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tidy(nodes): move all field things to fields.py

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Unfortunately, this is necessary to prevent circular imports at runtime.
This commit is contained in:
psychedelicious 2024-01-13 15:23:06 +11:00
parent 63ab5ff5a2
commit 992b02aa65
36 changed files with 552 additions and 608 deletions
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2
invokeai/app/api/routers/images.py
View File

@ -8,7 +8,7 @@ from fastapi.routing import APIRouter
from PIL import Image
from pydantic import BaseModel, Field, ValidationError
from invokeai.app.invocations.baseinvocation import MetadataField, MetadataFieldValidator
from invokeai.app.invocations.fields import MetadataField, MetadataFieldValidator
from invokeai.app.services.image_records.image_records_common import ImageCategory, ImageRecordChanges, ResourceOrigin
from invokeai.app.services.images.images_common import ImageDTO, ImageUrlsDTO
from invokeai.app.services.shared.pagination import OffsetPaginatedResults

3
invokeai/app/api_app.py
View File

@ -6,6 +6,7 @@ import sys
from invokeai.app.api.no_cache_staticfiles import NoCacheStaticFiles
from invokeai.version.invokeai_version import __version__
from .invocations.fields import InputFieldJSONSchemaExtra, OutputFieldJSONSchemaExtra
from .services.config import InvokeAIAppConfig
app_config = InvokeAIAppConfig.get_config()
@ -57,8 +58,6 @@ if True: # hack to make flake8 happy with imports coming after setting up the c
from .api.sockets import SocketIO
from .invocations.baseinvocation import (
BaseInvocation,
InputFieldJSONSchemaExtra,
OutputFieldJSONSchemaExtra,
UIConfigBase,
)

