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feat(ui): add support for custom field types

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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
This commit is contained in:
psychedelicious
2023-11-17 11:32:35 +11:00
parent 0d52430481
commit 86a74e929a
186 changed files with 5713 additions and 5704 deletions
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394
invokeai/app/invocations/baseinvocation.py
View File

@ -17,11 +17,15 @@ from pydantic_core import PydanticUndefined
from invokeai.app.services.config.config_default import InvokeAIAppConfig
from invokeai.app.shared.fields import FieldDescriptions
from invokeai.app.util.metaenum import MetaEnum
from invokeai.app.util.misc import uuid_string
from invokeai.backend.util.logging import InvokeAILogger
if TYPE_CHECKING:
from ..services.invocation_services import InvocationServices
logger = InvokeAILogger.get_logger()
class InvalidVersionError(ValueError):
pass
@ -31,7 +35,7 @@ class InvalidFieldError(TypeError):
pass
class Input(str, Enum):
class Input(str, Enum, metaclass=MetaEnum):
"""
The type of input a field accepts.
- `Input.Direct`: The field must have its value provided directly, when the invocation and field \
@ -45,86 +49,120 @@ class Input(str, Enum):
Any = "any"
class UIType(str, Enum):
class FieldKind(str, Enum, metaclass=MetaEnum):
"""
Type hints for the UI.
If a field should be provided a data type that does not exactly match the python type of the field, \
use this to provide the type that should be used instead. See the node development docs for detail \
on adding a new field type, which involves client-side changes.
The kind of field.
- `Input`: An input field on a node.
- `Output`: An output field on a node.
- `Internal`: A field which is treated as an input, but cannot be used in node definitions. Metadata is
one example. It is provided to nodes via the WithMetadata class, and we want to reserve the field name
"metadata" for this on all nodes. `FieldKind` is used to short-circuit the field name validation logic,
allowing "metadata" for that field.
- `NodeAttribute`: The field is a node attribute. These are fields which are not inputs or outputs,
but which are used to store information about the node. For example, the `id` and `type` fields are node
attributes.
The presence of this in `json_schema_extra["field_kind"]` is used when initializing node schemas on app
startup, and when generating the OpenAPI schema for the workflow editor.
"""
# region Primitives
Boolean = "boolean"
Color = "ColorField"
Conditioning = "ConditioningField"
Control = "ControlField"
Float = "float"
Image = "ImageField"
Integer = "integer"
Latents = "LatentsField"
String = "string"
# endregion
Input = "input"
Output = "output"
Internal = "internal"
NodeAttribute = "node_attribute"
# region Collection Primitives
BooleanCollection = "BooleanCollection"
ColorCollection = "ColorCollection"
ConditioningCollection = "ConditioningCollection"
ControlCollection = "ControlCollection"
FloatCollection = "FloatCollection"
ImageCollection = "ImageCollection"
IntegerCollection = "IntegerCollection"
LatentsCollection = "LatentsCollection"
StringCollection = "StringCollection"
# endregion
# region Polymorphic Primitives
BooleanPolymorphic = "BooleanPolymorphic"
ColorPolymorphic = "ColorPolymorphic"
ConditioningPolymorphic = "ConditioningPolymorphic"
ControlPolymorphic = "ControlPolymorphic"
FloatPolymorphic = "FloatPolymorphic"
ImagePolymorphic = "ImagePolymorphic"
IntegerPolymorphic = "IntegerPolymorphic"
LatentsPolymorphic = "LatentsPolymorphic"
StringPolymorphic = "StringPolymorphic"
# endregion
class UIType(str, Enum, metaclass=MetaEnum):
"""
Type hints for the UI for situations in which the field type is not enough to infer the correct UI type.
# region Models
MainModel = "MainModelField"
- Model Fields
The most common node-author-facing use will be for model fields. Internally, there is no difference
between SD-1, SD-2 and SDXL model fields - they all use the class `MainModelField`. To ensure the
base-model-specific UI is rendered, use e.g. `ui_type=UIType.SDXLMainModelField` to indicate that
the field is an SDXL main model field.
- Any Field
We cannot infer the usage of `typing.Any` via schema parsing, so you *must* use `ui_type=UIType.Any` to
indicate that the field accepts any type. Use with caution. This cannot be used on outputs.
