tests(ui): add parseFieldType.test.ts

Unfortunately you cannot test for both a specific type of error and match its message. Splitting the error classes makes it easier to test expected error conditions.

docs/contributing/INVOCATIONS.md

@@ -9,11 +9,15 @@ complex functionality.
 
 ## Invocations Directory
 
-InvokeAI Nodes can be found in the `invokeai/app/invocations` directory. These can be used as examples to create your own nodes.
+InvokeAI Nodes can be found in the `invokeai/app/invocations` directory. These
+can be used as examples to create your own nodes.
 
-New nodes should be added to a subfolder in `nodes` direction found at the root level of the InvokeAI installation location. Nodes added to this folder will be able to be used upon application startup. 
+New nodes should be added to a subfolder in `nodes` direction found at the root
+level of the InvokeAI installation location. Nodes added to this folder will be
+able to be used upon application startup.
 
 Example `nodes` subfolder structure:
+
 ```py
 ├── __init__.py # Invoke-managed custom node loader
 │
@@ -30,14 +34,14 @@ Example `nodes`  subfolder structure:
         └── fancy_node.py
 ```
 
-Each node folder must have an `__init__.py` file that imports its nodes. Only nodes imported in the `__init__.py` file are loaded.
- See the README in the nodes folder for more examples: 
+Each node folder must have an `__init__.py` file that imports its nodes. Only
+nodes imported in the `__init__.py` file are loaded. See the README in the nodes
+folder for more examples:
 
 ```py
 from .cool_node import CoolInvocation
 ```
 
-
 ## Creating A New Invocation
 
 In order to understand the process of creating a new Invocation, let us actually
@@ -131,7 +135,6 @@ from invokeai.app.invocations.primitives import ImageField
 class ResizeInvocation(BaseInvocation):
     '''Resizes an image'''
 
-    # Inputs
     image: ImageField = InputField(description="The input image")
     width: int = InputField(default=512, ge=64, le=2048, description="Width of the new image")
     height: int = InputField(default=512, ge=64, le=2048, description="Height of the new image")
@@ -167,7 +170,6 @@ from invokeai.app.invocations.primitives import ImageField
 class ResizeInvocation(BaseInvocation):
     '''Resizes an image'''
 
-    # Inputs
     image: ImageField = InputField(description="The input image")
     width: int = InputField(default=512, ge=64, le=2048, description="Width of the new image")
     height: int = InputField(default=512, ge=64, le=2048, description="Height of the new image")
@@ -197,7 +199,6 @@ from invokeai.app.invocations.image import ImageOutput
 class ResizeInvocation(BaseInvocation):
     '''Resizes an image'''
 
-    # Inputs
     image: ImageField = InputField(description="The input image")
     width: int = InputField(default=512, ge=64, le=2048, description="Width of the new image")
     height: int = InputField(default=512, ge=64, le=2048, description="Height of the new image")
@@ -229,30 +230,17 @@ class ResizeInvocation(BaseInvocation):
     height: int = InputField(default=512, ge=64, le=2048, description="Height of the new image")
 
     def invoke(self, context: InvocationContext) -> ImageOutput:
-        # Load the image using InvokeAI's predefined Image Service. Returns the PIL image.
-        image = context.services.images.get_pil_image(self.image.image_name)
+        # Load the input image as a PIL image
+        image = context.images.get_pil(self.image.image_name)
 
-        # Resizing the image
+        # Resize the image
         resized_image = image.resize((self.width, self.height))
 
-        # Save the image using InvokeAI's predefined Image Service. Returns the prepared PIL image.
-        output_image = context.services.images.create(
-            image=resized_image,
-            image_origin=ResourceOrigin.INTERNAL,
-            image_category=ImageCategory.GENERAL,
-            node_id=self.id,
-            session_id=context.graph_execution_state_id,
-            is_intermediate=self.is_intermediate,
-        )
+        # Save the image
+        image_dto = context.images.save(image=resized_image)
 
-        # Returning the Image
-        return ImageOutput(
-            image=ImageField(
-                image_name=output_image.image_name,
-            ),
-            width=output_image.width,
-            height=output_image.height,
-        )
+        # Return an ImageOutput
+        return ImageOutput.build(image_dto)
 ```
 
 **Note:** Do not be overwhelmed by the `ImageOutput` process. InvokeAI has a
@@ -343,27 +331,25 @@ class ImageColorStringOutput(BaseInvocationOutput):
 
 That's all there is to it.
 
-<!-- TODO: DANGER - we probably do not want people to create their own field types, because this requires a lot of work on the frontend to accomodate.
-
 ### Custom Input Fields
 
 Now that you know how to create your own Invocations, let us dive into slightly
 more advanced topics.
 
 While creating your own Invocations, you might run into a scenario where the
-existing input types in InvokeAI do not meet your requirements. In such cases,
-you can create your own input types.
+existing fields in InvokeAI do not meet your requirements. In such cases, you
+can create your own fields.
 
 Let us create one as an example. Let us say we want to create a color input
 field that represents a color code. But before we start on that here are some
 general good practices to keep in mind.
 
-**Good Practices**
+### Best Practices
 
 - There is no naming convention for input fields but we highly recommend that
   you name it something appropriate like `ColorField`.
 - It is not mandatory but it is heavily recommended to add a relevant
-  `docstring` to describe your input field.
+  `docstring` to describe your field.
 - Keep your field in the same file as the Invocation that it is made for or in
   another file where it is relevant.
 
@@ -378,10 +364,13 @@ class ColorField(BaseModel):
     pass
 ```
 
-Perfect. Now let us create our custom inputs for our field. This is exactly
-similar how you created input fields for your Invocation. All the same rules
-apply. Let us create four fields representing the _red(r)_, _blue(b)_,
-_green(g)_ and _alpha(a)_ channel of the color.
+Perfect. Now let us create the properties for our field. This is similar to how
+you created input fields for your Invocation. All the same rules apply. Let us
+create four fields representing the _red(r)_, _blue(b)_, _green(g)_ and
+_alpha(a)_ channel of the color.
+
+> Technically, the properties are _also_ called fields - but in this case, it
+> refers to a `pydantic` field.
 
 ```python
 class ColorField(BaseModel):
@@ -396,25 +385,11 @@ That's it. We now have a new input field type that we can use in our Invocations
 like this.
 
