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LLMII uses a local AI to label metadata and index images. It does not rely on a cloud service or database.

A visual language model runs on your computer and is used to create captions and keywords for images in a directory tree. The generated information is then added to each image file's metadata. The images can then be indexed, searched, and organized by by their descriptions using any tool you like that can access the common metadata fields. The files themselves can be moved, renamed, copied, and edited without affecting the metadata.

On your first run you will need to choose a model to run. Your system specs will be shown next to state of the art models. When you launch the indexer the model will be downloaded to the LLMII 'resources' directory. From that point the entire toolset is running completely locally.

• Image Analysis: Utilizes a local AI model to generate a list of keywords and a caption for each image
• Metadata Enhancement: Can automatically edit image metadata with generated tags
• Local Processing: All processing is done locally on your machine
• Multi-Format Support: Handles a wide range of image formats, including all major raw camera files
• User-Friendly GUI: Includes a GUI and installer. Relies on Koboldcpp, a single executable, for all AI functionality
• Simple Model Selection: Choose a the state of the art model and it will be automatically downloaded and configured
• Completely Automatic Backend Configuration: The AI backend (KoboldCpp) will be downloaded and configured with optimal settings
• GPU Acceleration: Will use Apple Metal, Nvidia CUDA, or AMD (Vulkan) hardware if available to greatly speed inference
• Cross-Platform: Supports Windows, macOS ARM, and Linux
• Stop and Start Capability: Can stop and start without having to reprocess all the files again
• One or Two Step Processing: Can do keywords and a simple caption in one step, or keywords and a detailed caption in two steps
• Highly Configurable: You are in control of everything

It is recommended to have a discrete graphics processor in your machine.

This tool verifies keywords and de-pluralizes them using rules that apply to English. Using it to generate keywords in other languages may have strange results.

This tool operates directly on image file metadata. It will write to one or more of the following fields:

1. MWG:Keyword
2. MWG:Description
3. XMP:Identifier
4. XMP:Status

The "Status" and "Identifier" fields are used to track the processing state of images. The "Description" field is used for the image caption, and "Subject" or "Keyword" fields are used to hold keywords.

The use of the Identifier tag means you can manage your files and add new files, and run the tool as many times as you like without worrying about reprocessing the files that were previously keyworded by the tool.

• Python 3.8 or higher
• KoboldCPP

1. Clone the repository or download theZIP file and extract it

2. InstallPython for Windows

3. Runllmii-windows.bat

1. Clone the repository or download theZIP file and extract it

2. Install Python 3.7 or higher if not already installed. You can use Homebrew:

   brew install python

3. Install ExifTool:

   brew install exiftool

4. Run the script:

   ./llmii.sh

5. If KoboldCpp fails to run, open a terminal in the 'resources' folder:

   xattr -cr ./resources/koboldcpp-mac-arm64chmod +x ./resources/koboldcpp-mac-arm64

1. Clone the repository or download and extract the ZIP file

2. Install Python 3.8 or higher if not already installed. Use your distribution's package manager, for example on Ubuntu:

   sudo apt-get updatesudo apt-get install python3 python3-pip

3. Install ExifTool. On Ubuntu:

   sudo apt-get install libimage-exiftool-perl

4. Run the script:

   ./llmii.sh

5. If KoboldCpp fails to run, open a terminal in the 'resources' folder:

   chmod +x ./resources/koboldcpp-linux-x64

1. Launch the LLMII GUI:

   • On Windows: Runllmii-windows.bat
   • On macOS/Linux: Run./llmii.sh

2. Ensure KoboldCPP is running. Wait until you see the following message in the KoboldCPP window:

   Please connect to custom endpoint at http://localhost:5001

3. Configure the indexing settings in the GUI

4. Click "Run Image Indexer" to start the process

5. Monitor the progress in the output area of the GUI.

Press the HELP button in the settings dialog.

Consultthe wiki for detailed information.

Contributions are welcome! Please feel free to submit a Pull Request.

This project is licensed under the GPLv3 License - see the LICENSE file for details.

• ExifTool for metadata manipulation
• KoboldCPP for local AI processing and for the GPU information logic
• PyQt6 for the GUI framework
• Fix Busted JSON andJson Repair for help with mangled JSON parsing