mirror of
https://github.com/invoke-ai/InvokeAI
synced 2024-08-30 20:32:17 +00:00
tidy(nodes): move all field things to fields.py
Unfortunately, this is necessary to prevent circular imports at runtime.
This commit is contained in:
psychedelicious
@ -12,10 +12,11 @@ from types import UnionType
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from typing import TYPE_CHECKING, Any, Callable, ClassVar, Iterable, Literal, Optional, Type, TypeVar, Union, cast
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import semver
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from pydantic import BaseModel, ConfigDict, Field, RootModel, TypeAdapter, create_model
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from pydantic.fields import FieldInfo, _Unset
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from pydantic import BaseModel, ConfigDict, Field, create_model
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from pydantic.fields import FieldInfo
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from pydantic_core import PydanticUndefined
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from invokeai.app.invocations.fields import FieldKind, Input
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from invokeai.app.services.config.config_default import InvokeAIAppConfig
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from invokeai.app.services.workflow_records.workflow_records_common import WorkflowWithoutID
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from invokeai.app.shared.fields import FieldDescriptions
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@ -52,393 +53,6 @@ class Classification(str, Enum, metaclass=MetaEnum):
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Prototype = "prototype"
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class Input(str, Enum, metaclass=MetaEnum):
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"""
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The type of input a field accepts.
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- `Input.Direct`: The field must have its value provided directly, when the invocation and field \
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are instantiated.
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- `Input.Connection`: The field must have its value provided by a connection.
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- `Input.Any`: The field may have its value provided either directly or by a connection.
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"""
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Connection = "connection"
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Direct = "direct"
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Any = "any"
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class FieldKind(str, Enum, metaclass=MetaEnum):
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"""
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The kind of field.
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- `Input`: An input field on a node.
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- `Output`: An output field on a node.
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- `Internal`: A field which is treated as an input, but cannot be used in node definitions. Metadata is
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one example. It is provided to nodes via the WithMetadata class, and we want to reserve the field name
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"metadata" for this on all nodes. `FieldKind` is used to short-circuit the field name validation logic,
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allowing "metadata" for that field.
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- `NodeAttribute`: The field is a node attribute. These are fields which are not inputs or outputs,
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but which are used to store information about the node. For example, the `id` and `type` fields are node
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attributes.
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The presence of this in `json_schema_extra["field_kind"]` is used when initializing node schemas on app
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startup, and when generating the OpenAPI schema for the workflow editor.
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"""
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Input = "input"
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Output = "output"
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Internal = "internal"
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NodeAttribute = "node_attribute"
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class UIType(str, Enum, metaclass=MetaEnum):
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"""
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Type hints for the UI for situations in which the field type is not enough to infer the correct UI type.
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- Model Fields
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The most common node-author-facing use will be for model fields. Internally, there is no difference
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between SD-1, SD-2 and SDXL model fields - they all use the class `MainModelField`. To ensure the
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base-model-specific UI is rendered, use e.g. `ui_type=UIType.SDXLMainModelField` to indicate that
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the field is an SDXL main model field.
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- Any Field
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We cannot infer the usage of `typing.Any` via schema parsing, so you *must* use `ui_type=UIType.Any` to
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indicate that the field accepts any type. Use with caution. This cannot be used on outputs.
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- Scheduler Field
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Special handling in the UI is needed for this field, which otherwise would be parsed as a plain enum field.
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- Internal Fields
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Similar to the Any Field, the `collect` and `iterate` nodes make use of `typing.Any`. To facilitate
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handling these types in the client, we use `UIType._Collection` and `UIType._CollectionItem`. These
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should not be used by node authors.
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- DEPRECATED Fields
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These types are deprecated and should not be used by node authors. A warning will be logged if one is
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used, and the type will be ignored. They are included here for backwards compatibility.
