DeepFaceLab 2.0 Installation Tutorial (AMD NVIDIA Intel HD)

Deepfakery
11 May 202105:36

TLDRThe DeepFaceLab 2.0 Installation Guide provides a step-by-step tutorial on downloading and installing the software from the official GitHub repository. It covers various builds for different hardware, including NVIDIA RTX 3000 series and CPUs with AVX instructions. The guide also discusses system requirements, recommends enabling Hardware Accelerated GPU Scheduling on Windows 10, and offers tips for optimizing performance. Additionally, it gives an overview of the software's components and workspace setup, ensuring users are ready to create deepfakes with ease.

Takeaways

  • 😀 Visit the official DeepFaceLab repository at github.com/iperov/deepfacelab for downloads and resources.
  • 🔍 Scroll down to 'Releases' to find builds for different operating systems and hardware configurations.
  • 💻 Choose the appropriate build based on your system's hardware, such as NVIDIA RTX 3000 series or up to RTX 2080 Ti.
  • 🚀 For CPU-only training, select the build labeled '10) makes CPU only' which modifies TensorFlow.
  • 🎯 The DirectX 12 build supports a range of hardware including AMD, Intel, and NVIDIA devices with DirectX 12 on Windows 10.
  • 🔗 If unable to run the latest builds, consider the DeepFaceLab 1.0 OpenCL build, though it's no longer maintained.
  • 🌐 There's also a version for Google Colab, allowing free cloud training with the need for a desktop version to prepare files.
  • 📥 Download the chosen build and extract the files using a zip program, being cautious of potential security software warnings.
  • ⚙️ No installation is needed; DeepFaceLab is ready to use after extraction, but ensure system performance settings are optimized.
  • 🛠️ Ensure the correct build is chosen, device drivers are updated, and system settings like Hardware Accelerated GPU Scheduling are enabled for best performance.

Q & A

  • Where can I find the official DeepFaceLab repository?

    -You can find the official DeepFaceLab repository 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 build for 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, which is linked in the video description.

  • Can DeepFaceLab 2.0 be run on a CPU?

    -Yes, DeepFaceLab 2.0 can be run on a CPU with the '10) makes CPU only' build, which installs an older version of TensorFlow.

  • What is the DirectX 12 build of DeepFaceLab 2.0 and what hardware does it support?

    -The DirectX 12 build of DeepFaceLab 2.0 can be used with AMD, Intel, and NVIDIA devices that support DirectX 12 on Windows 10, including 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 are the system requirements for running DeepFaceLab?

    -DeepFaceLab is designed to run on Windows 10 and Linux, with the best results coming from using a high-end NVIDIA GPU. It also recommends having up-to-date device drivers and enabling Hardware Accelerated GPU Scheduling on Windows 10.

  • How do I install DeepFaceLab after downloading it?

    -There is no installation process for DeepFaceLab. After downloading, you can simply double-click the self-extracting .exe file or use a zip program to extract the files. Once extracted, the program is ready to use.

  • What are the main components of the DeepFaceLab software?

    -The main components include the DeepFaceLab code, additional packages and software, workspace folder, and sample video data. The workspace folder contains subfolders for images, model files, and video files.

  • How do I prepare my files for creating a deepfake with DeepFaceLab?

    -You need to place your source face set in the Data_src folder and the destination video in the Data_dst folder within the workspace. These files can be in many common video formats, and you should rename them accordingly.

Outlines

00:00

💻 DeepFaceLab 2.0 Installation Guide

This paragraph provides a comprehensive guide on how to download and install DeepFaceLab 2.0, a software used for creating deepfakes. It directs users to the official GitHub repository for the software, where they can find the open-source code, issue queue, and other resources. The guide explains the different builds available for various hardware, including NVIDIA RTX 3000 series and up to RTX 2080 Ti, as well as a CPU-only build with AVX instruction set. It also mentions the DirectX 12 build compatible with AMD, Intel, and NVIDIA devices on Windows 10. An outdated DeepFaceLab 1.0 OpenCL build and a Google Colab version are also discussed. The paragraph advises users on how to download the files, what to expect during installation, and system requirements for optimal performance, including enabling Hardware Accelerated GPU Scheduling and disabling Windows animations for better resource utilization. It also touches on potential issues with external media and computer sleep settings during the training process.

