AI Image Auto Tagger

The successor of WD14 tagger and an alternative to DeepDanbooru - now with a metadata saving feature for NSFW-oriented gallery tagging (optimized for digiKam). Currently using wd-vit-tagger-v3 model by SmilingWolf which is newer than WD14 and latest DeepDanbooru. Using CUDA and ONNX library over Gradio WEBUI. Tested on Windows.

Features

• Output: There are 2 output modes: embedding tags directly into images for galleries OR .txt files for AI training
• Supported models: Latest WaifuDiffusion v3 tagger architecture featuring SmilingWolf/wd-vit-tagger-v3 model
• Supported files: JPG/JPEG (recommended), PNG, WEBP, GIF, BMP(no metadata)
• Easy user interface: By utilizing gradio for GUI, the usage of this script should be smooth
• Process subdirectories: recursively goes through all directories within a given one. In case of writing tags to .txt, it mirrors the folder structure
• User preferred threshold: Using the gradio slider, the user can adjust the threshold of the tagger model
• Hide rating tags: You can choose whether to output the rating tags (Like "General", "Explicit", "Questionable", etc) or not
• Character tags first: This feature makes the character name tag appear in front of others
• Remove separator: This function will remove the standard separator "_" of the tags in the output caption
• Overwrite existing metadata tags: wipes clean any existing tags in metadata before writing new ones (XMP:Subject and IPTC:Keywords)

How to run

Python >3.10 and CUDA GPU is required to run this script. Download from https://www.python.org/downloads
ExifTool >12.15 is required. Download from https://exiftool.org

Steps to run:

1. Clone this repository git clone https://github.com/Deiwulf/AI-image-auto-tagger.git OR download as a zip and extract
2. Navigate to the directory cd AI-image-auto-tagger
3. Set up a virtual environment python -m venv venv *
4. Activate the new venv: *
   • Windows: venv\scripts\activate
5. Install the requirements pip install -r requirements.txt
   • Optionally visit https://pytorch.org/ and install one fitting your system for a performance boost (~69% here)
6. Run the script python wdv3tagger.py OR use start.bat on Windows (using venv)

* Virtual environment is optional, but recommended to keep this isolated. You can skip to step 5 if you want to install and run in a global environment

Disclaimer

This has been thoroughly tested, but still do be savvy and back up before running, and report issues if any.

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