We use pydantic to validate a union of valid invocations when instantiating a graph.
Previously, we constructed the union while creating the `Graph` class. This introduces a dependency on the order of imports.
For example, consider a setup where we have 3 invocations in the app:
- Python executes the module where `FirstInvocation` is defined, registering `FirstInvocation`.
- Python executes the module where `SecondInvocation` is defined, registering `SecondInvocation`.
- Python executes the module where `Graph` is defined. A union of invocations is created and used to define the `Graph.nodes` field. The union contains `FirstInvocation` and `SecondInvocation`.
- Python executes the module where `ThirdInvocation` is defined, registering `ThirdInvocation`.
- A graph is created that includes `ThirdInvocation`. Pydantic validates the graph using the union, which does not know about `ThirdInvocation`, raising a `ValidationError` about an unknown invocation type.
This scenario has been particularly problematic in tests, where we may create invocations dynamically. The test files have to be structured in such a way that the imports happen in the right order. It's a major pain.
This PR refactors the validation of graph nodes to resolve this issue:
- `BaseInvocation` gets a new method `get_typeadapter`. This builds a pydantic `TypeAdapter` for the union of all registered invocations, caching it after the first call.
- `Graph.nodes`'s type is widened to `dict[str, BaseInvocation]`. This actually is a nice bonus, because we get better type hints whenever we reference `some_graph.nodes`.
- A "plain" field validator takes over the validation logic for `Graph.nodes`. "Plain" validators totally override pydantic's own validation logic. The validator grabs the `TypeAdapter` from `BaseInvocation`, then validates each node with it. The validation is identical to the previous implementation - we get the same errors.
`BaseInvocationOutput` gets the same treatment.
- Replace AnyModelLoader with ModelLoaderRegistry
- Fix type check errors in multiple files
- Remove apparently unneeded `get_model_config_enum()` method from model manager
- Remove last vestiges of old model manager
- Updated tests and documentation
resolve conflict with seamless.py
- Rename old "model_management" directory to "model_management_OLD" in order to catch
dangling references to original model manager.
- Caught and fixed most dangling references (still checking)
- Rename lora, textual_inversion and model_patcher modules
- Introduce a RawModel base class to simplfy the Union returned by the
model loaders.
- Tidy up the model manager 2-related tests. Add useful fixtures, and
a finalizer to the queue and installer fixtures that will stop the
services and release threads.
- Replace legacy model manager service with the v2 manager.
- Update invocations to use new load interface.
- Fixed many but not all type checking errors in the invocations. Most
were unrelated to model manager
- Updated routes. All the new routes live under the route tag
`model_manager_v2`. To avoid confusion with the old routes,
they have the URL prefix `/api/v2/models`. The old routes
have been de-registered.
- Added a pytest for the loader.
- Updated documentation in contributing/MODEL_MANAGER.md
- Implement new model loader and modify invocations and embeddings
- Finish implementation loaders for all models currently supported by
InvokeAI.
- Move lora, textual_inversion, and model patching support into
backend/embeddings.
- Restore support for model cache statistics collection (a little ugly,
needs work).
- Fixed up invocations that load and patch models.
- Move seamless and silencewarnings utils into better location
Turns out they are just different enough in purpose that the implementations would be rather unintuitive. I've made a separate ObjectSerializer service to handle tensors and conditioning.
Refined the class a bit too.
- New generic class `PickleStorageBase`, implements the same API as `LatentsStorageBase`, use for storing non-serializable data via pickling
- Implementation `PickleStorageTorch` uses `torch.save` and `torch.load`, same as `LatentsStorageDisk`
- Add `tensors: PickleStorageBase[torch.Tensor]` to `InvocationServices`
- Add `conditioning: PickleStorageBase[ConditioningFieldData]` to `InvocationServices`
- Remove `latents` service and all `LatentsStorage` classes
- Update `InvocationContext` and all usage of old `latents` service to use the new services/context wrapper methods
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.
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
