Easy DeepFaceLab Tutorial for 2022 and beyond

The video begins with a welcome to a Deep Face Lab tutorial. The presenter emphasizes the simplicity and condensed nature of the tutorial, which is aimed at getting viewers started with Deep Face Lab without delving into professional or high-end results. The tutorial focuses on the basics of using the software to create a 'fun deep fake.' The presenter directs viewers to the Deep Face Lab GitHub page, where they can download the software. They mention different versions available, with a recommendation for Nvidia card users to use the DirectX 12 options. The presenter also covers the process of downloading and extracting the software, highlighting the large file size and providing instructions for Windows PCs on how to run the executable file.

The tutorial continues with instructions on setting up Deep Face Lab for video processing. The presenter explains how to extract images from a video file, using Robert Downey Jr. as an example. They guide viewers through the process of running the 'extract images from video' batch file, discussing default settings and the decision to use PNG as the output image format. The presenter also covers the extraction of frames per second (FPS) and the process of extracting images from a second video file, featuring Elon Musk. They emphasize the potential time-consuming nature of video processing, depending on video length and complexity.

The presenter moves on to the next step of the tutorial, which involves extracting face sets from the previously extracted images. They explain the importance of focusing on the face area only, as the software does not require information about the background or other elements in the image. The tutorial demonstrates how to use the 'face set extract' batch file for both the source and destination videos. The presenter discusses various options within the software, such as face type and image size, but opts for the default settings. They also touch on the option to write debug images, which can be helpful for troubleshooting.

The tutorial enters the training phase, where the software learns to align and merge the source and destination faces. The presenter discusses the various training options available and selects 'sahd' for its effectiveness with lower-quality videos. They guide viewers through the process of setting up the training parameters, including the decision to use the GPU, the number of iterations, and the randomization of face selection. The presenter also explains the importance of iterations in achieving a realistic face swap and demonstrates the training process, showing the software's progress in aligning the faces.

The presenter covers the post-processing steps to refine the deep fake video. They explain how to use the 'merge SAE HD' option to combine the trained face alignments and introduce keyboard shortcuts for further video enhancement. The tutorial demonstrates techniques to improve the video quality, such as eroding the mask to soften edges and blurring the mask to reduce artifacts. The presenter also shows how to apply these settings to all frames and process the remaining frames for a final video output. They conclude with instructions on how to create the final video file and compare the original and the deep fake versions.

In the final part of the tutorial, the presenter wraps up by showcasing the result of the deep fake video and emphasizing the ease of creating such content with Deep Face Lab. They acknowledge the limitations of their example due to the relatively low number of iterations and encourage viewers to experiment with higher iterations for better results. The presenter invites viewers to share their creations and provides a call to action for feedback and suggestions for future tutorials. They express gratitude for the viewers' support of their new channel and sign off with a friendly farewell.