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"""
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# region Model Field Types
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SDXLMainModel = "SDXLMainModelField"
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SDXLRefinerModel = "SDXLRefinerModelField"
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ONNXModel = "ONNXModelField"
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VaeModel = "VAEModelField"
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LoRAModel = "LoRAModelField"
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ControlNetModel = "ControlNetModelField"
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IPAdapterModel = "IPAdapterModelField"
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# endregion
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# region Misc Field Types
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Scheduler = "SchedulerField"
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Any = "AnyField"
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# endregion
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# region Internal Field Types
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_Collection = "CollectionField"
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_CollectionItem = "CollectionItemField"
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# endregion
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# region DEPRECATED
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Boolean = "DEPRECATED_Boolean"
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Color = "DEPRECATED_Color"
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Conditioning = "DEPRECATED_Conditioning"
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Control = "DEPRECATED_Control"
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Float = "DEPRECATED_Float"
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Image = "DEPRECATED_Image"
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Integer = "DEPRECATED_Integer"
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Latents = "DEPRECATED_Latents"
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String = "DEPRECATED_String"
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BooleanCollection = "DEPRECATED_BooleanCollection"
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ColorCollection = "DEPRECATED_ColorCollection"
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ConditioningCollection = "DEPRECATED_ConditioningCollection"
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ControlCollection = "DEPRECATED_ControlCollection"
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FloatCollection = "DEPRECATED_FloatCollection"
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ImageCollection = "DEPRECATED_ImageCollection"
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IntegerCollection = "DEPRECATED_IntegerCollection"
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LatentsCollection = "DEPRECATED_LatentsCollection"
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StringCollection = "DEPRECATED_StringCollection"
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BooleanPolymorphic = "DEPRECATED_BooleanPolymorphic"
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ColorPolymorphic = "DEPRECATED_ColorPolymorphic"
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ConditioningPolymorphic = "DEPRECATED_ConditioningPolymorphic"
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ControlPolymorphic = "DEPRECATED_ControlPolymorphic"
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FloatPolymorphic = "DEPRECATED_FloatPolymorphic"
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ImagePolymorphic = "DEPRECATED_ImagePolymorphic"
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IntegerPolymorphic = "DEPRECATED_IntegerPolymorphic"
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LatentsPolymorphic = "DEPRECATED_LatentsPolymorphic"
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StringPolymorphic = "DEPRECATED_StringPolymorphic"
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MainModel = "DEPRECATED_MainModel"
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UNet = "DEPRECATED_UNet"
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Vae = "DEPRECATED_Vae"
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CLIP = "DEPRECATED_CLIP"
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Collection = "DEPRECATED_Collection"
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CollectionItem = "DEPRECATED_CollectionItem"
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Enum = "DEPRECATED_Enum"
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WorkflowField = "DEPRECATED_WorkflowField"
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IsIntermediate = "DEPRECATED_IsIntermediate"
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BoardField = "DEPRECATED_BoardField"
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MetadataItem = "DEPRECATED_MetadataItem"
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MetadataItemCollection = "DEPRECATED_MetadataItemCollection"
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MetadataItemPolymorphic = "DEPRECATED_MetadataItemPolymorphic"
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MetadataDict = "DEPRECATED_MetadataDict"
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# endregion
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class UIComponent(str, Enum, metaclass=MetaEnum):
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"""
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The type of UI component to use for a field, used to override the default components, which are
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inferred from the field type.
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"""
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None_ = "none"
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Textarea = "textarea"
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Slider = "slider"
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class InputFieldJSONSchemaExtra(BaseModel):
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"""
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Extra attributes to be added to input fields and their OpenAPI schema. Used during graph execution,
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and by the workflow editor during schema parsing and UI rendering.
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"""
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input: Input
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orig_required: bool
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field_kind: FieldKind
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default: Optional[Any] = None
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orig_default: Optional[Any] = None
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ui_hidden: bool = False
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ui_type: Optional[UIType] = None
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ui_component: Optional[UIComponent] = None
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ui_order: Optional[int] = None
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ui_choice_labels: Optional[dict[str, str]] = None
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model_config = ConfigDict(
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validate_assignment=True,
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json_schema_serialization_defaults_required=True,
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)
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class OutputFieldJSONSchemaExtra(BaseModel):
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"""
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Extra attributes to be added to input fields and their OpenAPI schema. Used by the workflow editor
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during schema parsing and UI rendering.
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"""
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field_kind: FieldKind
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ui_hidden: bool
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ui_type: Optional[UIType]
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ui_order: Optional[int]
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model_config = ConfigDict(
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validate_assignment=True,
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json_schema_serialization_defaults_required=True,
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)
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def InputField(
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# copied from pydantic's Field
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# TODO: Can we support default_factory?
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default: Any = _Unset,
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default_factory: Callable[[], Any] | None = _Unset,
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title: str | None = _Unset,
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description: str | None = _Unset,
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pattern: str | None = _Unset,
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strict: bool | None = _Unset,
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gt: float | None = _Unset,
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ge: float | None = _Unset,
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lt: float | None = _Unset,
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le: float | None = _Unset,
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multiple_of: float | None = _Unset,
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allow_inf_nan: bool | None = _Unset,
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max_digits: int | None = _Unset,
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decimal_places: int | None = _Unset,
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min_length: int | None = _Unset,
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max_length: int | None = _Unset,
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# custom
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input: Input = Input.Any,
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ui_type: Optional[UIType] = None,
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ui_component: Optional[UIComponent] = None,
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ui_hidden: bool = False,
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ui_order: Optional[int] = None,
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ui_choice_labels: Optional[dict[str, str]] = None,
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) -> Any:
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"""
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Creates an input field for an invocation.