 ```python
-color: ColorField = Field(default=ColorField(r=0, g=0, b=0, a=0), description='Background color of an image')
+color: ColorField = InputField(default=ColorField(r=0, g=0, b=0, a=0), description='Background color of an image')
 ```
 
-### Custom Components For Frontend
+### Using the custom field
 
-Every backend input type should have a corresponding frontend component so the
-UI knows what to render when you use a particular field type.
+When you start the UI, your custom field will be automatically recognized.
 
-If you are using existing field types, we already have components for those. So
-you don't have to worry about creating anything new. But this might not always
-be the case. Sometimes you might want to create new field types and have the
-frontend UI deal with it in a different way.
-
-This is where we venture into the world of React and Javascript and create our
-own new components for our Invocations. Do not fear the world of JS. It's
-actually pretty straightforward.
-
-Let us create a new component for our custom color field we created above. When
-we use a color field, let us say we want the UI to display a color picker for
-the user to pick from rather than entering values. That is what we will build
-now.
--->
+Custom fields only support connection inputs in the Workflow Editor.

invokeai/app/api/dependencies.py

@@ -2,9 +2,14 @@
 
 from logging import Logger
 
+import torch
+
 from invokeai.app.services.item_storage.item_storage_memory import ItemStorageMemory
+from invokeai.app.services.object_serializer.object_serializer_disk import ObjectSerializerDisk
+from invokeai.app.services.object_serializer.object_serializer_forward_cache import ObjectSerializerForwardCache
 from invokeai.app.services.shared.sqlite.sqlite_util import init_db
 from invokeai.backend.model_manager.metadata import ModelMetadataStore
+from invokeai.backend.stable_diffusion.diffusion.conditioning_data import ConditioningFieldData
 from invokeai.backend.util.logging import InvokeAILogger
 from invokeai.version.invokeai_version import __version__
 
@@ -23,8 +28,6 @@ from ..services.invocation_queue.invocation_queue_memory import MemoryInvocation
 from ..services.invocation_services import InvocationServices
 from ..services.invocation_stats.invocation_stats_default import InvocationStatsService
 from ..services.invoker import Invoker
-from ..services.latents_storage.latents_storage_disk import DiskLatentsStorage
-from ..services.latents_storage.latents_storage_forward_cache import ForwardCacheLatentsStorage
 from ..services.model_install import ModelInstallService
 from ..services.model_manager.model_manager_default import ModelManagerService
 from ..services.model_records import ModelRecordServiceSQL
@@ -68,6 +71,9 @@ class ApiDependencies:
         logger.debug(f"Internet connectivity is {config.internet_available}")
 
         output_folder = config.output_path
+        if output_folder is None:
+            raise ValueError("Output folder is not set")
+
         image_files = DiskImageFileStorage(f"{output_folder}/images")
 
         db = init_db(config=config, logger=logger, image_files=image_files)
@@ -84,7 +90,12 @@ class ApiDependencies:
         image_records = SqliteImageRecordStorage(db=db)
         images = ImageService()
         invocation_cache = MemoryInvocationCache(max_cache_size=config.node_cache_size)
-        latents = ForwardCacheLatentsStorage(DiskLatentsStorage(f"{output_folder}/latents"))
+        tensors = ObjectSerializerForwardCache(
+            ObjectSerializerDisk[torch.Tensor](output_folder / "tensors", ephemeral=True)
+        )
+        conditioning = ObjectSerializerForwardCache(
+            ObjectSerializerDisk[ConditioningFieldData](output_folder / "conditioning", ephemeral=True)
+        )
         model_manager = ModelManagerService(config, logger)
         model_record_service = ModelRecordServiceSQL(db=db)
         download_queue_service = DownloadQueueService(event_bus=events)
@@ -117,7 +128,6 @@ class ApiDependencies:
             image_records=image_records,
             images=images,
             invocation_cache=invocation_cache,
-            latents=latents,
             logger=logger,
             model_manager=model_manager,
             model_records=model_record_service,
@@ -131,6 +141,8 @@ class ApiDependencies:
             session_queue=session_queue,
             urls=urls,
             workflow_records=workflow_records,
+            tensors=tensors,
+            conditioning=conditioning,
         )
 
         ApiDependencies.invoker = Invoker(services)

invokeai/app/api/routers/images.py

@@ -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

invokeai/app/api_app.py

@@ -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,
     )
 

invokeai/app/invocations/baseinvocation.py

@@ -12,13 +12,16 @@ 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
+from invokeai.app.services.shared.invocation_context import InvocationContext
 from invokeai.app.util.metaenum import MetaEnum
 from invokeai.app.util.misc import uuid_string
 from invokeai.backend.util.logging import InvokeAILogger
@@ -52,393 +55,6 @@ 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 +76,6 @@ 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.
@@ -632,7 +221,7 @@ class BaseInvocation(ABC, BaseModel):
         """Invoke with provided context and return outputs."""
         pass
 
-    def invoke_internal(self, context: InvocationContext) -> BaseInvocationOutput:
+    def invoke_internal(self, context: InvocationContext, services: "InvocationServices") -> BaseInvocationOutput:
         """
         Internal invoke method, calls `invoke()` after some prep.
         Handles optional fields that are required to call `invoke()` and invocation cache.
@@ -657,23 +246,23 @@ class BaseInvocation(ABC, BaseModel):
                     raise MissingInputException(self.model_fields["type"].default, field_name)
 
         # skip node cache codepath if it's disabled
-        if context.services.configuration.node_cache_size == 0:
+        if services.configuration.node_cache_size == 0:
             return self.invoke(context)
 
         output: BaseInvocationOutput
         if self.use_cache:
-            key = context.services.invocation_cache.create_key(self)
-            cached_value = context.services.invocation_cache.get(key)
+            key = services.invocation_cache.create_key(self)
+            cached_value = services.invocation_cache.get(key)
             if cached_value is None:
-                context.services.logger.debug(f'Invocation cache miss for type "{self.get_type()}": {self.id}')
+                services.logger.debug(f'Invocation cache miss for type "{self.get_type()}": {self.id}')
                 output = self.invoke(context)
-                context.services.invocation_cache.save(key, output)
+                services.invocation_cache.save(key, output)
                 return output
             else:
-                context.services.logger.debug(f'Invocation cache hit for type "{self.get_type()}": {self.id}')
+                services.logger.debug(f'Invocation cache hit for type "{self.get_type()}": {self.id}')
                 return cached_value
         else:
-            context.services.logger.debug(f'Skipping invocation cache for "{self.get_type()}": {self.id}')
+            services.logger.debug(f'Skipping invocation cache for "{self.get_type()}": {self.id}')
             return self.invoke(context)
 
     id: str = Field(
@@ -714,9 +303,7 @@ RESERVED_NODE_ATTRIBUTE_FIELD_NAMES = {
     "workflow",
 }
 
-RESERVED_INPUT_FIELD_NAMES = {
-    "metadata",
-}
+RESERVED_INPUT_FIELD_NAMES = {"metadata", "board"}
 
 RESERVED_OUTPUT_FIELD_NAMES = {"type"}
 
@@ -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__()

invokeai/app/invocations/collections.py

@@ -5,9 +5,11 @@ import numpy as np
 from pydantic import ValidationInfo, field_validator
 
 from invokeai.app.invocations.primitives import IntegerCollectionOutput
+from invokeai.app.services.shared.invocation_context import InvocationContext
 from invokeai.app.util.misc import SEED_MAX
 
-from .baseinvocation import BaseInvocation, InputField, InvocationContext, invocation
+from .baseinvocation import BaseInvocation, invocation
+from .fields import InputField
 
 
 @invocation(

invokeai/app/invocations/compel.py

@@ -1,14 +1,21 @@
-from dataclasses import dataclass
 from typing import List, Optional, Union
 
 import torch
 from compel import Compel, ReturnedEmbeddingsType
 from compel.prompt_parser import Blend, Conjunction, CrossAttentionControlSubstitute, FlattenedPrompt, Fragment
 
-from invokeai.app.invocations.primitives import ConditioningField, ConditioningOutput
-from invokeai.app.shared.fields import FieldDescriptions
+from invokeai.app.invocations.fields import (
+    FieldDescriptions,
+    Input,
+    InputField,
+    OutputField,
+    UIComponent,
+)
+from invokeai.app.invocations.primitives import ConditioningOutput
+from invokeai.app.services.shared.invocation_context import InvocationContext
 from invokeai.backend.stable_diffusion.diffusion.conditioning_data import (
     BasicConditioningInfo,
+    ConditioningFieldData,
     ExtraConditioningInfo,
     SDXLConditioningInfo,
 )
@@ -20,21 +27,12 @@ from ..util.ti_utils import extract_ti_triggers_from_prompt
 from .baseinvocation import (
     BaseInvocation,
     BaseInvocationOutput,
-    Input,
-    InputField,
-    InvocationContext,
-    OutputField,
-    UIComponent,
     invocation,
     invocation_output,
 )
 from .model import ClipField
 
-
-@dataclass
-class ConditioningFieldData:
-    conditionings: List[BasicConditioningInfo]
-    # unconditioned: Optional[torch.Tensor]
+# unconditioned: Optional[torch.Tensor]
 
 
 # class ConditioningAlgo(str, Enum):
@@ -48,7 +46,7 @@ class ConditioningFieldData:
     title="Prompt",
     tags=["prompt", "compel"],
     category="conditioning",
-    version="1.0.0",
+    version="1.0.1",
 )
 class CompelInvocation(BaseInvocation):
     """Parse prompt using compel package to conditioning."""
@@ -66,25 +64,17 @@ class CompelInvocation(BaseInvocation):
 
     @torch.no_grad()
     def invoke(self, context: InvocationContext) -> ConditioningOutput:
-        tokenizer_info = context.services.model_manager.get_model(
-            **self.clip.tokenizer.model_dump(),
-            context=context,
-        )
-        text_encoder_info = context.services.model_manager.get_model(
-            **self.clip.text_encoder.model_dump(),
-            context=context,
-        )
+        tokenizer_info = context.models.load(**self.clip.tokenizer.model_dump())
+        text_encoder_info = context.models.load(**self.clip.text_encoder.model_dump())
 
         def _lora_loader():
             for lora in self.clip.loras:
-                lora_info = context.services.model_manager.get_model(
-                    **lora.model_dump(exclude={"weight"}), context=context
-                )
+                lora_info = context.models.load(**lora.model_dump(exclude={"weight"}))
                 yield (lora_info.context.model, lora.weight)
                 del lora_info
             return
 
-        # loras = [(context.services.model_manager.get_model(**lora.dict(exclude={"weight"})).context.model, lora.weight) for lora in self.clip.loras]
+        # loras = [(context.models.get(**lora.dict(exclude={"weight"})).context.model, lora.weight) for lora in self.clip.loras]
 
         ti_list = []
         for trigger in extract_ti_triggers_from_prompt(self.prompt):
@@ -93,11 +83,10 @@ class CompelInvocation(BaseInvocation):
                 ti_list.append(
                     (
                         name,
-                        context.services.model_manager.get_model(
+                        context.models.load(
                             model_name=name,
                             base_model=self.clip.text_encoder.base_model,
                             model_type=ModelType.TextualInversion,
-                            context=context,
                         ).context.model,
                     )
                 )
@@ -128,7 +117,7 @@ class CompelInvocation(BaseInvocation):
 
             conjunction = Compel.parse_prompt_string(self.prompt)
 
-            if context.services.configuration.log_tokenization:
+            if context.config.get().log_tokenization:
                 log_tokenization_for_conjunction(conjunction, tokenizer)
 
             c, options = compel.build_conditioning_tensor_for_conjunction(conjunction)
@@ -149,14 +138,9 @@ class CompelInvocation(BaseInvocation):
             ]
         )
 
-        conditioning_name = f"{context.graph_execution_state_id}_{self.id}_conditioning"
-        context.services.latents.save(conditioning_name, conditioning_data)
+        conditioning_name = context.conditioning.save(conditioning_data)
 
-        return ConditioningOutput(
-            conditioning=ConditioningField(
-                conditioning_name=conditioning_name,
-            ),
-        )
+        return ConditioningOutput.build(conditioning_name)
 
 
 class SDXLPromptInvocationBase:
@@ -169,14 +153,8 @@ class SDXLPromptInvocationBase:
         lora_prefix: str,
         zero_on_empty: bool,
     ):
-        tokenizer_info = context.services.model_manager.get_model(
-            **clip_field.tokenizer.model_dump(),
-            context=context,
-        )
-        text_encoder_info = context.services.model_manager.get_model(
-            **clip_field.text_encoder.model_dump(),
