On

Xu Yang, Changxing Ding, Zhibin Hong, Junhao Huang, Jin Tao, Xiangmin Xu

TODO List

• Release inference code
• Release model weights
• Release training code
• Release evaluation code

Environments

conda env create -f environment.yml
conda activate TPD

Data Preparation

Weights

Download the pretrained checkpoint and save it in the checkpoints folder like:

checkpoints
|-- release
	|-- TPD_240epochs.ckpt

Datasets

Download the VITON-HD dataset from here.

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

datasets/VITONHD/
test | train | validation(copied from test)
|-- agnostic-mask
|-- agnostic-v3.2
|-- cloth
|-- cloth_mask
|-- image
|-- image-densepose
|-- image-parse-agnostic-v3.2
|-- image-parse-v3
|-- openpose_img
|-- openpose_json

Inference

Refer to 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 first and save it in the checkpoints folder. Then run 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

Evaluation

Prepare

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

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

Run utils/generate_GT.py to generate GT images with 384*512 resolution

Commands

Refer to calculate_metrics/calculate_metrics.sh

Acknowledgements

Thanks to Paint-by-Example, our code is heavily borrowed from it.

Citation

@misc{yang2024texturepreserving,
      title={Texture-Preserving Diffusion Models for High-Fidelity Virtual Try-On}, 
      author={Xu Yang and Changxing Ding and Zhibin Hong and Junhao Huang and Jin Tao and Xiangmin Xu},
      year={2024},
      eprint={2404.01089},
      archivePrefix={arXiv},
      primaryClass={cs.CV}
}