A specialized suite of custom nodes for ComfyUI designed to hunt down and visualize generation parameters (prompts, steps, samplers, etc.) embedded in AI-generated images. It works across different formats and platforms (ComfyUI, Automatic1111, Civitai, and more).
The primary goal of this tool is to reveal hidden prompt strings and workflow structures from AI-generated content. Whether it's a PNG with a complex node graph or a JPEG with a text-based prompt hidden in EXIF data, this inspector brings it to light.
This extension includes three essential nodes to complete the inspection workflow:
- Purpose: Provides a flexible way to load images.
- Function: Unlike the standard loader, it allows you to input a filename via string or absolute path, making it easier to integrate with dynamic file-handling workflows.
- Purpose: The "brain" that scans the image file for generation data.
- Function:
- PNG Deep Scan: Extracts full ComfyUI/A1111 prompt and workflow JSONs.
- JPEG/EXIF Recovery: Deep-scans EXIF sub-IFDs to find hidden
UserCommentandImageDescriptiontags often used by Civitai and Tensor.art. - Smart JSON Recovery: Detects JSON structures even when they are buried inside plain-text metadata.
- Purpose: Provides a human-readable visualization of the raw data.
- Function: Transforms the raw metadata string into an interactive, foldable tree view directly on the ComfyUI canvas. It highlights potential prompt areas in orange for quick identification.
- Hidden Prompt Extraction: Recovers data from JPEGs where standard tools fail.
- Workflow Reconstruction Support: Outputs raw JSON strings that can be used to understand complex node setups.
- Multi-Platform Compatibility: Supports metadata formats from ComfyUI, Automatic1111, and various web-based generators.
cd ComfyUI/custom_nodes
git clone https://github.com/TakkunRed/ComfyUI-Metadata-Inspector.git- Place Load Image w/ Name.
- Connect it to Extract Metadata.
- Connect the metadata_json output to JSON Tree Viewer.
- Input your image path and run the queue to see the hidden prompt data.
MIT