- Scheduler Field
Special handling in the UI is needed for this field, which otherwise would be parsed as a plain enum field.
- Internal Fields
Similar to the Any Field, the `collect` and `iterate` nodes make use of `typing.Any`. To facilitate
handling these types in the client, we use `UIType._Collection` and `UIType._CollectionItem`. These
should not be used by node authors.
"""
# region Model Field Types
SDXLMainModel = "SDXLMainModelField"
SDXLRefinerModel = "SDXLRefinerModelField"
ONNXModel = "ONNXModelField"
VaeModel = "VaeModelField"
VaeModel = "VAEModelField"
LoRAModel = "LoRAModelField"
ControlNetModel = "ControlNetModelField"
IPAdapterModel = "IPAdapterModelField"
UNet = "UNetField"
Vae = "VaeField"
CLIP = "ClipField"
# endregion
# region Iterate/Collect
Collection = "Collection"
CollectionItem = "CollectionItem"
# region Misc Field Types
Scheduler = "SchedulerField"
Any = "AnyField"
# endregion
# region Misc
Enum = "enum"
Scheduler = "Scheduler"
WorkflowField = "WorkflowField"
IsIntermediate = "IsIntermediate"
BoardField = "BoardField"
Any = "Any"
MetadataItem = "MetadataItem"
MetadataItemCollection = "MetadataItemCollection"
MetadataItemPolymorphic = "MetadataItemPolymorphic"
MetadataDict = "MetadataDict"
# region Internal Field Types
_Collection = "CollectionField"
_CollectionItem = "CollectionItemField"
# endregion
# region DEPRECATED
Boolean = "DEPRECATED_Boolean"
Color = "DEPRECATED_Color"
Conditioning = "DEPRECATED_Conditioning"
Control = "DEPRECATED_Control"
Float = "DEPRECATED_Float"
Image = "DEPRECATED_Image"
Integer = "DEPRECATED_Integer"
Latents = "DEPRECATED_Latents"
String = "DEPRECATED_String"
BooleanCollection = "DEPRECATED_BooleanCollection"
ColorCollection = "DEPRECATED_ColorCollection"
ConditioningCollection = "DEPRECATED_ConditioningCollection"
ControlCollection = "DEPRECATED_ControlCollection"
FloatCollection = "DEPRECATED_FloatCollection"
ImageCollection = "DEPRECATED_ImageCollection"
IntegerCollection = "DEPRECATED_IntegerCollection"
LatentsCollection = "DEPRECATED_LatentsCollection"
StringCollection = "DEPRECATED_StringCollection"
BooleanPolymorphic = "DEPRECATED_BooleanPolymorphic"
ColorPolymorphic = "DEPRECATED_ColorPolymorphic"
ConditioningPolymorphic = "DEPRECATED_ConditioningPolymorphic"
ControlPolymorphic = "DEPRECATED_ControlPolymorphic"
FloatPolymorphic = "DEPRECATED_FloatPolymorphic"
ImagePolymorphic = "DEPRECATED_ImagePolymorphic"
IntegerPolymorphic = "DEPRECATED_IntegerPolymorphic"
LatentsPolymorphic = "DEPRECATED_LatentsPolymorphic"
StringPolymorphic = "DEPRECATED_StringPolymorphic"
MainModel = "DEPRECATED_MainModel"
UNet = "DEPRECATED_UNet"
Vae = "DEPRECATED_Vae"
CLIP = "DEPRECATED_CLIP"
Collection = "DEPRECATED_Collection"
CollectionItem = "DEPRECATED_CollectionItem"
Enum = "DEPRECATED_Enum"
WorkflowField = "DEPRECATED_WorkflowField"
IsIntermediate = "DEPRECATED_IsIntermediate"
BoardField = "DEPRECATED_BoardField"
MetadataItem = "DEPRECATED_MetadataItem"
MetadataItemCollection = "DEPRECATED_MetadataItemCollection"
MetadataItemPolymorphic = "DEPRECATED_MetadataItemPolymorphic"
MetadataDict = "DEPRECATED_MetadataDict"
# endregion
class UIComponent(str, Enum):
class UIComponent(str, Enum, metaclass=MetaEnum):
"""
The type of UI component to use for a field, used to override the default components, which are \
The type of UI component to use for a field, used to override the default components, which are
inferred from the field type.