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This is a wrapper for Pydantic's [Field](https://docs.pydantic.dev/latest/api/fields/#pydantic.fields.Field) \
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that adds a few extra parameters to support graph execution and the node editor UI.
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:param Input input: [Input.Any] The kind of input this field requires. \
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`Input.Direct` means a value must be provided on instantiation. \
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`Input.Connection` means the value must be provided by a connection. \
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`Input.Any` means either will do.
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:param UIType ui_type: [None] Optionally provides an extra type hint for the UI. \
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In some situations, the field's type is not enough to infer the correct UI type. \
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For example, model selection fields should render a dropdown UI component to select a model. \
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Internally, there is no difference between SD-1, SD-2 and SDXL model fields, they all use \
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`MainModelField`. So to ensure the base-model-specific UI is rendered, you can use \
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`UIType.SDXLMainModelField` to indicate that the field is an SDXL main model field.
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:param UIComponent ui_component: [None] Optionally specifies a specific component to use in the UI. \
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The UI will always render a suitable component, but sometimes you want something different than the default. \
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For example, a `string` field will default to a single-line input, but you may want a multi-line textarea instead. \
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For this case, you could provide `UIComponent.Textarea`.
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:param bool ui_hidden: [False] Specifies whether or not this field should be hidden in the UI.
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:param int ui_order: [None] Specifies the order in which this field should be rendered in the UI.
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:param dict[str, str] ui_choice_labels: [None] Specifies the labels to use for the choices in an enum field.
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"""
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json_schema_extra_ = InputFieldJSONSchemaExtra(
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input=input,
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ui_type=ui_type,
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ui_component=ui_component,
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ui_hidden=ui_hidden,
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ui_order=ui_order,
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ui_choice_labels=ui_choice_labels,
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field_kind=FieldKind.Input,
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orig_required=True,
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)
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"""
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There is a conflict between the typing of invocation definitions and the typing of an invocation's
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`invoke()` function.
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On instantiation of a node, the invocation definition is used to create the python class. At this time,
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any number of fields may be optional, because they may be provided by connections.
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On calling of `invoke()`, however, those fields may be required.
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For example, consider an ResizeImageInvocation with an `image: ImageField` field.
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`image` is required during the call to `invoke()`, but when the python class is instantiated,
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the field may not be present. This is fine, because that image field will be provided by a
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connection from an ancestor node, which outputs an image.
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This means we want to type the `image` field as optional for the node class definition, but required
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for the `invoke()` function.
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If we use `typing.Optional` in the node class definition, the field will be typed as optional in the
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`invoke()` method, and we'll have to do a lot of runtime checks to ensure the field is present - or
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any static type analysis tools will complain.
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To get around this, in node class definitions, we type all fields correctly for the `invoke()` function,
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but secretly make them optional in `InputField()`. We also store the original required bool and/or default
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value. When we call `invoke()`, we use this stored information to do an additional check on the class.
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"""
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if default_factory is not _Unset and default_factory is not None:
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default = default_factory()
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logger.warn('"default_factory" is not supported, calling it now to set "default"')
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# These are the args we may wish pass to the pydantic `Field()` function
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field_args = {
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"default": default,
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"title": title,
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"description": description,
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"pattern": pattern,
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"strict": strict,
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"gt": gt,
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"ge": ge,
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"lt": lt,
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"le": le,
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"multiple_of": multiple_of,
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"allow_inf_nan": allow_inf_nan,
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"max_digits": max_digits,
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"decimal_places": decimal_places,
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"min_length": min_length,
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"max_length": max_length,
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}
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# We only want to pass the args that were provided, otherwise the `Field()`` function won't work as expected
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provided_args = {k: v for (k, v) in field_args.items() if v is not PydanticUndefined}
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# Because we are manually making fields optional, we need to store the original required bool for reference later
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json_schema_extra_.orig_required = default is PydanticUndefined
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# Make Input.Any and Input.Connection fields optional, providing None as a default if the field doesn't already have one
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if input is Input.Any or input is Input.Connection:
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default_ = None if default is PydanticUndefined else default
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provided_args.update({"default": default_})
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if default is not PydanticUndefined:
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# Before invoking, we'll check for the original default value and set it on the field if the field has no value
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json_schema_extra_.default = default
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json_schema_extra_.orig_default = default
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elif default is not PydanticUndefined:
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default_ = default
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provided_args.update({"default": default_})
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json_schema_extra_.orig_default = default_
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return Field(
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**provided_args,
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json_schema_extra=json_schema_extra_.model_dump(exclude_none=True),
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)
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def OutputField(
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# copied from pydantic's Field
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default: Any = _Unset,
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title: str | None = _Unset,
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description: str | None = _Unset,
|
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pattern: str | None = _Unset,
|
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strict: bool | None = _Unset,
|
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gt: float | None = _Unset,
|
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ge: float | None = _Unset,
|
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lt: float | None = _Unset,
|
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le: float | None = _Unset,
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multiple_of: float | None = _Unset,
|
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allow_inf_nan: bool | None = _Unset,
|
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max_digits: int | None = _Unset,
|
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decimal_places: int | None = _Unset,
|
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min_length: int | None = _Unset,
|
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max_length: int | None = _Unset,
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# custom
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ui_type: Optional[UIType] = None,
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ui_hidden: bool = False,
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ui_order: Optional[int] = None,
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) -> Any:
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"""
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Creates an output field for an invocation output.