-            context=context,
-        )
+        tokenizer_info = context.models.load(**clip_field.tokenizer.model_dump())
+        text_encoder_info = context.models.load(**clip_field.text_encoder.model_dump())
 
         # return zero on empty
         if prompt == "" and zero_on_empty:
@@ -200,14 +178,12 @@ class SDXLPromptInvocationBase:
 
         def _lora_loader():
             for lora in clip_field.loras:
-                lora_info = context.services.model_manager.get_model(
-                    **lora.model_dump(exclude={"weight"}), context=context
-                )
+                lora_info = context.models.load(**lora.model_dump(exclude={"weight"}))
                 yield (lora_info.context.model, lora.weight)
                 del lora_info
             return
 
-        # loras = [(context.services.model_manager.get_model(**lora.dict(exclude={"weight"})).context.model, lora.weight) for lora in self.clip.loras]
+        # loras = [(context.models.get(**lora.dict(exclude={"weight"})).context.model, lora.weight) for lora in self.clip.loras]
 
         ti_list = []
         for trigger in extract_ti_triggers_from_prompt(prompt):
@@ -216,11 +192,10 @@ class SDXLPromptInvocationBase:
                 ti_list.append(
                     (
                         name,
-                        context.services.model_manager.get_model(
+                        context.models.load(
                             model_name=name,
                             base_model=clip_field.text_encoder.base_model,
                             model_type=ModelType.TextualInversion,
-                            context=context,
                         ).context.model,
                     )
                 )
@@ -253,7 +228,7 @@ class SDXLPromptInvocationBase:
 
             conjunction = Compel.parse_prompt_string(prompt)
 
-            if context.services.configuration.log_tokenization:
+            if context.config.get().log_tokenization:
                 # TODO: better logging for and syntax
                 log_tokenization_for_conjunction(conjunction, tokenizer)
 
@@ -286,7 +261,7 @@ class SDXLPromptInvocationBase:
     title="SDXL Prompt",
     tags=["sdxl", "compel", "prompt"],
     category="conditioning",
-    version="1.0.0",
+    version="1.0.1",
 )
 class SDXLCompelPromptInvocation(BaseInvocation, SDXLPromptInvocationBase):
     """Parse prompt using compel package to conditioning."""
@@ -368,14 +343,9 @@ class SDXLCompelPromptInvocation(BaseInvocation, SDXLPromptInvocationBase):
             ]
         )
 
-        conditioning_name = f"{context.graph_execution_state_id}_{self.id}_conditioning"
-        context.services.latents.save(conditioning_name, conditioning_data)
+        conditioning_name = context.conditioning.save(conditioning_data)
 
-        return ConditioningOutput(
-            conditioning=ConditioningField(
-                conditioning_name=conditioning_name,
-            ),
-        )
+        return ConditioningOutput.build(conditioning_name)
 
 
 @invocation(
@@ -383,7 +353,7 @@ class SDXLCompelPromptInvocation(BaseInvocation, SDXLPromptInvocationBase):
     title="SDXL Refiner Prompt",
     tags=["sdxl", "compel", "prompt"],
     category="conditioning",
-    version="1.0.0",
+    version="1.0.1",
 )
 class SDXLRefinerCompelPromptInvocation(BaseInvocation, SDXLPromptInvocationBase):
     """Parse prompt using compel package to conditioning."""
@@ -421,14 +391,9 @@ class SDXLRefinerCompelPromptInvocation(BaseInvocation, SDXLPromptInvocationBase
             ]
         )
 
-        conditioning_name = f"{context.graph_execution_state_id}_{self.id}_conditioning"
-        context.services.latents.save(conditioning_name, conditioning_data)
+        conditioning_name = context.conditioning.save(conditioning_data)
 
-        return ConditioningOutput(
-            conditioning=ConditioningField(
-                conditioning_name=conditioning_name,
-            ),
-        )
+        return ConditioningOutput.build(conditioning_name)
 
 
 @invocation_output("clip_skip_output")

invokeai/app/invocations/constants.py

@@ -0,0 +1,14 @@
+from typing import Literal
+
+from invokeai.backend.stable_diffusion.schedulers import SCHEDULER_MAP
+
+LATENT_SCALE_FACTOR = 8
+"""
+HACK: Many nodes are currently hard-coded to use a fixed latent scale factor of 8. This is fragile, and will need to
+be addressed if future models use a different latent scale factor. Also, note that there may be places where the scale
+factor is hard-coded to a literal '8' rather than using this constant.
+The ratio of image:latent dimensions is LATENT_SCALE_FACTOR:1, or 8:1.
+"""
+
+SCHEDULER_NAME_VALUES = Literal[tuple(SCHEDULER_MAP.keys())]
+"""A literal type representing the valid scheduler names."""

invokeai/app/invocations/controlnet_image_processors.py

@@ -25,22 +25,25 @@ 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.primitives import ImageField, ImageOutput
+from invokeai.app.invocations.fields import (
+    FieldDescriptions,
+    ImageField,
+    Input,
+    InputField,
+    OutputField,
+    WithBoard,
+    WithMetadata,
+)
+from invokeai.app.invocations.primitives import 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.app.services.shared.invocation_context import InvocationContext
 from invokeai.backend.image_util.depth_anything import DepthAnythingDetector
 from invokeai.backend.image_util.dw_openpose import DWOpenposeDetector
+from invokeai.backend.model_management.models.base import BaseModelType
 
-from ...backend.model_management import BaseModelType
 from .baseinvocation import (
     BaseInvocation,
     BaseInvocationOutput,
-    Input,
-    InputField,
-    InvocationContext,
-    OutputField,
-    WithMetadata,
     invocation,
     invocation_output,
 )
@@ -140,7 +143,7 @@ class ControlNetInvocation(BaseInvocation):
 
 
 # This invocation exists for other invocations to subclass it - do not register with @invocation!
-class ImageProcessorInvocation(BaseInvocation, WithMetadata):
+class ImageProcessorInvocation(BaseInvocation, WithMetadata, WithBoard):
     """Base class for invocations that preprocess images for ControlNet"""
 
     image: ImageField = InputField(description="The image to process")
@@ -150,22 +153,13 @@ class ImageProcessorInvocation(BaseInvocation, WithMetadata):
         return image
 
     def invoke(self, context: InvocationContext) -> ImageOutput:
-        raw_image = context.services.images.get_pil_image(self.image.image_name)
+        raw_image = context.images.get_pil(self.image.image_name)
         # image type should be PIL.PngImagePlugin.PngImageFile ?
         processed_image = self.run_processor(raw_image)
 
         # currently can't see processed image in node UI without a showImage node,
         #    so for now setting image_type to RESULT instead of INTERMEDIATE so will get saved in gallery
-        image_dto = context.services.images.create(
-            image=processed_image,
-            image_origin=ResourceOrigin.INTERNAL,
-            image_category=ImageCategory.CONTROL,
-            session_id=context.graph_execution_state_id,
-            node_id=self.id,
-            is_intermediate=self.is_intermediate,
-            metadata=self.metadata,
-            workflow=context.workflow,
-        )
+        image_dto = context.images.save(image=processed_image)
 
         """Builds an ImageOutput and its ImageField"""
         processed_image_field = ImageField(image_name=image_dto.image_name)
@@ -184,7 +178,7 @@ class ImageProcessorInvocation(BaseInvocation, WithMetadata):
     title="Canny Processor",
     tags=["controlnet", "canny"],
     category="controlnet",
-    version="1.2.0",
+    version="1.2.1",
 )
 class CannyImageProcessorInvocation(ImageProcessorInvocation):
     """Canny edge detection for ControlNet"""
@@ -207,7 +201,7 @@ class CannyImageProcessorInvocation(ImageProcessorInvocation):
     title="HED (softedge) Processor",
     tags=["controlnet", "hed", "softedge"],
     category="controlnet",
-    version="1.2.0",
+    version="1.2.1",
 )
 class HedImageProcessorInvocation(ImageProcessorInvocation):
     """Applies HED edge detection to image"""
@@ -236,7 +230,7 @@ class HedImageProcessorInvocation(ImageProcessorInvocation):
     title="Lineart Processor",
     tags=["controlnet", "lineart"],
     category="controlnet",
-    version="1.2.0",
+    version="1.2.1",
 )
 class LineartImageProcessorInvocation(ImageProcessorInvocation):
     """Applies line art processing to image"""
@@ -258,7 +252,7 @@ class LineartImageProcessorInvocation(ImageProcessorInvocation):
     title="Lineart Anime Processor",
     tags=["controlnet", "lineart", "anime"],
     category="controlnet",
-    version="1.2.0",
+    version="1.2.1",
 )
 class LineartAnimeImageProcessorInvocation(ImageProcessorInvocation):
     """Applies line art anime processing to image"""
@@ -281,7 +275,7 @@ class LineartAnimeImageProcessorInvocation(ImageProcessorInvocation):
     title="Midas Depth Processor",
     tags=["controlnet", "midas"],
     category="controlnet",
-    version="1.2.0",
+    version="1.2.1",
 )
 class MidasDepthImageProcessorInvocation(ImageProcessorInvocation):
     """Applies Midas depth processing to image"""
@@ -308,7 +302,7 @@ class MidasDepthImageProcessorInvocation(ImageProcessorInvocation):
     title="Normal BAE Processor",
     tags=["controlnet"],
     category="controlnet",
-    version="1.2.0",
+    version="1.2.1",
 )
 class NormalbaeImageProcessorInvocation(ImageProcessorInvocation):
     """Applies NormalBae processing to image"""
@@ -325,7 +319,7 @@ class NormalbaeImageProcessorInvocation(ImageProcessorInvocation):
 
 
 @invocation(
-    "mlsd_image_processor", title="MLSD Processor", tags=["controlnet", "mlsd"], category="controlnet", version="1.2.0"
+    "mlsd_image_processor", title="MLSD Processor", tags=["controlnet", "mlsd"], category="controlnet", version="1.2.1"
 )
 class MlsdImageProcessorInvocation(ImageProcessorInvocation):
     """Applies MLSD processing to image"""
@@ -348,7 +342,7 @@ class MlsdImageProcessorInvocation(ImageProcessorInvocation):
 
 
 @invocation(
-    "pidi_image_processor", title="PIDI Processor", tags=["controlnet", "pidi"], category="controlnet", version="1.2.0"
+    "pidi_image_processor", title="PIDI Processor", tags=["controlnet", "pidi"], category="controlnet", version="1.2.1"
 )
 class PidiImageProcessorInvocation(ImageProcessorInvocation):
     """Applies PIDI processing to image"""
@@ -375,7 +369,7 @@ class PidiImageProcessorInvocation(ImageProcessorInvocation):
     title="Content Shuffle Processor",
     tags=["controlnet", "contentshuffle"],
     category="controlnet",
-    version="1.2.0",
+    version="1.2.1",
 )
 class ContentShuffleImageProcessorInvocation(ImageProcessorInvocation):
     """Applies content shuffle processing to image"""
@@ -405,7 +399,7 @@ class ContentShuffleImageProcessorInvocation(ImageProcessorInvocation):
     title="Zoe (Depth) Processor",
     tags=["controlnet", "zoe", "depth"],
     category="controlnet",
-    version="1.2.0",
+    version="1.2.1",
 )
 class ZoeDepthImageProcessorInvocation(ImageProcessorInvocation):
     """Applies Zoe depth processing to image"""
@@ -421,7 +415,7 @@ class ZoeDepthImageProcessorInvocation(ImageProcessorInvocation):
     title="Mediapipe Face Processor",
     tags=["controlnet", "mediapipe", "face"],
     category="controlnet",
-    version="1.2.0",
+    version="1.2.1",
 )
 class MediapipeFaceProcessorInvocation(ImageProcessorInvocation):
     """Applies mediapipe face processing to image"""
@@ -444,7 +438,7 @@ class MediapipeFaceProcessorInvocation(ImageProcessorInvocation):
     title="Leres (Depth) Processor",
     tags=["controlnet", "leres", "depth"],
     category="controlnet",
-    version="1.2.0",
+    version="1.2.1",
 )
 class LeresImageProcessorInvocation(ImageProcessorInvocation):
     """Applies leres processing to image"""
@@ -473,7 +467,7 @@ class LeresImageProcessorInvocation(ImageProcessorInvocation):
     title="Tile Resample Processor",
     tags=["controlnet", "tile"],
     category="controlnet",
-    version="1.2.0",
+    version="1.2.1",
 )
 class TileResamplerProcessorInvocation(ImageProcessorInvocation):
     """Tile resampler processor"""
@@ -513,7 +507,7 @@ class TileResamplerProcessorInvocation(ImageProcessorInvocation):
     title="Segment Anything Processor",
     tags=["controlnet", "segmentanything"],
     category="controlnet",
-    version="1.2.0",
+    version="1.2.1",
 )
 class SegmentAnythingProcessorInvocation(ImageProcessorInvocation):
     """Applies segment anything processing to image"""
@@ -555,7 +549,7 @@ class SamDetectorReproducibleColors(SamDetector):
     title="Color Map Processor",
     tags=["controlnet"],
     category="controlnet",
-    version="1.2.0",
+    version="1.2.1",
 )
 class ColorMapImageProcessorInvocation(ImageProcessorInvocation):
     """Generates a color map from the provided image"""

invokeai/app/invocations/cv.py

@@ -5,22 +5,24 @@ import cv2 as cv
 import numpy
 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 invokeai.app.invocations.fields import ImageField
+from invokeai.app.invocations.primitives import ImageOutput
+from invokeai.app.services.shared.invocation_context import InvocationContext
 
-from .baseinvocation import BaseInvocation, InputField, InvocationContext, WithMetadata, invocation
+from .baseinvocation import BaseInvocation, invocation
+from .fields import InputField, WithBoard, WithMetadata
 
 