05:06

🔧 DeepFaceLab Software Overview and Testing

The second paragraph offers an overview of the DeepFaceLab software, detailing the main components and their purposes. It explains that the software comes with all necessary files and folders for creating deepfakes, including the code, additional packages, and sample video data. The paragraph describes the structure of the main folder, which contains numbered files and folders that guide users through the deepfake creation process. It also discusses the 'internal' folder that houses the code and required libraries, and the 'workspace' folder where all deepfake data and files are stored. The paragraph further explains the purpose of the 'Data_src' and 'Data_dst' folders for source and destination videos, respectively. It concludes by encouraging users to test the software with default settings and to reach out with any installation queries, and promotes further tutorials and professional services for those interested in deepfake creation.

Mindmap

Keywords

💡DeepFaceLab

DeepFaceLab is an open-source software project used for creating deepfake videos. In the context of the video, it is the primary tool being discussed for generating synthetic media. The script provides a guide on how to download and install DeepFaceLab from its official GitHub repository, indicating its significance in the tutorial.

💡GitHub

GitHub is a web-based platform for version control and collaboration, where users can host and review code, manage projects, and build software. The script mentions GitHub as the location to find the official DeepFaceLab repository, highlighting its role as a central hub for accessing the software's source code and related resources.

💡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 applications. The script specifies that a build of DeepFaceLab is available that is specifically optimized for this series, emphasizing the importance of having compatible hardware for efficient deepfake creation.

💡CUDA

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

💡AVX instruction set

AVX, or Advanced Vector Extensions, is a set of extensions to the x86 and x86-64 instruction set architecture for microprocessors from Intel and AMD. It is used to enhance performance on applications that can take advantage of its capabilities. The script notes that DeepFaceLab can be trained on a CPU with the AVX instruction set, showing an alternative to GPU usage.

💡DirectX 12

DirectX 12 is a low-level graphics API developed by Microsoft, designed to increase the performance of applications, especially in gaming. The script mentions that a DirectX 12 build of DeepFaceLab can be used with various hardware, including AMD, Intel, and NVIDIA devices, showcasing the software's versatility across different platforms.

💡Self-extracting .exe file

A self-extracting .exe file is a type of executable file that contains compressed data and automatically extracts it to a specified location when run. The script describes the process of using such a file to install DeepFaceLab, indicating that it is a straightforward method for users to get the software up and running.

💡Hardware Accelerated GPU Scheduling

Hardware Accelerated GPU Scheduling is a feature in Windows 10 that allows the operating system to better utilize the capabilities of modern GPUs. The script recommends enabling this feature for improved performance when running DeepFaceLab, highlighting its role in optimizing the software's operation.

💡Deepfake

A deepfake refers to a synthetic media in which a person's likeness is replaced with another's using artificial intelligence. The script is a tutorial for DeepFaceLab, a tool specifically designed for creating deepfakes, making this term central to the video's theme and content.

💡Workspace folder

The workspace folder, as mentioned in the script, is a directory within the DeepFaceLab software where all the deepfake data and files are stored. It is crucial for organizing the source and destination videos, as well as any generated images and model files, which are essential for the deepfake creation process.

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 all available Windows versions.

Different builds cater to various hardware, including NVIDIA RTX 3000 series and up to RTX 2080 Ti.

A CPU-only build is available for systems without NVIDIA GPUs.

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

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

Google Colab version allows for cloud-based training.

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

No installation is needed; the program is ready after extraction.

Ensure system drivers are up to date for optimal performance.

Enable Hardware Accelerated GPU Scheduling in Windows for better performance.

Disabling Windows animations can free up system resources.

Avoid using external media that may go to sleep, as it can cause issues with file access.

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

The workspace folder stores all deepfake data and files.

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

Default settings can be used for immediate testing of the software.

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