ADetailer is an extension for the Stable Diffusion WebUI that provides automatic object detection and masking for inpainting tasks. It integrates models like YOLOv8 and MediaPipe to detect faces, hands, persons, and other objects, creates masks from these detections, and then performs inpaint operations on the masked regions using the Stable Diffusion model. The extension features configurable detection parameters, mask preprocessing options, ControlNet integration, and advanced prompting capabilities. It follows the WebUI extension architecture with proper script callbacks and UI integration.
InsightFace is a comprehensive, production-grade face analysis ecosystem. It provides a Python package (`insightface`) with high-level APIs for face detection, recognition, attribute estimation, and face swapping, backed by ONNX Runtime. A C++ SDK (`inspireface`) enables on-device inference across Linux, Android, iOS, and embedded platforms with support for CUDA, TensorRT, CoreML, and Rockchip NPUs. A FastAPI server offers REST endpoints for face search and verification with SQLite persistence and license management. A PySide6 desktop GUI (Evaluation Studio) provides interactive workflows for verification, album management, and enterprise evaluation. The repository also contains extensive research code for training face recognition models (ArcFace, Partial FC, VPL) across PyTorch, MXNet, OneFlow, and PaddlePaddle, as well as detection (SCRFD, RetinaFace), 3D reconstruction, and face parsing. While the core application components are well-engineered and actively maintained, the research subprojects vary in maturity and may rely on outdated frameworks.
face_recognition is a Python library and CLI toolkit that wraps dlib's deep-learning-based face recognition models behind a simple, high-level API. It provides functions to detect face locations (HOG or CNN-based), extract 68-point or 5-point facial landmarks, compute 128-dimensional face encodings, and compare faces via Euclidean distance. The package ships two Click-based CLI entry points — `face_recognition` for batch identification against a known-people folder and `face_detection` for locating faces in images — both supporting multiprocessing and tunable tolerance/upsample parameters. The core logic lives in `face_recognition/api.py`, which is essentially a thin, well-abstracted wrapper around dlib detectors, shape predictors, and the ResNet face encoder, with coordinate conversion helpers to keep the public API in (top, right, bottom, left) tuple form. The repo also includes a large examples directory (webcam recognition, KNN/SVM classifiers, blink detection, batch GPU inference, Flask web service, Raspberry Pi integration) and multi-stage Docker builds for CPU and GPU deployment. Last tagged release is 1.4.0 (2020); the final commit on master dates to mid-2026 but the project shows little active development and many open issues, placing it in 'stale' maintenance status. Technical drift is medium: the codebase still references Python 2.7 in places, pins older dlib versions in some Dockerfiles, and has CI only through 3.10, while the ecosystem has moved to Python 3.12+ and dlib 19.24+. The API wrapper itself is clean and reusable, but the CLI modules and examples are too tightly coupled to the library and filesystem assumptions to be extracted as standalone components.