MAT: Mask-Aware Transformer for Large Hole Image Inpainting (CVPR2022, Oral)

Wenbo Li, Zhe Lin, Kun Zhou, Lu Qi, Yi Wang, Jiaya Jia

[Paper]

News

This is the official implementation of MAT. The training and testing code is released. We also provide our masks for CelebA-HQ-val and Places-val here.

Visualization

We present a transformer-based model (MAT) for large hole inpainting with high fidelity and diversity.

Compared to other methods, the proposed MAT restores more photo-realistic images with fewer artifacts.

Usage

1. Clone the repository.

   git clone https://github.com/fenglinglwb/MAT.git 

2. Install the dependencies.
   • Python 3.7
   • PyTorch 1.7.1
   • Cuda 11.0
   • Other packages

   pip install -r requirements.txt

Quick Test

1. We provide models trained on CelebA-HQ and Places365-Standard at 512x512 resolution. Download models from One Drive and put them into the 'pretrained' directory. The released models are retrained, and hence the visualization results may slightly differ from the paper.

2. Obtain inpainted results by running

   python generate_image.py --network model_path --dpath data_path --outdir out_path [--mpath mask_path]

   where the mask path is optional. If not assigned, random 512x512 masks will be generated. Note that 0 and 1 values in a mask refer to masked and remained pixels.

   For example, run

   python generate_image.py --network pretrained/CelebA-HQ.pkl --dpath test_sets/CelebA-HQ/images --mpath test_sets/CelebA-HQ/masks --outdir samples

   Note. Our implementation only supports generating an image whose size is a multiple of 512. You need to pad or resize the image to make its size a multiple of 512. Please pad the mask with 0 values.

Train

For example, if you want to train a model on Places, run a bash script with

python train.py \
    --outdir=output_path \
    --gpus=8 \
    --batch=32 \
    --metrics=fid36k5_full \
    --data=training_data_path \
    --data_val=val_data_path \
    --dataloader=datasets.dataset_512.ImageFolderMaskDataset \
    --mirror=True \
    --cond=False \
    --cfg=places512 \
    --aug=noaug \
    --generator=networks.mat.Generator \
    --discriminator=networks.mat.Discriminator \
    --loss=losses.loss.TwoStageLoss \
    --pr=0.1 \
    --pl=False \
    --truncation=0.5 \
    --style_mix=0.5 \
    --ema=10 \
    --lr=0.001

Description of arguments:

• outdir: output path for saving logs and models
• gpus: number of used gpus
• batch: number of images in all gpus
• metrics: find more metrics in 'metrics/metric_main.py'
• data: training data
• data_val: validation data
• dataloader: you can define your own dataloader
• mirror: use flip augmentation or not
• cond: use class info, default: false
• cfg: configuration, find more details in 'train.py'
• aug: use augmentation of style-gan-ada or not, default: false
• generator: you can define your own generator
• discriminator: you can define your own discriminator
• loss: you can define your own loss
• pr: ratio of perceptual loss
• pl: use path length regularization or not, default: false
• truncation: truncation ratio proposed in stylegan
• style_mix: style mixing ratio proposed in stylegan
• ema: exponoential moving averate, ~K samples
• lr: learning rate

Evaluation

We provide evaluation scrtips for FID/U-IDS/P-IDS/LPIPS/PSNR/SSIM/L1 metrics in the 'evaluation' directory. Only need to give paths of your results and GTs.

Citation

@inproceedings{li2022mat,
    title={MAT: Mask-Aware Transformer for Large Hole Image Inpainting},
    author={Li, Wenbo and Lin, Zhe and Zhou, Kun and Qi, Lu and Wang, Yi and Jia, Jiaya},
    booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition},
    year={2022}
}

License and Acknowledgement

The code and models in this repo are for research purposes only. Our code is bulit upon StyleGAN2-ADA.