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
Node authors may now create their own arbitrary/custom field types. Any pydantic model is supported.
Two notes:
1. Your field type's class name must be unique.
Suggest prefixing fields with something related to the node pack as a kind of namespace.
2. Custom field types function as connection-only fields.
For example, if your custom field has string attributes, you will not get a text input for that attribute when you give a node a field with your custom type.
This is the same behaviour as other complex fields that don't have custom UIs in the workflow editor - like, say, a string collection.
feat(ui): fix tooltips for custom types
We need to hold onto the original type of the field so they don't all just show up as "Unknown".
fix(ui): fix ts error with custom fields
feat(ui): custom field types connection validation
In the initial commit, a custom field's original type was added to the *field templates* only as `originalType`. Custom fields' `type` property was `"Custom"`*. This allowed for type safety throughout the UI logic.
*Actually, it was `"Unknown"`, but I changed it to custom for clarity.
Connection validation logic, however, uses the *field instance* of the node/field. Like the templates, *field instances* with custom types have their `type` set to `"Custom"`, but they didn't have an `originalType` property. As a result, all custom fields could be connected to all other custom fields.
To resolve this, we need to add `originalType` to the *field instances*, then switch the validation logic to use this instead of `type`.
This ended up needing a bit of fanagling:
- If we make `originalType` a required property on field instances, existing workflows will break during connection validation, because they won't have this property. We'd need a new layer of logic to migrate the workflows, adding the new `originalType` property.
While this layer is probably needed anyways, typing `originalType` as optional is much simpler. Workflow migration logic can come layer.
(Technically, we could remove all references to field types from the workflow files, and let the templates hold all this information. This feels like a significant change and I'm reluctant to do it now.)
- Because `originalType` is optional, anywhere we care about the type of a field, we need to use it over `type`. So there are a number of `field.originalType ?? field.type` expressions. This is a bit of a gotcha, we'll need to remember this in the future.
- We use `Array.prototype.includes()` often in the workflow editor, e.g. `COLLECTION_TYPES.includes(type)`. In these cases, the const array is of type `FieldType[]`, and `type` is is `FieldType`.
Because we now support custom types, the arg `type` is now widened from `FieldType` to `string`.
This causes a TS error. This behaviour is somewhat controversial (see https://github.com/microsoft/TypeScript/issues/14520). These expressions are now rewritten as `COLLECTION_TYPES.some((t) => t === type)` to satisfy TS. It's logically equivalent.
fix(ui): typo
feat(ui): add CustomCollection and CustomPolymorphic field types
feat(ui): add validation for CustomCollection & CustomPolymorphic types
- Update connection validation for custom types
- Use simple string parsing to determine if a field is a collection or polymorphic type.
- No longer need to keep a list of collection and polymorphic types.
- Added runtime checks in `baseinvocation.py` to ensure no fields are named in such a way that it could mess up the new parsing
chore(ui): remove errant console.log
fix(ui): rename 'nodes.currentConnectionFieldType' -> 'nodes.connectionStartFieldType'
This was confusingly named and kept tripping me up. Renamed to be consistent with the `reactflow` `ConnectionStartParams` type.
fix(ui): fix ts error
feat(nodes): add runtime check for custom field names
"Custom", "CustomCollection" and "CustomPolymorphic" are reserved field names.
chore(ui): add TODO for revising field type names
wip refactor fieldtype structured
wip refactor field types
wip refactor types
wip refactor types
fix node layout
refactor field types
chore: mypy
organisation
organisation
organisation
fix(nodes): fix field orig_required, field_kind and input statuses
feat(nodes): remove broken implementation of default_factory on InputField
Use of this could break connection validation due to the difference in node schemas required fields and invoke() required args.
Removed entirely for now. It wasn't ever actually used by the system, because all graphs always had values provided for fields where default_factory was used.
Also, pydantic is smart enough to not reuse the same object when specifying a default value - it clones the object first. So, the common pattern of `default_factory=list` is extraneous. It can just be `default=[]`.
fix(nodes): fix InputField name validation
workflow validation
validation
chore: ruff
feat(nodes): fix up baseinvocation comments
fix(ui): improve typing & logic of buildFieldInputTemplate
improved error handling in parseFieldType
fix: back compat for deprecated default_factory and UIType
feat(nodes): do not show node packs loaded log if none loaded
chore(ui): typegen
We have a number of shared classes, objects, and functions that are used in multiple places. This causes circular import issues.
This commit creates a new `app/shared/` module to hold these shared classes, objects, and functions.
