DeOldify.NET

C# implementation of Jason Antic's DeOldify(https://github.com/jantic/DeOldify) Only for photos for now!

How to run

On Windows 7, 8, 8.1, 10, 11

• Make sure that .NET Framework 4.5 (4.6 for SIMD-accelerated version) or higher is installed on your computer.

• You can use any bit depth (x32 or x64), but on a 32-bit system you will not be able to process large images due to the limited amount of memory.

• SIMD is supported only in 64-bit mode. On a 32-bit machine, you should use the regular version.

• At least 3 GB 1.5 GB with new convolution algorithm of free RAM is required to run.

• Download and unpack the repository, then download DeOldify.hmodel from the releases(https://github.com/ColorfulSoft/DeOldify.NET/releases/download/Weights/DeOldify.hmodel) and place it in Implementation\src\Resources.

• Run Compile.bat or Compile.simd.bat for SIMD-accelerated version

• The DeOldify.NET.win.exe or DeOldify.NET.win.simd.exe file will appear in the Implementation\Release folder. The application is ready to work!

• Use!

On Linux (Tested on Mint)

• We recommend that the first step is to update everything. It may take time, but it's worth it:

sudo apt-get update
sudo apt-get upgrade

• Install Mono:

sudo apt-get install mono-complete

• Download sources.

Using git and terminal

git clone https://github.com/ColorfulSoft/DeOldify.NET.git
cd DeOldify.NET
wget https://github.com/ColorfulSoft/DeOldify.NET/releases/download/Weights/DeOldify.hmodel -O Implementation/src/Resources/DeOldify.hmodel

Using GUI

• Download and unpack the repository.

• Download DeOldify.hmodel from the releases(https://github.com/ColorfulSoft/DeOldify.NET/releases/download/Weights/DeOldify.hmodel) and place it in Implementation/src/Resources.

• Run Compile.sh bash Compile.sh or Compile.simd.sh bash Compile.simd.sh

• The DeOldify.NET.linux.exe or DeOldify.NET.linux.simd.exe file will appear in the Implementation/Release folder. The application is ready to work!

• Run application using mono DeOldify.NET.linux.exe or mono DeOldify.NET.linux.simd.exe command in terminal or double click as in Windows.

• Use!

Please note, that DeOldify.NET using Mono is a bit slower, than using .NET Framework

Examples

New algorithms

DeOldify.NET has become a platform for testing the latest highly optimized algorithms, which will then be applied in the System.AI project. In this section you can find some information about the results of the experiments.

Patch2Vec Conv2d

The meaning of most fast convolution algorithms, such as im2col or im2row, involves bringing the convolution to matrix multiplication, which allows optimizing memory access operations by using the processor cache. However, such methods either require a buffer for srcC * kernelY * kernelH * dstH * dstW elements, which is extremely irrational. The proposed patch2vec method unwraps each patch of the input image on the fly, and then applies all convolution filters to it. This implementation is not inferior in efficiency to classical algorithms like im2col, and in practice even surpasses them. The buffer for this algorithm will have the size of srcC * kernelY * kernelX, which is much smaller than in the case of similar methods. Moreover, patch2vec does not impose restrictions on the convolution parameters, unlike, for example, the Shmuel Vinograd method. The proposed algorithm is difficult to fit into classical machine learning frameworks due to the fact that they are focused on using GEMM as the core. Pure C#-based implementations make it easy to do this. In DeOldify.NET two versions of the patch2vec conv2d algorithm are implemented - with and without SIMD support. You can choose, which version to use by executing the corresponding compilation command file. Vectorization is implemented through the Vector4 structure of the System.Numerics namespace. Vectorization is only available for x86-64 processors at version .NET Framework 4.6 and higher, or when using Mono newer than 2008.

Method	Time (ms)
im2col	123902
patch2vec	114970
patch2vec + simd	33270

Updates

• [27.04.2022] - DeOldify.NET has become a testing ground for the latest optimized algorithms. The Conv2d layer has been optimized and now requires significantly less memory. Support for SIMD vectorization will allow you to get about a fourfold increase in performance.
• [29.10.2021] - Big refactoring and code clean up
• [29.10.2021] - Linux support
• [16.09.2021] - Fixed a memory leak issue