DeepFaceLab 2.0 Installation Tutorial (AMD NVIDIA Intel HD)

Deepfakery
11 May 202105:36

TLDRThis tutorial guides you through installing DeepFaceLab 2.0, detailing the process of downloading from GitHub, selecting the appropriate build for your hardware, and extracting the files. It covers system requirements, with a focus on NVIDIA GPUs, and provides tips for optimizing performance on Windows 10. The guide also introduces the software's components, explaining the purpose of each file and folder, and how to prepare your own videos for deepfaking.

Takeaways

  • 😀 Visit the official DeepFaceLab repository on GitHub for the latest builds and resources.
  • 🔍 Choose the appropriate build for your hardware, such as NVIDIA RTX 3000 series or up to RTX 2080 Ti.
  • 💾 Ensure your system meets the requirements and keep your drivers up to date for optimal performance.
  • 🌐 The '10) makes CPU only' build allows training on a CPU with AVX instruction set.
  • 🖥️ 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 for those unable to run newer builds.
  • 🌐 DeepFaceLab is also available for Google Colab, allowing cloud-based training.
  • 📂 After downloading, extract the files with a zip program, and be aware of potential warnings from antivirus software.
  • ⚙️ No installation is needed; DeepFaceLab is ready to use once files are extracted.
  • 🛠️ Enable Hardware Accelerated GPU Scheduling and consider disabling Windows animations for better performance.
  • 📁 Organize your deepfake data in the workspace folder with separate folders for source and destination videos.

Q & A

  • Where can you 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 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 on NVIDIA's CUDA Compute Compatibility list provided in the description.

  • What does the '10) makes CPU only' build of DeepFaceLab do?

    -The '10) makes CPU only' build modifies the software to install an older version of TensorFlow, allowing training on a CPU with AVX instruction set.

  • Which 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 for Google Colab, allowing users to train for free in the cloud.

  • 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 you install DeepFaceLab once you've downloaded it?

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

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

    -The 'workspace' folder in DeepFaceLab is where all your deepfake data and files will be stored, including images, model files, and video files.

  • What should you do if you encounter issues with DeepFaceLab not finding files on external media?

    -If DeepFaceLab has trouble finding files on external media, try placing DeepFaceLab in your Windows root folder and override your computer's sleep settings to prevent it from going to sleep during training.

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, an open-source software for creating deepfakes. Users are directed to the official GitHub repository where they can find the latest builds for Windows 10, Linux, and Google Colab. The guide explains the different builds available based on system hardware, with specific recommendations for NVIDIA GPUs and CPU-only options. It also covers system requirements, installation process, and post-installation settings, including enabling Hardware Accelerated GPU Scheduling and disabling Windows animations for better performance. The paragraph concludes with an overview of the software components and workspace setup, preparing users to begin the deepfake creation process.

05:06

🔧 DeepFaceLab Usage and Support

The second paragraph offers guidance on using DeepFaceLab with default settings for immediate deepfake creation. It invites viewers to ask questions about the software's download and installation in the video's comment section and encourages them to explore additional tutorials and subscribe for more content. The paragraph also promotes professional services for creating deepfakes and provides an email address for inquiries.

Mindmap

Keywords

💡DeepFaceLab

DeepFaceLab is an open-source software tool used for creating deepfake videos, which are synthetic media where a person's face is replaced with another's. 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 software is designed to work with various hardware configurations, including NVIDIA GPUs and CPUs with AVX instruction sets.

💡GitHub

GitHub is a web-based platform for version control and collaboration, where developers can host and review code, manage projects, and build software. In the video, GitHub is mentioned as the official source to download DeepFaceLab from the repository of its developer, iperov. It's where users can find the latest releases and builds of the software.

💡NVIDIA RTX 3000 series

The NVIDIA RTX 3000 series refers to a line of graphics processing units (GPUs) designed for high-performance computing and gaming. The video script specifies that there is a build of DeepFaceLab 2.0 specifically optimized for this series, indicating that it can take advantage of the advanced capabilities of these GPUs for creating deepfakes.

💡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, known as GPGPU (General-Purpose computing on Graphics Processing Units). The script mentions that the NVIDIA build of DeepFaceLab supports GPUs with CUDA 3.5 and higher.

💡AVX instruction set

AVX (Advanced Vector Extensions) is a set of extensions to the x86 and x86-64 instruction set architecture for microprocessors from Intel and AMD. These extensions are designed to improve performance on floating-point, SIMD (Single Instruction, Multiple Data), and integer instructions. The video mentions that DeepFaceLab can be trained on a CPU with the AVX instruction set.

💡DirectX 12

DirectX 12 is a low-level graphics API developed by Microsoft for programming graphics-related hardware. It is used in the video to describe a build of DeepFaceLab that can be used with devices from AMD, Intel, and NVIDIA, provided that DirectX 12 is running on Windows 10. This build is meant to leverage the capabilities of modern graphics hardware.

💡Google Colab

Google Colab is a cloud-based interactive computing environment that allows users to write and execute code through a web browser. It is mentioned in the video as a platform where users can train DeepFaceLab models for free, although it requires one of the desktop versions for preparing files. This suggests that Google Colab can be used for the computational aspects of deepfake creation.

💡Self-extracting .exe file

A self-extracting .exe file is a type of executable file that contains compressed data and, when run, extracts the data to a specified location. In the context of the video, this refers to the method of installation for DeepFaceLab, where users can simply double-click the self-extracting file to begin using the software without traditional installation procedures.

💡Hardware Accelerated GPU Scheduling

Hardware Accelerated GPU Scheduling is a feature in Windows 10 that allows the operating system to directly schedule GPU workloads, potentially improving performance and efficiency. The video suggests enabling this feature for better DeepFaceLab performance, indicating that it can help with the processing of deepfake videos.

💡Deepfake

A deepfake is a media file that has been altered to replace a person's face with another's using artificial intelligence and machine learning techniques. The video's main theme revolves around the creation of deepfakes using DeepFaceLab, with the tutorial focusing on the software's installation and setup for this purpose.

Highlights

DeepFaceLab 2.0 is available on GitHub 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.

There's a CPU-only build that modifies the software to use an older version of TensorFlow.

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

DeepFaceLab 1.0 OpenCL build is an older, no longer maintained version.

Google Colab version allows for cloud-based training.

Download the appropriate self-extracting .exe file or use a zip program for installation.

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

Ensure system drivers are up to date for optimal performance.

Enable Hardware Accelerated GPU Scheduling in Windows for better DeepFaceLab performance.

Disable Windows animations and effects to increase available resources.

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

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

The internal folder includes DeepFaceLab code, CUDA, Python, and FFmpeg libraries.

The workspace folder stores all deepfake data and files.

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

DeepFaceLab is designed to run on Windows 10 and Linux for the best results.

High-end NVIDIA GPUs are recommended for optimal DeepFaceLab performance.

System memory usage is not highly impacted during the deepfake process.

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