DeepFaceLab 2.0 Installation Tutorial (AMD NVIDIA Intel HD)

Deepfakery
11 May 202105:36

TLDRThis tutorial guides users through installing DeepFaceLab 2.0, a deepfake software, available on GitHub. It offers builds for various hardware, including NVIDIA RTX 3000 series and CPUs with AVX instruction set. The guide covers system requirements, installation steps, and software overview. It highlights the need for compatible hardware, updated drivers, and Windows settings adjustments for optimal performance. The workspace folder structure is explained for organizing deepfake data. The tutorial encourages users to explore more tutorials and subscribe for updates.

Takeaways

  • 😀 Visit the official DeepFaceLab repository on GitHub to download the software.
  • 🔍 Scroll down to 'Releases' to find builds for Windows 10, Linux, and Google Colab.
  • 💻 Choose the build that matches your system hardware, such as NVIDIA RTX 3000 series or up to RTX 2080 Ti.
  • 🔗 Ensure you have the correct build and that your device drivers are up to date.
  • 📂 The '10) makes CPU only' build modifies the software to work with an older version of TensorFlow.
  • 🖥️ The DirectX 12 build supports a range of devices from AMD, Intel, and NVIDIA on Windows 10.
  • 🚫 If you can't run the latest builds, consider the DeepFaceLab 1.0 OpenCL build, though it's no longer maintained.
  • ☁️ There's a version of DeepFaceLab for Google Colab, allowing cloud-based training.
  • 📁 After downloading, extract the files using a zip program; no installation is required for DeepFaceLab.
  • 🛠️ Follow the recommended system performance settings for optimal use, such as enabling Hardware Accelerated GPU Scheduling on Windows 10.

Q & A

  • Where can you find the official DeepFaceLab repository?

    -The official DeepFaceLab repository can be found on GitHub at github.com/iperov/deepfacelab.

  • What are the different builds available for DeepFaceLab 2.0?

    -DeepFaceLab 2.0 offers builds for NVIDIA RTX 3000 series, NVIDIA up to RTX 2080 Ti, CPU only with AVX instruction set, and DirectX 12 compatible with AMD, Intel, and NVIDIA devices.

  • What is the minimum requirement for the NVIDIA RTX 3000 series build of DeepFaceLab 2.0?

    -The NVIDIA RTX 3000 series build requires an NVIDIA 3000 series GPU.

  • How can you check if your NVIDIA GPU is compatible with DeepFaceLab 2.0?

    -You can check your NVIDIA GPU's compatibility by referring to NVIDIA's CUDA Compute Compatibility list provided in the description of the video.

  • Is it possible to train DeepFaceLab on a CPU?

    -Yes, you can train DeepFaceLab on a CPU with AVX instruction set by using the '10) makes CPU only' build, which installs an older version of TensorFlow.

  • What hardware is supported by the DirectX 12 build of DeepFaceLab?

    -The DirectX 12 build supports AMD Radeon R5, R7, and R9 200 series or newer, Intel HD Graphics 500 series or newer, and NVIDIA GeForce GTX 900 series or newer.

  • What is the process for downloading DeepFaceLab?

    -To download DeepFaceLab, you right-click on the file, select 'download', and then choose 'standard download'.

  • How do you handle Microsoft Defender's warning when extracting DeepFaceLab files?

    -If Microsoft Defender prevents the extraction as an unrecognized application, click 'More Info' and then 'Run anyway' to proceed with the extraction.

  • What are the recommended system performance settings for running DeepFaceLab?

    -DeepFaceLab is designed for Windows 10 and Linux, with high-end NVIDIA GPUs recommended for best results. Ensure device drivers are up to date, and consider enabling Hardware Accelerated GPU Scheduling and disabling Windows animations and effects for better performance.

  • What is the purpose of the 'internal' folder in DeepFaceLab?

    -The 'internal' folder contains the DeepFaceLab code and additional software and required libraries such as CUDA, Python, and FFmpeg.

  • How do you prepare your files for creating a deepfake with DeepFaceLab?

    -You prepare your files by placing the source face set in the 'Data_src' folder and the destination video in the 'Data_dst' folder within the workspace.

Outlines

00:00

💻 DeepFaceLab 2.0 Installation and Setup

This paragraph provides a step-by-step guide on how to download and install DeepFaceLab 2.0. It directs users to the official repository on GitHub, where they can find the open-source code, issue queue, and other resources. It explains the different builds available for various hardware configurations, including specific builds for NVIDIA RTX 3000 series GPUs and builds that support CUDA 3.5 and higher. It also mentions a CPU-only build and a DirectX 12 build compatible with a range of devices. Additionally, it covers the installation process, system requirements, and recommended settings for optimal performance, such as enabling Hardware Accelerated GPU Scheduling and disabling Windows animations. The paragraph concludes with an overview of the software components and workspace folder structure.