-@invocation("cv_inpaint", title="OpenCV Inpaint", tags=["opencv", "inpaint"], category="inpaint", version="1.2.0")
-class CvInpaintInvocation(BaseInvocation, WithMetadata):
+@invocation("cv_inpaint", title="OpenCV Inpaint", tags=["opencv", "inpaint"], category="inpaint", version="1.2.1")
+class CvInpaintInvocation(BaseInvocation, WithMetadata, WithBoard):
     """Simple inpaint using opencv."""
 
     image: ImageField = InputField(description="The image to inpaint")
     mask: ImageField = InputField(description="The mask to use when inpainting")
 
     def invoke(self, context: InvocationContext) -> ImageOutput:
-        image = context.services.images.get_pil_image(self.image.image_name)
-        mask = context.services.images.get_pil_image(self.mask.image_name)
+        image = context.images.get_pil(self.image.image_name)
+        mask = context.images.get_pil(self.mask.image_name)
 
         # Convert to cv image/mask
         # TODO: consider making these utility functions
@@ -34,18 +36,6 @@ class CvInpaintInvocation(BaseInvocation, WithMetadata):
         # TODO: consider making a utility function
         image_inpainted = Image.fromarray(cv.cvtColor(cv_inpainted, cv.COLOR_BGR2RGB))
 
-        image_dto = context.services.images.create(
-            image=image_inpainted,
-            image_origin=ResourceOrigin.INTERNAL,
-            image_category=ImageCategory.GENERAL,
-            node_id=self.id,
-            session_id=context.graph_execution_state_id,
-            is_intermediate=self.is_intermediate,
-            workflow=context.workflow,
-        )
+        image_dto = context.images.save(image=image_inpainted)
 
-        return ImageOutput(
-            image=ImageField(image_name=image_dto.image_name),
-            width=image_dto.width,
-            height=image_dto.height,
-        )
+        return ImageOutput.build(image_dto)

invokeai/app/invocations/facetools.py

@@ -13,15 +13,13 @@ 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.primitives import ImageField, ImageOutput
-from invokeai.app.services.image_records.image_records_common import ImageCategory, ResourceOrigin
+from invokeai.app.invocations.fields import ImageField, InputField, OutputField, WithBoard, WithMetadata
+from invokeai.app.invocations.primitives import ImageOutput
+from invokeai.app.services.image_records.image_records_common import ImageCategory
+from invokeai.app.services.shared.invocation_context import InvocationContext
 
 
 @invocation_output("face_mask_output")
@@ -306,37 +304,37 @@ def extract_face(
 
     # Adjust the crop boundaries to stay within the original image's dimensions
     if x_min < 0:
-        context.services.logger.warning("FaceTools --> -X-axis padding reached image edge.")
+        context.logger.warning("FaceTools --> -X-axis padding reached image edge.")
         x_max -= x_min
         x_min = 0
     elif x_max > mask.width:
-        context.services.logger.warning("FaceTools --> +X-axis padding reached image edge.")
+        context.logger.warning("FaceTools --> +X-axis padding reached image edge.")
         x_min -= x_max - mask.width
         x_max = mask.width
 
     if y_min < 0:
-        context.services.logger.warning("FaceTools --> +Y-axis padding reached image edge.")
+        context.logger.warning("FaceTools --> +Y-axis padding reached image edge.")
         y_max -= y_min
         y_min = 0
     elif y_max > mask.height:
-        context.services.logger.warning("FaceTools --> -Y-axis padding reached image edge.")
+        context.logger.warning("FaceTools --> -Y-axis padding reached image edge.")
         y_min -= y_max - mask.height
         y_max = mask.height
 
     # Ensure the crop is square and adjust the boundaries if needed
     if x_max - x_min != crop_size:
-        context.services.logger.warning("FaceTools --> Limiting x-axis padding to constrain bounding box to a square.")
+        context.logger.warning("FaceTools --> Limiting x-axis padding to constrain bounding box to a square.")
         diff = crop_size - (x_max - x_min)
         x_min -= diff // 2
         x_max += diff - diff // 2
 
     if y_max - y_min != crop_size:
-        context.services.logger.warning("FaceTools --> Limiting y-axis padding to constrain bounding box to a square.")
+        context.logger.warning("FaceTools --> Limiting y-axis padding to constrain bounding box to a square.")
         diff = crop_size - (y_max - y_min)
         y_min -= diff // 2
         y_max += diff - diff // 2
 
-    context.services.logger.info(f"FaceTools --> Calculated bounding box (8 multiple): {crop_size}")
+    context.logger.info(f"FaceTools --> Calculated bounding box (8 multiple): {crop_size}")
 
     # Crop the output image to the specified size with the center of the face mesh as the center.
     mask = mask.crop((x_min, y_min, x_max, y_max))
@@ -368,7 +366,7 @@ def get_faces_list(
 
     # Generate the face box mask and get the center of the face.
     if not should_chunk:
-        context.services.logger.info("FaceTools --> Attempting full image face detection.")
+        context.logger.info("FaceTools --> Attempting full image face detection.")
         result = generate_face_box_mask(
             context=context,
             minimum_confidence=minimum_confidence,
@@ -380,7 +378,7 @@ def get_faces_list(
             draw_mesh=draw_mesh,
         )
     if should_chunk or len(result) == 0:
-        context.services.logger.info("FaceTools --> Chunking image (chunk toggled on, or no face found in full image).")
+        context.logger.info("FaceTools --> Chunking image (chunk toggled on, or no face found in full image).")
         width, height = image.size
         image_chunks = []
         x_offsets = []
@@ -399,7 +397,7 @@ def get_faces_list(
                 x_offsets.append(x)
                 y_offsets.append(0)
                 fx += increment
-                context.services.logger.info(f"FaceTools --> Chunk starting at x = {x}")
+                context.logger.info(f"FaceTools --> Chunk starting at x = {x}")
         elif height > width:
             # Portrait - slice the image vertically
             fy = 0.0
@@ -411,10 +409,10 @@ def get_faces_list(
                 x_offsets.append(0)
                 y_offsets.append(y)
                 fy += increment
-                context.services.logger.info(f"FaceTools --> Chunk starting at y = {y}")
+                context.logger.info(f"FaceTools --> Chunk starting at y = {y}")
 
         for idx in range(len(image_chunks)):
-            context.services.logger.info(f"FaceTools --> Evaluating faces in chunk {idx}")
+            context.logger.info(f"FaceTools --> Evaluating faces in chunk {idx}")
             result = result + generate_face_box_mask(
                 context=context,
                 minimum_confidence=minimum_confidence,
@@ -428,7 +426,7 @@ def get_faces_list(
 
         if len(result) == 0:
             # Give up
-            context.services.logger.warning(
+            context.logger.warning(
                 "FaceTools --> No face detected in chunked input image. Passing through original image."
             )
 
@@ -437,7 +435,7 @@ def get_faces_list(
     return all_faces
 
 
-@invocation("face_off", title="FaceOff", tags=["image", "faceoff", "face", "mask"], category="image", version="1.2.0")
+@invocation("face_off", title="FaceOff", tags=["image", "faceoff", "face", "mask"], category="image", version="1.2.1")
 class FaceOffInvocation(BaseInvocation, WithMetadata):
     """Bound, extract, and mask a face from an image using MediaPipe detection"""
 
@@ -470,11 +468,11 @@ class FaceOffInvocation(BaseInvocation, WithMetadata):
         )
 
         if len(all_faces) == 0:
-            context.services.logger.warning("FaceOff --> No faces detected. Passing through original image.")
+            context.logger.warning("FaceOff --> No faces detected. Passing through original image.")
             return None
 
         if self.face_id > len(all_faces) - 1:
-            context.services.logger.warning(
+            context.logger.warning(
                 f"FaceOff --> Face ID {self.face_id} is outside of the number of faces detected ({len(all_faces)}). Passing through original image."
             )
             return None
@@ -486,7 +484,7 @@ class FaceOffInvocation(BaseInvocation, WithMetadata):
         return face_data
 
     def invoke(self, context: InvocationContext) -> FaceOffOutput:
-        image = context.services.images.get_pil_image(self.image.image_name)
+        image = context.images.get_pil(self.image.image_name)
         result = self.faceoff(context=context, image=image)
 
         if result is None:
@@ -500,24 +498,9 @@ class FaceOffInvocation(BaseInvocation, WithMetadata):
             x = result["x_min"]
             y = result["y_min"]
 
-        image_dto = context.services.images.create(
-            image=result_image,
-            image_origin=ResourceOrigin.INTERNAL,
-            image_category=ImageCategory.GENERAL,
-            node_id=self.id,
-            session_id=context.graph_execution_state_id,
-            is_intermediate=self.is_intermediate,
-            workflow=context.workflow,
-        )
+        image_dto = context.images.save(image=result_image)
 
-        mask_dto = context.services.images.create(
-            image=result_mask,
-            image_origin=ResourceOrigin.INTERNAL,
-            image_category=ImageCategory.MASK,
-            node_id=self.id,
-            session_id=context.graph_execution_state_id,
-            is_intermediate=self.is_intermediate,
-        )
+        mask_dto = context.images.save(image=result_mask, image_category=ImageCategory.MASK)
 
         output = FaceOffOutput(
             image=ImageField(image_name=image_dto.image_name),
@@ -531,7 +514,7 @@ class FaceOffInvocation(BaseInvocation, WithMetadata):
         return output
 
 
-@invocation("face_mask_detection", title="FaceMask", tags=["image", "face", "mask"], category="image", version="1.2.0")
+@invocation("face_mask_detection", title="FaceMask", tags=["image", "face", "mask"], category="image", version="1.2.1")
 class FaceMaskInvocation(BaseInvocation, WithMetadata):
     """Face mask creation using mediapipe face detection"""
 
@@ -580,7 +563,7 @@ class FaceMaskInvocation(BaseInvocation, WithMetadata):
 
             if len(intersected_face_ids) == 0:
                 id_range_str = ",".join([str(id) for id in id_range])
-                context.services.logger.warning(
+                context.logger.warning(
                     f"Face IDs must be in range of detected faces - requested {self.face_ids}, detected {id_range_str}. Passing through original image."
                 )
                 return FaceMaskResult(
@@ -616,27 +599,12 @@ class FaceMaskInvocation(BaseInvocation, WithMetadata):
         )
 
     def invoke(self, context: InvocationContext) -> FaceMaskOutput:
-        image = context.services.images.get_pil_image(self.image.image_name)
+        image = context.images.get_pil(self.image.image_name)
         result = self.facemask(context=context, image=image)
 
-        image_dto = context.services.images.create(
-            image=result["image"],
-            image_origin=ResourceOrigin.INTERNAL,
-            image_category=ImageCategory.GENERAL,
-            node_id=self.id,
-            session_id=context.graph_execution_state_id,
-            is_intermediate=self.is_intermediate,
-            workflow=context.workflow,
-        )
+        image_dto = context.images.save(image=result["image"])
 
-        mask_dto = context.services.images.create(
-            image=result["mask"],
-            image_origin=ResourceOrigin.INTERNAL,
-            image_category=ImageCategory.MASK,
-            node_id=self.id,
-            session_id=context.graph_execution_state_id,
-            is_intermediate=self.is_intermediate,
-        )
+        mask_dto = context.images.save(image=result["mask"], image_category=ImageCategory.MASK)
 
         output = FaceMaskOutput(
             image=ImageField(image_name=image_dto.image_name),
@@ -649,9 +617,9 @@ class FaceMaskInvocation(BaseInvocation, WithMetadata):
 
 
 @invocation(
-    "face_identifier", title="FaceIdentifier", tags=["image", "face", "identifier"], category="image", version="1.2.0"
+    "face_identifier", title="FaceIdentifier", tags=["image", "face", "identifier"], category="image", version="1.2.1"
 )
-class FaceIdentifierInvocation(BaseInvocation, WithMetadata):
+class FaceIdentifierInvocation(BaseInvocation, WithMetadata, WithBoard):
     """Outputs an image with detected face IDs printed on each face. For use with other FaceTools."""
 
     image: ImageField = InputField(description="Image to face detect")
@@ -705,21 +673,9 @@ class FaceIdentifierInvocation(BaseInvocation, WithMetadata):
         return image
 
     def invoke(self, context: InvocationContext) -> ImageOutput:
-        image = context.services.images.get_pil_image(self.image.image_name)
+        image = context.images.get_pil(self.image.image_name)
         result_image = self.faceidentifier(context=context, image=image)
 
-        image_dto = context.services.images.create(
-            image=result_image,
-            image_origin=ResourceOrigin.INTERNAL,
-            image_category=ImageCategory.GENERAL,
-            node_id=self.id,
-            session_id=context.graph_execution_state_id,
-            is_intermediate=self.is_intermediate,
-            workflow=context.workflow,
-        )
+        image_dto = context.images.save(image=result_image)
 
-        return ImageOutput(
