DeepFaceLab 2.0 Faceset Extract Tutorial

Deepfakery

12 Jul 202114:25

TLDRDeepFaceLab 2.0's Face Set Extract Tutorial offers a comprehensive guide to creating high-quality face sets for deepfaking. It covers extracting frames from videos, refining face sets by removing unwanted faces and bad alignments, and fixing poor alignments. The tutorial also addresses extracting from multiple videos and images, and aligning faces manually for challenging subjects. By following these steps, users can prepare face sets for realistic deepfake creations.

Takeaways

😀 DeepFaceLab 2.0 is used for face set extraction, which is crucial for creating deepfakes.
🎥 The process starts with extracting individual frames from source and destination videos.
🔍 Face set images are then extracted from these video frames, focusing on the faces present.
🗑 Unwanted faces and poorly aligned images are removed to ensure quality.
🛠️ Poor alignments in the destination face set can be fixed to improve the final deepfake.
✂️ The source face set is trimmed to match the destination set, optimizing the deepfake process.
📁 DeepFaceLab allows for extraction from multiple videos, still images, and image sequences.
🖼️ The software uses filenames to set the original filenames of face set images, making organization key.
🔧 An optional video trimmer is available for adjusting the length of videos before extraction.
🌐 The face type selected influences the area of the face available for training, impacting realism.
🗂️ Cleaning the face set involves deleting unwanted or poorly aligned faces to enhance the deepfake quality.

Q & A

What is the purpose of the DeepFaceLab 2.0 Faceset Extract Tutorial?
-The tutorial aims to guide users through the process of creating high-quality face sets for deepfaking by extracting and preparing face images from source and destination videos.
What are the initial steps in the face set extraction process?
-The initial steps include extracting individual frame images from source and destination videos, then extracting face set images from these frames, and removing unwanted faces and bad alignments.
How can users extract images from video data_src in DeepFaceLab?
-Users can navigate to the DeepFaceLab workspace folder, rename their source video to data_src, and run the script '2) extract images from video data_src' to extract frames at a chosen frames per second and output image type.
What is the significance of choosing the correct frames per second (FPS) during extraction?
-Selecting the correct FPS allows users to extract fewer frames, which can be beneficial for managing file size and processing time, especially with long videos.
Why might someone choose to use PNG over JPEG when extracting images?
-PNG is a lossless format that preserves higher image quality compared to JPEG, which is compressed and can result in quality loss. This choice is recommended when high-quality face sets are needed for deepfaking.
How can users handle multiple source videos or still images in DeepFaceLab?
-Users can separate multiple sources by appending prefixes to filenames or using a script for batch renaming. For still images or sequences, they can be directly placed into the data_src folder.
What is the optional video trimmer in DeepFaceLab used for?
-The optional video trimmer allows users to cut their destination or source videos to specific start and end times, and specify audio tracks and bitrate for the output file.
How does the automatic face set extraction work in DeepFaceLab?
-The automatic extractor processes all files without interruption, detecting and extracting faces from the images. It requires users to choose a device, face type, image size, and other parameters before starting the extraction.
What is the role of the 'data_src view aligned result' tool in the cleaning process?
-This tool opens the extracted face set in an image browser, allowing users to review and delete unwanted faces, bad alignments, and duplicates to refine the face set for deepfaking.
Why is it important to trim the source faceset to match the destination faceset?
-Trimming the source faceset ensures that the training process uses relevant image information, matching the range and style of the destination faceset, which can improve the deepfake result and reduce unnecessary processing time.

Outlines

00:00

😀 DeepFaceLab 2.0 Face Set Extraction Overview

This paragraph introduces the DeepFaceLab 2.0 software and provides an overview of the face set extraction process. It involves extracting individual frame images from source and destination videos, followed by the extraction of face set images from these frames. The process includes removing unwanted faces and bad alignments, fixing poor alignments in the destination face set, and trimming the source face set to match the destination. The tutorial covers the use of videos, still images, image sequences, face set cleanup, and alignment debugging. By the end, users will be able to create high-quality face sets for deepfaking. The tutorial assumes that DeepFaceLab is already installed and that various videos and images are ready for use.

05:00

📸 Extracting Images and Setting Up Face Sets

This section details the process of extracting images from videos and setting up face sets for deepfaking. It starts with importing source videos into the DeepFaceLab workspace, renaming them to 'data_src' for software recognition. The tutorial then guides through extracting frames per second (FPS) to manage video length and selecting output image types, recommending PNG for quality. After extraction, users are instructed to return to the workspace folder and handle still images or image sequences by placing them in the 'data_src' folder. For multiple sources, files should be renamed with prefixes. The tutorial also covers optional video trimming and denoising for the destination video 'data_dst'. The process of extracting destination video images is explained, emphasizing the use of all files without frame rate choice and selecting PNG for output format.

10:01

🔍 Extracting and Cleaning Source Face Sets

The paragraph explains how to extract the source face set images for deepfake creation. It describes two extraction modes: automatic and manual, with the automatic mode being sufficient for most cases. The process involves selecting a device for extraction, choosing face type (e.g., whole face type 'WF'), setting the maximum number of faces per image, determining image size, and selecting JPEG compression quality. The tutorial also instructs on writing debug images for alignment verification. After extraction, users are guided to clean the face set by deleting unwanted faces, bad alignments, and duplicates using the XNView image browser. The cleaning process includes filtering images by file name and face properties, removing false detections, and ensuring accurate alignment.

