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.
This repository implements a deep learning image inpainting model for watermark removal, based on the Contextual Attention (CVPR 2018) and Gated Convolution (ICCV 2019) papers. It provides a CLI tool (main.py) that loads a pre-trained TensorFlow 1.x checkpoint, applies a user-supplied mask (defaulting to a specific watermark pattern like iStock), and outputs the inpainted image. The code includes the full model definition (inpaint_model.py), custom operations (inpaint_ops.py), preprocessing utilities, batch inference scripts, and a Dockerfile. The architecture is a two-stage generator with a spectral normalization discriminator, trained on datasets like CelebA-HQ and Places2. However, the repository is primarily designed for inference; training code is present but not the primary focus. The project is heavily dependent on TensorFlow 1.15, the neuralgym library, and deprecated tf.contrib APIs, making it incompatible with modern TensorFlow 2.x without significant refactoring. The codebase is well-structured academically but not production-ready, with no tests and minimal documentation beyond the README.