This class works the same way as `WithMetadata` - it simply adds a `board` field to the node. The context wrapper function is able to pull the board id from this. This allows image-outputting nodes to get a board field "for free", and have their outputs automatically saved to it.
This is a breaking change for node authors who may have a field called `board`, because it makes `board` a reserved field name. I'll look into how to avoid this - maybe by naming this invoke-managed field `_board` to avoid collisions?
Supporting changes:
- `WithBoard` is added to all image-outputting nodes, giving them the ability to save to board.
- Unused, duplicate `WithMetadata` and `WithWorkflow` classes are deleted from `baseinvocation.py`. The "real" versions are in `fields.py`.
- Remove `LinearUIOutputInvocation`. Now that all nodes that output images also have a `board` field by default, this node is no longer necessary. See comment here for context: https://github.com/invoke-ai/InvokeAI/pull/5491#discussion_r1480760629
- Without `LinearUIOutputInvocation`, the `ImagesInferface.update` method is no longer needed, and removed.
Note: This commit does not bump all node versions. I will ensure that is done correctly before merging the PR of which this commit is a part.
Note: A followup commit will implement the frontend changes to support this change.
- The config is already cached by the config class's `get_config()` method.
- The config mutates itself in its `root_path` property getter. Freezing the class makes any attempt to grab a path from the config error. Unfortunately this means we cannot easily freeze the class without fiddling with the inner workings of `InvokeAIAppConfig`, which is outside the scope here.
Update all invocations to use the new context. The changes are all fairly simple, but there are a lot of them.
Supporting minor changes:
- Patch bump for all nodes that use the context
- Update invocation processor to provide new context
- Minor change to `EventServiceBase` to accept a node's ID instead of the dict version of a node
- Minor change to `ModelManagerService` to support the new wrapped context
- Fanagling of imports to avoid circular dependencies
With these changes, the Docker image can be built and executed
successfully on hosts with AMD devices with ROCm acceleration.
Previously, a ROCm-enabled version of torch would be installed, but
later removed during installation of InvokeAI itself. This was caused by
InvokeAI needing a newer torch version than was previously installed.
The fix consists of multiple components:
* Update the hardcoded versions of torch and torchvision to the versions
currently used in pyproject.toml, so that a new version need not be
installed during installation of InvokeAI.
* Specify --extra-index-url on installation of InvokeAI so that even if
a verison mismatch occurs, the correct torch version should still be
installed. This also necessitates changing --index-url to
--extra-index-url for the Torch repo. Otherwise non-torch dependencies
would not be found.
* In run.sh, build the image for the selected service.
## 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
- [ ] No
## Description
## Related Tickets & Documents
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- Related Issue #
- Closes #
## QA Instructions, Screenshots, Recordings
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## Merge Plan
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## 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?
* new workflow tab UI - still using shared state with workflow editor tab
* polish workflow details
* remove workflow tab, add edit/view mode to workflow slice and get that working to switch between within editor tab
* UI updates for view/edit mode
* cleanup
* add warning to view mode
* lint
* start with isTouched false
* working on styling mode toggle
* more UX iteration
* lint
* cleanup
* save original field values to state, add indicator if they have been changed and give user choice to reset
* lint
* fix import and commit translation
* dont switch to view mode when loading a workflow
* warns before clearing editor
* use folder icon
* fix(ui): track do not erase value when resetting field value
- When adding an exposed field, we need to add it to originalExposedFieldValues
- When removing an exposed field, we need to remove it from originalExposedFieldValues
- add `useFieldValue` and `useOriginalFieldValue` hooks to encapsulate related logic
* feat(ui): use IconButton for workflow view/edit button
* feat(ui): change icon for new workflow
It was the same as the workflow tab icon, confusing bc you think it's going to somehow take you to the tab.
* feat(ui): use render props for NewWorkflowConfirmationAlertDialog
There was a lot of potentially sensitive logic shared between the new workflow button and menu items. Also, two instances of ConfirmationAlertDialog.
Using a render prop deduplicates the logic & components
* fix(ui): do not mark workflow touched when loading workflow
This was occurring because the `nodesChanged` action is called by reactflow when loading a workflow. Specifically, it calculates and sets the node dimensions as it loads.
The existing logic set `isTouched` whenever this action was called.
The changes reactflow emits have types, and we can use the change types and data to determine if a change should result in the workflow being marked as touched.
* chore(ui): lint
* chore(ui): lint
* delete empty file
---------
Co-authored-by: Mary Hipp <maryhipp@Marys-MacBook-Air.local>
Co-authored-by: psychedelicious <4822129+psychedelicious@users.noreply.github.com>
Methods `get_node` and `complete` were typed as returning a dynamically created unions `InvocationsUnion` and `InvocationOutputsUnion`, respectively.
Static type analysers cannot work with dynamic objects, so these methods end up as effectively un-annotated, returning `Unknown`.
They now return `BaseInvocation` and `BaseInvocationOutput`, respectively, which are the superclasses of all members of each union. This gives us the best type annotation that is possible.
Note: the return types of these methods are never introspected, so it doesn't really matter what they are at runtime.
