This easy DeepFaceLab 2.0 deepfake tutorial will have you creating deepfakes in just a few hours! By the end of this deepfake guide you will be familiar with the basic deepfake process and ready to move on to more advanced projects!
How to download and install DeepFaceLab 2.0 deepfake software for AMD, NVIDIA, Intel HD, and CPU. DFL 2.0 can run on Windows, Linux, Google Colab, and other cloud machine learning platforms.
How to extract faces in DeepFaceLab 2.0. We’ll go over extracting images from both the source and destination videos, how to extract faces from multiple videos, using still images and image sequences, faceset cleanup, and alignment debugging.
In this DeepFaceLab XSeg tutorial I’ll go over what XSeg is and some important terminology, then we’ll use the generic mask to shortcut the entire process. After that we’ll do a deep dive into XSeg editing, training the model, and applying the masks to your facesets, and making backups along the way. I’ll also cover some ways of dealing with obstructions in front of the face.
How to download and use premade Celebrity Facesets for DeepFaceLab deepfakes. A faceset is a collection of images you can use along with your own videos to create deepfakes of anyone you choose. Many members of the DeepFaceLab Community have contributed their time to extract, clean up, label, and upload a growing archive of celebrity facesets.
How to pretrain models for DeepFaceLab deepfakes. A pretrained model is created with a pretrain faceset consisting of thousands of images with a wide variety of angles, facial expressions, color, and lighting conditions. DeepFaceLab includes such a faceset derived from Flickr-Faces-HQ Dataset.
These DeepFaceLab utility scripts are used to perform operations on your aligned images, a few of which are vital to the deepfake training process. I’ll show you things like resizing and enhancing facesets and how to edit your images outside DFL!