In recent years, the field of image inpainting has developed rapidly, but most deep methods are strongly tied to the resolution of the images on which they were trained. A slight resolution increase leads to artifacts and unsatisfactory filling quality. These methods are therefore unsuitable for interactive image processing. In this article, we propose a method that solves the problem of inpainting arbitrary-size images. We also describe a way to better restore texture fragments in the filled area. Moreover, this approach can work with existing inpainting models, making them resolution independent. We also created a GIMP plugin that implements our technique.

• Python 3.7
• Install requirements with pip install -r requirements.txt

1. Download weights and save it in weights folder.
2. Put your images as shown in Test/Inputs
3. Run: python test.py

You can find all the images involved in our comparison here

1. GIMP 2.10
2. Ubuntu 18.04 LTS
3. macOS Mojave 10.14.6

1. Open GIMP and go to Preferences -> Folders -> Plug-ins, add the folder gimp-plugins from this repo and close GIMP.
2. Download weights and save it in gimp-plugins/Inpainting/weights folder.
3. Open terminal and run:
   bash installGimpML.sh
4. Open GIMP.

You can find example of usage: youtube.
Please note that the mask must be exactly binary. Otherwise the filling result will be terrible.
To do this, remove antialiasing in selection:

@article{Moskalenko_2020,
	doi = {10.51130/graphicon-2020-2-4-18},
	url = {https://doi.org/10.51130%2Fgraphicon-2020-2-4-18},
	year = 2020,
	month = {dec},
	pages = {short18--1--short18--9},
	author = {Andrey Moskalenko and Mikhail Erofeev and Dmitriy Vatolin},
	title = {Deep Two-Stage High-Resolution Image Inpainting},
	journal = {Proceedings of the 30th International Conference on Computer Graphics and Machine Vision ({GraphiCon} 2020). Part 2}
} 

We are largely benefiting from:
[1] https://github.com/hughplay/DFNet
[2] https://github.com/kritiksoman/GIMP-ML/