There was a bit of wonk with default workflows. On every app startup, we wiped them all out and recreated them with new IDs. This is a quick-and-dirty way to ensure default workflows are always in sync.
Unfortunately, it also means default workflows are newly-created entities on every app load. Any thumbnails associated to them will be lost (bc they have new IDs), and `updated_at` doesn't work.
This changes makes default workflows stable entities.
The workflows we bundle in the python package in JSON format are still the source of truth for default workflows, but the startup logic that syncs them to the user DB is a bit smarter.
- All bundled workflows have an ID. It is prefixed with "default_" for clarity.
- Any default workflows in the user's DB that are not in the bundled default workflows are deleted from the DB.
- Any bundled default workflows that are not in the user's DB are added to the DB.
- If a default workflow in the user's DB does not match the content of its corresponding bundled workflow, it is updated in the DB.
The end result is that default workflows are still kept in sync for the user, but they don't change their identity.
We may now add thumbnails to default workflows, and sorting by `updated_at` is now meaningful.
## Summary
Upgrade ruff version to 0.9.9 and format existing code.
## Related Issues / Discussions
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## QA Instructions
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## Merge Plan
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## Checklist
- [ ] _The PR has a short but descriptive title, suitable for a
changelog_
- [ ] _Tests added / updated (if applicable)_
- [ ] _Documentation added / updated (if applicable)_
- [ ] _Updated `What's New` copy (if doing a release after this PR)_
This allows our tests to run in an isolated environment. For tests taht implicitly depend on import behaviour, this can prevent side-effects.
The function should only be used for tests.
by adding a layer with all the pytorch dependencies that don't change
most of the time.
## Summary
Every time the [`main` docker
images](https://github.com/invoke-ai/InvokeAI/pkgs/container/invokeai)
rebuild and I pull `main-cuda`, it gets another 3+ GB, which seems like
about a zillion times too much since most things don't change from one
commit on `main` to the next.
This is an attempt to follow the guidance in [Using uv in Docker:
Intermediate
Layers](https://docs.astral.sh/uv/guides/integration/docker/#intermediate-layers)
so there's one layer that installs all the dependencies—including
PyTorch with its bundled nvidia libraries—_before_ the project's own
frequently-changing files are copied in to the image.
## Related Issues / Discussions
- [Improved docker layer cache with
uv](https://discord.com/channels/1020123559063990373/1329975172022927370)
- [astral: Can `uv pip install` torch, but not `uv sync`
it](https://discord.com/channels/1039017663004942429/1329986610770612347)
## QA Instructions
Hopefully the CI system building the docker images is sufficient.
But there is one change to `pyproject.toml` related to xformers, so it'd
be worth checking that `python -m xformers.info` still says it has
triton on the platforms that expect it.
## Merge Plan
I don't expect this to be a disruptive merge.
(An earlier revision of this PR moved the venv, but I've reverted that
change at ebr's recommendation.)
## Checklist
- [ ] _The PR has a short but descriptive title, suitable for a
changelog_
- [ ] _Tests added / updated (if applicable)_
- [ ] _Documentation added / updated (if applicable)_
- [ ] _Updated `What's New` copy (if doing a release after this PR)_
