TagGUI

Cross-platform desktop application for quickly adding and editing image tags and captions, aimed towards creators of image datasets for generative AI models like Stable Diffusion.

Features

• Keyboard-friendly interface for fast tagging
• Tag autocomplete based on your own most-used tags
• Integrated Stable Diffusion token counter
• Automatic caption and tag generation with models including CogVLM, LLaVA, WD Tagger, and many more
• Batch tag operations for renaming, deleting, and sorting tags
• Advanced image list filtering

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 inside. You may have to install 7-Zip to extract the files if you don't have it on your system.

• macOS users: There is no macOS release because it requires a device running the OS, and I do not have one. You can still install and run the program manually (see below).
• Linux users: You may need to install libxcb-cursor0. (See this Stack Overflow answer.) You may also have to install python3.11-dev or python3.10-dev (depending on your Python version) if you get an error while trying to use a CogVLM2 model. (See this issue.)

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.

Automatic Captioning

In addition to manual tagging, you can automatically generate captions or tags 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 select the captioning model you want to use in the Auto-Captioner pane. If you have a local directory containing previously downloaded models, you can set it in File -> Settings to include the models in the model list. Click the Start Auto-Captioning button to start captioning. You can select multiple images to batch generate captions for all of them. It can take up to several minutes to download and load a model when you first use it, but subsequent generations will be much faster.

Captioning parameters

Prompt: Instructions given to the captioning model. Prompt formats are handled automatically based on the selected model. You can use the following template variables to dynamically insert information about each image into the prompt:

• {tags}: The tags of the image, separated by commas.
• {name}: The file name of the image without the extension.
• {directory} or {folder}: The name of the directory containing the image.

An example prompt using a template variable could be Describe the image using the following tags as context: {tags}. With this prompt, {tags} would be replaced with the existing tags of each image before the prompt is sent to the model.

Start caption with: Generated captions will start with this text.

Remove tag separators in caption: If checked, tag separators (commas by default) will be removed from the generated captions.

Discourage from caption: Words or phrases that should not be present in the generated captions. You can separate multiple words or phrases with commas (,). For example, you can put appears,seems,possibly to prevent the model from using an uncertain tone in the captions. The words may still be generated due to limitations related to tokenization.

Include in caption: Words or phrases that should be present somewhere in the generated captions. You can separate multiple words or phrases with commas (,). You can also allow the captioning model to choose from a group of words or phrases by separating them with |. For example, if you put cat,orange|white|black, the model will attempt to generate captions that contain the word cat and either orange, white, or black. It is not guaranteed that all of your specifications will be met.

Tags to exclude (WD Tagger models): Tags that should not be generated, separated by commas.

Many of the other generation parameters are described in the Hugging Face documentation.

Advanced Image List Filtering

The basic functionality of filtering for images that contain a certain tag is available by clicking on the tag in the All Tags pane. In addition to this, you can construct more complex filters in the Filter Images box at the top of the Images pane.

Click here to see the full documentation for the filter syntax.

Filter criteria

These are the prefixes you can use to specify the filter criteria you want to apply:

• tag:: Images that have the filter term as a tag
  • tag:cat will match images with the tag cat.
• caption: Images that contain the filter term in the caption
  • The caption is the list of tags as a single string, as it appears in the .txt file.
  • caption:cat will match images that have cat anywhere in the caption. For example, images with the tag orange cat or the tag catastrophe.
• name: Images that contain the filter term in the file name
  • name:cat will match images such as cat-1.jpg or large_cat.png.
• path: Images that contain the filter term in the full file path
  • path:cat will match images such as C:\Users\cats\dog.jpg or /home/dogs/cat.jpg.
• You can also use a filter term with no prefix to filter for images that contain the term in either the caption or the file path.
  • cat will match images containing cat in the caption or file path.

The following are prefixes for numeric filters. The operators = (== also works), !=, <, >, <=, and >= are used to specify the type of comparison.

• tags: Images that have the specified number of tags
  • tags:=13 will match images that have exactly 13 tags.
  • tags:!=7 will match images that do not have exactly 7 tags (images with less than 7 tags or more than 7 tags).
• chars: Images that have the specified number of characters in the caption
  • chars:<100 will match images that have less than 100 characters in the caption.
  • chars:>=30 will match images that have 30 or more characters in the caption.
• tokens: Images that have the specified number of tokens in the caption
  • tokens:>75 will match images that have more than 75 tokens in the caption.
  • tokens:<=50 will match images that have 50 or fewer tokens in the caption.

Spaces and quotes

If the filter term contains spaces, you must enclose it in quotes (either single or double quotes). For example, to find images with the tag orange cat, you must use tag:"orange cat" or tag:'orange cat'. If you have both spaces and quotes in the filter term, you can escape the quotes with backslashes. For example, you can use tag:"orange \"cat\"" for the tag orange "cat". An alternative is to use different types of quotes for the outer and inner quotes, like so: tag:'orange "cat"'.

Wildcards

You can use the * character as a wildcard to match any number of any characters, and the ? character to match any single character. For example, tag:*cat will match images with tags like orange cat, large cat, and cat.

Combining filters

Logical operators can be used to combine multiple filters:

• NOT: Images that do not match the filter
  • NOT tag:cat will match images that do not have the tag cat.
• AND: Images that match both filters before and after the operator
  • tag:cat AND tag:orange will match images that have both the tag cat and the tag orange.
• OR: Images that match either filter before or after the operator
  • tag:cat OR tag:dog will match images that have either the tag cat or the tag dog, or both.

The lowercase versions of these operators will also work: not, and, and or.

The operator precedence is NOT > AND > OR, so by default, NOT will be evaluated first, then AND, then OR. You can use parentheses to change this order. For example, in tag:cat AND (tag:orange OR tag:white), the OR will be evaluated first, matching images that have the tag cat and either the tag orange or the tag white. You can nest parentheses and operators to create arbitrarily complex filters.

Controls

• ⭐ Previous / next image: Ctrl+Up / Down (just Up / Down also works in some cases)
• Jump to the first untagged image: Ctrl+J
• Focus the Filter Images box: Alt+F
• Focus the Add Tag box: Alt+A
• Focus the Image Tags list: Alt+I
• Focus the Search Tags box: Alt+S
• Focus the Start Auto-Captioning button: Alt+C

Images pane

• First / last image: Home / End
• Select multiple images: Hold Ctrl or Shift and click the images
• Select all images: Ctrl+A
• Invert selection: Ctrl+I
• Right-clicking on an image will bring up the context menu, which includes actions such as copying and pasting tags and moving or copying selected images to another directory.

Image Tags pane

• Add a tag: Type the tag into the Add Tag box and press Enter
• ⭐ Add the first tag suggested by autocomplete: Ctrl+Enter
• Add a tag to multiple images: Select the images in the image list add the tag
• 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 and click the tags

All Tags pane

• Show all images containing a tag: Select the tag (When Tag click action is set to Filter images for tag)
• Add a tag to selected images: Click the tag (When Tag click action is set to Add tag to selected images)
• 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

The Edit menu contains additional features for batch tag operations, such as Find and Replace (Ctrl+R) and Batch Reorder Tags (Ctrl+B).