DeepFaceLab 2.0 Installation Tutorial (AMD NVIDIA Intel HD)
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- 🚫 If you can't run the latest builds, consider the DeepFaceLab 1.0 OpenCL build, though it's no longer maintained.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
💻 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.
🔧 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
GitHub
NVIDIA RTX 3000 series
CUDA
AVX instruction set
DirectX 12
Self-extracting .exe file
Hardware Accelerated GPU Scheduling
Deepfake
Workspace folder
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.