Improving Diffusion Models for Authentic Virtual Try-on in the Wild

This is an official implementation of paper 'Improving Diffusion Models for Authentic Virtual Try-on in the Wild'

• paper
• project page

🤗 Try our huggingface Demo

 

TODO LIST

• demo model
• inference code
• training code

Requirements

git clone https://github.com/yisol/IDM-VTON.git
cd IDM-VTON

conda env create -f environment.yaml
conda activate idm

Data preparation

VITON-HD

You can download VITON-HD dataset from VITON-HD.
After download VITON-HD dataset, move vitonhd_test_tagged.json into the test folder.
Structure of the Dataset directory should be as follows.

train
|-- ...

test
|-- image
|-- image-densepose
|-- agnostic-mask
|-- cloth
|-- vitonhd_test_tagged.json

DressCode

You can download DressCode dataset from DressCode.
We provide pre-computed densepose images and captions for garments here.
We used detectron2 for obtaining densepose images.
After download DressCode dataset, place image-densepose directories and caption text files as follows.

DressCode
|-- dresses
    |-- images
    |-- image-densepose
    |-- dc_caption.txt
    |-- ...
|-- lower_body
    |-- images
    |-- image-densepose
    |-- dc_caption.txt
    |-- ...
|-- upper_body
    |-- images
    |-- image-densepose
    |-- dc_caption.txt
    |-- ...

Inference

VITON-HD

Inference using python file with arguments.

accelerate launch inference.py \
    --width 768 --height 1024 --num_inference_steps 30 \
    --output_dir "result" \
    --unpaired \
    --data_dir "DATA_DIR" \
    --seed 42 \
    --test_batch_size 2 \
    --guidance_scale 2.0

You can simply run with the script file.

sh inference.sh

DressCode

For DressCode dataset, put the category you want to generate images via category argument.

accelerate launch inference_dc.py \
    --width 768 --height 1024 --num_inference_steps 30 \
    --output_dir "result" \
    --unpaired \
    --data_dir "DATA_DIR" \
    --seed 42 
    --test_batch_size 2
    --guidance_scale 2.0
    --category "upper_body" 

You can simply run with the script file.

sh inference.sh

Acknowledgements

For the demo, GPUs are supported from zerogpu, and auto masking generation codes are based on OOTDiffusion and DCI-VTON.
Parts of the code are based on IP-Adapter.

Citation

@article{choi2024improving,
  title={Improving Diffusion Models for Virtual Try-on},
  author={Choi, Yisol and Kwak, Sangkyung and Lee, Kyungmin and Choi, Hyungwon and Shin, Jinwoo},
  journal={arXiv preprint arXiv:2403.05139},
  year={2024}
}

License

The codes and checkpoints in this repository are under the CC BY-NC-SA 4.0 license.