[CVPR2024] StableVITON: Learning Semantic Correspondence with Latent Diffusion Model for Virtual Try-On
This repository is the official implementation of StableVITON
StableVITON: Learning Semantic Correspondence with Latent Diffusion Model for Virtual Try-On
Jeongho Kim, Gyojung Gu, Minho Park, Sunghyun Park, Jaegul Choo
-
Inference code -
Release model weights -
Training code
git clone https://github.com/rlawjdghek/StableVITON
cd StableVITON
conda create --name StableVITON python=3.10 -y
conda activate StableVITON
# install packages
pip install torch==2.0.0+cu117 torchvision==0.15.1+cu117 torchaudio==2.0.1 --index-url https://download.pytorch.org/whl/cu117
pip install pytorch-lightning==1.5.0
pip install einops
pip install opencv-python==4.7.0.72
pip install matplotlib
pip install omegaconf
pip install albumentations
pip install transformers==4.33.2
pip install xformers==0.0.19
pip install triton==2.0.0
pip install open-clip-torch==2.19.0
pip install diffusers==0.20.2
pip install scipy==1.10.1
conda install -c anaconda ipython -yOur checkpoint on VITONHD have been released!
You can download the VITON-HD dataset from here.
For both training and inference, the following dataset structure is required:
train
|-- image
|-- image-densepose
|-- agnostic
|-- agnostic-mask
|-- cloth
|-- cloth_mask
|-- gt_cloth_warped_mask (for ATV loss)
test
|-- image
|-- image-densepose
|-- agnostic
|-- agnostic-mask
|-- cloth
|-- cloth_mask
The VITON-HD dataset serves as a benchmark and provides an agnostic mask. However, you can attempt virtual try-on on arbitrary images using segmentation tools like SAM. Please note that for densepose, you should use the same densepose model as used in VITON-HD.
#### paired
CUDA_VISIBLE_DEVICES=4 python inference.py \
--config_path ./configs/VITONHD.yaml \
--batch_size 4 \
--model_load_path <model weight path> \
--save_dir <save directory>
#### unpaired
CUDA_VISIBLE_DEVICES=4 python inference.py \
--config_path ./configs/VITONHD.yaml \
--batch_size 4 \
--model_load_path <model weight path> \
--unpair \
--save_dir <save directory>
#### paired repaint
CUDA_VISIBLE_DEVICES=4 python inference.py \
--config_path ./configs/VITONHD.yaml \
--batch_size 4 \
--model_load_path <model weight path>t \
--repaint \
--save_dir <save directory>
#### unpaired repaint
CUDA_VISIBLE_DEVICES=4 python inference.py \
--config_path ./configs/VITONHD.yaml \
--batch_size 4 \
--model_load_path <model weight path> \
--unpair \
--repaint \
--save_dir <save directory>You can also preserve the unmasked region by '--repaint' option.
For VITON training, we increased the first block of U-Net from 9 to 13 channels (add zero conv) based on the Paint-by-Example (PBE) model. Therefore, you should download the modified checkpoint (named as 'VITONHD_PBE_pose.ckpt') from the Link and place it in the './ckpts/' folder first.
Additionally, for more refined person texture, we utilized a VAE fine-tuned on the VITONHD dataset. You should also download the checkpoint (named as VITONHD_VAE_finetuning.ckpt') from the Link and place it in the './ckpts/' folder.
### Base model training
CUDA_VISIBLE_DEVICES=3,4 python train.py \
--config_name VITONHD \
--transform_size shiftscale3 hflip \
--transform_color hsv bright_contrast \
--save_name Base_test
### ATV loss finetuning
CUDA_VISIBLE_DEVICES=5,6 python train.py \
--config_name VITONHD \
--transform_size shiftscale3 hflip \
--transform_color hsv bright_contrast \
--use_atv_loss \
--resume_path <first stage model path> \
--save_name ATVloss_testIf you find our work useful for your research, please cite us:
@artical{kim2023stableviton,
title={StableVITON: Learning Semantic Correspondence with Latent Diffusion Model for Virtual Try-On},
author={Kim, Jeongho and Gu, Gyojung and Park, Minho and Park, Sunghyun and Choo, Jaegul},
booktitle={arXiv preprint arxiv:2312.01725},
year={2023}
}
Acknowledgements Sunghyun Park is the corresponding author.
Licensed under the CC BY-NC-SA 4.0 license (https://creativecommons.org/licenses/by-nc-sa/4.0/legalcode).