"""
@ -133,7 +171,7 @@ class UIComponent(str, Enum):
Slider = "slider"
class _InputField(BaseModel):
class InputFieldJSONSchemaExtra(BaseModel):
"""
*DO NOT USE*
This helper class is used to tell the client about our custom field attributes via OpenAPI
@ -142,12 +180,15 @@ class _InputField(BaseModel):
"""
input: Input
ui_hidden: bool
ui_type: Optional[UIType]
ui_component: Optional[UIComponent]
ui_order: Optional[int]
ui_choice_labels: Optional[dict[str, str]]
item_default: Optional[Any]
orig_required: bool
field_kind: FieldKind
default: Optional[Any] = None
orig_default: Optional[Any] = None
ui_hidden: bool = False
ui_type: Optional[UIType] = None
ui_component: Optional[UIComponent] = None
ui_order: Optional[int] = None
ui_choice_labels: Optional[dict[str, str]] = None
model_config = ConfigDict(
validate_assignment=True,
@ -155,7 +196,7 @@ class _InputField(BaseModel):
)
class _OutputField(BaseModel):
class OutputFieldJSONSchemaExtra(BaseModel):
"""
*DO NOT USE*
This helper class is used to tell the client about our custom field attributes via OpenAPI
@ -163,6 +204,7 @@ class _OutputField(BaseModel):
purpose in the backend.
"""
field_kind: FieldKind
ui_hidden: bool
ui_type: Optional[UIType]
ui_order: Optional[int]
@ -180,6 +222,7 @@ def get_type(klass: BaseModel) -> str:
def InputField(
# copied from pydantic's Field
# TODO: Can we support default_factory?
default: Any = _Unset,
default_factory: Callable[[], Any] | None = _Unset,
title: str | None = _Unset,
@ -203,12 +246,11 @@ def InputField(
ui_hidden: bool = False,
ui_order: Optional[int] = None,
ui_choice_labels: Optional[dict[str, str]] = None,
item_default: Optional[Any] = None,
) -> Any:
"""
Creates an input field for an invocation.
This is a wrapper for Pydantic's [Field](https://docs.pydantic.dev/1.10/usage/schema/#field-customization) \
This is a wrapper for Pydantic's [Field](https://docs.pydantic.dev/latest/api/fields/#pydantic.fields.Field) \
that adds a few extra parameters to support graph execution and the node editor UI.
:param Input input: [Input.Any] The kind of input this field requires. \
@ -228,28 +270,59 @@ def InputField(
For example, a `string` field will default to a single-line input, but you may want a multi-line textarea instead. \
For this case, you could provide `UIComponent.Textarea`.
: param bool ui_hidden: [False] Specifies whether or not this field should be hidden in the UI.
:param bool ui_hidden: [False] Specifies whether or not this field should be hidden in the UI.
: param int ui_order: [None] Specifies the order in which this field should be rendered in the UI. \
:param int ui_order: [None] Specifies the order in which this field should be rendered in the UI.
: param bool item_default: [None] Specifies the default item value, if this is a collection input. \
Ignored for non-collection fields.
:param dict[str, str] ui_choice_labels: [None] Specifies the labels to use for the choices in an enum field.
"""
json_schema_extra_: dict[str, Any] = {
"input": input,
"ui_type": ui_type,
"ui_component": ui_component,
"ui_hidden": ui_hidden,
"ui_order": ui_order,
"item_default": item_default,
"ui_choice_labels": ui_choice_labels,
"_field_kind": "input",
}
json_schema_extra_ = InputFieldJSONSchemaExtra(
input=input,
ui_type=ui_type,
ui_component=ui_component,
ui_hidden=ui_hidden,
ui_order=ui_order,
ui_choice_labels=ui_choice_labels,
field_kind=FieldKind.Input,
orig_required=True,
)
"""
There is a conflict between the typing of invocation definitions and the typing of an invocation's
`invoke()` function.
On instantiation of a node, the invocation definition is used to create the python class. At this time,
any number of fields may be optional, because they may be provided by connections.
On calling of `invoke()`, however, those fields may be required.