453
invokeai/app/invocations/baseinvocation.py
View File

@ -12,10 +12,11 @@ from types import UnionType
from typing import TYPE_CHECKING, Any, Callable, ClassVar, Iterable, Literal, Optional, Type, TypeVar, Union, cast
import semver
from pydantic import BaseModel, ConfigDict, Field, RootModel, TypeAdapter, create_model
from pydantic.fields import FieldInfo, _Unset
from pydantic import BaseModel, ConfigDict, Field, create_model
from pydantic.fields import FieldInfo
from pydantic_core import PydanticUndefined
from invokeai.app.invocations.fields import FieldKind, Input
from invokeai.app.services.config.config_default import InvokeAIAppConfig
from invokeai.app.services.workflow_records.workflow_records_common import WorkflowWithoutID
from invokeai.app.shared.fields import FieldDescriptions
@ -52,393 +53,6 @@ class Classification(str, Enum, metaclass=MetaEnum):
Prototype = "prototype"
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 \
are instantiated.
- `Input.Connection`: The field must have its value provided by a connection.
- `Input.Any`: The field may have its value provided either directly or by a connection.
"""
Connection = "connection"
Direct = "direct"
Any = "any"
class FieldKind(str, Enum, metaclass=MetaEnum):
"""
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.
"""
Input = "input"
Output = "output"
Internal = "internal"
NodeAttribute = "node_attribute"
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.
- 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.
- DEPRECATED Fields
These types are deprecated and should not be used by node authors. A warning will be logged if one is
used, and the type will be ignored. They are included here for backwards compatibility.
"""
# region Model Field Types
SDXLMainModel = "SDXLMainModelField"
SDXLRefinerModel = "SDXLRefinerModelField"
ONNXModel = "ONNXModelField"
VaeModel = "VAEModelField"
LoRAModel = "LoRAModelField"
ControlNetModel = "ControlNetModelField"
IPAdapterModel = "IPAdapterModelField"
# endregion
# region Misc Field Types
Scheduler = "SchedulerField"
Any = "AnyField"
# endregion
# 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, metaclass=MetaEnum):
"""
The type of UI component to use for a field, used to override the default components, which are
inferred from the field type.
"""
None_ = "none"
Textarea = "textarea"
Slider = "slider"
class InputFieldJSONSchemaExtra(BaseModel):
"""
Extra attributes to be added to input fields and their OpenAPI schema. Used during graph execution,
and by the workflow editor during schema parsing and UI rendering.
"""
input: Input
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,
json_schema_serialization_defaults_required=True,
)
class OutputFieldJSONSchemaExtra(BaseModel):
"""
Extra attributes to be added to input fields and their OpenAPI schema. Used by the workflow editor
during schema parsing and UI rendering.
"""
field_kind: FieldKind
ui_hidden: bool
ui_type: Optional[UIType]
ui_order: Optional[int]
model_config = ConfigDict(
validate_assignment=True,
json_schema_serialization_defaults_required=True,
)
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,
description: str | None = _Unset,
pattern: str | None = _Unset,
strict: bool | None = _Unset,
gt: float | None = _Unset,
ge: float | None = _Unset,
lt: float | None = _Unset,
le: float | None = _Unset,
multiple_of: float | None = _Unset,
allow_inf_nan: bool | None = _Unset,
max_digits: int | None = _Unset,
decimal_places: int | None = _Unset,
min_length: int | None = _Unset,
max_length: int | None = _Unset,
# custom
input: Input = Input.Any,
ui_type: Optional[UIType] = None,
ui_component: Optional[UIComponent] = None,
ui_hidden: bool = False,
ui_order: Optional[int] = None,
ui_choice_labels: Optional[dict[str, str]] = None,
) -> Any:
"""
Creates an input field for an invocation.
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. \
`Input.Direct` means a value must be provided on instantiation. \
`Input.Connection` means the value must be provided by a connection. \
`Input.Any` means either will do.
:param UIType ui_type: [None] Optionally provides an extra type hint for the UI. \
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 UIComponent ui_component: [None] Optionally specifies a specific component to use in the UI. \
The UI will always render a suitable component, but sometimes you want something different than the default. \
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 int ui_order: [None] Specifies the order in which this field should be rendered in the UI.
:param dict[str, str] ui_choice_labels: [None] Specifies the labels to use for the choices in an enum field.
"""
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()
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,
"title": title,
"description": description,
"pattern": pattern,
"strict": strict,
"gt": gt,
"ge": ge,
"lt": lt,
"le": le,
"multiple_of": multiple_of,
"allow_inf_nan": allow_inf_nan,
"max_digits": max_digits,
"decimal_places": decimal_places,
"min_length": min_length,
"max_length": max_length,
}
# 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}
# 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
# 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 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_.orig_default = default_
return Field(
**provided_args,
json_schema_extra=json_schema_extra_.model_dump(exclude_none=True),
)
def OutputField(
# copied from pydantic's Field
default: Any = _Unset,
title: str | None = _Unset,
description: str | None = _Unset,
pattern: str | None = _Unset,
strict: bool | None = _Unset,
gt: float | None = _Unset,
ge: float | None = _Unset,
lt: float | None = _Unset,
le: float | None = _Unset,
multiple_of: float | None = _Unset,
allow_inf_nan: bool | None = _Unset,
max_digits: int | None = _Unset,
decimal_places: int | None = _Unset,
min_length: int | None = _Unset,
max_length: int | None = _Unset,
# custom
ui_type: Optional[UIType] = None,
ui_hidden: bool = False,
ui_order: Optional[int] = None,
) -> Any:
"""
Creates an output field for an invocation output.
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. \
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. \
:param int ui_order: [None] Specifies the order in which this field should be rendered in the UI. \
"""
return Field(
default=default,
title=title,
description=description,
pattern=pattern,
strict=strict,
gt=gt,
ge=ge,
lt=lt,
le=le,
multiple_of=multiple_of,
allow_inf_nan=allow_inf_nan,
max_digits=max_digits,
decimal_places=decimal_places,
min_length=min_length,
max_length=max_length,
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),
)
class UIConfigBase(BaseModel):
"""
Provides additional node configuration to the UI.
@ -460,33 +74,6 @@ class UIConfigBase(BaseModel):
)
class InvocationContext:
"""Initialized and provided to on execution of invocations."""
services: InvocationServices
graph_execution_state_id: str
queue_id: str
queue_item_id: int
queue_batch_id: str
workflow: Optional[WorkflowWithoutID]
def __init__(
self,
services: InvocationServices,
queue_id: str,
queue_item_id: int,
queue_batch_id: str,
graph_execution_state_id: str,
workflow: Optional[WorkflowWithoutID],
):
self.services = services
self.graph_execution_state_id = graph_execution_state_id
self.queue_id = queue_id
self.queue_item_id = queue_item_id
self.queue_batch_id = queue_batch_id
self.workflow = workflow
class BaseInvocationOutput(BaseModel):
"""
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__()

3
invokeai/app/invocations/collections.py
View File

@ -7,7 +7,8 @@ from pydantic import ValidationInfo, field_validator
from invokeai.app.invocations.primitives import IntegerCollectionOutput
from invokeai.app.util.misc import SEED_MAX
from .baseinvocation import BaseInvocation, InputField, InvocationContext, invocation
from .baseinvocation import BaseInvocation, InvocationContext, invocation
from .fields import InputField
@invocation(

6
invokeai/app/invocations/compel.py
View File

@ -5,8 +5,8 @@ import torch
from compel import Compel, ReturnedEmbeddingsType
from compel.prompt_parser import Blend, Conjunction, CrossAttentionControlSubstitute, FlattenedPrompt, Fragment
from invokeai.app.invocations.fields import FieldDescriptions, Input, InputField, OutputField, UIComponent
from invokeai.app.invocations.primitives import ConditioningField, ConditioningOutput
from invokeai.app.shared.fields import FieldDescriptions
from invokeai.backend.stable_diffusion.diffusion.conditioning_data import (
BasicConditioningInfo,
ExtraConditioningInfo,
@ -20,11 +20,7 @@ from ..util.ti_utils import extract_ti_triggers_from_prompt
from .baseinvocation import (
BaseInvocation,
BaseInvocationOutput,
Input,
InputField,
InvocationContext,
OutputField,
UIComponent,
invocation,
invocation_output,
)

