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

Star ⭐ us if you like it!

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, refer here for more details.

After download the 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

or, 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" 

or, you can simply run with the script file.

sh inference.sh

Acknowledgements

For the demo, GPUs are supported from ZeroGPU, and masking generation codes are based on OOTDiffusion and DCI-VTON.

Parts of our 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.