This is the code for ACM multimedia 2022 “T-former: An Efficient Transformer for Image Inpainting”
python - m visdom.server
python train.py --no_flip --no_rotation --no_augment --img_file your_data --lr 1e-4
python train.py --no_flip --no_rotation --no_augment --img_file your_data --lr 1e-5 --continue_train
python test.py --batchSize 1 --mask_type 3 --img_file your_data --mask_file your_mask your_data
If you are interested in this work, please consider citing:
@inproceedings{tformer_image_inpainting,
author = {Deng, Ye and Hui, Siqi and Zhou, Sanping and Meng, Deyu and Wang, Jinjun},
title = {T-former: An Efficient Transformer for Image Inpainting},
year = {2022},
isbn = {9781450392037},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
doi = {10.1145/3503161.3548446},
pages = {6559–6568},
numpages = {10},
series = {MM '22}
}