6
invokeai/app/invocations/controlnet_image_processors.py
View File

@ -25,10 +25,10 @@ from controlnet_aux.util import HWC3, ade_palette
from PIL import Image
from pydantic import BaseModel, ConfigDict, Field, field_validator, model_validator
from invokeai.app.invocations.fields import FieldDescriptions, Input, InputField, OutputField, WithMetadata
from invokeai.app.invocations.primitives import ImageField, ImageOutput
from invokeai.app.invocations.util import validate_begin_end_step, validate_weights
from invokeai.app.services.image_records.image_records_common import ImageCategory, ResourceOrigin
from invokeai.app.shared.fields import FieldDescriptions
from invokeai.backend.image_util.depth_anything import DepthAnythingDetector
from invokeai.backend.image_util.dw_openpose import DWOpenposeDetector
@ -36,11 +36,7 @@ from ...backend.model_management import BaseModelType
from .baseinvocation import (
BaseInvocation,
BaseInvocationOutput,
Input,
InputField,
InvocationContext,
OutputField,
WithMetadata,
invocation,
invocation_output,
)

3
invokeai/app/invocations/cv.py
View File

@ -8,7 +8,8 @@ from PIL import Image, ImageOps
from invokeai.app.invocations.primitives import ImageField, ImageOutput
from invokeai.app.services.image_records.image_records_common import ImageCategory, ResourceOrigin
from .baseinvocation import BaseInvocation, InputField, InvocationContext, WithMetadata, invocation
from .baseinvocation import BaseInvocation, InvocationContext, invocation
from .fields import InputField, WithMetadata
@invocation("cv_inpaint", title="OpenCV Inpaint", tags=["opencv", "inpaint"], category="inpaint", version="1.2.0")

4
invokeai/app/invocations/facetools.py
View File

@ -13,13 +13,11 @@ from pydantic import field_validator
import invokeai.assets.fonts as font_assets
from invokeai.app.invocations.baseinvocation import (
BaseInvocation,
InputField,
InvocationContext,
OutputField,
WithMetadata,
invocation,
invocation_output,
)
from invokeai.app.invocations.fields import InputField, OutputField, WithMetadata
from invokeai.app.invocations.primitives import ImageField, ImageOutput
from invokeai.app.services.image_records.image_records_common import ImageCategory, ResourceOrigin

