DeepFaceLab 2.0 Installation Tutorial (AMD NVIDIA Intel HD)

Deepfakery
11 May 202105:36

TLDRThe DeepFaceLab 2.0 Installation Guide offers a step-by-step tutorial on downloading and installing the software from the official GitHub repository. It details the selection of the appropriate build based on hardware, such as NVIDIA RTX 3000 series or CPU with AVX instruction set. The guide also covers system requirements, installation process, and software components, emphasizing the need for a high-end NVIDIA GPU for optimal performance. Additionally, it provides tips on system settings to enhance DeepFaceLab's functionality and mentions the availability of a Google Colab version for cloud-based training.

Takeaways

  • 😀 Visit the official DeepFaceLab repository on GitHub for the latest builds and resources.
  • 🔍 Choose the appropriate build based on your system hardware, such as NVIDIA RTX 3000 series or up to RTX 2080 Ti.
  • 💾 The '10) makes CPU only' build is designed for CPU training with AVX instruction set support.
  • 🖥️ The DirectX 12 build is compatible with a range of devices including AMD, Intel, and NVIDIA with DirectX 12 on Windows 10.
  • ⚙️ DeepFaceLab 1.0 OpenCL build is an older, less maintained version that can be used if other builds are incompatible.
  • 🌐 There's a version of DeepFaceLab available for Google Colab, allowing cloud-based training.
  • 📥 Download the selected build and extract the files using a zip program, acknowledging any security warnings as false positives.
  • 🛠️ No installation is needed; DeepFaceLab is ready to use after extraction, but ensure system performance settings are optimized.
  • 💻 DeepFaceLab performs best on Windows 10 with high-end NVIDIA GPUs, and keeping device drivers up to date is recommended.
  • 🔧 Windows 10 users can enhance performance by enabling Hardware Accelerated GPU Scheduling and disabling unnecessary animations and effects.
  • 📁 The workspace folder in DeepFaceLab is crucial for organizing deepfake data and files, with specific subfolders for source and destination videos.

Q & A

  • Where can I find the official DeepFaceLab repository?

    -You can find the official DeepFaceLab repository at github.com/iperov/deepfacelab.

  • What are the different builds available for DeepFaceLab 2.0?

    -The builds available include NVIDIA RTX 3000 series build, NVIDIA up to RTX 2080 Ti build, a CPU-only build with AVX instruction set, and a DirectX 12 build 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 I check if my NVIDIA GPU is compatible with DeepFaceLab 2.0?

    -You can check your NVIDIA GPU's compatibility on NVIDIA's CUDA Compute Compatibility list provided in the description.

  • Can I train DeepFaceLab on a CPU?

    -Yes, you can train on a CPU with AVX instruction set by downloading the file labeled '10) makes CPU only'.

  • 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.

  • Is there a version of DeepFaceLab for Google Colab?

    -Yes, there is a version of DeepFaceLab available for Google Colab, allowing you to train in the cloud for free.

  • What should I do after downloading the DeepFaceLab files?

    -After downloading, double-click the self-extracting .exe file or use a zip program to extract the files. DeepFaceLab is ready to use once extracted.

  • Are there any recommended system performance settings for running DeepFaceLab?

    -Yes, it is recommended to use a high-end NVIDIA GPU, keep device drivers up to date, enable Hardware Accelerated GPU Scheduling on Windows 10, and disable Windows animations and effects to increase available resources.

  • Where should I place the source and destination video files for DeepFaceLab?

    -Place the source face set in the 'Data_src' folder and the destination video in the 'Data_dst' folder within the workspace.

  • How can I get support or ask questions about DeepFaceLab?

    -You can leave questions in the comments section of the tutorial video or email deepfakery@deepfakery.net for professional services.

Outlines

00:00

💻 DeepFaceLab 2.0 Installation Guide

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 for the latest releases and builds compatible with various operating systems and hardware configurations. The guide emphasizes the necessity of choosing the correct build based on the user's NVIDIA GPU or opting for a CPU-only version. It also covers the installation process, which involves extracting the downloaded files without the need for a formal installer. Additionally, it offers tips for system performance optimization, such as enabling Hardware Accelerated GPU Scheduling and disabling unnecessary Windows animations and effects. The paragraph concludes with a brief overview of the software's components and workspace structure, explaining the purpose of each file and folder.