For example, consider an ResizeImageInvocation with an `image: ImageField` field.
`image` is required during the call to `invoke()`, but when the python class is instantiated,
the field may not be present. This is fine, because that image field will be provided by a
connection from an ancestor node, which outputs an image.
This means we want to type the `image` field as optional for the node class definition, but required
for the `invoke()` function.
If we use `typing.Optional` in the node class definition, the field will be typed as optional in the
`invoke()` method, and we'll have to do a lot of runtime checks to ensure the field is present - or
any static type analysis tools will complain.
To get around this, in node class definitions, we type all fields correctly for the `invoke()` function,
but secretly make them optional in `InputField()`. We also store the original required bool and/or default
value. When we call `invoke()`, we use this stored information to do an additional check on the class.
"""
if default_factory is not _Unset and default_factory is not None:
default = default_factory()
del default_factory
logger.warn('"default_factory" is not supported, calling it now to set "default"')
# These are the args we may wish pass to the pydantic `Field()` function
field_args = {
"default": default,
"default_factory": default_factory,
"title": title,
"description": description,
"pattern": pattern,
@ -266,70 +339,34 @@ def InputField(
"max_length": max_length,
}
"""
Invocation definitions have their fields typed correctly for their `invoke()` functions.
This typing is often more specific than the actual invocation definition requires, because
fields may have values provided only by connections.
For example, consider an ResizeImageInvocation with an `image: ImageField` field.
`image` is required during the call to `invoke()`, but when the python class is instantiated,
the field may not be present. This is fine, because that image field will be provided by a
an ancestor node that outputs the image.
So we'd like to type that `image` field as `Optional[ImageField]`. If we do that, however, then
we need to handle a lot of extra logic in the `invoke()` function to check if the field has a
value or not. This is very tedious.
Ideally, the invocation definition would be able to specify that the field is required during
invocation, but optional during instantiation. So the field would be typed as `image: ImageField`,
but when calling the `invoke()` function, we raise an error if the field is not present.
To do this, we need to do a bit of fanagling to make the pydantic field optional, and then do
extra validation when calling `invoke()`.
There is some additional logic here to cleaning create the pydantic field via the wrapper.
"""
# Filter out field args not provided
# We only want to pass the args that were provided, otherwise the `Field()`` function won't work as expected
provided_args = {k: v for (k, v) in field_args.items() if v is not PydanticUndefined}
if (default is not PydanticUndefined) and (default_factory is not PydanticUndefined):
raise ValueError("Cannot specify both default and default_factory")
# Because we are manually making fields optional, we need to store the original required bool for reference later
json_schema_extra_.orig_required = default is PydanticUndefined
# because we are manually making fields optional, we need to store the original required bool for reference later
if default is PydanticUndefined and default_factory is PydanticUndefined:
json_schema_extra_.update({"orig_required": True})
else:
json_schema_extra_.update({"orig_required": False})
# make Input.Any and Input.Connection fields optional, providing None as a default if the field doesn't already have one
if (input is Input.Any or input is Input.Connection) and default_factory is PydanticUndefined:
# Make Input.Any and Input.Connection fields optional, providing None as a default if the field doesn't already have one
if input is Input.Any or input is Input.Connection:
default_ = None if default is PydanticUndefined else default
provided_args.update({"default": default_})
if default is not PydanticUndefined:
# before invoking, we'll grab the original default value and set it on the field if the field wasn't provided a value
json_schema_extra_.update({"default": default})
json_schema_extra_.update({"orig_default": default})
elif default is not PydanticUndefined and default_factory is PydanticUndefined:
# Before invoking, we'll check for the original default value and set it on the field if the field has no value
json_schema_extra_.default = default
json_schema_extra_.orig_default = default
elif default is not PydanticUndefined:
default_ = default
provided_args.update({"default": default_})
json_schema_extra_.update({"orig_default": default_})
elif default_factory is not PydanticUndefined:
provided_args.update({"default_factory": default_factory})
# TODO: cannot serialize default_factory...
# json_schema_extra_.update(dict(orig_default_factory=default_factory))
json_schema_extra_.orig_default = default_
return Field(
**provided_args,
json_schema_extra=json_schema_extra_,
json_schema_extra=json_schema_extra_.model_dump(exclude_none=True),
)
def OutputField(
# copied from pydantic's Field
default: Any = _Unset,
default_factory: Callable[[], Any] | None = _Unset,
title: str | None = _Unset,
description: str | None = _Unset,
pattern: str | None = _Unset,
@ -362,13 +399,12 @@ def OutputField(
`MainModelField`. So to ensure the base-model-specific UI is rendered, you can use \
`UIType.SDXLMainModelField` to indicate that the field is an SDXL main model field.