501
invokeai/app/invocations/fields.py Normal file
View File

@ -0,0 +1,501 @@
from enum import Enum
from typing import Any, Callable, Optional
from pydantic import BaseModel, ConfigDict, Field, RootModel, TypeAdapter
from pydantic.fields import _Unset
from pydantic_core import PydanticUndefined
from invokeai.app.util.metaenum import MetaEnum
from invokeai.backend.util.logging import InvokeAILogger
logger = InvokeAILogger.get_logger()
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.
- 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.
- DEPRECATED Fields
These types are deprecated and should not be used by node authors. A warning will be logged if one is
used, and the type will be ignored. They are included here for backwards compatibility.
"""
# region Model Field Types
SDXLMainModel = "SDXLMainModelField"
SDXLRefinerModel = "SDXLRefinerModelField"
ONNXModel = "ONNXModelField"
VaeModel = "VAEModelField"
LoRAModel = "LoRAModelField"
ControlNetModel = "ControlNetModelField"
IPAdapterModel = "IPAdapterModelField"
# endregion
# region Misc Field Types
Scheduler = "SchedulerField"
Any = "AnyField"
# endregion
# 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"
class UIComponent(str, Enum, metaclass=MetaEnum):
"""
The type of UI component to use for a field, used to override the default components, which are
inferred from the field type.
"""
None_ = "none"
Textarea = "textarea"
Slider = "slider"
class FieldDescriptions:
denoising_start = "When to start denoising, expressed a percentage of total steps"
denoising_end = "When to stop denoising, expressed a percentage of total steps"
cfg_scale = "Classifier-Free Guidance scale"
cfg_rescale_multiplier = "Rescale multiplier for CFG guidance, used for models trained with zero-terminal SNR"
scheduler = "Scheduler to use during inference"
positive_cond = "Positive conditioning tensor"
negative_cond = "Negative conditioning tensor"
noise = "Noise tensor"
clip = "CLIP (tokenizer, text encoder, LoRAs) and skipped layer count"
unet = "UNet (scheduler, LoRAs)"
vae = "VAE"
cond = "Conditioning tensor"
controlnet_model = "ControlNet model to load"
vae_model = "VAE model to load"
lora_model = "LoRA model to load"
main_model = "Main model (UNet, VAE, CLIP) to load"
sdxl_main_model = "SDXL Main model (UNet, VAE, CLIP1, CLIP2) to load"
sdxl_refiner_model = "SDXL Refiner Main Modde (UNet, VAE, CLIP2) to load"
onnx_main_model = "ONNX Main model (UNet, VAE, CLIP) to load"
lora_weight = "The weight at which the LoRA is applied to each model"
compel_prompt = "Prompt to be parsed by Compel to create a conditioning tensor"
raw_prompt = "Raw prompt text (no parsing)"
sdxl_aesthetic = "The aesthetic score to apply to the conditioning tensor"
skipped_layers = "Number of layers to skip in text encoder"
seed = "Seed for random number generation"
steps = "Number of steps to run"
width = "Width of output (px)"
height = "Height of output (px)"
control = "ControlNet(s) to apply"
ip_adapter = "IP-Adapter to apply"
t2i_adapter = "T2I-Adapter(s) to apply"
denoised_latents = "Denoised latents tensor"
latents = "Latents tensor"
strength = "Strength of denoising (proportional to steps)"
metadata = "Optional metadata to be saved with the image"
metadata_collection = "Collection of Metadata"
metadata_item_polymorphic = "A single metadata item or collection of metadata items"
metadata_item_label = "Label for this metadata item"
metadata_item_value = "The value for this metadata item (may be any type)"
workflow = "Optional workflow to be saved with the image"
interp_mode = "Interpolation mode"
torch_antialias = "Whether or not to apply antialiasing (bilinear or bicubic only)"
fp32 = "Whether or not to use full float32 precision"
precision = "Precision to use"
tiled = "Processing using overlapping tiles (reduce memory consumption)"
detect_res = "Pixel resolution for detection"
image_res = "Pixel resolution for output image"
safe_mode = "Whether or not to use safe mode"
scribble_mode = "Whether or not to use scribble mode"
scale_factor = "The factor by which to scale"
blend_alpha = (
"Blending factor. 0.0 = use input A only, 1.0 = use input B only, 0.5 = 50% mix of input A and input B."
)
num_1 = "The first number"
num_2 = "The second number"
mask = "The mask to use for the operation"
board = "The board to save the image to"
image = "The image to process"
tile_size = "Tile size"
inclusive_low = "The inclusive low value"
exclusive_high = "The exclusive high value"
decimal_places = "The number of decimal places to round to"
freeu_s1 = 'Scaling factor for stage 1 to attenuate the contributions of the skip features. This is done to mitigate the "oversmoothing effect" in the enhanced denoising process.'
freeu_s2 = 'Scaling factor for stage 2 to attenuate the contributions of the skip features. This is done to mitigate the "oversmoothing effect" in the enhanced denoising process.'
freeu_b1 = "Scaling factor for stage 1 to amplify the contributions of backbone features."
freeu_b2 = "Scaling factor for stage 2 to amplify the contributions of backbone features."
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 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 \
are instantiated.
- `Input.Connection`: The field must have its value provided by a connection.
- `Input.Any`: The field may have its value provided either directly or by a connection.
"""
Connection = "connection"
Direct = "direct"
Any = "any"
class FieldKind(str, Enum, metaclass=MetaEnum):
"""
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.
"""
Input = "input"
Output = "output"
Internal = "internal"
NodeAttribute = "node_attribute"
class InputFieldJSONSchemaExtra(BaseModel):
"""
Extra attributes to be added to input fields and their OpenAPI schema. Used during graph execution,
and by the workflow editor during schema parsing and UI rendering.
"""
input: Input
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,
json_schema_serialization_defaults_required=True,
)
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__()
class OutputFieldJSONSchemaExtra(BaseModel):
"""
Extra attributes to be added to input fields and their OpenAPI schema. Used by the workflow editor
during schema parsing and UI rendering.
"""
field_kind: FieldKind
ui_hidden: bool
ui_type: Optional[UIType]
ui_order: Optional[int]
model_config = ConfigDict(
validate_assignment=True,
json_schema_serialization_defaults_required=True,
)
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,
description: str | None = _Unset,
pattern: str | None = _Unset,
strict: bool | None = _Unset,
gt: float | None = _Unset,
ge: float | None = _Unset,
lt: float | None = _Unset,
le: float | None = _Unset,
multiple_of: float | None = _Unset,
allow_inf_nan: bool | None = _Unset,
max_digits: int | None = _Unset,
decimal_places: int | None = _Unset,
min_length: int | None = _Unset,
max_length: int | None = _Unset,
# custom
input: Input = Input.Any,
ui_type: Optional[UIType] = None,
ui_component: Optional[UIComponent] = None,
ui_hidden: bool = False,
ui_order: Optional[int] = None,
ui_choice_labels: Optional[dict[str, str]] = None,
) -> Any:
"""
Creates an input field for an invocation.
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. \
`Input.Direct` means a value must be provided on instantiation. \
`Input.Connection` means the value must be provided by a connection. \
`Input.Any` means either will do.
:param UIType ui_type: [None] Optionally provides an extra type hint for the UI. \
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 UIComponent ui_component: [None] Optionally specifies a specific component to use in the UI. \
The UI will always render a suitable component, but sometimes you want something different than the default. \
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 int ui_order: [None] Specifies the order in which this field should be rendered in the UI.
:param dict[str, str] ui_choice_labels: [None] Specifies the labels to use for the choices in an enum field.
"""
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()
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,
"title": title,
"description": description,
"pattern": pattern,
"strict": strict,
"gt": gt,
"ge": ge,
"lt": lt,
"le": le,
"multiple_of": multiple_of,
"allow_inf_nan": allow_inf_nan,
"max_digits": max_digits,
"decimal_places": decimal_places,
"min_length": min_length,
"max_length": max_length,
}
# 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}
# 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
# 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 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_.orig_default = default_
return Field(
**provided_args,
json_schema_extra=json_schema_extra_.model_dump(exclude_none=True),
)
def OutputField(
# copied from pydantic's Field
default: Any = _Unset,
title: str | None = _Unset,
description: str | None = _Unset,
pattern: str | None = _Unset,
strict: bool | None = _Unset,
gt: float | None = _Unset,
ge: float | None = _Unset,
lt: float | None = _Unset,
le: float | None = _Unset,
multiple_of: float | None = _Unset,
allow_inf_nan: bool | None = _Unset,
max_digits: int | None = _Unset,
decimal_places: int | None = _Unset,
min_length: int | None = _Unset,
max_length: int | None = _Unset,
# custom
ui_type: Optional[UIType] = None,
ui_hidden: bool = False,
ui_order: Optional[int] = None,
) -> Any:
"""
Creates an output field for an invocation output.
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. \
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. \
:param int ui_order: [None] Specifies the order in which this field should be rendered in the UI. \
"""
return Field(
default=default,
title=title,
description=description,
pattern=pattern,
strict=strict,
gt=gt,
ge=ge,
lt=lt,
le=le,
multiple_of=multiple_of,
allow_inf_nan=allow_inf_nan,
max_digits=max_digits,
decimal_places=decimal_places,
min_length=min_length,
max_length=max_length,
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),
)
# endregion

