DEEPFAKE Tutorial: A Beginners Guide (using DeepFace Lab)

Cinecom.net
10 Dec 201911:54

TLDRIn this tutorial, Jordy from Cinecom.net explores deepfake technology using DeepFace Lab, a tool that swaps faces in videos. He discusses the process of selecting a scene, gathering high-quality face data, and preparing the workspace. The video covers installing deepfake software, extracting images, cleaning up faces, and training AI for facial recognition. Jordy emphasizes the importance of powerful hardware, showcasing MSI's P100 desktop with an NVIDIA RTX 2080Ti for efficient deepfaking. The tutorial concludes with tips for finalizing the deepfake video and enhancing it with video editing software.

Takeaways

  • 💻 The tutorial is sponsored by MSI and features their P100 desktop series and PS341WU monitor.
  • 🎬 The video discusses creating deepfakes, using the example of a Christmas video featuring faces swapped onto characters from 'Home Alone'.
  • 👨‍🏫 Chris, a deepfake specialist, provides detailed explanations and bonus tips on the process.
  • 📹 For best results, gather high-quality video footage of the subject's face from multiple angles, ideally around 20 minutes of footage.
  • 💇‍♂️ If using your own face, mimic the expressions of the character in the video for better alignment.
  • 🤖 Deepfake software like Faceswap and Deep Face Lab are used, with the tutorial focusing on the latter.
  • 🖼️ The process involves extracting images from videos, cleaning up face data, and training the AI with the collected data.
  • 💾 A powerful computer with a good GPU, like the NVIDIA RTX 2080Ti, is recommended for the resource-intensive deepfake training.
  • 🔧 The tutorial covers how to adjust settings for training and converting the deepfake, including batch size and gradient clipping.
  • ⏱️ Deepfake training can take several days, depending on the complexity and the computer's hardware.
  • 🎞️ Post-processing in video editing software can further refine the deepfake result, such as color correction and mask adjustments.

Q & A

  • What is the main purpose of the video?

    -The main purpose of the video is to provide a beginner's guide to creating deepfakes using DeepFace Lab software.

  • What is the significance of the MSI P100 in the video?

    -The MSI P100 is highlighted as a powerful desktop series with high-end hardware suitable for creative tasks like deepfaking, which requires significant computing power.

  • What is the role of the PS341WU monitor in the video?

    -The PS341WU monitor is mentioned for its color accuracy and 5k resolution, which is ideal for editing 4k videos on a single monitor, thus beneficial for deepfake editing.

  • What is the first step in the deepfake process as described in the video?

    -The first step in the deepfake process is to pick a favorite scene from a film or shoot something oneself to use as the base for the deepfake.

  • Why is it important to gather high-quality face data for deepfaking?

    -High-quality face data is crucial for deepfaking because it ensures the AI has enough detailed information to accurately swap faces, leading to better results.

  • What is the recommended video length for capturing facial expressions for deepfaking?

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

  • How does the video suggest handling multiple actors in a scene when deepfaking?

    -When multiple actors appear on screen, the video suggests using editing software to cover up the faces of those not being deepfaked.

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

    -The two main deepfake software programs mentioned are Faceswap and Deep Face Lab.

  • Why is it important to manually clean up the extracted faces during the deepfake process?

    -Manually cleaning up the extracted faces is important to remove blurred faces, non-face elements, and incorrect orientations, which ensures the AI has clean and accurate data to learn from.

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

    -SAE (Shallow Appearance Embedding) is a training method that provides great results in deepfaking but requires significant computer resources, particularly video memory.

  • How does the video suggest improving the final deepfake result?

    -The video suggests doing a final convert to 'Lossless+Alpha' to allow further tweaking of the face in a video editor like After Effects, which can significantly enhance the final result.

Outlines

00:00

🖥️ Introduction to Deepfake Technology and MSI P100 Desktop

The video begins with an introduction by Jordy from Cinecom.net, who expresses excitement over the opportunity to work with MSI's new desktop series, the P100, after previously using the P65 laptop. Jordy discusses setting up a home office with a new desk, chair, and lights to complement the aesthetic and power of the P100. The video also introduces the Prestige monitor, the PS341WU, which boasts a 5K resolution and color accuracy ideal for editing 4K videos. Jordy mentions an upcoming Christmas video featuring deepfake technology, where faces are swapped with characters from 'Home Alone,' and credits Chris, a deepfake specialist, for his contributions and expertise. The tutorial is set to cover the basics of deepfake technology, starting with selecting a scene and gathering face data, emphasizing the need for high-quality video and clean source data for optimal results.

05:02

🔧 Deepfake Process: Software Installation and Face Data Preparation

The tutorial continues with a step-by-step guide on installing deepfake software, specifically Deep Face Lab, and preparing face data for the deepfake process. Jordy explains the importance of selecting the appropriate software build based on the user's graphics card and installing the necessary files. The video demonstrates how to clear the workspace, import source and destination video clips, and extract images from these clips at a recommended frame rate to balance data quantity and processing speed. The tutorial then covers the extraction of faces from the image sequences using the S3FD method, followed by a manual cleanup of the extracted faces to ensure only the clearest and most relevant images are used. Jordy also discusses the training process, emphasizing the trial-and-error nature of deepfaking and the impact of computer resources on the training's success.

