- use simpler pattern for migration dependencies
- move SqliteDatabase & migration to utility method `init_db`, use this in both the app and tests, ensuring the same db schema is used in both
This fixes a problem with `Annotated` which prevented us from using pydantic's `Field` to specify a discriminator for a union. We had to use FastAPI's `Body` as a workaround.
* selector added
* ref and useeffect added
* scrolling done using useeffect
* fixed scroll and changed the ref name
* fixed scroll again
* created hook for scroll logic
* feat(ui): debounce metadata fetch by 300ms
This vastly reduces the network requests when using the arrow keys to quickly skim through images.
* feat(ui): extract logic to determine virtuoso scrollToIndex align
This needs to be used in `useNextPrevImage()` to ensure the scrolling puts the image at the top or bottom appropriately
* feat(ui): add debounce to image workflow hook
This was spamming network requests like the metadata query
---------
Co-authored-by: psychedelicious <4822129+psychedelicious@users.noreply.github.com>
Invocations now have a classification:
- Stable: LTS
- Beta: LTS planned, API may change
- Prototype: No LTS planned, API may change, may be removed entirely
The `@invocation` decorator has a new arg `classification`, and an enum `Classification` is added to `baseinvocation.py`.
The default is Stable; this is a non-breaking change.
The classification is presented in the node header as a hammer icon (Beta) or flask icon (prototype).
The icon has a tooltip briefly describing the classification.
## What type of PR is this? (check all applicable)
- [ ] Refactor
- [X] Feature
- [ ] Bug Fix
- [ ] Optimization
- [ ] Documentation Update
- [ ] Community Node Submission
## Have you discussed this change with the InvokeAI team?
- [X] Yes
- [ ] No, because:
## Have you updated all relevant documentation?
- [ ] Yes
- [X] No
## Description
1. The new model manager sqlite3-based configuration record storage
system is automatically populated with probed values from existing
models found in the models path when `invokeai-web` starts up for the
first time. However, the user's customization of these models in
`invokeai.yaml`, including such things as the prediction type and model
description, are not automatically copied over. This PR enhances the
`invokeai-migrate-models-to-db` script so that any customized
configuration data from `invokeai.yaml` replaces the original probed
values. This script only needs to be run once, but it does not hurt to
run it additional times. In the near future, I'm going to register this
module with psychedelicious's sqlite migration system so that the update
happens automatically during database migration.
2. The SQL-based model config record system stores a JSON version of the
config, as well as several fields that are broken out into individual
columns for search/indexing purposes. This PR keeps the JSON and the
broken-out fields in sync using the `json_extract()` sqlite3 function to
populate the broken out `base`, `type`, `name`, `path` and `format`
fields in the `model_config` table.
3. Finally, this PR fixes the annoying `invokeai-web` shutdown message:
`TypeError: ModelInstallService.stop() takes 1 positional argument but 2
were given`
## Related Tickets & Documents
- Related Issue #
- Closes #
## QA Instructions, Screenshots, Recordings
If you've run `invokeai-web` at any time since PR #5039, your
`invokeai.db` will have a `model_config` table containing probe
information from all models in the invokeai models directory as well as
those in `autoimport` (if applicable). However, any models present in
`models.yaml` whose paths are outside these directories will not be
present. To add them, and to update the description and other values
from `models.yaml`, run the command `invokeai-migrate-models-to-db`. You
should see the missing models added to the database table with the
correct information.
<!--
Please provide steps on how to test changes, any hardware or
software specifications as well as any other pertinent information.
-->
## Added/updated tests?
- [X] Yes
- [ ] No : _please replace this line with details on why tests
have not been included_
## [optional] Are there any post deployment tasks we need to perform?
## What type of PR is this? (check all applicable)
- [ ] Refactor
- [X] Feature
- [ ] Bug Fix
- [ ] Optimization
- [ ] Documentation Update
- [ ] Community Node Submission
## Have you discussed this change with the InvokeAI team?
- [X] Yes
- [ ] No, because:
## Have you updated all relevant documentation?
- [X] Yes
- [ ] No
## Description
This PR does three things:
1) It separates out the script that creates the installer zipfile
(`create_installer.sh`) from the script that tags the repository with
the current release version (now called `tag_release.sh`)
2) It adds new targets to Makefile for running the installer script and
tagging.
3) It adds a `help` target that lists the Makefile targets:
```
$ make help
Developer commands:
ruff Run ruff, fixing any safely-fixable errors and formatting
ruff-unsafe Run ruff, fixing all fixable errors and formatting
mypy Run mypy using the config in pyproject.toml to identify type mismatches and other coding errors
mypy-all Run mypy ignoring the config in pyproject.tom but still ignoring missing imports
frontend-build Build the frontend in order to run on localhost:9090
frontend-dev Run the frontend in developer mode on localhost:5173
installer-zip Build the installer .zip file for the current version
tag-release Tag the GitHub repository with the current version (use at release time only!)
```
`help` is also the default target so that the help message will print
out when only `make` is issued.
## Related Tickets & Documents
<!--
For pull requests that relate or close an issue, please include them
below.
For example having the text: "closes #1234" would connect the current
pull
request to issue 1234. And when we merge the pull request, Github will
automatically close the issue.
-->
- Related Issue #
- Closes #
## QA Instructions, Screenshots, Recordings
<!--
Please provide steps on how to test changes, any hardware or
software specifications as well as any other pertinent information.
-->
## Added/updated tests?
- [ ] Yes
- [X] No: not needed
## [optional] Are there any post deployment tasks we need to perform?
## What type of PR is this? (check all applicable)
- [ ] Refactor
- [x] Feature
- [ ] Bug Fix
- [ ] Optimization
- [ ] Documentation Update
- [ ] Community Node Submission
## Have you discussed this change with the InvokeAI team?
- [x] Yes
- [ ] No, because:
## Have you updated all relevant documentation?
- [ ] Yes
- [x] No
## Description
Additional tile generation nodes of
CalculateImageTilesEvenSplitInvocation &
CalculateImageTilesMinimumOverlapInvocation
Additional blending method of merge_tiles_with_seam_blending
Updated Node MergeTilesToImageInvocation with seam blending
## Related Tickets & Documents
<!--
For pull requests that relate or close an issue, please include them
below.
For example having the text: "closes #1234" would connect the current
pull
request to issue 1234. And when we merge the pull request, Github will
automatically close the issue.
-->
- Related Issue #
- Closes #
## QA Instructions, Screenshots, Recordings
<!--
Please provide steps on how to test changes, any hardware or
software specifications as well as any other pertinent information.
-->
## Added/updated tests?
- [ ] Yes
- [ ] No : _please replace this line with details on why tests
have not been included_
## [optional] Are there any post deployment tasks we need to perform?
Simplifies a couple things:
- Init is more straightforward
- It's clear in the migrator that the connection we are working with is related to the SqliteDatabase
- Simplify init args to path (None means use memory), logger, and verbose
- Add docstrings to SqliteDatabase (it had almost none)
- Update all usages of the class
CONTAINER_UID is used for the user ID within the container, however I noticed the UID was hard coded to 1000 in the Dockerfile chown -R command.
This leaves the default as 1000, but allows it to be overrriden by setting CONTAINER_UID.
