Easy DeepFaceLab Tutorial for 2022 and beyond

DigitalDaz
3 Nov 202227:10

TLDRThis tutorial offers a straightforward guide to using DeepFaceLab for creating deepfake videos. It begins with downloading and setting up the software, then progresses through extracting images from videos, creating face sets, and aligning faces. The tutorial emphasizes using default settings for simplicity and suggests running the training process for at least 100,000 iterations for optimal results. Viewers are encouraged to experiment with the software and share their creations.

Takeaways

  • 😀 The tutorial introduces a simplified method for using DeepFaceLab to create deepfake videos.
  • 🔧 The process does not aim for professional or high-end results but focuses on ease of use and basic functionality.
  • 💻 Users are guided to download DeepFaceLab from GitHub, with a preference for the NVIDIA-optimized versions if using an NVIDIA graphics card.
  • 📂 The tutorial explains how to extract images from a video source and destination, which are necessary for the deepfake creation.
  • 📝 It details the steps to extract face sets from the images, focusing only on the facial features and ignoring the background or other elements.
  • 🤖 The software is set to train the model by aligning and merging the source and destination faces to create a realistic effect.
  • 🕒 The tutorial mentions that training can take varying amounts of time depending on the quality and length of the videos.
  • 🎛️ Default settings are recommended for beginners, with the option to tweak settings for better results after gaining experience.
  • 🔁 The importance of running a sufficient number of iterations for the best training outcome is emphasized, suggesting a range of 100,000 to 150,000 iterations.
  • 🖥️ The final step involves merging the trained images into a video, with tips on using keyboard commands to refine the deepfake effect.
  • 📹 The tutorial concludes with a comparison of the original and the deepfake video, highlighting the potential of DeepFaceLab despite using a limited number of iterations.

Q & A

  • What is the tutorial about?

    -The tutorial is about using DeepFaceLab to create deepfake videos. It provides a simple and condensed guide to get started with the software without delving into professional or high-end results.

  • Where can you find DeepFaceLab?

    -DeepFaceLab can be found on its GitHub page, which can be accessed by searching for 'DeepFaceLab' on Google or following the link provided in the description of the tutorial video.

  • What are the different versions of DeepFaceLab available for download?

    -There are two DirectX 12 options available for download: the latest 2022 version and the previous year's 2021 version. Additionally, there are versions specifically tuned for Nvidia graphics cards.

  • Why would someone choose the Nvidia-tuned version of DeepFaceLab?

    -The Nvidia-tuned versions of DeepFaceLab are recommended for users with Nvidia cards because the software is more optimized for Nvidia hardware, potentially providing better performance and results.

  • What is the first step in creating a deepfake video with DeepFaceLab?

    -The first step is to extract images from the source video. This is done using the 'extract images from video' utility provided with DeepFaceLab.

  • How does the software handle different frames per second (FPS) in the video?

    -The software defaults to an FPS of 48, but if zero is entered, it allows the user to decide the FPS. The software then extracts frames accordingly.

  • What is the purpose of extracting face sets from the images?

    -Extracting face sets involves creating a map of just the faces in the images, ignoring all other details. This helps the software focus solely on the facial features for the deepfake process.

  • What does the training process in DeepFaceLab involve?

    -The training process involves aligning the source face onto the destination face. The software tries to match them as closely as possible, merging faces when necessary to create a realistic effect.

  • How long does the training process typically take?

    -The training time can vary greatly depending on the quality and length of the videos. It can take from minutes to hours or even days for very long videos or high iteration counts.

  • What are some tips for improving the final output video's quality?

    -Tips for improving the final output include running the training for a higher number of iterations (ideally 100,000), using the 'a' key to adjust the alpha roll mask, and the 'e' and 'd' keys to adjust the blur mask, then applying these settings to all frames.

  • How does the final video size compare to the original video?

    -The final output video tries to maintain a similar file size to the original, with minimal increase. In the tutorial, the original video was 102 megabytes and the output was 105 megabytes.

Outlines

00:00

😀 Introduction to Deep Face Lab Tutorial

The speaker welcomes viewers to a tutorial on Deep Face Lab, emphasizing that the tutorial will be straightforward and not delve into professional-level results. The focus is on enabling users to quickly start using the software for creating fun deepfakes. The tutorial begins with instructions to download Deep Face Lab from its GitHub page, with a recommendation to use the DirectX 12 version if the user has an NVIDIA card, due to its optimization for NVIDIA hardware. The speaker also mentions downloading the software via a mega link and provides a brief guide on extracting the executable file, noting that the file size is approximately 3GB.

05:01

🖥️ Setting Up Deep Face Lab

The tutorial continues with the setup process of Deep Face Lab. The speaker explains how to extract images from a video file, using Robert Downey Jr.'s video as an example. The process involves running a batch file that extracts every frame from the video into the 'data source' folder. The speaker uses default settings for this step, including the output image format set to PNG and the default frames per second (FPS) set to 48. The speaker then demonstrates how to extract images from a second video, featuring Elon Musk, using the same method. The tutorial emphasizes the simplicity of the process, with the speaker choosing to use default settings to keep the tutorial accessible for beginners.

10:02

🔍 Extracting Face Sets from Images

The speaker proceeds to the next step, which involves extracting face sets from the previously extracted images. The goal is to isolate the faces from the background and other unnecessary details. The speaker guides viewers on how to use a batch file to extract face sets from the Robert Downey Jr. video, specifying default settings such as 'whole face' for the face type and a maximum image size of 512. The speaker also mentions the option to write debug images to a folder if the software encounters faces it cannot process, but notes that this is not usually necessary. The process is repeated for the Elon Musk video, and the speaker pauses the tutorial to allow time for the extraction process to complete.

