[2022.10.03] Model for FFHQ-512 is available. (Link)
[2022.09.10] We could provide all testing images of Places and CelebA inpainted by our MAT and other methods. Since there are too many images, please send an email to wenboli@cse.cuhk.edu.hk and explain your needs.
[2022.06.21] We provide a SOTA Places-512 model (Places_512_FullData.pkl) trained with full Places data (8M images). It achieves significant improvements on all metrics.
Model | Data | Small Mask | Large Mask | ||||
---|---|---|---|---|---|---|---|
FID↓ | P-IDS↑ | U-IDS↑ | FID↓ | P-IDS↑ | U-IDS↑ | ||
MAT (Ours) | 8M | 0.78 | 31.72 | 43.71 | 1.96 | 23.42 | 38.34 |
MAT (Ours) | 1.8M | 1.07 | 27.42 | 41.93 | 2.90 | 19.03 | 35.36 |
CoModGAN | 8M | 1.10 | 26.95 | 41.88 | 2.92 | 19.64 | 35.78 |
LaMa-Big | 4.5M | 0.99 | 22.79 | 40.58 | 2.97 | 13.09 | 32.39 |
[2022.06.19] We have uploaded the CelebA-HQ-256 model and masks. Because the original model was lost, we retrained the model so that the results may slightly differ from the reported ones.
Thank Replicate for providing a web demo for our MAT. But I didn't check if this demo is correct. You are recommended to use our models as following.
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.
It is highly recommanded to adopt Conda/MiniConda to manage the environment to avoid some compilation errors.
git clone https://github.com/fenglinglwb/MAT.git
pip install -r requirements.txt
We provide models trained on CelebA-HQ, FFHQ 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.
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.
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:
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.
We also provide our masks for CelebA-HQ-val and Places-val here.
@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}
}
The code and models in this repo are for research purposes only. Our code is bulit upon StyleGAN2-ADA.