ADetailer is a script extension for AUTOMATIC1111's Stable Diffusion Web UI that performs automatic object detection and inpainting. After an initial image is generated, it runs detection models (YOLOv8, YOLO World, MediaPipe face detection/mesh) to identify objects such as faces, hands, or persons. It creates masks from the detections and performs targeted inpainting on those regions using separate prompts, denoising strength, and other configurable parameters. The extension supports multiple detection models per run, ControlNet integration for guided inpainting, and various mask preprocessing options (dilation/erosion, offset, merge, invert, filtering by area/confidence). It also provides a REST API for querying version, schema, and available models. Architecturally, ADetailer is a plugin that hooks into the webui's script system. It consists of several internal modules: `adetailer/` contains the core detection logic (ultralytics and mediapipe wrappers), mask processing functions, argument validation using pydantic, and utility functions for model management. The `aaaaaa/` module provides webui-specific helpers for compatibility, UI generation, and rich traceback formatting. The `controlnet_ext/` module handles ControlNet integration for both standard and Forge versions. The main script `scripts/!adetailer.py` orchestrates the entire pipeline: argument parsing, detection, mask preprocessing, and inpainting for each detected object. Key features include: support for multiple detection models simultaneously, configurable per-tab settings, dynamic denoising strength based on bounding box size, optimal crop size calculation for inpainting (SDXL and free modes), XYZ grid support for batch experimentation, and a comprehensive settings panel in the webui. The codebase demonstrates good engineering practices with type hints, pydantic validation, comprehensive tests, and clean separation of concerns.