DEEPFAKE Tutorial: A Beginners Guide (using DeepFace Lab)

Cinecom.net
10 Dec 201911:54

TLDRThis tutorial, sponsored by MSI, guides beginners through the deepfake process using DeepFace Lab. It covers setting up a home office, choosing a scene, gathering face data, and installing deepfake software. The video emphasizes the importance of high-quality source data and demonstrates steps like extracting images, cleaning up faces, and training the AI. It also highlights the MSI P100's powerful hardware, which is ideal for deepfaking, and concludes with tips for refining the final deepfake video using video editing software.

Takeaways

  • 🖥️ The video is sponsored by MSI and features their new P100 desktop series and PS341WU monitor.
  • 🎬 The tutorial focuses on creating a deepfake video using DeepFace Lab software.
  • 🤝 The video is a collaboration with Chris, a specialist in deepfake technology, who provides insights and tips.
  • 🎥 To start, gather high-quality video footage of the face to be swapped and the destination video.
  • 📹 For best results, capture a 20-minute video with varied facial expressions and angles.
  • 💻 DeepFace Lab is the software of choice for this tutorial, with detailed installation and setup instructions provided.
  • 🔍 The process involves extracting images from videos, cleaning up face data, and training the AI with the collected data.
  • 💾 The tutorial emphasizes the importance of having powerful hardware, like MSI's P100, to handle the resource-intensive deepfake process.
  • 🕒 Deepfake training can take several days, depending on the complexity and the hardware capabilities.
  • 🎞️ Post-training, the video can be converted and further refined using video editing software for enhanced realism.
  • 👨‍🏫 The tutorial concludes with a recommendation to explore more advanced techniques and to check out VFXChris's work for deeper insights into deepfaking.

Q & A

  • What is the primary focus of the video tutorial?

    -The primary focus of the video tutorial is to provide a beginner's guide on creating deepfakes using DeepFace Lab software.

  • What is the significance of the MSI P100 mentioned in the script?

    -The MSI P100 is highlighted as a powerful desktop computer with high-end hardware, specifically designed for creative tasks like deepfaking, which requires significant processing power.

  • What is the recommended duration for the video clips used in deepfaking?

    -It is recommended to have a video clip of around 20 minutes that contains a variety of facial expressions from different angles for better deepfake results.

  • Why is it important to have high-quality video for deepfaking?

    -High-quality video is crucial for deepfaking because it ensures that the facial features are clearly visible and not obstructed, which helps the AI to learn and replicate the face more accurately.

  • What is the role of the PS341WU monitor in the deepfake process?

    -The PS341WU monitor, with its 5k resolution and color accuracy, is perfect for editing 4k videos on a single monitor, which is beneficial for the detailed work involved in deepfaking.

  • Why is it necessary to cover up faces of other actors when preparing the source video?

    -Covering up other actors' faces is necessary to ensure that the deepfake software focuses only on the target face, leading to cleaner source data and better final results.

  • What are the two main deepfake software programs mentioned in the tutorial?

    -The two main deepfake software programs mentioned are Faceswap and Deep Face Lab, with the tutorial focusing on the latter.

  • What does FPS stand for in the context of the tutorial, and why is it important?

    -FPS stands for frames per second, which is important because selecting an appropriate FPS can balance the amount of data the AI processes, avoiding excessive computation without compromising the quality of the deepfake.

  • How does the tutorial suggest cleaning up extracted faces from the image sequence?

    -The tutorial suggests manually cleaning up the extracted faces by removing blurred faces, non-facial elements, or incorrectly oriented faces to ensure the best results in the deepfake process.

  • What is the significance of the 'SAE' method mentioned in the training section?

    -SAE, or Synchronized Autoencoders, is a training method that provides great results in deepfaking. It requires a powerful GPU and is mentioned as a method that can handle the intensive video memory requirements of deepfake training.

  • Why is it recommended to do a final convert to 'Lossless+Alpha' after the deepfake is created?

    -A final convert to 'Lossless+Alpha' is recommended because it allows for further tweaking of the face in a video editor, such as color correction or mask adjustments, due to the transparency provided by the alpha channel.

Outlines

00:00

🎥 Introduction to Deepfake Tutorial

The video is sponsored by MSI and features the P100 desktop series and the PS341WU monitor. The presenter, Jordy from Cinecom.net, introduces a tutorial on deepfake technology, explaining its potential beyond face swapping. They discuss an upcoming Christmas video featuring faces swapped onto characters from 'Home Alone' and acknowledge the expertise of Chris, a specialist in deepfake, who provides insights and tips. The tutorial begins with selecting a scene or shooting new footage, emphasizing the importance of high-quality video and sufficient facial expression data for the best results.

05:02

🖥 Deepfake Software Setup and Face Extraction

The tutorial continues with instructions on installing deepfake software, specifically Deep Face Lab, and choosing the appropriate build based on the user's graphics card. It details the process of preparing the workspace, exporting videos, and extracting images from the source and destination clips. The importance of manually cleaning up extracted faces to ensure the best results is highlighted, along with the steps to extract faces using the S3FD method. The video also touches on the differences between various face extraction methods, suggesting resources for further learning.

10:04

💻 Deepfake Training and Post-Processing

The video script describes the training phase of deepfaking, where AI learns to match facial features using the prepared data. It mentions the use of SAE as a training method due to its effectiveness, and the importance of computer resources, particularly video memory, in this process. The script promotes the MSI P100's powerful GPU and other specifications as ideal for deepfake tasks. It also covers the training settings, emphasizing the need for stability and the potential need for trial and error. The final steps involve converting the trained data into a movie file and providing a bonus tip on further enhancing the deepfake result using video editing software like After Effects.