-            image=ImageField(image_name=image_dto.image_name),
-            width=image_dto.width,
-            height=image_dto.height,
-        )
+        return ImageOutput.build(image_dto)

invokeai/app/invocations/fields.py

@@ -0,0 +1,565 @@
+from enum import Enum
+from typing import Any, Callable, Optional, Tuple
+
+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 ImageField(BaseModel):
+    """An image primitive field"""
+
+    image_name: str = Field(description="The name of the image")
+
+
+class BoardField(BaseModel):
+    """A board primitive field"""
+
+    board_id: str = Field(description="The id of the board")
+
+
+class DenoiseMaskField(BaseModel):
+    """An inpaint mask field"""
+
+    mask_name: str = Field(description="The name of the mask image")
+    masked_latents_name: Optional[str] = Field(default=None, description="The name of the masked image latents")
+
+
+class LatentsField(BaseModel):
+    """A latents tensor primitive field"""
+
+    latents_name: str = Field(description="The name of the latents")
+    seed: Optional[int] = Field(default=None, description="Seed used to generate this latents")
+
+
+class ColorField(BaseModel):
+    """A color primitive field"""
+
+    r: int = Field(ge=0, le=255, description="The red component")
+    g: int = Field(ge=0, le=255, description="The green component")
+    b: int = Field(ge=0, le=255, description="The blue component")
+    a: int = Field(ge=0, le=255, description="The alpha component")
+
+    def tuple(self) -> Tuple[int, int, int, int]:
+        return (self.r, self.g, self.b, self.a)
+
+
+class ConditioningField(BaseModel):
+    """A conditioning tensor primitive value"""
+
+    conditioning_name: str = Field(description="The name of conditioning tensor")
+    # endregion
+
+
+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):
+    """
+    Inherit from this class if your node needs a metadata input field.
+    """
+
+    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 WithBoard(BaseModel):
+    """
+    Inherit from this class if your node needs a board input field.
+    """
+
+    board: Optional[BoardField] = Field(
+        default=None,
+        description=FieldDescriptions.board,
+        json_schema_extra=InputFieldJSONSchemaExtra(
+            field_kind=FieldKind.Internal,
+            input=Input.Direct,
+            orig_required=False,
+        ).model_dump(exclude_none=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),
+    )

invokeai/app/invocations/infill.py

@@ -6,14 +6,16 @@ from typing import Literal, Optional, get_args
 import numpy as np
 from PIL import Image, ImageOps
 
-from invokeai.app.invocations.primitives import ColorField, ImageField, ImageOutput
-from invokeai.app.services.image_records.image_records_common import ImageCategory, ResourceOrigin
+from invokeai.app.invocations.fields import ColorField, ImageField
+from invokeai.app.invocations.primitives import ImageOutput
+from invokeai.app.services.shared.invocation_context import InvocationContext
 from invokeai.app.util.misc import SEED_MAX
 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, invocation
+from .fields import InputField, WithBoard, WithMetadata
 from .image import PIL_RESAMPLING_MAP, PIL_RESAMPLING_MODES
 
 
@@ -118,8 +120,8 @@ def tile_fill_missing(im: Image.Image, tile_size: int = 16, seed: Optional[int]
     return si
 
 
-@invocation("infill_rgba", title="Solid Color Infill", tags=["image", "inpaint"], category="inpaint", version="1.2.0")
-class InfillColorInvocation(BaseInvocation, WithMetadata):
+@invocation("infill_rgba", title="Solid Color Infill", tags=["image", "inpaint"], category="inpaint", version="1.2.1")
+class InfillColorInvocation(BaseInvocation, WithMetadata, WithBoard):
     """Infills transparent areas of an image with a solid color"""
 
     image: ImageField = InputField(description="The image to infill")
@@ -129,33 +131,20 @@ class InfillColorInvocation(BaseInvocation, WithMetadata):
     )
 
     def invoke(self, context: InvocationContext) -> ImageOutput:
-        image = context.services.images.get_pil_image(self.image.image_name)
+        image = context.images.get_pil(self.image.image_name)
 
         solid_bg = Image.new("RGBA", image.size, self.color.tuple())
         infilled = Image.alpha_composite(solid_bg, image.convert("RGBA"))
 
         infilled.paste(image, (0, 0), image.split()[-1])
 
-        image_dto = context.services.images.create(
-            image=infilled,
-            image_origin=ResourceOrigin.INTERNAL,
-            image_category=ImageCategory.GENERAL,
-            node_id=self.id,
-            session_id=context.graph_execution_state_id,
-            is_intermediate=self.is_intermediate,
-            metadata=self.metadata,
-            workflow=context.workflow,
-        )
+        image_dto = context.images.save(image=infilled)
 
-        return ImageOutput(
-            image=ImageField(image_name=image_dto.image_name),
-            width=image_dto.width,
-            height=image_dto.height,
-        )
+        return ImageOutput.build(image_dto)
 
 
-@invocation("infill_tile", title="Tile Infill", tags=["image", "inpaint"], category="inpaint", version="1.2.1")
-class InfillTileInvocation(BaseInvocation, WithMetadata):
+@invocation("infill_tile", title="Tile Infill", tags=["image", "inpaint"], category="inpaint", version="1.2.2")
+class InfillTileInvocation(BaseInvocation, WithMetadata, WithBoard):
     """Infills transparent areas of an image with tiles of the image"""
 
     image: ImageField = InputField(description="The image to infill")
@@ -168,33 +157,20 @@ class InfillTileInvocation(BaseInvocation, WithMetadata):
     )
 
     def invoke(self, context: InvocationContext) -> ImageOutput:
-        image = context.services.images.get_pil_image(self.image.image_name)
+        image = context.images.get_pil(self.image.image_name)
 
         infilled = tile_fill_missing(image.copy(), seed=self.seed, tile_size=self.tile_size)
         infilled.paste(image, (0, 0), image.split()[-1])
 
-        image_dto = context.services.images.create(
-            image=infilled,
-            image_origin=ResourceOrigin.INTERNAL,
-            image_category=ImageCategory.GENERAL,
-            node_id=self.id,
-            session_id=context.graph_execution_state_id,
-            is_intermediate=self.is_intermediate,
-            metadata=self.metadata,
-            workflow=context.workflow,
-        )
+        image_dto = context.images.save(image=infilled)
 
-        return ImageOutput(
-            image=ImageField(image_name=image_dto.image_name),
-            width=image_dto.width,
-            height=image_dto.height,
-        )
+        return ImageOutput.build(image_dto)
 
 
 @invocation(
-    "infill_patchmatch", title="PatchMatch Infill", tags=["image", "inpaint"], category="inpaint", version="1.2.0"
+    "infill_patchmatch", title="PatchMatch Infill", tags=["image", "inpaint"], category="inpaint", version="1.2.1"
 )
-class InfillPatchMatchInvocation(BaseInvocation, WithMetadata):
+class InfillPatchMatchInvocation(BaseInvocation, WithMetadata, WithBoard):
     """Infills transparent areas of an image using the PatchMatch algorithm"""
 
     image: ImageField = InputField(description="The image to infill")
@@ -202,7 +178,7 @@ class InfillPatchMatchInvocation(BaseInvocation, WithMetadata):
     resample_mode: PIL_RESAMPLING_MODES = InputField(default="bicubic", description="The resampling mode")
 
     def invoke(self, context: InvocationContext) -> ImageOutput:
-        image = context.services.images.get_pil_image(self.image.image_name).convert("RGBA")
+        image = context.images.get_pil(self.image.image_name).convert("RGBA")
 
         resample_mode = PIL_RESAMPLING_MAP[self.resample_mode]
 
@@ -227,77 +203,38 @@ class InfillPatchMatchInvocation(BaseInvocation, WithMetadata):
         infilled.paste(image, (0, 0), mask=image.split()[-1])
         # image.paste(infilled, (0, 0), mask=image.split()[-1])
 
-        image_dto = context.services.images.create(
-            image=infilled,
-            image_origin=ResourceOrigin.INTERNAL,
-            image_category=ImageCategory.GENERAL,
-            node_id=self.id,
-            session_id=context.graph_execution_state_id,
-            is_intermediate=self.is_intermediate,
-            metadata=self.metadata,
-            workflow=context.workflow,
-        )
+        image_dto = context.images.save(image=infilled)
 
-        return ImageOutput(
-            image=ImageField(image_name=image_dto.image_name),
-            width=image_dto.width,
-            height=image_dto.height,
-        )
+        return ImageOutput.build(image_dto)
 
 
-@invocation("infill_lama", title="LaMa Infill", tags=["image", "inpaint"], category="inpaint", version="1.2.0")
-class LaMaInfillInvocation(BaseInvocation, WithMetadata):
+@invocation("infill_lama", title="LaMa Infill", tags=["image", "inpaint"], category="inpaint", version="1.2.1")
+class LaMaInfillInvocation(BaseInvocation, WithMetadata, WithBoard):
     """Infills transparent areas of an image using the LaMa model"""
 
     image: ImageField = InputField(description="The image to infill")
 
     def invoke(self, context: InvocationContext) -> ImageOutput:
-        image = context.services.images.get_pil_image(self.image.image_name)
+        image = context.images.get_pil(self.image.image_name)
 
         infilled = infill_lama(image.copy())
 
-        image_dto = context.services.images.create(
-            image=infilled,
-            image_origin=ResourceOrigin.INTERNAL,
-            image_category=ImageCategory.GENERAL,
-            node_id=self.id,
-            session_id=context.graph_execution_state_id,
-            is_intermediate=self.is_intermediate,
-            metadata=self.metadata,
-            workflow=context.workflow,
-        )
+        image_dto = context.images.save(image=infilled)
 
-        return ImageOutput(
-            image=ImageField(image_name=image_dto.image_name),
-            width=image_dto.width,
-            height=image_dto.height,
-        )
+        return ImageOutput.build(image_dto)
 
 
-@invocation("infill_cv2", title="CV2 Infill", tags=["image", "inpaint"], category="inpaint", version="1.2.0")
-class CV2InfillInvocation(BaseInvocation, WithMetadata):
+@invocation("infill_cv2", title="CV2 Infill", tags=["image", "inpaint"], category="inpaint", version="1.2.1")
+class CV2InfillInvocation(BaseInvocation, WithMetadata, WithBoard):
     """Infills transparent areas of an image using OpenCV Inpainting"""
 
     image: ImageField = InputField(description="The image to infill")
 
     def invoke(self, context: InvocationContext) -> ImageOutput:
-        image = context.services.images.get_pil_image(self.image.image_name)
+        image = context.images.get_pil(self.image.image_name)
 
         infilled = infill_cv2(image.copy())
 
-        image_dto = context.services.images.create(
-            image=infilled,
-            image_origin=ResourceOrigin.INTERNAL,
-            image_category=ImageCategory.GENERAL,
-            node_id=self.id,
-            session_id=context.graph_execution_state_id,
-            is_intermediate=self.is_intermediate,
-            metadata=self.metadata,
-            workflow=context.workflow,
-        )
+        image_dto = context.images.save(image=infilled)
 
-        return ImageOutput(
-            image=ImageField(image_name=image_dto.image_name),
-            width=image_dto.width,
-            height=image_dto.height,
-        )
+        return ImageOutput.build(image_dto)

invokeai/app/invocations/ip_adapter.py

@@ -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.app.services.shared.invocation_context import InvocationContext
 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
 
@@ -65,7 +62,7 @@ class IPAdapterOutput(BaseInvocationOutput):
     ip_adapter: IPAdapterField = OutputField(description=FieldDescriptions.ip_adapter, title="IP-Adapter")
 
 
-@invocation("ip_adapter", title="IP-Adapter", tags=["ip_adapter", "control"], category="ip_adapter", version="1.1.1")
+@invocation("ip_adapter", title="IP-Adapter", tags=["ip_adapter", "control"], category="ip_adapter", version="1.1.2")
 class IPAdapterInvocation(BaseInvocation):
     """Collects IP-Adapter info to pass to other nodes."""
 
@@ -98,7 +95,7 @@ class IPAdapterInvocation(BaseInvocation):
 
     def invoke(self, context: InvocationContext) -> IPAdapterOutput:
         # Lookup the CLIP Vision encoder that is intended to be used with the IP-Adapter model.
-        ip_adapter_info = context.services.model_manager.model_info(
+        ip_adapter_info = context.models.get_info(
             self.ip_adapter_model.model_name, self.ip_adapter_model.base_model, ModelType.IPAdapter
         )
         # HACK(ryand): This is bad for a couple of reasons: 1) we are bypassing the model manager to read the model
@@ -107,7 +104,7 @@ class IPAdapterInvocation(BaseInvocation):
         # is currently messy due to differences between how the model info is generated when installing a model from
         # disk vs. downloading the model.
         image_encoder_model_id = get_ip_adapter_image_encoder_model_id(
-            os.path.join(context.services.configuration.get_config().models_path, ip_adapter_info["path"])
+            os.path.join(context.config.get().models_path, ip_adapter_info["path"])
         )
         image_encoder_model_name = image_encoder_model_id.split("/")[-1].strip()
         image_encoder_model = CLIPVisionModelField(