5
invokeai/app/invocations/image.py
View File

@ -7,19 +7,16 @@ import cv2
import numpy
from PIL import Image, ImageChops, ImageFilter, ImageOps
from invokeai.app.invocations.fields import FieldDescriptions, Input, InputField, WithMetadata
from invokeai.app.invocations.primitives import BoardField, ColorField, ImageField, ImageOutput
from invokeai.app.services.image_records.image_records_common import ImageCategory, ImageRecordChanges, ResourceOrigin
from invokeai.app.shared.fields import FieldDescriptions
from invokeai.backend.image_util.invisible_watermark import InvisibleWatermark
from invokeai.backend.image_util.safety_checker import SafetyChecker
from .baseinvocation import (
BaseInvocation,
Classification,
Input,
InputField,
InvocationContext,
WithMetadata,
invocation,
)

3
invokeai/app/invocations/infill.py
View File

@ -13,7 +13,8 @@ from invokeai.backend.image_util.cv2_inpaint import cv2_inpaint
from invokeai.backend.image_util.lama import LaMA
from invokeai.backend.image_util.patchmatch import PatchMatch
from .baseinvocation import BaseInvocation, InputField, InvocationContext, WithMetadata, invocation
from .baseinvocation import BaseInvocation, InvocationContext, invocation
from .fields import InputField, WithMetadata
from .image import PIL_RESAMPLING_MAP, PIL_RESAMPLING_MODES

5
invokeai/app/invocations/ip_adapter.py
View File

@ -7,16 +7,13 @@ from pydantic import BaseModel, ConfigDict, Field, field_validator, model_valida
from invokeai.app.invocations.baseinvocation import (
BaseInvocation,
BaseInvocationOutput,
Input,
InputField,
InvocationContext,
OutputField,
invocation,
invocation_output,
)
from invokeai.app.invocations.fields import FieldDescriptions, Input, InputField, OutputField
from invokeai.app.invocations.primitives import ImageField
from invokeai.app.invocations.util import validate_begin_end_step, validate_weights
from invokeai.app.shared.fields import FieldDescriptions
from invokeai.backend.model_management.models.base import BaseModelType, ModelType
from invokeai.backend.model_management.models.ip_adapter import get_ip_adapter_image_encoder_model_id

7
invokeai/app/invocations/latent.py
View File

@ -23,6 +23,7 @@ from diffusers.schedulers import SchedulerMixin as Scheduler
from pydantic import field_validator
from torchvision.transforms.functional import resize as tv_resize
from invokeai.app.invocations.fields import FieldDescriptions, Input, InputField, OutputField, UIType, WithMetadata
from invokeai.app.invocations.ip_adapter import IPAdapterField
from invokeai.app.invocations.primitives import (
DenoiseMaskField,
@ -35,7 +36,6 @@ from invokeai.app.invocations.primitives import (
)
from invokeai.app.invocations.t2i_adapter import T2IAdapterField
from invokeai.app.services.image_records.image_records_common import ImageCategory, ResourceOrigin
from invokeai.app.shared.fields import FieldDescriptions
from invokeai.app.util.controlnet_utils import prepare_control_image
from invokeai.app.util.step_callback import stable_diffusion_step_callback
from invokeai.backend.ip_adapter.ip_adapter import IPAdapter, IPAdapterPlus
@ -59,12 +59,7 @@ from ...backend.util.devices import choose_precision, choose_torch_device
from .baseinvocation import (
BaseInvocation,
BaseInvocationOutput,
Input,
InputField,
InvocationContext,
OutputField,
UIType,
WithMetadata,
invocation,
invocation_output,
)

4
invokeai/app/invocations/math.py
View File

@ -5,10 +5,10 @@ from typing import Literal
import numpy as np
from pydantic import ValidationInfo, field_validator
from invokeai.app.invocations.fields import FieldDescriptions, InputField
from invokeai.app.invocations.primitives import FloatOutput, IntegerOutput
from invokeai.app.shared.fields import FieldDescriptions
from .baseinvocation import BaseInvocation, InputField, InvocationContext, invocation
from .baseinvocation import BaseInvocation, InvocationContext, invocation
@invocation("add", title="Add Integers", tags=["math", "add"], category="math", version="1.0.0")

