angeloshredder/taggui

Desktop application for quickly tagging images

TagGUI

Cross-platform desktop application for quickly tagging images, aimed towards creators of image datasets for generative AI models like Stable Diffusion. Written in Python using PySide6.

Features

• Keyboard-friendly interface for fast tagging
• Tag autocomplete based on your own most-used tags
• Integrated Stable Diffusion token counter
• Batch tag renaming and deleting
• BLIP-2 caption generation
• Automatic dark mode based on system settings

Installation

The easiest way to use the application is to download the latest release from the releases page. Choose the appropriate file for your operating system, extract it wherever you want, and run the executable file shortcut inside. You will have to install 7-Zip to extract the files if you don't have it on your system. No additional dependencies are required.

Alternatively, you can install manually by cloning this repository and installing the dependencies in requirements.txt. Run taggui/run_gui.py to start the program. Python 3.11 is recommended, but Python 3.10 should also work.

Usage

Load the directory containing your images by clicking the Load Directory button in the center of the window (or File -> Load Directory). Tags are loaded from .txt files in the directory with the same names as the images. Any changes you make to the tags are also automatically saved to these .txt files.

You can change the settings in File -> Settings. Panes can be resized, undocked, moved around, or placed on top of each other to create a tabbed interface.

BLIP-2 Captioning (New in v1.2.0)

In addition to manual tagging, you can use the BLIP-2 model to automatically generate captions for your images inside TagGUI. GPU generation requires a compatible NVIDIA GPU, and CPU generation is also supported.

To use the feature, select the images you want to caption in the image list, then click the Caption With BLIP-2 button in the BLIP-2 Captioner pane. You can select a single image to get a caption for that image, or multiple images to batch generate captions for all of them. It can take up to several minutes to download and load the model when you first use it, but subsequent generations will be much faster.

You can put some text inside the Start caption with: box to make the model generate captions that start with that text. For example, you can write A photo of a person wearing to get captions that describe the clothing of the subject. Additional generation parameters such as the minimum number of tokens and the repetition penalty can be viewed and changed by clicking the Show Advanced Settings button. If you want to know more about what each parameter does, you can read the Hugging Face documentation.

Controls

• Focus the image list: Alt+L
• Focus the Add Tag box: Alt+A
• Focus the Search Tags box: Alt+S
• Focus the Caption With BLIP-2 button: Alt+C

Images pane

• Previous / next image: Up / Down arrow keys
• First / last image: Home / End

Image Tags pane

• Add a tag: Type the tag into the Add Tag box and press Enter
• Add the first tag suggested by autocomplete: Press Ctrl+Enter
• Delete a tag: Select the tag and press Delete
• Rename a tag: Double-click the tag, or select the tag and press F2
• Reorder tags: Drag and drop the tags
• Select multiple tags: Hold Ctrl or Shift while selecting the tags
• Previous / next image: Up / Down arrow keys while in the Add Tag box

All Tags pane

• Show all images containing a tag: Select the tag
• Go back to showing all images: Click the Clear Image Filter button
• Delete all instances of a tag: Select the tag and press Delete
• Rename all instances of a tag: Double-click the tag, or select the tag and press F2