shshim0513 / dci-vton-virtual-try-on

[ACM Multimedia 2023] Taming the Power of Diffusion Models for High-Quality Virtual Try-On with Appearance Flow.

DCI-VTON-Virtual-Try-On

This is the official repository for the following paper:

Taming the Power of Diffusion Models for High-Quality Virtual Try-On with Appearance Flow [arxiv]

Junhong Gou, Siyu Sun, Jianfu Zhang, Jianlou Si, Chen Qian, Liqing Zhang
Accepted by ACM MM 2023.

News

• 2023-12-06 We have updated the selection strategy of inpainting mask similar to VITON-HD and HR-VTON in cp_dataset_v2.py. The pretrained model based on this new masking strategy is available from Google Drive.

Overview

Abstract:
Virtual try-on is a critical image synthesis task that aims to transfer clothes from one image to another while preserving the details of both humans and clothes. While many existing methods rely on Generative Adversarial Networks (GANs) to achieve this, flaws can still occur, particularly at high resolutions. Recently, the diffusion model has emerged as a promising alternative for generating high-quality images in various applications. However, simply using clothes as a condition for guiding the diffusion model to inpaint is insufficient to maintain the details of the clothes. To overcome this challenge, we propose an exemplar-based inpainting approach that leverages a warping module to guide the diffusion model's generation effectively. The warping module performs initial processing on the clothes, which helps to preserve the local details of the clothes. We then combine the warped clothes with clothes-agnostic person image and add noise as the input of diffusion model. Additionally, the warped clothes is used as local conditions for each denoising process to ensure that the resulting output retains as much detail as possible. Our approach effectively utilizes the power of the diffusion model, and the incorporation of the warping module helps to produce high-quality and realistic virtual try-on results. Experimental results on VITON-HD demonstrate the effectiveness and superiority of our method.

Getting Started

Installation

Diffusion Model

1. Clone the repository

git clone https://github.com/bcmi/DCI-VTON-Virtual-Try-On.git
cd DCI-VTON-Virtual-Try-On

2. Install Python dependencies

conda env create -f environment.yaml
conda activate dci-vton

3. Download the pretrained vgg checkpoint and put it in models/vgg/

Warping Module

4. Clone the PF-AFN repository

git clone https://github.com/geyuying/PF-AFN.git

5. Move the code to the corresponding directory

cp -r DCI-VTON-Virtual-Try-On/warp/train/* PF-AFN/PF-AFN_train/
cp -r DCI-VTON-Virtual-Try-On/warp/test/* PF-AFN/PF-AFN_test/

Data Preparation

VITON-HD

1. Download VITON-HD dataset
2. Download pre-warped cloth image/mask from Google Drive or Baidu Cloud and put it under your VITON-HD dataset

After these, the folder structure should look like this (the unpaired-cloth* only included in test directory):

├── VITON-HD
|   ├── test_pairs.txt
|   ├── train_pairs.txt
│   ├── [train | test]
|   |   ├── image
│   │   │   ├── [000006_00.jpg | 000008_00.jpg | ...]
│   │   ├── cloth
│   │   │   ├── [000006_00.jpg | 000008_00.jpg | ...]
│   │   ├── cloth-mask
│   │   │   ├── [000006_00.jpg | 000008_00.jpg | ...]
│   │   ├── cloth-warp
│   │   │   ├── [000006_00.jpg | 000008_00.jpg | ...]
│   │   ├── cloth-warp-mask
│   │   │   ├── [000006_00.jpg | 000008_00.jpg | ...]
│   │   ├── unpaired-cloth-warp
│   │   │   ├── [000006_00.jpg | 000008_00.jpg | ...]
│   │   ├── unpaired-cloth-warp-mask
│   │   │   ├── [000006_00.jpg | 000008_00.jpg | ...]

Inference

VITON-HD

Please download the pretrained model from Google Drive or Baidu Cloud.

Warping Module

To test the warping module, first move the warp_viton.pth to checkpoints directory:

mv warp_viton.pth PF-AFN/PF-AFN_test/checkpoints

Then run the following command:

cd PF-AFN/PF-AFN_test
sh test_VITON.sh

After inference, you can put the results in the VITON-HD for inference and training of the diffusion model.

Diffusion Model

To quickly test our diffusion model, run the following command:

python test.py --plms --gpu_id 0 \
--ddim_steps 100 \
--outdir results/viton \
--config configs/viton512.yaml \
--ckpt /CHECKPOINT_PATH/viton512.ckpt \
--dataroot /DATASET_PATH/ \
--n_samples 8 \
--seed 23 \
--scale 1 \
--H 512 \
--W 512 \
--unpaired

or just simply run:

sh test.sh

Training

Warping Module

To train the warping module, just run following commands:

cd PF-AFN/PF-AFN_train/
sh train_VITON.sh

Diffusion Model

We utilize the pretrained Paint-by-Example as initialization, please download the pretrained models from Google Drive and save the model to directory checkpoints.

To train a new model on VITON-HD, you should first modify the dataroot of VITON-HD dataset in configs/viton512.yaml and then use main.py for training. For example,

python -u main.py \
--logdir models/dci-vton \
--pretrained_model checkpoints/model.ckpt \
--base configs/viton512.yaml \
--scale_lr False

or simply run:

sh train.sh

Acknowledgements

Our code is heavily borrowed from Paint-by-Example. We also thank PF-AFN, our warping module depends on it.

Citation

@inproceedings{gou2023taming,
  title={Taming the Power of Diffusion Models for High-Quality Virtual Try-On with Appearance Flow},
  author={Gou, Junhong and Sun, Siyu and Zhang, Jianfu and Si, Jianlou and Qian, Chen and Zhang, Liqing},
  booktitle={Proceedings of the 31st ACM International Conference on Multimedia},
  year={2023}
}