6
invokeai/app/invocations/metadata.py
View File

@ -5,20 +5,16 @@ from pydantic import BaseModel, ConfigDict, Field
from invokeai.app.invocations.baseinvocation import (
BaseInvocation,
BaseInvocationOutput,
InputField,
InvocationContext,
MetadataField,
OutputField,
UIType,
invocation,
invocation_output,
)
from invokeai.app.invocations.controlnet_image_processors import ControlField
from invokeai.app.invocations.fields import FieldDescriptions, InputField, MetadataField, OutputField, UIType
from invokeai.app.invocations.ip_adapter import IPAdapterModelField
from invokeai.app.invocations.model import LoRAModelField, MainModelField, VAEModelField
from invokeai.app.invocations.primitives import ImageField
from invokeai.app.invocations.t2i_adapter import T2IAdapterField
from invokeai.app.shared.fields import FieldDescriptions
from ...version import __version__

5
invokeai/app/invocations/model.py
View File

@ -3,17 +3,14 @@ from typing import List, Optional
from pydantic import BaseModel, ConfigDict, Field
from invokeai.app.shared.fields import FieldDescriptions
from invokeai.app.invocations.fields import FieldDescriptions, Input, InputField, OutputField
from invokeai.app.shared.models import FreeUConfig
from ...backend.model_management import BaseModelType, ModelType, SubModelType
from .baseinvocation import (
BaseInvocation,
BaseInvocationOutput,
Input,
InputField,
InvocationContext,
OutputField,
invocation,
invocation_output,
)

4
invokeai/app/invocations/noise.py
View File

@ -4,17 +4,15 @@
import torch
from pydantic import field_validator
from invokeai.app.invocations.fields import FieldDescriptions, InputField, OutputField
from invokeai.app.invocations.latent import LatentsField
from invokeai.app.shared.fields import FieldDescriptions
from invokeai.app.util.misc import SEED_MAX
from ...backend.util.devices import choose_torch_device, torch_dtype
from .baseinvocation import (
BaseInvocation,
BaseInvocationOutput,
InputField,
InvocationContext,
OutputField,
invocation,
invocation_output,
)

16
invokeai/app/invocations/onnx.py
View File

@ -11,9 +11,17 @@ from diffusers.image_processor import VaeImageProcessor
from pydantic import BaseModel, ConfigDict, Field, field_validator
from tqdm import tqdm
from invokeai.app.invocations.fields import (
FieldDescriptions,
Input,
InputField,
OutputField,
UIComponent,
UIType,
WithMetadata,
)
from invokeai.app.invocations.primitives import ConditioningField, ConditioningOutput, ImageField, ImageOutput
from invokeai.app.services.image_records.image_records_common import ImageCategory, ResourceOrigin
from invokeai.app.shared.fields import FieldDescriptions
from invokeai.app.util.step_callback import stable_diffusion_step_callback
from invokeai.backend import BaseModelType, ModelType, SubModelType
@ -24,13 +32,7 @@ from ..util.ti_utils import extract_ti_triggers_from_prompt
from .baseinvocation import (
BaseInvocation,
BaseInvocationOutput,
Input,
InputField,
InvocationContext,
OutputField,
UIComponent,
UIType,
WithMetadata,
invocation,
invocation_output,
)

3
invokeai/app/invocations/param_easing.py
View File

@ -41,7 +41,8 @@ from matplotlib.ticker import MaxNLocator
from invokeai.app.invocations.primitives import FloatCollectionOutput
from .baseinvocation import BaseInvocation, InputField, InvocationContext, invocation
from .baseinvocation import BaseInvocation, InvocationContext, invocation
from .fields import InputField
@invocation(

6
invokeai/app/invocations/primitives.py
View File

@ -5,16 +5,12 @@ from typing import Optional, Tuple
import torch
from pydantic import BaseModel, Field
from invokeai.app.shared.fields import FieldDescriptions
from invokeai.app.invocations.fields import FieldDescriptions, Input, InputField, OutputField, UIComponent
from .baseinvocation import (
BaseInvocation,
BaseInvocationOutput,
Input,
InputField,
InvocationContext,
OutputField,
UIComponent,
invocation,
invocation_output,
)

3
invokeai/app/invocations/prompt.py
View File

@ -7,7 +7,8 @@ from pydantic import field_validator
from invokeai.app.invocations.primitives import StringCollectionOutput
from .baseinvocation import BaseInvocation, InputField, InvocationContext, UIComponent, invocation
from .baseinvocation import BaseInvocation, InvocationContext, invocation
from .fields import InputField, UIComponent
@invocation(

6
invokeai/app/invocations/sdxl.py
View File

@ -1,14 +1,10 @@
from invokeai.app.shared.fields import FieldDescriptions
from invokeai.app.invocations.fields import FieldDescriptions, Input, InputField, OutputField, UIType
from ...backend.model_management import ModelType, SubModelType
from .baseinvocation import (
BaseInvocation,
BaseInvocationOutput,
Input,
InputField,
InvocationContext,
OutputField,
UIType,
invocation,
invocation_output,
)