Initially, only `FreeUConfig` and `FieldDescriptions` are moved here. This resolves a circular import issue with custom nodes.
Other shared classes, objects, and functions will be moved here in future commits.
- Refactor how metadata is handled to support a user-defined metadata in graphs
- Update workflow embed handling
- Update UI to work with these changes
- Update tests to support metadata/workflow changes
- Remove the add-to-board node
- Create `BoardField` field type & add it to `save_image` node
- Add UI for `BoardField`
- Tighten up some loose types
- Make `save_image` node, in workflow editor, default to not intermediate
- Patch bump `save_image`
The `@invocation` decorator is extended with an optional `version` arg. On execution of the decorator, the version string is parsed using the `semver` package (this was an indirect dependency and has been added to `pyproject.toml`).
All built-in nodes are set with `version="1.0.0"`.
The version is added to the OpenAPI Schema for consumption by the client.
Initial support for polymorphic field types. Polymorphic types are a single of or list of a specific type. For example, `Union[str, list[str]]`.
Polymorphics do not yet have support for direct input in the UI (will come in the future). They will be forcibly set as Connection-only fields, in which case users will not be able to provide direct input to the field.
If a polymorphic should present as a singleton type - which would allow direct input - the node must provide an explicit type hint.
For example, `DenoiseLatents`' `CFG Scale` is polymorphic, but in the node editor, we want to present this as a number input. In the node definition, the field is given `ui_type=UIType.Float`, which tells the UI to treat this as a `float` field.
The connection validation logic will prevent connecting a collection to `CFG Scale` in this situation, because it is typed as `float`. The workaround is to disable validation from the settings to make this specific connection. A future improvement will resolve this.
This also introduces better support for collection field types. Like polymorphics, collection types are parsed automatically by the client and do not need any specific type hints.
Also like polymorphics, there is no support yet for direct input of collection types in the UI.
- Disabling validation in workflow editor now displays the visual hints for valid connections, but lets you connect to anything.
- Added `ui_order: int` to `InputField` and `OutputField`. The UI will use this, if present, to order fields in a node UI. See usage in `DenoiseLatents` for an example.
- Updated the field colors - duplicate colors have just been lightened a bit. It's not perfect but it was a quick fix.
- Field handles for collections are the same color as their single counterparts, but have a dark dot in the center of them.
- Field handles for polymorphics are a rounded square with dot in the middle.
- Removed all fields that just render `null` from `InputFieldRenderer`, replaced with a single fallback
- Removed logic in `zValidatedWorkflow`, which checked for existence of node templates for each node in a workflow. This logic introduced a circular dependency, due to importing the global redux `store` in order to get the node templates within a zod schema. It's actually fine to just leave this out entirely; The case of a missing node template is handled by the UI. Fixing it otherwise would introduce a substantial headache.
- Fixed the `ControlNetInvocation.control_model` field default, which was a string when it shouldn't have one.
All invocation metadata (type, title, tags and category) are now defined in decorators.
The decorators add the `type: Literal["invocation_type"]: "invocation_type"` field to the invocation.
Category is a new invocation metadata, but it is not used by the frontend just yet.
- `@invocation()` decorator for invocations
```py
@invocation(
"sdxl_compel_prompt",
title="SDXL Prompt",
tags=["sdxl", "compel", "prompt"],
category="conditioning",
)
class SDXLCompelPromptInvocation(BaseInvocation, SDXLPromptInvocationBase):
...
```
- `@invocation_output()` decorator for invocation outputs
```py
@invocation_output("clip_skip_output")
class ClipSkipInvocationOutput(BaseInvocationOutput):
...
```
- update invocation docs
- add category to decorator
- regen frontend types
Refine concept of "parameter" nodes to "primitives":
- integer
- float
- string
- boolean
- image
- latents
- conditioning
- color
Each primitive has:
- A field definition, if it is not already python primitive value. The field is how this primitive value is passed between nodes. Collections are lists of the field in node definitions. ex: `ImageField` & `list[ImageField]`
- A single output class. ex: `ImageOutput`
- A collection output class. ex: `ImageCollectionOutput`
- A node, which functions to load or pass on the primitive value. ex: `ImageInvocation` (in this case, `ImageInvocation` replaces `LoadImage`)
Plus a number of related changes:
- Reorganize these into `primitives.py`
- Update all nodes and logic to use primitives
- Consolidate "prompt" outputs into "string" & "mask" into "image" (there's no reason for these to be different, the function identically)
- Update default graphs & tests
- Regen frontend types & minor frontend tidy related to changes
