Open-source alternatives to DeepFaceLab

StyleGAN is a TensorFlow-based generative adversarial network framework designed for the synthesis of high-resolution synthetic imagery. It utilizes a style-based generator architecture to create realistic visual assets from latent vectors, focusing on the production of high-fidelity images. The system incorporates style mixing and stochastic noise injection to control visual attributes and fine-grained details. It uses adaptive instance normalization and progressive resolution upsampling to manage image quality and variety across different resolutions. The framework covers the full lifecycl

StarGAN is a PyTorch image-to-image translation framework designed to synthesize visual styles and attributes across multiple domains. It implements a generative adversarial network that serves as a deep learning image translator for modifying specific visual characteristics within an image dataset. The framework uses a single unified model to handle translations between multiple image domains rather than requiring separate pairs of models. It is a research implementation that learns mappings between different image attributes without the need for paired training data. The project covers the

PRNet is a Python library for 3D facial reconstruction. It uses a deep learning regression model to predict 3D facial geometry and vertex colors from a single 2D input image to generate a textured mesh. The project provides tools for digital face swapping, allowing the replacement of a target face with a new image and blending textures to match the original pose. It also includes a framework for face texture swapping and blending to fit specific 3D poses. Additional capabilities cover facial analysis, including the detection and alignment of facial landmarks and the estimation of head pose a

Rope is a graphical user interface for swapping faces in images and videos. It functions as a deepfake video editor and image face swapper that utilizes pre-trained deep learning models to replace identities in visual media. The tool includes specialized capabilities for AI video post-production, such as occlusion-aware blending to handle foreground objects and mouth-parsing refinement to align facial expressions. It also serves as an AI face restoration tool, using saliency-based restoration to recover clarity and sharpness in swapped facial regions. The software provides a pipeline for vis

This project is a Stable Diffusion WebUI extension that provides a graphical interface for personalized portrait generation and AI photo editing. It allows users to train custom identity models from a small set of uploaded images to create consistent digital versions of specific people. The extension includes a virtual try-on system that replaces clothing in images by aligning reference garments with template bodies. It also features tools for face swapping in both static images and videos, as well as a portrait animator that transforms static images into dynamic videos using reference-guided

clmtrackr is a JavaScript computer vision library designed for facial landmark detection and real-time tracking. It implements Constrained Local Models to identify specific coordinate points on a human face within video feeds or static images. The project functions as a real-time face warping engine and expression analysis tool. It can distort facial images via parametric models to create caricatures or identify and label emotional states such as happiness, sadness, anger, and surprise based on feature coordinates. The library covers a broad range of capabilities including automatic and manu

LivePortrait is a computer vision framework designed for portrait animation and generative video synthesis. It functions as a deep learning system that transfers facial expressions and head movements from a driving video source onto a static image or an existing portrait video, effectively decoupling the subject's identity from the dynamic motion patterns. The framework utilizes keypoint-based motion retargeting and implicit 3D latent representations to map movements across different subjects, including both human and animal portraits. By employing canonical motion normalization and feature-s

Deep-Live-Cam is a generative video transformation tool designed for real-time facial manipulation and cinematic enhancement. It functions as a local-first AI runtime, performing all media processing directly on the user's hardware to ensure complete data privacy without external network dependencies. By utilizing a high-performance processing pipeline, the application enables live face swapping and interactive video modifications during active streaming sessions or on pre-recorded media. The system distinguishes itself through a hardware-abstraction execution layer that dynamically routes co

lite.ai.toolkit is a C++ computer vision toolkit designed for edge AI deployment. It enables the execution of pre-trained models for object detection, image classification, and segmentation on resource-constrained devices. The project features a multi-backend inference engine that supports the ONNX model runtime, allowing AI models to run across different hardware targets. It includes a GPU-accelerated pipeline specifically for NVIDIA hardware to reduce latency and increase processing speed. The toolkit covers a broad range of facial analysis capabilities, including emotion detection, gender

SC-FEGAN : Face Editing Generative Adversarial Network with User's Sketch and Color (ICCV 2019)

Openface is a deep learning toolkit designed for facial recognition and identity verification. It provides a comprehensive pipeline for detecting faces, aligning landmarks, and transforming facial images into compact numerical vectors. By utilizing these embeddings, the system enables identity classification and similarity comparison through geometric distance calculations. The project distinguishes itself by integrating research-oriented diagnostic tools alongside its core recognition capabilities. It includes utilities for visualizing high-dimensional feature clusters, inspecting internal c

Official implementation of GANimation. In this work we introduce a novel GAN conditioning scheme based on Action Units (AU) annotations, which describe in a continuous manifold the anatomical facial movements defining a human expression. Our approach permits controlling the magnitude of…

Pulse is a face image super-resolution tool and self-supervised image enhancer. It functions as a generative model image upsampler and latent space optimization tool designed to increase photo resolution and recover image details. The system differentiates itself by using latent space exploration and spherical constraints to find high-fidelity matches within a generative model. It employs geodesic distance measurement and spherical latent space optimization to regularize representations and maintain parameter radii during the recovery process. The project covers facial image restoration thro

Faceswap is a comprehensive framework for automated media manipulation and neural face synthesis. It provides a modular pipeline that manages the entire lifecycle of facial feature extraction, deep learning model training, and image conversion. By coordinating complex computer vision workflows, the system enables users to map facial identities between source and destination datasets while maintaining structural alignment and lighting consistency across video frames. The project distinguishes itself through a highly extensible plugin-based architecture that handles hardware-accelerated process