invokeai/app/invocations/latent.py

@@ -23,21 +23,29 @@ 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.constants import LATENT_SCALE_FACTOR, SCHEDULER_NAME_VALUES
+from invokeai.app.invocations.fields import (
+    ConditioningField,
+    DenoiseMaskField,
+    FieldDescriptions,
+    ImageField,
+    Input,
+    InputField,
+    LatentsField,
+    OutputField,
+    UIType,
+    WithBoard,
+    WithMetadata,
+)
 from invokeai.app.invocations.ip_adapter import IPAdapterField
 from invokeai.app.invocations.primitives import (
-    DenoiseMaskField,
     DenoiseMaskOutput,
-    ImageField,
     ImageOutput,
-    LatentsField,
     LatentsOutput,
-    build_latents_output,
 )
 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.services.shared.invocation_context import InvocationContext
 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
 from invokeai.backend.model_management.models import ModelType, SilenceWarnings
 from invokeai.backend.stable_diffusion.diffusion.conditioning_data import ConditioningData, IPAdapterConditioningInfo
@@ -59,16 +67,9 @@ from ...backend.util.devices import choose_precision, choose_torch_device
 from .baseinvocation import (
     BaseInvocation,
     BaseInvocationOutput,
-    Input,
-    InputField,
-    InvocationContext,
-    OutputField,
-    UIType,
-    WithMetadata,
     invocation,
     invocation_output,
 )
-from .compel import ConditioningField
 from .controlnet_image_processors import ControlField
 from .model import ModelInfo, UNetField, VaeField
 
@@ -77,18 +78,10 @@ if choose_torch_device() == torch.device("mps"):
 
 DEFAULT_PRECISION = choose_precision(choose_torch_device())
 
-SAMPLER_NAME_VALUES = Literal[tuple(SCHEDULER_MAP.keys())]
-
-# HACK: Many nodes are currently hard-coded to use a fixed latent scale factor of 8. This is fragile, and will need to
-# be addressed if future models use a different latent scale factor. Also, note that there may be places where the scale
-# factor is hard-coded to a literal '8' rather than using this constant.
-# The ratio of image:latent dimensions is LATENT_SCALE_FACTOR:1, or 8:1.
-LATENT_SCALE_FACTOR = 8
-
 
 @invocation_output("scheduler_output")
 class SchedulerOutput(BaseInvocationOutput):
-    scheduler: SAMPLER_NAME_VALUES = OutputField(description=FieldDescriptions.scheduler, ui_type=UIType.Scheduler)
+    scheduler: SCHEDULER_NAME_VALUES = OutputField(description=FieldDescriptions.scheduler, ui_type=UIType.Scheduler)
 
 
 @invocation(
@@ -101,7 +94,7 @@ class SchedulerOutput(BaseInvocationOutput):
 class SchedulerInvocation(BaseInvocation):
     """Selects a scheduler."""
 
-    scheduler: SAMPLER_NAME_VALUES = InputField(
+    scheduler: SCHEDULER_NAME_VALUES = InputField(
         default="euler",
         description=FieldDescriptions.scheduler,
         ui_type=UIType.Scheduler,
@@ -116,7 +109,7 @@ class SchedulerInvocation(BaseInvocation):
     title="Create Denoise Mask",
     tags=["mask", "denoise"],
     category="latents",
-    version="1.0.0",
+    version="1.0.1",
 )
 class CreateDenoiseMaskInvocation(BaseInvocation):
     """Creates mask for denoising model run."""
@@ -144,7 +137,7 @@ class CreateDenoiseMaskInvocation(BaseInvocation):
     @torch.no_grad()
     def invoke(self, context: InvocationContext) -> DenoiseMaskOutput:
         if self.image is not None:
-            image = context.services.images.get_pil_image(self.image.image_name)
+            image = context.images.get_pil(self.image.image_name)
             image = image_resized_to_grid_as_tensor(image.convert("RGB"))
             if image.dim() == 3:
                 image = image.unsqueeze(0)
@@ -152,33 +145,26 @@ class CreateDenoiseMaskInvocation(BaseInvocation):
             image = None
 
         mask = self.prep_mask_tensor(
-            context.services.images.get_pil_image(self.mask.image_name),
+            context.images.get_pil(self.mask.image_name),
         )
 
         if image is not None:
-            vae_info = context.services.model_manager.get_model(
-                **self.vae.vae.model_dump(),
-                context=context,
-            )
+            vae_info = context.models.load(**self.vae.vae.model_dump())
 
             img_mask = tv_resize(mask, image.shape[-2:], T.InterpolationMode.BILINEAR, antialias=False)
             masked_image = image * torch.where(img_mask < 0.5, 0.0, 1.0)
             # TODO:
             masked_latents = ImageToLatentsInvocation.vae_encode(vae_info, self.fp32, self.tiled, masked_image.clone())
 
-            masked_latents_name = f"{context.graph_execution_state_id}__{self.id}_masked_latents"
-            context.services.latents.save(masked_latents_name, masked_latents)
+            masked_latents_name = context.tensors.save(tensor=masked_latents)
         else:
             masked_latents_name = None
 
-        mask_name = f"{context.graph_execution_state_id}__{self.id}_mask"
-        context.services.latents.save(mask_name, mask)
+        mask_name = context.tensors.save(tensor=mask)
 
-        return DenoiseMaskOutput(
-            denoise_mask=DenoiseMaskField(
+        return DenoiseMaskOutput.build(
             mask_name=mask_name,
             masked_latents_name=masked_latents_name,
-            ),
         )
 
 
@@ -189,10 +175,7 @@ def get_scheduler(
     seed: int,
 ) -> Scheduler:
     scheduler_class, scheduler_extra_config = SCHEDULER_MAP.get(scheduler_name, SCHEDULER_MAP["ddim"])
-    orig_scheduler_info = context.services.model_manager.get_model(
-        **scheduler_info.model_dump(),
-        context=context,
-    )
+    orig_scheduler_info = context.models.load(**scheduler_info.model_dump())
     with orig_scheduler_info as orig_scheduler:
         scheduler_config = orig_scheduler.config
 
@@ -221,7 +204,7 @@ def get_scheduler(
     title="Denoise Latents",
     tags=["latents", "denoise", "txt2img", "t2i", "t2l", "img2img", "i2i", "l2l"],
     category="latents",
-    version="1.5.1",
+    version="1.5.2",
 )
 class DenoiseLatentsInvocation(BaseInvocation):
     """Denoises noisy latents to decodable images"""
@@ -249,7 +232,7 @@ class DenoiseLatentsInvocation(BaseInvocation):
         description=FieldDescriptions.denoising_start,
     )
     denoising_end: float = InputField(default=1.0, ge=0, le=1, description=FieldDescriptions.denoising_end)
-    scheduler: SAMPLER_NAME_VALUES = InputField(
+    scheduler: SCHEDULER_NAME_VALUES = InputField(
         default="euler",
         description=FieldDescriptions.scheduler,
         ui_type=UIType.Scheduler,
@@ -307,22 +290,6 @@ class DenoiseLatentsInvocation(BaseInvocation):
                 raise ValueError("cfg_scale must be greater than 1")
         return v
 
-    # TODO: pass this an emitter method or something? or a session for dispatching?
-    def dispatch_progress(
-        self,
-        context: InvocationContext,
-        source_node_id: str,
-        intermediate_state: PipelineIntermediateState,
-        base_model: BaseModelType,
-    ) -> None:
-        stable_diffusion_step_callback(
-            context=context,
-            intermediate_state=intermediate_state,
-            node=self.model_dump(),
-            source_node_id=source_node_id,
-            base_model=base_model,
-        )
-
     def get_conditioning_data(
         self,
         context: InvocationContext,
@@ -330,11 +297,11 @@ class DenoiseLatentsInvocation(BaseInvocation):
         unet,
         seed,
     ) -> ConditioningData:
-        positive_cond_data = context.services.latents.get(self.positive_conditioning.conditioning_name)
+        positive_cond_data = context.conditioning.load(self.positive_conditioning.conditioning_name)
         c = positive_cond_data.conditionings[0].to(device=unet.device, dtype=unet.dtype)
         extra_conditioning_info = c.extra_conditioning
 
-        negative_cond_data = context.services.latents.get(self.negative_conditioning.conditioning_name)
+        negative_cond_data = context.conditioning.load(self.negative_conditioning.conditioning_name)
         uc = negative_cond_data.conditionings[0].to(device=unet.device, dtype=unet.dtype)
 
         conditioning_data = ConditioningData(
@@ -422,17 +389,16 @@ class DenoiseLatentsInvocation(BaseInvocation):
         controlnet_data = []
         for control_info in control_list:
             control_model = exit_stack.enter_context(
-                context.services.model_manager.get_model(
+                context.models.load(
                     model_name=control_info.control_model.model_name,
                     model_type=ModelType.ControlNet,
                     base_model=control_info.control_model.base_model,
-                    context=context,
                 )
             )
 
             # control_models.append(control_model)
             control_image_field = control_info.image
-            input_image = context.services.images.get_pil_image(control_image_field.image_name)
+            input_image = context.images.get_pil(control_image_field.image_name)
             # self.image.image_type, self.image.image_name
             # FIXME: still need to test with different widths, heights, devices, dtypes
             #        and add in batch_size, num_images_per_prompt?
@@ -490,19 +456,17 @@ class DenoiseLatentsInvocation(BaseInvocation):
         conditioning_data.ip_adapter_conditioning = []
         for single_ip_adapter in ip_adapter:
             ip_adapter_model: Union[IPAdapter, IPAdapterPlus] = exit_stack.enter_context(
-                context.services.model_manager.get_model(
+                context.models.load(
                     model_name=single_ip_adapter.ip_adapter_model.model_name,
                     model_type=ModelType.IPAdapter,
                     base_model=single_ip_adapter.ip_adapter_model.base_model,
-                    context=context,
                 )
             )
 
-            image_encoder_model_info = context.services.model_manager.get_model(
+            image_encoder_model_info = context.models.load(
                 model_name=single_ip_adapter.image_encoder_model.model_name,
                 model_type=ModelType.CLIPVision,
                 base_model=single_ip_adapter.image_encoder_model.base_model,
-                context=context,
             )
 
             # `single_ip_adapter.image` could be a list or a single ImageField. Normalize to a list here.
@@ -510,7 +474,7 @@ class DenoiseLatentsInvocation(BaseInvocation):
             if not isinstance(single_ipa_images, list):
                 single_ipa_images = [single_ipa_images]
 
-            single_ipa_images = [context.services.images.get_pil_image(image.image_name) for image in single_ipa_images]
+            single_ipa_images = [context.images.get_pil(image.image_name) for image in single_ipa_images]
 
             # TODO(ryand): With some effort, the step of running the CLIP Vision encoder could be done before any other
             # models are needed in memory. This would help to reduce peak memory utilization in low-memory environments.
@@ -554,13 +518,12 @@ class DenoiseLatentsInvocation(BaseInvocation):
 
         t2i_adapter_data = []
         for t2i_adapter_field in t2i_adapter:
-            t2i_adapter_model_info = context.services.model_manager.get_model(
+            t2i_adapter_model_info = context.models.load(
                 model_name=t2i_adapter_field.t2i_adapter_model.model_name,
                 model_type=ModelType.T2IAdapter,
                 base_model=t2i_adapter_field.t2i_adapter_model.base_model,
-                context=context,
             )
-            image = context.services.images.get_pil_image(t2i_adapter_field.image.image_name)
+            image = context.images.get_pil(t2i_adapter_field.image.image_name)
 
             # The max_unet_downscale is the maximum amount that the UNet model downscales the latent image internally.
             if t2i_adapter_field.t2i_adapter_model.base_model == BaseModelType.StableDiffusion1:
@@ -647,14 +610,14 @@ class DenoiseLatentsInvocation(BaseInvocation):
 
         return num_inference_steps, timesteps, init_timestep
 
-    def prep_inpaint_mask(self, context, latents):
+    def prep_inpaint_mask(self, context: InvocationContext, latents):
         if self.denoise_mask is None:
             return None, None
 
-        mask = context.services.latents.get(self.denoise_mask.mask_name)
+        mask = context.tensors.load(self.denoise_mask.mask_name)
         mask = tv_resize(mask, latents.shape[-2:], T.InterpolationMode.BILINEAR, antialias=False)
         if self.denoise_mask.masked_latents_name is not None:
-            masked_latents = context.services.latents.get(self.denoise_mask.masked_latents_name)
+            masked_latents = context.tensors.load(self.denoise_mask.masked_latents_name)
         else:
             masked_latents = None
 
@@ -666,11 +629,11 @@ class DenoiseLatentsInvocation(BaseInvocation):
             seed = None
             noise = None
             if self.noise is not None:
-                noise = context.services.latents.get(self.noise.latents_name)
+                noise = context.tensors.load(self.noise.latents_name)
                 seed = self.noise.seed
 
             if self.latents is not None:
-                latents = context.services.latents.get(self.latents.latents_name)
+                latents = context.tensors.load(self.latents.latents_name)
                 if seed is None:
                     seed = self.latents.seed
 
@@ -696,27 +659,17 @@ class DenoiseLatentsInvocation(BaseInvocation):