📊 Sorting and Optimizing Face Sets

This section discusses the use of sorting tools to refine the face sets by removing unwanted and extremely similar images. It covers various sorting methods like histogram similarity, pitch, yaw, and blur to identify and delete poor quality or misaligned images. The tutorial explains how to recover original filenames after sorting and the use of 'best faces' sorting methods to select a variety of faces with different properties. It also advises on using debug images to find and remove additional bad alignments.

🖼️ Extracting and Refining Destination Face Sets

The paragraph outlines the steps for extracting and cleaning the destination face set, which is crucial for ensuring that all faces in the final deepfake are represented. It mentions four extraction methods, focusing on the automatic extraction with manual fix. The cleaning process involves removing unwanted faces and bad alignments, and manually re-extracting poorly aligned faces. The tutorial emphasizes the importance of keeping as many images as possible to avoid losing faces in the final deepfake.

✂️ Trimming Source Face Set to Match Destination

The final paragraph discusses the importance of trimming the source face set to match the destination face set's range and style. It guides users through sorting face sets by yaw, pitch, brightness, and hue to compare and adjust the source material to fit the destination's characteristics. The goal is to provide DeepFaceLab with a suitable range of image information to recreate the destination faces effectively. The tutorial concludes with an invitation for questions and suggestions for further learning and professional services.

Mindmap

Keywords

💡DeepFaceLab

DeepFaceLab is an open-source software tool used for creating deepfakes, which are synthetic media in which a person's face is replaced with another person's face in a video. In the context of the video, DeepFaceLab is the main software being used to demonstrate the process of face set extraction, which is a crucial step in the deepfake creation process.

💡Face Set Extraction

Face set extraction refers to the process of extracting individual face images from video frames or still images. This is a fundamental step in creating deepfakes, as it provides the raw material for training the AI to swap faces. The video tutorial walks through the steps of extracting face sets from source and destination videos using DeepFaceLab.

💡Source Video

The source video is the original video from which face images are extracted to create the deepfake. It contains the face that will be 'swapped' onto another person in the final video. The script mentions renaming the source video to 'data_src' for recognition by DeepFaceLab.

💡Destination Video

The destination video is the video that will have its faces replaced with those from the source video in the deepfake process. The tutorial explains how to extract face images from the destination video, which is renamed to 'data_dst' for processing.

💡Frame Extraction

Frame extraction is the process of extracting individual frames from a video. This is an initial step before face set extraction, as it breaks down the video into still images that can be processed by DeepFaceLab to identify and extract faces.

💡Alignment

Alignment in the context of deepfakes refers to the process of ensuring that the extracted face images are correctly oriented and positioned for the deepfake software to accurately map one face onto another. The script discusses manual and automatic alignment processes.

💡FPS (Frames Per Second)

Frames per second (FPS) is a measure of how many individual frames are displayed in one second of video. In the tutorial, the FPS setting is used to determine how many frames are extracted from the video, which can affect the quality and processing time of the deepfake.

💡PNG

PNG stands for Portable Network Graphics, a file format used for storing high-quality images with lossless compression. The script mentions choosing PNG as the output image type for higher quality face images during extraction.

💡JPEG

JPEG is a commonly used image file format that applies lossy compression, which can reduce file size but may decrease image quality. It is mentioned as an alternative to PNG in the context of choosing an output image type for extraction.

💡Debug Images

Debug images are images that include visual aids such as landmarks and bounding boxes to help identify and correct alignment issues. The script explains the option to generate debug images during face set extraction to assist in the alignment debugging process.

💡Deepfake

A deepfake is a video or other media in which a person's face is replaced with a different face using AI and machine learning techniques. The video tutorial is focused on teaching viewers how to prepare face sets for creating high-quality deepfakes using DeepFaceLab.

Highlights

DeepFaceLab 2.0 introduces a comprehensive face set extraction process for deepfaking.

The process begins with extracting individual frames from source and destination videos.

Face set images are then extracted from the video frame images.

Unwanted faces and bad alignments are removed to refine the face set.

Poor alignments in the destination face set can be manually fixed.

The source face set is trimmed to match the destination face set for optimal results.

DeepFaceLab can handle multiple videos and still images for face set creation.

Face set cleanup and alignment debugging are crucial steps in the process.

DeepFaceLab provides a video trimmer for adjusting source and destination videos.

The software allows for the extraction of images at different frame rates.

Users can choose between lossless PNG or compressed JPEG for output image type.

DeepFaceLab supports batch processing of file renaming for multiple source videos.

The automatic extractor processes files without interruption, while manual mode allows for frame-by-frame alignment.

Face type selection is a critical decision affecting the area of the face available for training.

The max number of faces from image setting controls the number of faces extracted per frame.

Image size and JPEG compression quality are adjustable for balance between quality and file size.

Debug images with face alignment landmarks and bounding boxes aid in identifying poorly aligned images.

Data cleaning involves deleting unwanted faces, bad alignments, and duplicate images for a refined face set.

Sorting tools help in removing unnecessary images based on various criteria like similarity and alignment.

The destination face set should be kept as comprehensive as possible to ensure all desired faces are transferred in the final deepfake.

Manual re-extract allows for the selective re-alignment of poorly aligned faces.

Trimming the source face set to match the destination's range and style optimizes the training process.