Patches and contributions are very welcome!
If you are facing an issue or want to report a bug, please check existing issues.
Having a look at the log files can provide additional information for troubleshooting. You can find them in the .logs
subfolder of the plugin installation folder (ai_diffusion
). There is also a link in the plugin's connection settings.
When you open a new issue, please attach the log files. Other useful information to include: OS (Windows/Linux/Mac), Krita version, Plugin version, GPU vendor.
You can create or improve a translation for the plugin interface into your language.
Language files are stored in the ai_diffusion/langauge
folder. Each translation has its own
file, typically named using a language code (eg. en.json
for English).
If no file for your language exists, you can use the new_language.json.template
file and
rename it. It will show up in the plugin's language settings after a restart.
You can check existing translations here - this might be more up-to-date than your local installation!
To edit a localization file, open the file in a text editor and provide translations for each of the english text strings. For example, to provide a German translation, it could look like this:
{
"id": "de",
"name": "Deutsch",
"translations": {
"(Custom)": "(Benutzerdefiniert)",
"<No text prompt>": "<Keine Text-Eingabe>",
"Active": "Aktiv",
"Add Content": "Inhalte hinzufügen",
"Could not find LoRA '{lora}' used by sampler preset '{name}'": "LoRA '{lora}' konnte nicht gefunden werden, wird aber von Sampler '{name}' benutzt",
...
}
}
Important: {placeholders}
must be left unmodified! They will be replaced with actual content during runtime.
To update an existing translation (eg. after the plugin has been updated and new text was added)
simply search for entries which are null
. These are valid, but not translated yet.
For bigger changes, it makes sense to create an issue first to discuss a proposal before time is comitted.
You can submit your changes by opening a pull request.
The easiest way to run a development version of the plugin is to use symlinks:
git clone
the repository into a location of your choicegit submodule update --init
- in the pykrita folder where Krita expects plugins:
- create a symlink to the
ai_diffusion
folder - create a symlink to
ai_diffusion.desktop
- create a symlink to the
The codebase uses black for formatting. You can check locally by running black
in the repository root, or use an IDE integration.
Code style follows the official Python recommendations. Only exception: no ALL_CAPS
.
Type annotations should be used where types can't be inferred. Basic type checks are enabled for the project and should not report errors.
The Krita
module is special in that it is usually only available when running inside Krita. To make type checking work an interface file is located in scripts/typeshed
.
You can run pyright
from the repository root to perform type checks on the entire codebase. This is also done by the CI.
Configuration for VSCode with Pylance (.vscode/settings.json):
{
"python.analysis.typeCheckingMode": "basic",
"python.analysis.exclude": [
"scripts/typeshed/**",
"ai_diffusion/websockets/**"
]
}
There are tests, although with some caveats currently.
To install dependencies for tests run:
pip install -r requirements.txt
Tests are run from the project root via pytest:
pytest tests
Some tests require a running ComfyUI server. This should be automated... but for now it's not.
Generating images is tested. Because it takes a lot of time the number of tests is limited. Images are not compared in most cases, as they tend to frequently change with updates to dependencies.
Functionality which uses Krita's API is not tested. It just doesn't work outside Krita without a comprehensive mock.
UI is not tested. Because UI.
Everything else has tests. Mostly. If effort is reasonable, tests are expected. They help being confident about making changes.
Testing changes to the installer is annoying because of the file sizes involved. There are some things that help. You can preload model files with the following script:
python scripts/download_models.py --minimal scripts/downloads
This will download the minimum required models and store them in scripts/downloads
.
The following command does some automated testing for installation and upgrade. It starts a local file server which pulls preloaded models, so it's reasonably fast and doesn't download the entire internet.
pytest tests/test_server.py --test-install -vs
You can also run the file server manually. Then you can start Krita with the HOSTMAP
environment variable set, and it will map HuggingFace & civit.ai links to localhost.
python scripts/file_server.py
HOSTMAP=1 /your/krita/install/krita