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https://github.com/invoke-ai/InvokeAI
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3fb116155b
- 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.
47 lines
2.0 KiB
Python
47 lines
2.0 KiB
Python
import os
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# The pydantic app config can be documented automatically using mkdocs, but this requires that the docstring
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# for the class is kept up to date. We use a pydantic model for the app config. Each config setting is a field
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# with a `description` parameter. It is tedious to update both the description _and_ the docstring for the class.
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#
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# This script parses the pydantic model, generates a valid docstring and replaces the existing docstring in the file,
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# so you don't need to worry about keeping the docstring up to date.
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#
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# A test is provided to ensure that the docstring is up to date. If the test fails, run this script.
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def main():
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# Change working directory to the repo root
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os.chdir(os.path.abspath(os.path.join(os.path.dirname(__file__), "..")))
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from invokeai.app.services.config.config_default import generate_config_docstrings
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docstring = generate_config_docstrings()
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# Replace the docstring in the file
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with open("invokeai/app/services/config/config_default.py", "r") as f:
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lines = f.readlines()
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# Find the class definition line
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class_def_index = next(i for i, line in enumerate(lines) if "class InvokeAIAppConfig" in line)
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# Find the existing docstring start and end lines
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docstring_start_index = next(i for i, line in enumerate(lines[class_def_index:]) if '"""' in line) + class_def_index
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docstring_end_index = (
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next(i for i, line in enumerate(lines[docstring_start_index + 1 :]) if '"""' in line)
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+ docstring_start_index
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+ 1
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)
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# Replace the existing docstring with the new one, plus some line breaks in between. This _should_ result in a
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# correctly-formatted file with no syntax errors.
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lines = lines[:docstring_start_index] + [docstring, "\n"] + lines[docstring_end_index + 1 :]
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# Write the modified lines back to the file
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with open("invokeai/app/services/config/config_default.py", "w") as f:
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f.writelines(lines)
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if __name__ == "__main__":
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main()
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