15:11

🤖 Training the Deep Learning Model

After extracting face sets, the tutorial moves on to training the deep learning model. The speaker explains that this step involves aligning the source face (Robert Downey Jr.) onto the destination face (Elon Musk) as accurately as possible, with the software attempting to merge faces when perfect alignment isn't achievable. The speaker chooses the 'sahd' training option, believing it provides the best results for lower-quality videos. The speaker then walks through the training settings, again using mostly default values, and starts the training process. The speaker mentions that the training time can vary greatly depending on the video's length and quality, and that they will aim for around 100,000 iterations for a good result.

20:11

🎭 Refining the Deepfake Video

The speaker demonstrates how to refine the deepfake video by adjusting the 'a-roll mask' and 'blur mask' to improve the blending of the source and destination faces. They use keyboard shortcuts to apply these adjustments to individual frames and then to all frames automatically. The speaker emphasizes the importance of not overdoing the blur effect to maintain clarity. They also show how to process the remaining frames and merge them into a final video using the 'merge SAE HD' option. The speaker provides a quick tip for dealing with black screens that may appear due to black frames in the video and concludes this section by showing the process of creating the final video file.

25:12

📹 Conclusion and Final Thoughts

In the final part of the tutorial, the speaker concludes the process by demonstrating the creation of the final video file and comparing it to the original. They note that the output video size is similar to the original, which is advantageous for uploading and sharing. The speaker plays both the original and the deepfake videos to show the improvement in face alignment and overall quality, despite the relatively short training time of 27 minutes and only 4,300 iterations. They encourage viewers to experiment with longer training times for better results and to share their creations. The speaker thanks the viewers for watching, invites feedback and suggestions for future tutorials, and signs off with a positive note.

Mindmap

Keywords

💡DeepFaceLab

DeepFaceLab is an open-source tool used for creating deepfake videos. In the context of the video, it is the primary software being used to demonstrate how to replace the face of one person with another in a video sequence. The tutorial walks viewers through the process of using DeepFaceLab to generate a 'fun deep fake', emphasizing its user-friendly nature despite its powerful capabilities.

💡GitHub

GitHub is a web-based platform primarily used for version control and collaboration in software development. In the video, GitHub is mentioned as the source to find the DeepFaceLab project, where users can download the latest release of the software. It highlights the collaborative and open-source nature of the project.

💡DirectX 12

DirectX 12 is a version of the DirectX API, which is used for multimedia tasks, especially game programming and video processing. The video mentions DirectX 12 options for DeepFaceLab, indicating that these versions are optimized for performance with DirectX 12 compatible hardware, providing better utilization of the graphics card's capabilities.

💡NVIDIA

NVIDIA is a company known for its graphics processing units (GPUs). The script suggests that DeepFaceLab is highly tuned for NVIDIA cards, implying that users with NVIDIA GPUs will likely experience better performance and results when creating deepfakes with this software.

💡EXE file

An EXE file is a type of Windows executable file that can run programs. In the tutorial, the presenter mentions downloading an EXE file for DeepFaceLab. The video also addresses a common concern about running EXE files, which is the potential security risk, and reassures viewers on how to proceed safely with the extraction process.

💡FPS (Frames Per Second)

Frames Per Second (FPS) is a measure of how many individual frames are displayed in one second of video. The script refers to FPS when discussing the default settings for video processing in DeepFaceLab. A higher FPS can result in smoother video playback, but it also increases the processing time and resources required.

💡Face Extraction

Face extraction is the process of identifying and extracting faces from images or video frames. In the context of the video, face extraction is a crucial step in creating deepfakes, as it allows the software to focus on the facial features that will be replaced or manipulated.

💡Iterations

In the script, iterations refer to the number of times the software processes the data to refine the deepfake effect. More iterations can lead to a more realistic outcome, but also increase the processing time. The tutorial suggests aiming for a certain number of iterations to achieve a balance between quality and processing time.

💡Merging

Merging in the context of the video refers to the final step in the deepfake creation process, where the source face is blended onto the destination face. The script explains that this step combines the aligned and processed images to create the final deepfake video.

💡Bitrate

Bitrate in video processing refers to the number of bits used per unit of time to represent the video. A higher bitrate generally means better video quality but also larger file sizes. The video mentions bitrate in relation to the output video, indicating that DeepFaceLab tries to maintain a similar file size to the original video while still delivering a high-quality deepfake.

Highlights

Introduction to a simple and condensed DeepFaceLab tutorial.

Direct link to DeepFaceLab on GitHub for easy access.

Explanation of the different DeepFaceLab versions available for download.

Recommendation for Nvidia card users to select specific DeepFaceLab versions.

Instructions on downloading and extracting the DeepFaceLab executable.

Overview of the files included in the DeepFaceLab folder.

Tutorial on extracting images from a video using DeepFaceLab.

Default settings for extracting images and their implications.

Process of extracting face sets from the images for DeepFaceLab training.

Options for different face types in the face extraction process.

Importance of setting the correct image size for face extraction.

Explanation of the training process and its role in DeepFaceLab.

Guidance on selecting the appropriate training option for different video qualities.

Recommendation for the number of iterations for effective training.

Description of the face alignment and merging process in DeepFaceLab.

Use of keyboard commands to refine the face merging in the video.

Techniques to improve the video output quality using DeepFaceLab tools.

Final steps to merge the processed images into a video file.

Comparison of the original and the DeepFaceLab processed video.

Conclusion and call to action for viewers to try DeepFaceLab and share their results.