DeepFaceLab-Based AI Face-Change Tutorial

Introduction

AI face change is a product under Neuro Network and Machine Learning technologies. And this article aims to be a tutorial that could let people who do not have experience in Machine Learning to replace the face in one video with other faces.

Key Words: #deepfakes #faceswap #face-swap #deep-learning #deeplearning #deep-neural-networks #deepface #deep-face-swap #fakeapp #fake-app #neural-networks #neural-nets

What is DeepFaceLab?

DeepFaceLab is a tool that utilizes machine learning to replace faces in videos.  Github Project: Click here. DeepFaceLab does not have GUI but it does not require a high RAM (at least 2g).

1. Environment

Hardware requirement:

I. Graphics Card: Nvidia Graphic Card with CC(Compute Capability)>3.0 CC Chart, or at least GTX 1060 6G. AMD not be supported 🙁

II. CPU: Intel or AMD CPU recommended with RAM>8G

III. OS: Windows 10 x64

Software requirement:

I. Microsoft Visual Studio 2015: Download

in custom option, select Visual C++

II. CUDA 9.0 For Windows 10: Download

III. CuDNN 7.0.5: Download

Open the downloaded CuCNN zip, and copy the three files in it to CUDA’s install location

The Environment in this tutorial:

Graphics Card: Nvidia GTX GeForce 1070 8G

RAM: 16.0GB

CPU: Intel(R) Core(TM) i7-7700k 4.2GHz

OS: Windows 10 Pro 1903 – 18362.10024

2. DeepFaceLab

I. Install:

DeepFaceLab: Download

II. Preparation

1. Prepare two videos, and name them:

The video has the face need to be replaced as data_dst

The video has the face to replace to another video’s face as data_src

1. Put those two into the workspace

III. Processing

There are many .bat files, do as shown below:

(1) clear workspace

While doing a new model of Face Change, you want clear the previous data

(2) extract images from video data_src

Using Enter to skip (set as default)

(3.2) extract images from video data_dst FULL FPS

Using Enter to skip (set as default)

(4) data_src extract faces MT all GPU

Identify the faces in data_src and pull out

(4.2.2) data_src sort by similar histogram

(4.1) data_src check result

Delete unpleasant faces

(4.2.1) data_src sort by blur

(4.1) data_src check result

Delete unpleasant/non-target faces

(5) data_dst extract faces MT all GPU

(5.2) data_dst sort by similar histogram

(5.1) data_dst check results

Delete unpleasant faces

(5.3) data_dst sort by blur

(5.1) data_dst check results

Delete unpleasant faces

(6) train H128

Using Enter to skip (set as default)

Press P to update previews; press Enter to save and quit

We wait until the loss rate close to 0.02

(7) convert H128

Factors:

Press Enter to convert the whole frames

(8) converted to mp4

Whole video would be uploaded later, video was on but now is off due to copyrights violations