05:06

🔧 Testing DeepFaceLab and Seeking Further Assistance

The second paragraph discusses the ease of testing DeepFaceLab with default settings and invites viewers to ask questions about the software in the video's comment section. It also encourages viewers to explore more tutorials on creating deepfakes and to subscribe for further content. For professional services, it provides an email address for contact. This paragraph serves as a call to action for users to engage with the content and seek additional help if needed.

Mindmap

Keywords

DeepFaceLab

DeepFaceLab is an open-source software tool used for creating deepfake videos. It utilizes artificial intelligence to swap faces in videos with high accuracy. In the context of the video, DeepFaceLab 2.0 is the latest version of the software, and the tutorial focuses on its installation process. The script guides users through downloading the software from the official GitHub repository and selecting the appropriate build based on their system hardware.

GitHub

GitHub is a web-based platform that hosts and facilitates the version control and collaboration for software development projects using Git. In the video script, GitHub is mentioned as the source to access the official DeepFaceLab repository where users can find the latest releases of the software, including different builds for various hardware configurations.

NVIDIA RTX 3000 series

The NVIDIA RTX 3000 series refers to a line of high-performance graphics processing units (GPUs) designed for gaming and professional workloads. The script specifies that there is a build of DeepFaceLab 2.0 specifically optimized for and requiring an NVIDIA 3000 series GPU, indicating that users with these GPUs should use this build for better performance.

CUDA

CUDA (Compute Unified Device Architecture) is a parallel computing platform and application programming interface (API) model created by NVIDIA. It allows software developers to use NVIDIA GPUs for general purpose processing. The script mentions that the NVIDIA build of DeepFaceLab supports GPUs with CUDA 3.5 and higher, which is necessary for the software to function properly.

AVX instruction set

AVX (Advanced Vector Extensions) is a set of instructions for the x86 and x86-64 instruction set architecture for microprocessors from Intel and AMD. It is used to enhance performance on certain types of mathematical calculations. In the context of the script, the '10) makes CPU only' build of DeepFaceLab modifies the software to work on a CPU with AVX instructions, allowing training on systems without a compatible NVIDIA GPU.

DirectX 12

DirectX 12 is a low-level graphics API developed by Microsoft for programming graphics-related hardware. It is part of the DirectX API suite and is used by game developers to improve rendering performance. The script mentions that the DirectX 12 build of DeepFaceLab can be used with a range of devices from AMD, Intel, and NVIDIA, provided that DirectX 12 is supported on Windows 10.

Self-extracting .exe file

A self-extracting .exe file is a type of executable file that contains compressed data and has the ability to extract and install its contents automatically when run. In the script, users are instructed to double-click on the self-extracting .exe file to begin the extraction process of DeepFaceLab, which does not require a traditional installation procedure.

Hardware Accelerated GPU Scheduling

Hardware Accelerated GPU Scheduling is a feature in Windows 10 that allows the operating system to directly schedule GPU work, potentially improving performance. The script suggests enabling this feature in the system graphics settings for optimal performance when using DeepFaceLab.

Deepfake

A deepfake is a term used to describe a media file, usually a video, that has been altered using AI to replace a person's face with another person's face in a realistic manner. The script is a tutorial for DeepFaceLab, which is software designed to create deepfakes. The video guides users through the process of setting up the software for this purpose.

Workspace folder

In the context of the script, the workspace folder is a part of the DeepFaceLab software where all the deepfake data and files are stored. It includes subfolders for images, model files, and video files that are used in the deepfake creation process. The script instructs users to place their source and destination video files in the 'Data_src' and 'Data_dst' folders within the workspace.

Highlights

DeepFaceLab 2.0 is available on GitHub at the official repository.

Releases include builds for Windows 10, Linux, and Google Colab.

Choose the Mega.nz link for Windows versions.

Different builds are available based on system hardware.

The NVIDIA RTX 3000 series build requires an NVIDIA 3000 series GPU.

The NVIDIA up to RTX 2080 Ti build supports GPUs with CUDA 3.5 and higher.

CPU-only builds are available for systems without a compatible GPU.

The DirectX 12 build supports AMD, Intel, and NVIDIA devices with DirectX 12 on Windows 10.

Supported hardware includes AMD Radeon R5, R7, and R9 200 series or newer.

Intel HD Graphics 500 series or newer is also supported.

NVIDIA GeForce GTX 900 series or newer GPUs are compatible.

DeepFaceLab 1.0 OpenCL build is available for legacy systems.

Google Colab version allows cloud-based training.

Download the appropriate build and extract the files to start using DeepFaceLab.

DeepFaceLab does not require installation after extraction.

System performance settings are recommended for optimal use.

High-end NVIDIA GPUs are recommended for best results.

Ensure device drivers are up to date.

Enable Hardware Accelerated GPU Scheduling for Windows 10 users.

Disable Windows animations and effects to increase available resources.

Avoid using external media or hard drives that sleep when inactive.

DeepFaceLab's main components include the code, additional packages, and sample video data.

The workspace folder holds all deepfake data and files.

Data_src and Data_dst folders are for source and destination video files.

DeepFaceLab is ready to use with default settings for testing.

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