This allows users to create simple "profiles" via separate `invokeai.yaml` files.
- Remove `InvokeAIAppConfig.set_root()`, it's extraneous
- Remove `InvokeAIAppConfig.merge_from_file()`, it's extraneous
- Add `--config` to the app arg parser, add `InvokeAIAppConfig._config_file`, and consume in the config singleton getter
- `InvokeAIAppConfig.init_file_path` -> `InvokeAIAppConfig.config_file_path`
This flag acts as a proxy for the `get_config()` function to determine if the full application is running.
If it was, the config will set the root, do HF login, etc.
If not (e.g. it's called by an external script), all that stuff will be skipped.
HF login, legacy yaml confs, and default init file are all handled during app setup.
All directories are created as they are needed by the app.
No need to check for a valid root dir - we will make it if it doesn't exist.
Use the util function to calculate ram cache size on startup. This way, the `ram` setting will always be optimized for a system, even if they add or remove RAM. In other words, the default value is now dynamic.
When running the configurator, the `legacy_models_conf_path` was stripped when saving the config file. Then the migration logic didn't fire correctly, and the custom models.yaml paths weren't migrated into the db.
- Rework the logic to migrate this path by adding it to the config object as a normal field that is not excluded from serialization.
- Rearrange the models.yaml migration logic to remove the legacy path after migrating, then write the config file. This way, the legacy path doesn't stick around.
- Move the schema version into the config object.
- Back up the config file before attempting migration.
- Add tests to cover this edge case
Hold onto `conf_path` temporarily while migrating `invokeai.yaml` so that it gets migrated correctly as the model installer starts up. Stashed as `legacy_models_yaml_path` in the config, excluded from serialization.
We have two problems with how argparse is being utilized:
- We parse CLI args as the `api_app.py` file is read. This causes a problem pytest, which has an incompatible set of CLI args. Some tests import the FastAPI app, which triggers the config to parse CLI args, which receives the pytest args and fails.
- We've repeatedly had problems when something that uses the config is imported before the CLI args are parsed. When this happens, the root dir may not be set correctly, so we attempt to operate on incorrect paths.
To resolve these issues, we need to lift CLI arg parsing outside of the application code, but still let the application access the CLI args. We can create a external app entrypoint to do this.
- `InvokeAIArgs` is a simple helper class that parses CLI args and stores the result.
- `run_app()` is the new entrypoint. It first parses CLI args, then runs `invoke_api` to start the app.
The `invokeai-web` project script and `invokeai-web.py` dev script now call `run_app()` instead of `invoke_api()`.
The first time `get_config()` is called to get the singleton config object, it retrieves the args from `InvokeAIArgs`, sets the root dir if provided, then merges settings in from `invokeai.yaml`.
CLI arg parsing is now safely insulated from application code, but still accessible. And we don't need to worry about import order having an impact on anything, because by the time the app is running, we have already parsed CLI args. Whew!
This fixes an issue with `test_images.py`, which tests the bulk images routers and imports the whole FastAPI app. This triggers the config logic which fails on the test runner, because it has no `invokeai.yaml`.
Also probably just good for graceful fallback.
- `write_file` requires an destination file path
- `read_config` -> `merge_from_file`, if no path is provided, reads from `self.init_file_path`
- update app, tests to use new methods
- fix configurator, was overwriting config file data unexpectedly
Tweak the name of it so that incoming configs with the old default value of 6 have the setting stripped out. The result is all configs will now have the new, much better default value of 1.
Having this all in the `get_config` function makes testing hard. Move these two functions to their own methods, and call them on app startup explicitly.
- Remove OmegaConf. It functioned as an intermediary data format, between YAML/argparse and pydantic. It's not necessary - we can parse YAML or CLI args directly with pydantic.
- Remove dynamic CLI args. Only `root` is explicitly supported. This greatly simplifies config handling. Configuration is done by editing the YAML file. Frequently-used args can be added if there is a demand.
- A separate arg parser is created to handle the slimmed-down CLI args. It's run immediately in the `invokeai-web` script to handle `--version` and `--help`. It is also used inside the singleton config getter (see below).
- Remove categories from the config. Our settings model is mostly flat. Handling categories adds complexity for both us and users - we have to handle transforming a flat config to categorized config (and vice-versa), while users have to be careful with indentation in their YAML file.
- Add a `meta` key to the config file. Currently, this holds the config schema version only. It is not a part of the config object itself.
- Remove legacy settings that are no longer referenced, or were effectively no-op settings when referenced in code.
- Implement simple migration logic to for v3 configs. If migration is successful, the v3 config file is backed up to `invokeai.yaml.bak` and the new config written to `invokeai.yaml`.
- Previously, the singleton config was accessed by calling `InvokeAIAppConfig.get_config()`. This returned an instance of `InvokeAIAppConfig`, which _also_ has the `get_config` function. This created to a confusing situation where you weren't sure if you needed to call `get_config` or just use the config object. This method is replaced by a standalone `get_config` function which returns a singleton config object.
- Wrap CLI arg parsing (for `root`) and loading/migrating `invokeai.yaml` into the new `get_config()` function.
- Move `generate_config_docstrings` into standalone utility function.
- Make `root` a private attr (`_root`). This reduces the temptation to directly modify and or use this sensitive field and ensures it is neither serialized nor read from input data. Use `root_path` to access the resolved root path, or `set_root` to set the root to something.
BLAKE3 has poor performance on spinning disks when parallelized. See https://github.com/BLAKE3-team/BLAKE3/issues/31
- Replace `skip_model_hash` setting with `hashing_algorithm`. Any algorithm we support is accepted.
- Add `random` algorithm: hashes a UUID with BLAKE3 to create a random "hash". Equivalent to the previous skip functionality.
- Add `blake3_single` algorithm: hashes on a single thread using BLAKE3, fixes the aforementioned performance issue
- Update model probe to accept the algorithm to hash with as an optional arg, defaulting to `blake3`
- Update all calls of the probe to use the app's configured hashing algorithm
- Update an external script that probes models
- Update tests
- Move ModelHash into its own module to avoid circuclar import issues
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
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.