: param bool ui_hidden: [False] Specifies whether or not this field should be hidden in the UI. \
:param bool ui_hidden: [False] Specifies whether or not this field should be hidden in the UI. \
: param int ui_order: [None] Specifies the order in which this field should be rendered in the UI. \
:param int ui_order: [None] Specifies the order in which this field should be rendered in the UI. \
"""
return Field(
default=default,
default_factory=default_factory,
title=title,
description=description,
pattern=pattern,
@ -383,12 +419,12 @@ def OutputField(
decimal_places=decimal_places,
min_length=min_length,
max_length=max_length,
json_schema_extra={
"ui_type": ui_type,
"ui_hidden": ui_hidden,
"ui_order": ui_order,
"_field_kind": "output",
},
json_schema_extra=OutputFieldJSONSchemaExtra(
ui_type=ui_type,
ui_hidden=ui_hidden,
ui_order=ui_order,
field_kind=FieldKind.Output,
).model_dump(exclude_none=True),
)
@ -538,7 +574,7 @@ class BaseInvocation(ABC, BaseModel):
return signature(cls.invoke).return_annotation
@staticmethod
def json_schema_extra(schema: dict[str, Any], model_class: Type[BaseModel]) -> None:
def json_schema_extra(schema: dict[str, Any], model_class: Type[BaseModel], *args, **kwargs) -> None:
# Add the various UI-facing attributes to the schema. These are used to build the invocation templates.
uiconfig = getattr(model_class, "UIConfig", None)
if uiconfig and hasattr(uiconfig, "title"):
@ -604,15 +640,17 @@ class BaseInvocation(ABC, BaseModel):
id: str = Field(
default_factory=uuid_string,
description="The id of this instance of an invocation. Must be unique among all instances of invocations.",
json_schema_extra={"_field_kind": "internal"},
json_schema_extra={"field_kind": FieldKind.NodeAttribute},
)
is_intermediate: bool = Field(
default=False,
description="Whether or not this is an intermediate invocation.",
json_schema_extra={"ui_type": UIType.IsIntermediate, "_field_kind": "internal"},
json_schema_extra={"ui_type": "IsIntermediate", "field_kind": FieldKind.NodeAttribute},
)
use_cache: bool = Field(
default=True, description="Whether or not to use the cache", json_schema_extra={"_field_kind": "internal"}
default=True,
description="Whether or not to use the cache",
json_schema_extra={"field_kind": FieldKind.NodeAttribute},
)
UIConfig: ClassVar[Type[UIConfigBase]]
@ -629,12 +667,15 @@ class BaseInvocation(ABC, BaseModel):
TBaseInvocation = TypeVar("TBaseInvocation", bound=BaseInvocation)
RESERVED_INPUT_FIELD_NAMES = {
RESERVED_NODE_ATTRIBUTE_FIELD_NAMES = {
"id",
"is_intermediate",
"use_cache",
"type",
"workflow",
}
RESERVED_INPUT_FIELD_NAMES = {
"metadata",
}
@ -653,39 +694,56 @@ def validate_fields(model_fields: dict[str, FieldInfo], model_type: str) -> None
"""
Validates the fields of an invocation or invocation output:
- must not override any pydantic reserved fields
- must not end with "Collection" or "Polymorphic" as these are reserved for internal use
- must be created via `InputField`, `OutputField`, or be an internal field defined in this file
"""
for name, field in model_fields.items():
if name in RESERVED_PYDANTIC_FIELD_NAMES:
raise InvalidFieldError(f'Invalid field name "{name}" on "{model_type}" (reserved by pydantic)')
field_kind = (
# _field_kind is defined via InputField(), OutputField() or by one of the internal fields defined in this file
field.json_schema_extra.get("_field_kind", None) if field.json_schema_extra else None
)
if not field.annotation:
raise InvalidFieldError(f'Invalid field type "{name}" on "{model_type}" (missing annotation)')
if not isinstance(field.json_schema_extra, dict):
raise InvalidFieldError(
f'Invalid field definition for "{name}" on "{model_type}" (missing json_schema_extra dict)'
)
field_kind = field.json_schema_extra.get("field_kind", None)
# must have a field_kind
if field_kind is None or field_kind not in {"input", "output", "internal"}:
if not isinstance(field_kind, FieldKind):
raise InvalidFieldError(
f'Invalid field definition for "{name}" on "{model_type}" (maybe it\'s not an InputField or OutputField?)'