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|
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This is a wrapper for Pydantic's [Field](https://docs.pydantic.dev/1.10/usage/schema/#field-customization) \
|
||||
that adds a few extra parameters to support graph execution and the node editor UI.
|
||||
|
||||
:param UIType ui_type: [None] Optionally provides an extra type hint for the UI. \
|
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In some situations, the field's type is not enough to infer the correct UI type. \
|
||||
For example, model selection fields should render a dropdown UI component to select a model. \
|
||||
Internally, there is no difference between SD-1, SD-2 and SDXL model fields, they all use \
|
||||
`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. \
|
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|
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:param int ui_order: [None] Specifies the order in which this field should be rendered in the UI. \
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"""
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return Field(
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default=default,
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title=title,
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description=description,
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pattern=pattern,
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strict=strict,
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gt=gt,
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ge=ge,
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lt=lt,
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le=le,
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multiple_of=multiple_of,
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allow_inf_nan=allow_inf_nan,
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max_digits=max_digits,
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decimal_places=decimal_places,
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min_length=min_length,
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max_length=max_length,
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json_schema_extra=OutputFieldJSONSchemaExtra(
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ui_type=ui_type,
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ui_hidden=ui_hidden,
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ui_order=ui_order,
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field_kind=FieldKind.Output,
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).model_dump(exclude_none=True),
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)
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class UIConfigBase(BaseModel):
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"""
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Provides additional node configuration to the UI.
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@ -460,33 +74,6 @@ class UIConfigBase(BaseModel):
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)
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class InvocationContext:
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"""Initialized and provided to on execution of invocations."""
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||||
|
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services: InvocationServices
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graph_execution_state_id: str
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||||
queue_id: str
|
||||
queue_item_id: int
|
||||
queue_batch_id: str
|
||||
workflow: Optional[WorkflowWithoutID]
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||||
|
||||
def __init__(
|
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self,
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services: InvocationServices,
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queue_id: str,
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||||
queue_item_id: int,
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||||
queue_batch_id: str,
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||||
graph_execution_state_id: str,
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workflow: Optional[WorkflowWithoutID],
|
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):
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self.services = services
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||||
self.graph_execution_state_id = graph_execution_state_id
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||||
self.queue_id = queue_id
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||||
self.queue_item_id = queue_item_id
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self.queue_batch_id = queue_batch_id
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||||
self.workflow = workflow
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||||
|
||||
|
||||
class BaseInvocationOutput(BaseModel):
|
||||
"""
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||||
Base class for all invocation outputs.
|
||||
@ -926,37 +513,3 @@ def invocation_output(
|
||||
return cls
|
||||
|
||||
return wrapper
|
||||
|
||||
|
||||
class MetadataField(RootModel):
|
||||
"""
|
||||
Pydantic model for metadata with custom root of type dict[str, Any].
|
||||
Metadata is stored without a strict schema.
|
||||
"""
|
||||
|
||||
root: dict[str, Any] = Field(description="The metadata")
|
||||
|
||||
|
||||
MetadataFieldValidator = TypeAdapter(MetadataField)
|
||||
|
||||
|
||||
class WithMetadata(BaseModel):
|
||||
metadata: Optional[MetadataField] = Field(
|
||||
default=None,
|
||||
description=FieldDescriptions.metadata,
|
||||
json_schema_extra=InputFieldJSONSchemaExtra(
|
||||
field_kind=FieldKind.Internal,
|
||||
input=Input.Connection,
|
||||
orig_required=False,
|
||||
).model_dump(exclude_none=True),
|
||||
)
|
||||
|
||||
|
||||
class WithWorkflow:
|
||||
workflow = None
|
||||
|
||||
def __init_subclass__(cls) -> None:
|
||||
logger.warn(
|
||||
f"{cls.__module__.split('.')[0]}.{cls.__name__}: WithWorkflow is deprecated. Use `context.workflow` to access the workflow."
|
||||
)
|
||||
super().__init_subclass__()
|
||||
|
||||
Reference in New Issue
Block a user