Inpainting Network

This code is the implementation of the “Globally and Locally Consistent Image Completion” paper. It contains the pre-trained model and example usage code.

Inpainting Network
  • 種類 library
  • バージョン 2018年02月
  • プログラミング言語 lua
  • ライセンス CC-by-sa-nc 4.0
  • 依存関係 torch, nn, image, nngraph


This code provides an implementation of the research paper:

Globally and Locally Consistent Image Completion
Satoshi Iizuka, Edgar Simo-Serra, and Hiroshi Ishikawa
ACM Transaction on Graphics (SIGGRAPH), 2017

We learn to inpaint missing regions with a deep convolutional network. Our network completes images of arbitrary resolutions by filling in missing regions of any shape. We use global and local context discriminators to train the completion network to provide both locally and globally consistent results.

See our project page for more detailed information.


Copyright (C) <2017> <Satoshi Iizuka, Edgar Simo-Serra, Hiroshi Ishikawa>

This work is licensed under the Creative Commons
Attribution-NonCommercial-ShareAlike 4.0 International License. To view a copy
of this license, visit or
send a letter to Creative Commons, PO Box 1866, Mountain View, CA 94042, USA.

Satoshi Iizuka, Waseda University,
Edgar Simo-Serra, Waseda University,


The packages of nn, image, and nngraph should be a part of a standard Torch7 install. For information on how to install Torch7 please see the official torch documentation on the subject. The torch-opencv is OpenCV bindings for LuaJit+Torch, which can be installed via luarocks install cv after installing OpenCV 3.1. Please see the instruction page for more detailed information.


First, download the model by running the download script:


Basic usage is:

th inpaint.lua --input <input_image> --mask <mask_image>

The mask is a binary image (1 for pixels to be completed, 0 otherwise) and should be the same size as the input image.

Other options:

  • --gpu: Use GPU for the computation. cunn is required to use this option. Defaults to false.
  • --maxdim: Long edge dimension of the input image. Defaults to 500.
  • --nopostproc: Disable the post-processing. Defaults to false. If you fail to install the torch-opencv, use this option to avoid using the package.

For example:

th inpaint.lua --input example.png --mask example_mask.png

Best Performance

  • This model was trained on the Places2 dataset and thus best performance is for natural outdoor images.
  • While the model works on images of any size with arbitrary holes, we trained it on images with the smallest edges in the [256, 384] pixel range and random holes in the [96, 128] pixel range. Our model will work best on images with holes of those sizes.
  • Significantly large holes or extrapolation when the holes are at the border of images may fail to be filled in due to limited spatial support of the model.


  • This is developed on a Linux machine running Ubuntu 16.04 during late 2016.
  • Provided model and sample code is under a non-commercial creative commons license.


If you use this code please cite:

  author = {Satoshi Iizuka and Edgar Simo-Serra and Hiroshi Ishikawa},
  title = {{Globally and Locally Consistent Image Completion}},
  journal = "ACM Transactions on Graphics (Proc. of SIGGRAPH)",
  year = 2017,
  volume = 36,
  number = 4,
  pages = 107:1--107:14,
  articleno = 107,