mkdocs can autogenerate python class docs from its docstrings. Our config is a pydantic model.
It's tedious and error-prone to duplicate docstrings from the pydantic field descriptions to the class docstrings.
- Add helper function to generate a mkdocs-compatible docstring from the InvokeAIAppConfig class fields
Recently the schema for models was changed to a generic `ModelField`, and the UI was unable to derive the type of those fields. This didn't affect functionality, but it did break the styling of handles.
Add `ui_type` to the affected fields and update the UI to use the correct capitalizations.
A list of regex and token pairs is accepted. As a file is downloaded by the model installer, the URL is tested against the provided regex/token pairs. The token for the first matching regex is used during download, added as a bearer token.
When we change a model image, its URL remains the same. The browser will aggressively cache the image. The easiest way to fix this is to append a random query parameter to the URL whenever we build a model config in the API.
- All models are identified by a key and optionally a submodel type via new model `ModelField`. Previously, a few model types had their own class, but not all of them. This inconsistency just added complexity without any benefit.
- Update all invocation to use the new format.
- In the node API, models are loaded by key or an instance of `ModelField` as a convenience.
- Add an enriched model schema for metadata. It includes key, hash, name, base and type.