                 do_classifier_free_guidance=True,
             )
 
-            # Get the source node id (we are invoking the prepared node)
-            graph_execution_state = context.services.graph_execution_manager.get(context.graph_execution_state_id)
-            source_node_id = graph_execution_state.prepared_source_mapping[self.id]
-
             def step_callback(state: PipelineIntermediateState):
-                self.dispatch_progress(context, source_node_id, state, self.unet.unet.base_model)
+                context.util.sd_step_callback(state, self.unet.unet.base_model)
 
             def _lora_loader():
                 for lora in self.unet.loras:
-                    lora_info = context.services.model_manager.get_model(
-                        **lora.model_dump(exclude={"weight"}),
-                        context=context,
-                    )
+                    lora_info = context.models.load(**lora.model_dump(exclude={"weight"}))
                     yield (lora_info.context.model, lora.weight)
                     del lora_info
                 return
 
-            unet_info = context.services.model_manager.get_model(
-                **self.unet.unet.model_dump(),
-                context=context,
-            )
+            unet_info = context.models.load(**self.unet.unet.model_dump())
             with (
                 ExitStack() as exit_stack,
                 ModelPatcher.apply_freeu(unet_info.context.model, self.unet.freeu_config),
@@ -792,9 +745,8 @@ class DenoiseLatentsInvocation(BaseInvocation):
             if choose_torch_device() == torch.device("mps"):
                 mps.empty_cache()
 
-            name = f"{context.graph_execution_state_id}__{self.id}"
-            context.services.latents.save(name, result_latents)
-        return build_latents_output(latents_name=name, latents=result_latents, seed=seed)
+            name = context.tensors.save(tensor=result_latents)
+        return LatentsOutput.build(latents_name=name, latents=result_latents, seed=seed)
 
 
 @invocation(
@@ -802,9 +754,9 @@ class DenoiseLatentsInvocation(BaseInvocation):
     title="Latents to Image",
     tags=["latents", "image", "vae", "l2i"],
     category="latents",
-    version="1.2.0",
+    version="1.2.1",
 )
-class LatentsToImageInvocation(BaseInvocation, WithMetadata):
+class LatentsToImageInvocation(BaseInvocation, WithMetadata, WithBoard):
     """Generates an image from latents."""
 
     latents: LatentsField = InputField(
@@ -820,12 +772,9 @@ class LatentsToImageInvocation(BaseInvocation, WithMetadata):
 
     @torch.no_grad()
     def invoke(self, context: InvocationContext) -> ImageOutput:
-        latents = context.services.latents.get(self.latents.latents_name)
+        latents = context.tensors.load(self.latents.latents_name)
 
-        vae_info = context.services.model_manager.get_model(
-            **self.vae.vae.model_dump(),
-            context=context,
-        )
+        vae_info = context.models.load(**self.vae.vae.model_dump())
 
         with set_seamless(vae_info.context.model, self.vae.seamless_axes), vae_info as vae:
             latents = latents.to(vae.device)
@@ -854,7 +803,7 @@ class LatentsToImageInvocation(BaseInvocation, WithMetadata):
                 vae.to(dtype=torch.float16)
                 latents = latents.half()
 
-            if self.tiled or context.services.configuration.tiled_decode:
+            if self.tiled or context.config.get().tiled_decode:
                 vae.enable_tiling()
             else:
                 vae.disable_tiling()
@@ -878,22 +827,9 @@ class LatentsToImageInvocation(BaseInvocation, WithMetadata):
         if choose_torch_device() == torch.device("mps"):
             mps.empty_cache()
 
-        image_dto = context.services.images.create(
-            image=image,
-            image_origin=ResourceOrigin.INTERNAL,
-            image_category=ImageCategory.GENERAL,
-            node_id=self.id,
-            session_id=context.graph_execution_state_id,
-            is_intermediate=self.is_intermediate,
-            metadata=self.metadata,
-            workflow=context.workflow,
-        )
+        image_dto = context.images.save(image=image)
 
-        return ImageOutput(
-            image=ImageField(image_name=image_dto.image_name),
-            width=image_dto.width,
-            height=image_dto.height,
-        )
+        return ImageOutput.build(image_dto)
 
 
 LATENTS_INTERPOLATION_MODE = Literal["nearest", "linear", "bilinear", "bicubic", "trilinear", "area", "nearest-exact"]
@@ -904,7 +840,7 @@ LATENTS_INTERPOLATION_MODE = Literal["nearest", "linear", "bilinear", "bicubic",
     title="Resize Latents",
     tags=["latents", "resize"],
     category="latents",
-    version="1.0.0",
+    version="1.0.1",
 )
 class ResizeLatentsInvocation(BaseInvocation):
     """Resizes latents to explicit width/height (in pixels). Provided dimensions are floor-divided by 8."""
@@ -927,7 +863,7 @@ class ResizeLatentsInvocation(BaseInvocation):
     antialias: bool = InputField(default=False, description=FieldDescriptions.torch_antialias)
 
     def invoke(self, context: InvocationContext) -> LatentsOutput:
-        latents = context.services.latents.get(self.latents.latents_name)
+        latents = context.tensors.load(self.latents.latents_name)
 
         # TODO:
         device = choose_torch_device()
@@ -945,10 +881,8 @@ class ResizeLatentsInvocation(BaseInvocation):
         if device == torch.device("mps"):
             mps.empty_cache()
 
-        name = f"{context.graph_execution_state_id}__{self.id}"
-        # context.services.latents.set(name, resized_latents)
-        context.services.latents.save(name, resized_latents)
-        return build_latents_output(latents_name=name, latents=resized_latents, seed=self.latents.seed)
+        name = context.tensors.save(tensor=resized_latents)
+        return LatentsOutput.build(latents_name=name, latents=resized_latents, seed=self.latents.seed)
 
 
 @invocation(
@@ -956,7 +890,7 @@ class ResizeLatentsInvocation(BaseInvocation):
     title="Scale Latents",
     tags=["latents", "resize"],
     category="latents",
-    version="1.0.0",
+    version="1.0.1",
 )
 class ScaleLatentsInvocation(BaseInvocation):
     """Scales latents by a given factor."""
@@ -970,7 +904,7 @@ class ScaleLatentsInvocation(BaseInvocation):
     antialias: bool = InputField(default=False, description=FieldDescriptions.torch_antialias)
 
     def invoke(self, context: InvocationContext) -> LatentsOutput:
-        latents = context.services.latents.get(self.latents.latents_name)
+        latents = context.tensors.load(self.latents.latents_name)
 
         # TODO:
         device = choose_torch_device()
@@ -989,10 +923,8 @@ class ScaleLatentsInvocation(BaseInvocation):
         if device == torch.device("mps"):
             mps.empty_cache()
 
-        name = f"{context.graph_execution_state_id}__{self.id}"
-        # context.services.latents.set(name, resized_latents)
-        context.services.latents.save(name, resized_latents)
-        return build_latents_output(latents_name=name, latents=resized_latents, seed=self.latents.seed)
+        name = context.tensors.save(tensor=resized_latents)
+        return LatentsOutput.build(latents_name=name, latents=resized_latents, seed=self.latents.seed)
 
 
 @invocation(
@@ -1000,7 +932,7 @@ class ScaleLatentsInvocation(BaseInvocation):
     title="Image to Latents",
     tags=["latents", "image", "vae", "i2l"],
     category="latents",
-    version="1.0.0",
+    version="1.0.1",
 )
 class ImageToLatentsInvocation(BaseInvocation):
     """Encodes an image into latents."""
@@ -1061,12 +993,9 @@ class ImageToLatentsInvocation(BaseInvocation):
 
     @torch.no_grad()
     def invoke(self, context: InvocationContext) -> LatentsOutput:
-        image = context.services.images.get_pil_image(self.image.image_name)
+        image = context.images.get_pil(self.image.image_name)
 
-        vae_info = context.services.model_manager.get_model(
-            **self.vae.vae.model_dump(),
-            context=context,
-        )
+        vae_info = context.models.load(**self.vae.vae.model_dump())
 
         image_tensor = image_resized_to_grid_as_tensor(image.convert("RGB"))
         if image_tensor.dim() == 3:
@@ -1074,10 +1003,9 @@ class ImageToLatentsInvocation(BaseInvocation):
 
         latents = self.vae_encode(vae_info, self.fp32, self.tiled, image_tensor)
 
-        name = f"{context.graph_execution_state_id}__{self.id}"
         latents = latents.to("cpu")
-        context.services.latents.save(name, latents)
-        return build_latents_output(latents_name=name, latents=latents, seed=None)
+        name = context.tensors.save(tensor=latents)
+        return LatentsOutput.build(latents_name=name, latents=latents, seed=None)
 
     @singledispatchmethod
     @staticmethod
@@ -1097,7 +1025,7 @@ class ImageToLatentsInvocation(BaseInvocation):
     title="Blend Latents",
     tags=["latents", "blend"],
     category="latents",
-    version="1.0.0",
+    version="1.0.1",
 )
 class BlendLatentsInvocation(BaseInvocation):
     """Blend two latents using a given alpha. Latents must have same size."""
@@ -1113,8 +1041,8 @@ class BlendLatentsInvocation(BaseInvocation):
     alpha: float = InputField(default=0.5, description=FieldDescriptions.blend_alpha)
 
     def invoke(self, context: InvocationContext) -> LatentsOutput:
-        latents_a = context.services.latents.get(self.latents_a.latents_name)
-        latents_b = context.services.latents.get(self.latents_b.latents_name)
+        latents_a = context.tensors.load(self.latents_a.latents_name)
+        latents_b = context.tensors.load(self.latents_b.latents_name)
 
         if latents_a.shape != latents_b.shape:
             raise Exception("Latents to blend must be the same size.")
@@ -1168,10 +1096,8 @@ class BlendLatentsInvocation(BaseInvocation):
         if device == torch.device("mps"):
             mps.empty_cache()
 
-        name = f"{context.graph_execution_state_id}__{self.id}"
-        # context.services.latents.set(name, resized_latents)
-        context.services.latents.save(name, blended_latents)
-        return build_latents_output(latents_name=name, latents=blended_latents)
+        name = context.tensors.save(tensor=blended_latents)
+        return LatentsOutput.build(latents_name=name, latents=blended_latents)
 
 
 # The Crop Latents node was copied from @skunkworxdark's implementation here:
@@ -1181,7 +1107,7 @@ class BlendLatentsInvocation(BaseInvocation):
     title="Crop Latents",
     tags=["latents", "crop"],
     category="latents",
-    version="1.0.0",
+    version="1.0.1",
 )
 # TODO(ryand): Named `CropLatentsCoreInvocation` to prevent a conflict with custom node `CropLatentsInvocation`.
 # Currently, if the class names conflict then 'GET /openapi.json' fails.
@@ -1216,7 +1142,7 @@ class CropLatentsCoreInvocation(BaseInvocation):
     )
 
     def invoke(self, context: InvocationContext) -> LatentsOutput:
-        latents = context.services.latents.get(self.latents.latents_name)
+        latents = context.tensors.load(self.latents.latents_name)
 
         x1 = self.x // LATENT_SCALE_FACTOR
         y1 = self.y // LATENT_SCALE_FACTOR
@@ -1225,10 +1151,9 @@ class CropLatentsCoreInvocation(BaseInvocation):
 
         cropped_latents = latents[..., y1:y2, x1:x2]
 
-        name = f"{context.graph_execution_state_id}__{self.id}"
-        context.services.latents.save(name, cropped_latents)
+        name = context.tensors.save(tensor=cropped_latents)
 
-        return build_latents_output(latents_name=name, latents=cropped_latents)
+        return LatentsOutput.build(latents_name=name, latents=cropped_latents)
 
 
 @invocation_output("ideal_size_output")

invokeai/app/invocations/math.py

@@ -5,10 +5,11 @@ 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 invokeai.app.services.shared.invocation_context import InvocationContext
 
-from .baseinvocation import BaseInvocation, InputField, InvocationContext, invocation
+from .baseinvocation import BaseInvocation, invocation
 
 
 @invocation("add", title="Add Integers", tags=["math", "add"], category="math", version="1.0.0")

invokeai/app/invocations/metadata.py

@@ -5,20 +5,22 @@ 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,
+    ImageField,
+    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 invokeai.app.services.shared.invocation_context import InvocationContext
 
 from ...version import __version__
 

invokeai/app/invocations/model.py

@@ -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.services.shared.invocation_context import InvocationContext
 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,
 )
@@ -105,7 +102,7 @@ class LoRAModelField(BaseModel):
     title="Main Model",
     tags=["model"],
     category="model",
-    version="1.0.0",
+    version="1.0.1",
 )
 class MainModelLoaderInvocation(BaseInvocation):
     """Loads a main model, outputting its submodels."""
@@ -119,7 +116,7 @@ class MainModelLoaderInvocation(BaseInvocation):
         model_type = ModelType.Main
 
         # TODO: not found exceptions
-        if not context.services.model_manager.model_exists(
+        if not context.models.exists(
             model_name=model_name,
             base_model=base_model,
             model_type=model_type,
@@ -206,7 +203,7 @@ class LoraLoaderOutput(BaseInvocationOutput):
     clip: Optional[ClipField] = OutputField(default=None, description=FieldDescriptions.clip, title="CLIP")
 
 
