This repository is a production-grade face manipulation platform (facefusion). It operates as a modular monolith, combining a powerful CLI, a Gradio web UI, a job queue system, and a plugin-based processor architecture. The system ingests images or videos, detects faces using various ONNX models (RetinaFace, YOLO, etc.), and applies a configurable pipeline of processors (face swapping, enhancement, editing, age modification, lip-syncing, background removal, etc.). It relies heavily on ffmpeg for video/audio handling and ONNX Runtime for model inference. The architecture is exceptionally clean, with clear separation of concerns between the UI, CLI, workflows, jobs, and individual processors. It is not a research notebook or a simple script; it is a well-engineered application designed for end-to-end media processing, demonstrating high craftsmanship in state management, thread safety, and extensibility.
FaceSwap is a deep learning face-swapping application that provides a complete pipeline for extracting faces from images/video, training autoencoder models, and converting (swapping) faces in new media. It features a modular plugin architecture for detectors, aligners, maskers, identity extractors, training models, and converters, supporting multiple GPU backends (NVIDIA, AMD, Apple Silicon, CPU). The application includes both a command-line interface and a full tkinter-based GUI for managing projects, editing alignments manually, previewing conversions, and analyzing training progress. It also provides auxiliary tools for sorting faces, generating masks, manipulating alignments, and processing video via ffmpeg. The codebase is heavily research-oriented with custom loss functions (LPIPS, LDR-FLIP, GMSD, etc.), custom neural network layers (PixelShuffler, AdaIN), and advanced training features (learning rate finder, mixed precision, distributed training). While primarily an application, it contains significant data/research components in its model architectures and loss implementations.
Deep-Live-Cam is a real-time face swapping desktop application that uses ONNX Runtime with insightface models to swap faces in live webcam feeds or pre-recorded images/videos. The core pipeline involves face detection (RetinaFace), face recognition (ArcFace), and a face swap model (inswapper_128), with optional face enhancement via GFPGAN or GPEN-BFR ONNX models. The application provides a PySide6 GUI for selecting source/target faces, toggling options (mouth mask, many faces, Poisson blending, sharpening, temporal interpolation), and managing camera selection. It supports multiple execution providers: CUDA (NVIDIA), CoreML (Apple Silicon), DirectML (AMD/Intel), OpenVINO (Intel), and CPU fallback. Video processing can run in-memory via FFmpeg pipes (avoiding disk I/O) or via traditional frame extraction to PNG files. The architecture is modular: frame processors (face swapper, enhancers) follow a common interface, and the codebase includes optimizations for Apple Silicon (CoreML subgraph optimization, Pad decomposition, Shape/Gather folding) and CUDA (IO binding, CUDA graphs). The project also includes a benchmark pipeline and unit tests for core components.