  Modern image inpainting systems, despite the significant progress, often
struggle with large missing areas, complex geometric structures, and
high-resolution images. We find that one of the main reasons for that is the
lack of an effective receptive field in both the inpainting network and the
loss function. To alleviate this issue, we propose a new method called large
mask inpainting (LaMa). LaMa is based on i) a new inpainting network
architecture that uses fast Fourier convolutions (FFCs), which have the
image-wide receptive field; ii) a high receptive field perceptual loss; iii)
large training masks, which unlocks the potential of the first two components.
Our inpainting network improves the state-of-the-art across a range of datasets
and achieves excellent performance even in challenging scenarios, e.g.
completion of periodic structures. Our model generalizes surprisingly well to
resolutions that are higher than those seen at train time, and achieves this at
lower parameter&time costs than the competitive baselines. The code is
available at \url{https://github.com/saic-mdal/lama}.
