DeepFaceLab - AI Video Tools Tool

Overview

DeepFaceLab is an open-source deepfake creation toolkit focused on face extraction, neural-model training, and face replacement for video. It provides a pipeline of utilities—extract, train, convert—that together let users produce high-quality face swaps and realistic facial manipulations for research, film VFX, and hobbyist projects. According to the GitHub repository (https://github.com/iperov/DeepFaceLab), the project is licensed under GPL-3.0 and is maintained as a community-driven toolkit with prebuilt workflows and scripts for common GPU-accelerated setups. DeepFaceLab supports end-to-end workflows: detect and extract faces from source/target footage, train neural networks on those datasets, and merge the trained face back into target video with masks and post-processing. It is widely used by creators because it exposes multiple model architectures and mask/segmentation tools to balance quality and training time. The project is free to use under its GPL license, but creators should be mindful of ethical, legal, and privacy implications when producing face-swapped media.

GitHub Statistics

• Stars: 18,935
• Forks: 814
• Contributors: 19
• License: GPL-3.0
• Primary Language: Python
• Last Updated: 2024-11-13T19:29:05Z
• Latest Release: DF.wf.288res.384.92.72.22

The repository is active and well-established: according to the GitHub page it has 18,935 stars, 814 forks and 19 contributors, and is distributed under GPL-3.0. The last recorded commit in the provided repository metadata was on 2024-11-13, indicating recent maintenance. These metrics suggest a sizable user base and steady upstream activity, though the number of contributors is relatively small compared with its star count, which is typical for specialized tooling that attracts many users and a core group of maintainers.

Installation

Install via docker:

git clone https://github.com/iperov/DeepFaceLab.git

cd DeepFaceLab

docker build -t deepfacelab .

docker run --gpus all -it --rm -v $(pwd)/workspace:/workspace deepfacelab

Key Features

• End-to-end pipeline: face extraction, dataset preparation, model training, and conversion.
• Multiple model architectures to trade off speed versus visual fidelity.
• XSeg-style mask/segmentation tools for selective, realistic blending.
• GPU-accelerated training and conversion workflows (NVIDIA CUDA support typical).
• Workspace and batching scripts for reproducible training and automated steps.

Community

DeepFaceLab has a large user base and active community presence—18,935 GitHub stars and many tutorial creators. The repo’s commit history and community contributions indicate ongoing maintenance; public discussion, tutorials and third-party tools extend the ecosystem. Users should follow community guidelines and consider ethical/legal issues when sharing outputs.

Key Information

• Category: Video Tools
• Type: AI Video Tools Tool