)
if field_kind == "input" and name in RESERVED_INPUT_FIELD_NAMES:
if field_kind is FieldKind.Input and (
name in RESERVED_NODE_ATTRIBUTE_FIELD_NAMES or name in RESERVED_INPUT_FIELD_NAMES
):
raise InvalidFieldError(f'Invalid field name "{name}" on "{model_type}" (reserved input field name)')
if field_kind == "output" and name in RESERVED_OUTPUT_FIELD_NAMES:
if field_kind is FieldKind.Output and name in RESERVED_OUTPUT_FIELD_NAMES:
raise InvalidFieldError(f'Invalid field name "{name}" on "{model_type}" (reserved output field name)')
# internal fields *must* be in the reserved list
if (
field_kind == "internal"
and name not in RESERVED_INPUT_FIELD_NAMES
and name not in RESERVED_OUTPUT_FIELD_NAMES
):
if (field_kind is FieldKind.Internal) and name not in RESERVED_INPUT_FIELD_NAMES:
raise InvalidFieldError(
f'Invalid field name "{name}" on "{model_type}" (internal field without reserved name)'
)
# node attribute fields *must* be in the reserved list
if (
field_kind is FieldKind.NodeAttribute
and name not in RESERVED_NODE_ATTRIBUTE_FIELD_NAMES
and name not in RESERVED_OUTPUT_FIELD_NAMES
):
raise InvalidFieldError(
f'Invalid field name "{name}" on "{model_type}" (node attribute field without reserved name)'
)
ui_type = field.json_schema_extra.get("ui_type", None)
if isinstance(ui_type, str) and ui_type.startswith("DEPRECATED_"):
logger.warn(f"\"UIType.{ui_type.split('_')[-1]}\" is deprecated, ignoring")
field.json_schema_extra.pop("ui_type")
return None
@ -749,7 +807,7 @@ def invocation(
invocation_type_annotation = Literal[invocation_type] # type: ignore
invocation_type_field = Field(
title="type", default=invocation_type, json_schema_extra={"_field_kind": "internal"}
title="type", default=invocation_type, json_schema_extra={"field_kind": FieldKind.NodeAttribute}
)
docstring = cls.__doc__
@ -795,7 +853,9 @@ def invocation_output(
# Add the output type to the model.
output_type_annotation = Literal[output_type] # type: ignore
output_type_field = Field(title="type", default=output_type, json_schema_extra={"_field_kind": "internal"})
output_type_field = Field(
title="type", default=output_type, json_schema_extra={"field_kind": FieldKind.NodeAttribute}
)
docstring = cls.__doc__
cls = create_model(
@ -827,7 +887,7 @@ WorkflowFieldValidator = TypeAdapter(WorkflowField)
class WithWorkflow(BaseModel):
workflow: Optional[WorkflowField] = Field(
default=None, description=FieldDescriptions.workflow, json_schema_extra={"_field_kind": "internal"}
default=None, description=FieldDescriptions.workflow, json_schema_extra={"field_kind": FieldKind.NodeAttribute}
)
@ -845,5 +905,11 @@ MetadataFieldValidator = TypeAdapter(MetadataField)
class WithMetadata(BaseModel):
metadata: Optional[MetadataField] = Field(
default=None, description=FieldDescriptions.metadata, json_schema_extra={"_field_kind": "internal"}
default=None,
description=FieldDescriptions.metadata,
json_schema_extra=InputFieldJSONSchemaExtra(
field_kind=FieldKind.Internal,
input=Input.Connection,
orig_required=False,
).model_dump(exclude_none=True),
)