05:06

🔧 DeepFaceLab Usage and Support

The second paragraph addresses the practical use of DeepFaceLab, suggesting that users can start creating deepfakes with default settings immediately after installation. It invites viewers to engage with the content by asking questions in the video's comments section and encourages them to explore additional tutorials for more in-depth knowledge. The paragraph also promotes subscription for further educational content and offers a professional service contact email for those seeking expert assistance in deepfake creation.

Mindmap

Keywords

💡DeepFaceLab

DeepFaceLab is an open-source software used for creating deepfake videos. In the context of the video, it is the main subject of the tutorial, guiding users through the installation process. The software allows users to manipulate videos by swapping faces using artificial intelligence techniques.

💡GitHub

GitHub is a web-based platform for version control and collaboration used by developers. In the script, GitHub is mentioned as the place to access the official DeepFaceLab repository, where users can find the software's source code, releases, and other resources.

💡NVIDIA RTX 3000 series

The NVIDIA RTX 3000 series refers to a line of high-performance graphics processing units (GPUs) by NVIDIA. The script specifies that there is a build of DeepFaceLab specifically designed for systems with these GPUs, highlighting the importance of having compatible hardware for optimal software performance.

💡CUDA

CUDA (Compute Unified Device Architecture) is a parallel computing platform and application programming interface (API) model developed by NVIDIA. It allows software developers to use NVIDIA GPUs for general purpose processing. The script mentions that certain builds of DeepFaceLab require a GPU with CUDA compatibility.

💡DirectX 12

DirectX 12 is a collection of APIs designed for handling multimedia technologies, such as game programming and video processing, on Microsoft platforms. The script mentions that a DirectX 12 build of DeepFaceLab can be used with various hardware, including AMD, Intel, and NVIDIA devices.

💡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 itself to a user-specified location. In the video script, users are instructed to run this type of file to begin the DeepFaceLab installation process.

💡System Requirements

System requirements refer to the minimum hardware and software needed to run a particular software application. The script outlines the recommended system settings and hardware for running DeepFaceLab, emphasizing the need for a high-end NVIDIA GPU for optimal performance.

💡Hardware Accelerated GPU Scheduling

Hardware Accelerated GPU Scheduling is a feature in Windows 10 that allows the operating system to manage GPU resources more efficiently. The script suggests enabling this feature to potentially resolve errors and improve DeepFaceLab's performance.

💡Workspace folder

In the context of DeepFaceLab, the workspace folder is where all the deepfake data and files are stored. The script describes it as a central location for organizing the source and destination video files, as well as other necessary data for the deepfake creation process.

💡Deepfake

A deepfake is a synthetic media in which a person's likeness is replaced with another's using artificial intelligence. The video script is a tutorial for DeepFaceLab, a tool specifically designed for creating deepfakes by manipulating video files.

Highlights

DeepFaceLab 2.0 is available on GitHub for download.

Navigate to the Releases section for builds compatible with Windows 10, Linux, and Google Colab.

Choose the build that matches your hardware: NVIDIA RTX 3000 series, up to RTX 2080 Ti, or CPU with AVX instruction set.

The '10) makes CPU only' build modifies software to use an older TensorFlow version for CPU training.

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

DeepFaceLab 1.0 OpenCL build is an older, less maintained version.

Google Colab version allows cloud-based training but requires a desktop version for file preparation.

Download the self-extracting .exe file or use a zip program to extract DeepFaceLab.

DeepFaceLab does not require installation; it's ready to use after extraction.

Ensure system drivers are up to date and consider enabling Hardware Accelerated GPU Scheduling for optimal performance.

Disabling Windows animations and effects can free up resources for DeepFaceLab.

Place DeepFaceLab in your Windows root folder if using external media that may sleep.

Override computer sleep settings to prevent interruptions during training.

DeepFaceLab's main folder contains all files and folders needed for creating deepfakes.

The internal folder includes necessary software and libraries like CUDA, Python, and FFmpeg.

The workspace folder stores all deepfake data and files, with subfolders for images and model files.

Data_src and Data_dst folders are for source and destination videos respectively.

Use default settings to begin testing and creating deepfakes immediately.