Mindmap

Keywords

💡Deepfake

Deepfake refers to a technology that uses artificial intelligence, particularly deep learning, to create realistic but fake videos or audios. In the context of the video, deepfake is used to swap faces in a video, replacing the original face with another person's face. This is done by gathering a large amount of facial data from the person whose face is to be used and then training the AI to recognize and replicate facial expressions and features onto the target video.

💡DeepFace Lab

DeepFace Lab is a software tool specifically designed for creating deepfakes. It is mentioned in the video as the preferred choice over Faceswap for the tutorial. DeepFace Lab allows users to extract faces from videos, align them, and train the AI to produce a deepfake video. The script describes the process of using DeepFace Lab to replace faces in a scene, such as placing the tutorial creator's face onto a character from the movie 'Home Alone'.

💡Faceswap

Faceswap is a technique and also the name of a software that is used to swap faces in videos. While it is not the main software used in the tutorial, it is mentioned as an alternative to DeepFace Lab. Faceswap operates on a similar principle to DeepFace Lab, using AI to map one person's face onto another in a video, but it may differ in the algorithms and tools it provides for achieving the deepfake.

💡High-quality video

High-quality video is essential for deepfaking because it provides the AI with clear and detailed facial data. The video mentions that the source video should be of high quality with no obstructions in front of the face to ensure the best results. High-quality video allows for better extraction of facial features, which is crucial for the AI to learn and replicate the face accurately.

💡Facial expression

Facial expressions are the movements and positions of the face that convey emotions or reactions. In the context of the video, capturing a wide range of facial expressions is important for training the AI in deepfake software. The more expressions the AI is trained on, the more realistically it can replicate those expressions in the final deepfake video.

💡Frames per second (FPS)

Frames per second (FPS) is a measure of how many individual frames are displayed in one second of video. The video script mentions choosing a FPS of 7 or 8 when extracting images from the source video. This decision balances the need for detailed data with the computational efficiency, as using every frame (higher FPS) would generate too much data and slow down the AI training process.

💡CUDA

CUDA (Compute Unified Device Architecture) is a parallel computing platform and application programming interface (API) model created by NVIDIA. It allows software to use the processing power of NVIDIA GPUs for general-purpose processing, which is beneficial for deepfake creation as it can speed up the computationally intensive tasks. The video mentions downloading the CUDA build of DeepFace Lab if the user has an NVIDIA graphics card.

💡Batch files

Batch files are scripts in an operating system that execute a series of commands. In the video, batch files are used to automate the deepfake creation process, with each batch file corresponding to a step in the process, such as clearing the workspace or extracting faces from images. These files simplify the user's interaction with the deepfake software by running predefined commands.

💡Training (AI)

Training in the context of AI refers to the process of feeding data into an AI model so that it can learn patterns and make predictions or generate outputs. In the video, training involves using the facial data to teach the AI how to replicate the source face onto the destination video. The training process is time-consuming and requires a significant amount of computational power, as mentioned with the use of powerful hardware like the MSI P100.

💡Iterations

In the context of AI training, iterations refer to the number of times the AI processes the training data to learn and improve its model. The video script mentions that ideally, there should be at least 150,000 iterations for a good deepfake result. More iterations allow the AI to better understand and replicate the facial features and expressions, leading to higher quality deepfakes.

💡Lossless+Alpha

Lossless+Alpha is a file format used in video editing that preserves the highest quality of the video and includes an alpha channel for transparency. The video mentions doing a final conversion to this format to allow for further editing and tweaking of the deepfake in a video editor like After Effects. This step is optional but can significantly enhance the final result by allowing for color correction, mask adjustments, and other visual effects.

Highlights

MSI's P65 laptop and P100 desktop series are praised for their performance in video editing.

Setting up a home office with a new desk, chair, and lights enhances the creative workflow.

MSI's P100 desktop series is built for creative tasks, boasting impressive internal hardware.

The PS341WU monitor's 5k resolution and color accuracy are ideal for editing 4K videos.

Deepfake technology is introduced as an advanced form of face swapping with numerous applications.

A Christmas video project involves swapping faces onto characters from 'Home Alone'.

Expert advice from Chris, a deepfake specialist, is shared to enhance the tutorial's credibility.

Gathering high-quality facial data of celebrities or oneself is crucial for deepfake projects.

Editing software can be used to isolate and prepare the source and destination video clips.

Deep Face Lab is the chosen software for this tutorial, with installation instructions provided.

Extracting images from videos at 7-8 FPS is recommended to balance data quantity and processing speed.

Manual cleanup of extracted faces is necessary for optimal deepfake results.

Training the AI involves various methods, with SAE being a popular choice for its balance of quality and speed.

MSI P100's powerful GPU and CPU are highlighted as ideal for deepfake processing.

The 'Creator Center' software allows users to optimize system performance for specific tasks.

MSI's 'Silent Pro Cooling System' ensures stable operation during intensive deepfake processing.

Deepfake training requires patience, as the AI learns through numerous iterations.

Post-training, the deepfake video can be converted and further refined using video editing software.

A final conversion to 'Lossless+Alpha' format allows for advanced tweaking in a video editor.

The tutorial concludes with a comparison of the deepfake result before and after post-production tweaks.

VFXChris's expertise in deepfakes and visual effects is recommended for further learning.