4
invokeai/app/invocations/strings.py
View File

@ -5,13 +5,11 @@ import re
from .baseinvocation import (
BaseInvocation,
BaseInvocationOutput,
InputField,
InvocationContext,
OutputField,
UIComponent,
invocation,
invocation_output,
)
from .fields import InputField, OutputField, UIComponent
from .primitives import StringOutput

5
invokeai/app/invocations/t2i_adapter.py
View File

@ -5,17 +5,14 @@ from pydantic import BaseModel, ConfigDict, Field, field_validator, model_valida
from invokeai.app.invocations.baseinvocation import (
BaseInvocation,
BaseInvocationOutput,
Input,
InputField,
InvocationContext,
OutputField,
invocation,
invocation_output,
)
from invokeai.app.invocations.controlnet_image_processors import CONTROLNET_RESIZE_VALUES
from invokeai.app.invocations.fields import FieldDescriptions, Input, InputField, OutputField
from invokeai.app.invocations.primitives import ImageField
from invokeai.app.invocations.util import validate_begin_end_step, validate_weights
from invokeai.app.shared.fields import FieldDescriptions
from invokeai.backend.model_management.models.base import BaseModelType

5
invokeai/app/invocations/tiles.py
View File

@ -8,14 +8,11 @@ from invokeai.app.invocations.baseinvocation import (
BaseInvocation,
BaseInvocationOutput,
Classification,
Input,
InputField,
InvocationContext,
OutputField,
WithMetadata,
invocation,
invocation_output,
)
from invokeai.app.invocations.fields import Input, InputField, OutputField, WithMetadata
from invokeai.app.invocations.primitives import ImageField, ImageOutput
from invokeai.app.services.image_records.image_records_common import ImageCategory, ResourceOrigin
from invokeai.backend.tiles.tiles import (

3
invokeai/app/invocations/upscale.py
View File

@ -14,7 +14,8 @@ from invokeai.backend.image_util.basicsr.rrdbnet_arch import RRDBNet
from invokeai.backend.image_util.realesrgan.realesrgan import RealESRGAN
from invokeai.backend.util.devices import choose_torch_device
from .baseinvocation import BaseInvocation, InputField, InvocationContext, WithMetadata, invocation
from .baseinvocation import BaseInvocation, InvocationContext, invocation
from .fields import InputField, WithMetadata
# TODO: Populate this from disk?
# TODO: Use model manager to load?

2
invokeai/app/services/image_files/image_files_base.py
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@ -4,7 +4,7 @@ from typing import Optional
from PIL.Image import Image as PILImageType
from invokeai.app.invocations.baseinvocation import MetadataField
from invokeai.app.invocations.fields import MetadataField
from invokeai.app.services.workflow_records.workflow_records_common import WorkflowWithoutID

2
invokeai/app/services/image_files/image_files_disk.py
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@ -7,7 +7,7 @@ from PIL import Image, PngImagePlugin
from PIL.Image import Image as PILImageType
from send2trash import send2trash
from invokeai.app.invocations.baseinvocation import MetadataField
from invokeai.app.invocations.fields import MetadataField
from invokeai.app.services.invoker import Invoker
from invokeai.app.services.workflow_records.workflow_records_common import WorkflowWithoutID
from invokeai.app.util.thumbnails import get_thumbnail_name, make_thumbnail

2
invokeai/app/services/image_records/image_records_base.py
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@ -2,7 +2,7 @@ from abc import ABC, abstractmethod
from datetime import datetime
from typing import Optional
from invokeai.app.invocations.metadata import MetadataField
from invokeai.app.invocations.fields import MetadataField
from invokeai.app.services.shared.pagination import OffsetPaginatedResults
from .image_records_common import ImageCategory, ImageRecord, ImageRecordChanges, ResourceOrigin

2
invokeai/app/services/image_records/image_records_sqlite.py
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@ -3,7 +3,7 @@ import threading
from datetime import datetime
from typing import Optional, Union, cast
from invokeai.app.invocations.baseinvocation import MetadataField, MetadataFieldValidator
from invokeai.app.invocations.fields import MetadataField, MetadataFieldValidator
from invokeai.app.services.shared.pagination import OffsetPaginatedResults
from invokeai.app.services.shared.sqlite.sqlite_database import SqliteDatabase

2
invokeai/app/services/images/images_base.py
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@ -3,7 +3,7 @@ from typing import Callable, Optional
from PIL.Image import Image as PILImageType
from invokeai.app.invocations.baseinvocation import MetadataField
from invokeai.app.invocations.fields import MetadataField
from invokeai.app.services.image_records.image_records_common import (
ImageCategory,
ImageRecord,

2
invokeai/app/services/images/images_default.py
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@ -2,7 +2,7 @@ from typing import Optional
from PIL.Image import Image as PILImageType
from invokeai.app.invocations.baseinvocation import MetadataField
from invokeai.app.invocations.fields import MetadataField
from invokeai.app.services.invoker import Invoker
from invokeai.app.services.shared.pagination import OffsetPaginatedResults
from invokeai.app.services.workflow_records.workflow_records_common import WorkflowWithoutID