10:04

💻 Deepfake Training and Post-Processing with MSI P100

In this segment, Jordy delves into the training phase of deepfaking, using the SAE method for its quality results, and discusses the importance of computer hardware, particularly the MSI P100's powerful GPU and CPU, for handling the intensive deepfake tasks. He outlines the settings and precautions to take during the training process, such as enabling auto-backups and adjusting batch sizes to prevent crashes. Jordy then moves on to the conversion phase, explaining how to compile the trained data into a new video file and the importance of iterating over the training and conversion process to refine results. He also provides a bonus tip on doing a final conversion to 'Lossless+Alpha' for further tweaking in video editing software like After Effects. The video concludes with a comparison of the deepfake results with and without post-processing, and Jordy thanks MSI for their support and encourages viewers to stay creative.

Mindmap

Keywords

💡Deepfake

Deepfake refers to a technique that uses artificial intelligence, specifically deep learning, to create realistic but fake images or videos of a person. In the context of the video, deepfakes are used to swap faces in a video, such as placing the presenter's face onto characters from a movie. The video mentions working on a Christmas video where faces are swapped onto characters from 'Home Alone', showcasing the potential of deepfake technology in creative projects.

💡Faceswap

Faceswap is a simpler form of deepfake technology where the face of one person is swapped with another in a video or image. It is mentioned as a precursor to deepfake, indicating that deepfake technology has advanced beyond simple face swapping to create more realistic and complex manipulations. The video script describes deepfake as 'kind of an advanced faceswap', suggesting that while faceswap is a starting point, deepfake offers more sophisticated capabilities.

💡High-quality video

High-quality video is essential for deepfake processes because it provides the AI with clear and detailed facial data. The video script emphasizes the importance of high-quality source material, stating that 'the cleaner your source data is, the better the results will be'. This is crucial because the AI relies on clear images to accurately learn and replicate facial features and expressions.

💡FPS (Frames Per Second)

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 framerate of around 7 or 8 for extracting images from the video, which balances the need for detailed data with the computational efficiency of the AI. A higher FPS would provide more data but could slow down the AI training process without significantly improving results.

💡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 NVIDIA GPUs for general purpose processing. In the video script, it is mentioned as a build option for the deepfake software, indicating that users with NVIDIA graphics cards can take advantage of CUDA for improved performance in deepfake creation.

💡Batch files

Batch files are scripts in an operating system that execute a series of commands. In the context of the video, batch files are used to automate the deepfake process, with each batch file corresponding to a different step in the deepfake creation process. The script mentions that these files are numbered from 1-10, representing the different stages of the deepfake process.

💡Training (AI)

Training in the context of AI refers to the process of teaching a machine learning model to perform a specific task, such as learning to create deepfakes. The video script describes the AI training process as 'the artificial intelligence because that sounds way cooler', highlighting the complex and advanced nature of the technology. The training phase is crucial for the AI to learn how to accurately replicate and manipulate facial features.

💡Iterations

In machine learning, iterations refer to the number of times an algorithm processes the training data to improve its performance. The video script mentions that ideally, there should be at least 150,000 iterations for the AI to learn effectively, indicating that a higher number of iterations can lead to better results in the deepfake creation.

💡Lossless+Alpha

Lossless+Alpha refers to a video file format that retains the highest quality of the original data and includes transparency information (alpha channel). The video script suggests doing a final conversion to this format to allow for further manipulation in video editing software, such as color correction or mask adjustments. This provides a high degree of control over the final deepfake output.

💡Stable deepfake software

A stable deepfake software is one that can run without crashing or experiencing errors, which is crucial for long processing times. The video script mentions enabling 'Gradient Clipping' to ensure a more stable training process, highlighting the importance of software stability in deepfake creation, especially when dealing with large amounts of data and complex AI algorithms.

Highlights

MSI's P100 desktop series is recommended for deepfake editing due to its powerful internal hardware.

The PS341WU monitor is highlighted for its 5k resolution and color accuracy, ideal for 4k video editing.

Deepfake technology is described as an advanced form of faceswap with broader applications.

A Christmas video project involving deepfake of Home Alone characters is mentioned.

Expert Chris, specialized in deepfake, provided insights and tips for the tutorial.

The tutorial covers how to replace faces in video with high-quality results.

Gathering high-quality face data from celebrities or oneself is crucial for deepfake success.

The importance of filming oneself for 20 minutes to match expressions in the target video is emphasized.

Deep Face Lab is chosen over Faceswap for the deepfake software tutorial.

NVIDIA graphics card users are advised to download the CUDA build for Deep Face Lab.

Batch files in Deep Face Lab are used to execute different steps of the deepfake process.

The FPS for video frame extraction is recommended to be around 7 or 8 for efficiency.

Manual cleanup of extracted faces is necessary to ensure high-quality deepfake results.

SAE is chosen as the training method for deepfake due to its effectiveness.

MSI P100's high-end GPU and CPU are praised for handling deepfake's resource-intensive tasks.

The 'Silent Pro Cooling System' of MSI P100 ensures stable operation during long deepfake processes.

The tutorial advises on using default settings for the initial deepfake training run.

Deepfake training may require several days of processing time for optimal results.

Post-training, a 'Lossless+Alpha' conversion is suggested for further video editing flexibility.

Final results can be significantly enhanced with additional editing in software like After Effects.

The tutorial concludes with encouragement to stay creative and explore deepfake technology.