Facefusion is a modular framework designed for automated image and video manipulation, specializing in tasks such as face swapping, enhancement, and restoration. It functions as a computer vision processing pipeline that chains independent machine learning modules to perform complex transformations, including facial animation, age modification, and lip synchronization. The system is built to handle both real-time interactive feeds and large-scale batch processing tasks. The platform distinguishes itself through a highly extensible architecture that supports custom processing modules and inter

This project is a deep learning framework designed for facial landmark detection. It functions as a computer vision library that provides the necessary routines to locate precise points on human faces within images by generating spatial probability maps. The architecture distinguishes itself through high-resolution parallel branching, which maintains detailed visual representations throughout the entire network. By utilizing multi-scale feature fusion, the model repeatedly exchanges information across these parallel streams to integrate fine-grained spatial details with broader semantic conte

This project is a computer vision library designed for facial landmark detection and alignment. It provides a framework for identifying and mapping specific points on a human face in both two-dimensional and three-dimensional space, enabling the normalization of facial geometry and orientation across diverse images. The system utilizes a deep learning approach to extract precise facial coordinates, supporting tasks such as expression analysis and geometric modeling. By employing a stacked hourglass architecture, the model performs multi-stage feature refinement to capture spatial relationship

This is a PyTorch-based computer vision library for detecting 2D and 3D facial landmark coordinates. It functions as a facial landmark detector and reconstruction tool, utilizing deep learning to identify precise geometric points on human faces from image datasets. The library allows for the selection of specific detection backends to balance accuracy and processing speed. It supports the integration of precomputed bounding box files, which enables the system to bypass the initial detection phase and proceed directly to landmark extraction. The toolkit includes capabilities for batch image p

This project is a scientific computing framework for the .NET ecosystem, providing a comprehensive suite of libraries for numerical analysis, statistics, and mathematical optimization. It serves as a foundational toolkit for developing applications in machine learning, digital signal processing, and computer vision. The framework provides specialized toolkits for training and deploying predictive models, including neural networks, support vector machines, and decision trees. It further distinguishes itself with deep integrations for real-time visual analysis, such as object tracking and facia

Thumbor is a dynamic image processing service and proxy server that resizes, crops, and filters images on demand via URL parameters. It functions as a URL-based image manipulator that generates specific image versions from source assets to optimize web delivery. The system includes a smart cropping engine that uses facial feature detection and computer vision to automatically center thumbnails on the most relevant visual content. To prevent unauthorized parameter tampering and malicious requests, it employs signature-based request validation and domain-based source whitelisting. Broad capabi

face-api.js is a TensorFlow.js face recognition library and browser-based computer vision API. It provides tools for performing face detection, recognition, and landmark prediction within browsers and Node.js. The library includes a biometric identity descriptor generator that creates numerical vectors to compare identity and similarity between images. It features a facial landmark detection tool for mapping sixty-eight specific coordinate points on a face, as well as an age and gender estimation model. Its capabilities cover real-time facial analysis, including the recognition of facial exp

Pigo is a computer vision library written in Go for locating human faces in images and video streams. It provides tools for face detection, facial landmark identification, and pupil and eye localization. The project is implemented in pure Go to ensure portable execution without external dependencies. It supports compilation to WebAssembly, enabling face detection and image processing to run directly in web browsers without a backend. The library's capabilities include real-time face detection using classifier cascades and gaze tracking localization. It maps anatomical points on the face to a

OpenFace is an affective computing framework and facial behavior analysis toolkit designed for the extraction of facial features and the recognition of muscle movements to analyze human emotional behavior. It provides a research platform for analyzing human emotion and social interaction patterns through computer vision. The software implements a suite of tools for detecting facial landmarks, calculating head pose in 3D space relative to a camera, and tracking eye gaze by analyzing eye position and orientation. It also includes capabilities for facial action unit recognition to identify speci

facenet-pytorch is a facial recognition library for PyTorch that provides pretrained neural networks for detecting faces and extracting facial embeddings. It includes an MTCNN face detector for locating faces and landmarks, alongside an InceptionResnet face encoder to convert facial images into high-dimensional vectors for identity verification. The project provides tools for identity recognition by comparing facial embeddings using cosine similarity. It also supports facial video tracking to maintain identity consistency across consecutive frames and allows for the fine-tuning of pretrained

MMPose is a PyTorch-based pose estimation toolbox and deep learning training pipeline designed for detecting 2D and 3D keypoints on humans, animals, and faces. It serves as a computer vision model zoo and a framework for both 2D pose estimation and 3D pose lifting. The project is distinguished by its modular architecture and extensibility, employing a registry-based system and hierarchical configurations to allow for custom algorithm integration and model pipeline customization. It supports diverse estimation paradigms, including top-down, bottom-up, and two-stage pose lifting workflows. The

This is a Python facial recognition library designed to detect, encode, and identify human faces in images and video. It functions as a biometric identification tool that converts facial features into numerical encodings to compare and match identities. The library provides a computer vision command line interface for batch processing face detection and recognition tasks across image directories. It also supports a GPU accelerated vision API that utilizes CUDA and NVIDIA hardware to increase the speed of facial analysis and identification. Its capabilities cover human face detection and faci

This project is a computer vision tool designed to calculate the pitch, yaw, and roll of a human head in real time. It functions as a facial landmark detection library that identifies sixty-eight specific points on a face to enable precise orientation tracking from video streams. The system utilizes a high-performance inference engine to execute pre-trained machine learning models, ensuring that geometric analysis is performed efficiently on input frames. By mapping detected facial coordinates to a three-dimensional model, the software determines the camera-relative rotation of the head throu