Bing-su/adetailer
Adetailer
Adetailer is a Stable Diffusion inpainting extension and automated detail enhancer that identifies specific image regions to improve quality through targeted inpainting. It functions as an AI image masking tool that uses detection models to create precise masks for automated image editing.
The system distinguishes itself by integrating structural guides, such as depth and pose, to constrain the inpainting process and maintain anatomical consistency. It also supports object-specific prompt assignment, allowing unique text instructions to be mapped to multiple detected objects within a single image using separator tokens.
The tool provides a full pipeline for image refinement, covering automated mask generation, morphological mask transformations, and object filtering based on confidence thresholds and size ratios. It combines these capabilities into an iterative process of detection, masking, and diffusion to refine details without altering the global composition.
Features
- Image Enhancement Tools - Automates the process of improving specific image regions through detection-driven targeted inpainting.
- Iterative Image Inpainting - Executes a sequential pipeline of detection, masking, and diffusion to iteratively refine image details.
- Binary Mask Generators - Automatically generates binary masks around detected objects to isolate them for targeted processing.
- ControlNet Guidance - Integrates spatial control networks to maintain anatomical and structural consistency during localized inpainting.
- Image Inpainting - Performs targeted generative filling of specific image regions based on detected objects and tailored text prompts.
- Generative Masking - Automatically generates precise masks using detection models to isolate objects for targeted editing.
- Object-Specific Prompt Mapping - Provides the ability to map distinct inpainting prompts to different detected objects using separator tokens for targeted refinement.
- Stable Diffusion Ecosystem - Extends the Stable Diffusion ecosystem with automated object detection and targeted inpainting capabilities.
- AI Image Masking - Functions as an AI tool that uses detection models to automatically generate masks for image enhancement.
- Automated Image Enhancements - Automatically identifies image regions and applies unique prompts to improve visual quality via inpainting.
- Object Detection - Utilizes object detection to identify regions that should be processed or ignored based on size and confidence.
- Inpainting Constraint Plugins - Provides a bridge that uses depth and pose guides to constrain automated inpainting masks.
- Confidence Filtering - Implements a mechanism to prune detection results based on statistical reliability scores to prevent false positives.
- ControlNet Depth Replacements - Integrates depth, openpose, and lineart guides to provide structural constraints during the inpainting process.
- Object-Specific Prompt Mapping - Allows assigning unique text instructions to different detected objects within a single image.
- Morphological Operations - Uses dilation and erosion operations to adjust the boundaries of detected masks.
- Masking Tools - Provides tools for modifying detected masks via erosion, dilation, and offsets for precise control.
Star history
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