5
invokeai/app/services/shared/graph.py
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@ -13,14 +13,11 @@ from invokeai.app.invocations import * # noqa: F401 F403
from invokeai.app.invocations.baseinvocation import (
BaseInvocation,
BaseInvocationOutput,
Input,
InputField,
InvocationContext,
OutputField,
UIType,
invocation,
invocation_output,
)
from invokeai.app.invocations.fields import Input, InputField, OutputField, UIType
from invokeai.app.util.misc import uuid_string
# in 3.10 this would be "from types import NoneType"

67
invokeai/app/shared/fields.py
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@ -1,67 +0,0 @@
class FieldDescriptions:
denoising_start = "When to start denoising, expressed a percentage of total steps"
denoising_end = "When to stop denoising, expressed a percentage of total steps"
cfg_scale = "Classifier-Free Guidance scale"
cfg_rescale_multiplier = "Rescale multiplier for CFG guidance, used for models trained with zero-terminal SNR"
scheduler = "Scheduler to use during inference"
positive_cond = "Positive conditioning tensor"
negative_cond = "Negative conditioning tensor"
noise = "Noise tensor"
clip = "CLIP (tokenizer, text encoder, LoRAs) and skipped layer count"
unet = "UNet (scheduler, LoRAs)"
vae = "VAE"
cond = "Conditioning tensor"
controlnet_model = "ControlNet model to load"
vae_model = "VAE model to load"
lora_model = "LoRA model to load"
main_model = "Main model (UNet, VAE, CLIP) to load"
sdxl_main_model = "SDXL Main model (UNet, VAE, CLIP1, CLIP2) to load"
sdxl_refiner_model = "SDXL Refiner Main Modde (UNet, VAE, CLIP2) to load"
onnx_main_model = "ONNX Main model (UNet, VAE, CLIP) to load"
lora_weight = "The weight at which the LoRA is applied to each model"
compel_prompt = "Prompt to be parsed by Compel to create a conditioning tensor"
raw_prompt = "Raw prompt text (no parsing)"
sdxl_aesthetic = "The aesthetic score to apply to the conditioning tensor"
skipped_layers = "Number of layers to skip in text encoder"
seed = "Seed for random number generation"
steps = "Number of steps to run"
width = "Width of output (px)"
height = "Height of output (px)"
control = "ControlNet(s) to apply"
ip_adapter = "IP-Adapter to apply"
t2i_adapter = "T2I-Adapter(s) to apply"
denoised_latents = "Denoised latents tensor"
latents = "Latents tensor"
strength = "Strength of denoising (proportional to steps)"
metadata = "Optional metadata to be saved with the image"
metadata_collection = "Collection of Metadata"
metadata_item_polymorphic = "A single metadata item or collection of metadata items"
metadata_item_label = "Label for this metadata item"
metadata_item_value = "The value for this metadata item (may be any type)"
workflow = "Optional workflow to be saved with the image"
interp_mode = "Interpolation mode"
torch_antialias = "Whether or not to apply antialiasing (bilinear or bicubic only)"
fp32 = "Whether or not to use full float32 precision"
precision = "Precision to use"
tiled = "Processing using overlapping tiles (reduce memory consumption)"
detect_res = "Pixel resolution for detection"
image_res = "Pixel resolution for output image"
safe_mode = "Whether or not to use safe mode"
scribble_mode = "Whether or not to use scribble mode"
scale_factor = "The factor by which to scale"
blend_alpha = (
"Blending factor. 0.0 = use input A only, 1.0 = use input B only, 0.5 = 50% mix of input A and input B."
)
num_1 = "The first number"
num_2 = "The second number"
mask = "The mask to use for the operation"
board = "The board to save the image to"
image = "The image to process"
tile_size = "Tile size"
inclusive_low = "The inclusive low value"
exclusive_high = "The exclusive high value"
decimal_places = "The number of decimal places to round to"
freeu_s1 = 'Scaling factor for stage 1 to attenuate the contributions of the skip features. This is done to mitigate the "oversmoothing effect" in the enhanced denoising process.'
freeu_s2 = 'Scaling factor for stage 2 to attenuate the contributions of the skip features. This is done to mitigate the "oversmoothing effect" in the enhanced denoising process.'
freeu_b1 = "Scaling factor for stage 1 to amplify the contributions of backbone features."
freeu_b2 = "Scaling factor for stage 2 to amplify the contributions of backbone features."

2
invokeai/app/shared/models.py
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@ -1,6 +1,6 @@
from pydantic import BaseModel, Field
from invokeai.app.shared.fields import FieldDescriptions
from invokeai.app.invocations.fields import FieldDescriptions
class FreeUConfig(BaseModel):

3
tests/aa_nodes/test_nodes.py
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@ -3,12 +3,11 @@ from typing import Any, Callable, Union
from invokeai.app.invocations.baseinvocation import (
BaseInvocation,
BaseInvocationOutput,
InputField,
InvocationContext,
OutputField,
invocation,
invocation_output,
)
from invokeai.app.invocations.fields import InputField, OutputField
from invokeai.app.invocations.image import ImageField