-@invocation("lora_loader", title="LoRA", tags=["model"], category="model", version="1.0.0")
+@invocation("lora_loader", title="LoRA", tags=["model"], category="model", version="1.0.1")
 class LoraLoaderInvocation(BaseInvocation):
     """Apply selected lora to unet and text_encoder."""
 
@@ -232,7 +229,7 @@ class LoraLoaderInvocation(BaseInvocation):
         base_model = self.lora.base_model
         lora_name = self.lora.model_name
 
-        if not context.services.model_manager.model_exists(
+        if not context.models.exists(
             base_model=base_model,
             model_name=lora_name,
             model_type=ModelType.Lora,
@@ -288,7 +285,7 @@ class SDXLLoraLoaderOutput(BaseInvocationOutput):
     title="SDXL LoRA",
     tags=["lora", "model"],
     category="model",
-    version="1.0.0",
+    version="1.0.1",
 )
 class SDXLLoraLoaderInvocation(BaseInvocation):
     """Apply selected lora to unet and text_encoder."""
@@ -321,7 +318,7 @@ class SDXLLoraLoaderInvocation(BaseInvocation):
         base_model = self.lora.base_model
         lora_name = self.lora.model_name
 
-        if not context.services.model_manager.model_exists(
+        if not context.models.exists(
             base_model=base_model,
             model_name=lora_name,
             model_type=ModelType.Lora,
@@ -387,7 +384,7 @@ class VAEModelField(BaseModel):
     model_config = ConfigDict(protected_namespaces=())
 
 
-@invocation("vae_loader", title="VAE", tags=["vae", "model"], category="model", version="1.0.0")
+@invocation("vae_loader", title="VAE", tags=["vae", "model"], category="model", version="1.0.1")
 class VaeLoaderInvocation(BaseInvocation):
     """Loads a VAE model, outputting a VaeLoaderOutput"""
 
@@ -402,7 +399,7 @@ class VaeLoaderInvocation(BaseInvocation):
         model_name = self.vae_model.model_name
         model_type = ModelType.Vae
 
-        if not context.services.model_manager.model_exists(
+        if not context.models.exists(
             base_model=base_model,
             model_name=model_name,
             model_type=model_type,

invokeai/app/invocations/noise.py

@@ -4,17 +4,15 @@
 import torch
 from pydantic import field_validator
 
-from invokeai.app.invocations.latent import LatentsField
-from invokeai.app.shared.fields import FieldDescriptions
+from invokeai.app.invocations.constants import LATENT_SCALE_FACTOR
+from invokeai.app.invocations.fields import FieldDescriptions, InputField, LatentsField, OutputField
+from invokeai.app.services.shared.invocation_context import InvocationContext
 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,
 )
@@ -69,12 +67,12 @@ class NoiseOutput(BaseInvocationOutput):
     width: int = OutputField(description=FieldDescriptions.width)
     height: int = OutputField(description=FieldDescriptions.height)
 
-
-def build_noise_output(latents_name: str, latents: torch.Tensor, seed: int):
-    return NoiseOutput(
+    @classmethod
+    def build(cls, latents_name: str, latents: torch.Tensor, seed: int) -> "NoiseOutput":
+        return cls(
             noise=LatentsField(latents_name=latents_name, seed=seed),
-        width=latents.size()[3] * 8,
-        height=latents.size()[2] * 8,
+            width=latents.size()[3] * LATENT_SCALE_FACTOR,
+            height=latents.size()[2] * LATENT_SCALE_FACTOR,
         )
 
 
@@ -96,13 +94,13 @@ class NoiseInvocation(BaseInvocation):
     )
     width: int = InputField(
         default=512,
-        multiple_of=8,
+        multiple_of=LATENT_SCALE_FACTOR,
         gt=0,
         description=FieldDescriptions.width,
     )
     height: int = InputField(
         default=512,
-        multiple_of=8,
+        multiple_of=LATENT_SCALE_FACTOR,
         gt=0,
         description=FieldDescriptions.height,
     )
@@ -124,6 +122,5 @@ class NoiseInvocation(BaseInvocation):
             seed=self.seed,
             use_cpu=self.use_cpu,
         )
-        name = f"{context.graph_execution_state_id}__{self.id}"
-        context.services.latents.save(name, noise)
-        return build_noise_output(latents_name=name, latents=noise, seed=self.seed)
+        name = context.tensors.save(tensor=noise)
+        return NoiseOutput.build(latents_name=name, latents=noise, seed=self.seed)

invokeai/app/invocations/onnx.py

@@ -1,508 +0,0 @@
-# Copyright (c) 2023 Borisov Sergey (https://github.com/StAlKeR7779)
-
-import inspect
-
-# from contextlib import ExitStack
-from typing import List, Literal, Union
-
-import numpy as np
-import torch
-from diffusers.image_processor import VaeImageProcessor
-from pydantic import BaseModel, ConfigDict, Field, field_validator
-from tqdm import tqdm
-
-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
-
-from ...backend.model_management import ONNXModelPatcher
-from ...backend.stable_diffusion import PipelineIntermediateState
-from ...backend.util import choose_torch_device
-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,
-)
-from .controlnet_image_processors import ControlField
-from .latent import SAMPLER_NAME_VALUES, LatentsField, LatentsOutput, build_latents_output, get_scheduler
-from .model import ClipField, ModelInfo, UNetField, VaeField
-
-ORT_TO_NP_TYPE = {
-    "tensor(bool)": np.bool_,
-    "tensor(int8)": np.int8,
-    "tensor(uint8)": np.uint8,
-    "tensor(int16)": np.int16,
-    "tensor(uint16)": np.uint16,
-    "tensor(int32)": np.int32,
-    "tensor(uint32)": np.uint32,
-    "tensor(int64)": np.int64,
-    "tensor(uint64)": np.uint64,
-    "tensor(float16)": np.float16,
-    "tensor(float)": np.float32,
-    "tensor(double)": np.float64,
-}
-
-PRECISION_VALUES = Literal[tuple(ORT_TO_NP_TYPE.keys())]
-
-
-@invocation("prompt_onnx", title="ONNX Prompt (Raw)", tags=["prompt", "onnx"], category="conditioning", version="1.0.0")
-class ONNXPromptInvocation(BaseInvocation):
-    prompt: str = InputField(default="", description=FieldDescriptions.raw_prompt, ui_component=UIComponent.Textarea)
-    clip: ClipField = InputField(description=FieldDescriptions.clip, input=Input.Connection)
-
-    def invoke(self, context: InvocationContext) -> ConditioningOutput:
-        tokenizer_info = context.services.model_manager.get_model(
-            **self.clip.tokenizer.model_dump(),
-        )
-        text_encoder_info = context.services.model_manager.get_model(
-            **self.clip.text_encoder.model_dump(),
-        )
-        with tokenizer_info as orig_tokenizer, text_encoder_info as text_encoder:  # , ExitStack() as stack:
-            loras = [
-                (
-                    context.services.model_manager.get_model(**lora.model_dump(exclude={"weight"})).context.model,
-                    lora.weight,
-                )
-                for lora in self.clip.loras
-            ]
-
-            ti_list = []
-            for trigger in extract_ti_triggers_from_prompt(self.prompt):
-                name = trigger[1:-1]
-                try:
-                    ti_list.append(
-                        (
-                            name,
-                            context.services.model_manager.get_model(
-                                model_name=name,
-                                base_model=self.clip.text_encoder.base_model,
-                                model_type=ModelType.TextualInversion,
-                            ).context.model,
-                        )
-                    )
-                except Exception:
-                    # print(e)
-                    # import traceback
-                    # print(traceback.format_exc())
-                    print(f'Warn: trigger: "{trigger}" not found')
-            if loras or ti_list:
-                text_encoder.release_session()
-            with (
-                ONNXModelPatcher.apply_lora_text_encoder(text_encoder, loras),
-                ONNXModelPatcher.apply_ti(orig_tokenizer, text_encoder, ti_list) as (tokenizer, ti_manager),
-            ):
-                text_encoder.create_session()
-
-                # copy from
-                # https://github.com/huggingface/diffusers/blob/3ebbaf7c96801271f9e6c21400033b6aa5ffcf29/src/diffusers/pipelines/stable_diffusion/pipeline_onnx_stable_diffusion.py#L153
-                text_inputs = tokenizer(
-                    self.prompt,
-                    padding="max_length",
-                    max_length=tokenizer.model_max_length,
-                    truncation=True,
-                    return_tensors="np",
-                )
-                text_input_ids = text_inputs.input_ids
-                """
-                untruncated_ids = tokenizer(prompt, padding="max_length", return_tensors="np").input_ids
-
-                if not np.array_equal(text_input_ids, untruncated_ids):
-                    removed_text = self.tokenizer.batch_decode(
-                        untruncated_ids[:, self.tokenizer.model_max_length - 1 : -1]
-                    )
-                    logger.warning(
-                        "The following part of your input was truncated because CLIP can only handle sequences up to"
-                        f" {self.tokenizer.model_max_length} tokens: {removed_text}"
-                    )
-                """
-
-                prompt_embeds = text_encoder(input_ids=text_input_ids.astype(np.int32))[0]
-
-        conditioning_name = f"{context.graph_execution_state_id}_{self.id}_conditioning"
-
-        # TODO: hacky but works ;D maybe rename latents somehow?
-        context.services.latents.save(conditioning_name, (prompt_embeds, None))
-
-        return ConditioningOutput(
-            conditioning=ConditioningField(
-                conditioning_name=conditioning_name,
-            ),
-        )
-
-
-# Text to image
-@invocation(
-    "t2l_onnx",
-    title="ONNX Text to Latents",
-    tags=["latents", "inference", "txt2img", "onnx"],
-    category="latents",
-    version="1.0.0",
-)
-class ONNXTextToLatentsInvocation(BaseInvocation):
-    """Generates latents from conditionings."""
-
-    positive_conditioning: ConditioningField = InputField(
-        description=FieldDescriptions.positive_cond,
-        input=Input.Connection,
-    )
-    negative_conditioning: ConditioningField = InputField(
-        description=FieldDescriptions.negative_cond,
-        input=Input.Connection,
-    )
-    noise: LatentsField = InputField(
-        description=FieldDescriptions.noise,
-        input=Input.Connection,
-    )
-    steps: int = InputField(default=10, gt=0, description=FieldDescriptions.steps)
-    cfg_scale: Union[float, List[float]] = InputField(
-        default=7.5,
-        ge=1,
-        description=FieldDescriptions.cfg_scale,
-    )
-    scheduler: SAMPLER_NAME_VALUES = InputField(
-        default="euler", description=FieldDescriptions.scheduler, input=Input.Direct, ui_type=UIType.Scheduler
-    )
-    precision: PRECISION_VALUES = InputField(default="tensor(float16)", description=FieldDescriptions.precision)
-    unet: UNetField = InputField(
-        description=FieldDescriptions.unet,
-        input=Input.Connection,
-    )
-    control: Union[ControlField, list[ControlField]] = InputField(
-        default=None,
-        description=FieldDescriptions.control,
-    )
-    # seamless:   bool = InputField(default=False, description="Whether or not to generate an image that can tile without seams", )
-    # seamless_axes: str = InputField(default="", description="The axes to tile the image on, 'x' and/or 'y'")
-
-    @field_validator("cfg_scale")
-    def ge_one(cls, v):
-        """validate that all cfg_scale values are >= 1"""
-        if isinstance(v, list):
-            for i in v:
-                if i < 1:
-                    raise ValueError("cfg_scale must be greater than 1")
-        else:
-            if v < 1:
-                raise ValueError("cfg_scale must be greater than 1")
-        return v
-
-    # based on
-    # https://github.com/huggingface/diffusers/blob/3ebbaf7c96801271f9e6c21400033b6aa5ffcf29/src/diffusers/pipelines/stable_diffusion/pipeline_onnx_stable_diffusion.py#L375
-    def invoke(self, context: InvocationContext) -> LatentsOutput:
-        c, _ = context.services.latents.get(self.positive_conditioning.conditioning_name)
-        uc, _ = context.services.latents.get(self.negative_conditioning.conditioning_name)
-        graph_execution_state = context.services.graph_execution_manager.get(context.graph_execution_state_id)
-        source_node_id = graph_execution_state.prepared_source_mapping[self.id]
-        if isinstance(c, torch.Tensor):
-            c = c.cpu().numpy()
-        if isinstance(uc, torch.Tensor):
-            uc = uc.cpu().numpy()
-        device = torch.device(choose_torch_device())
-        prompt_embeds = np.concatenate([uc, c])
-
-        latents = context.services.latents.get(self.noise.latents_name)
-        if isinstance(latents, torch.Tensor):
-            latents = latents.cpu().numpy()
-
-        # TODO: better execution device handling
-        latents = latents.astype(ORT_TO_NP_TYPE[self.precision])
-
-        # get the initial random noise unless the user supplied it
-        do_classifier_free_guidance = True
-        # latents_dtype = prompt_embeds.dtype
-        # latents_shape = (batch_size * num_images_per_prompt, 4, height // 8, width // 8)
-        # if latents.shape != latents_shape:
-        #    raise ValueError(f"Unexpected latents shape, got {latents.shape}, expected {latents_shape}")
-
-        scheduler = get_scheduler(
-            context=context,
-            scheduler_info=self.unet.scheduler,
-            scheduler_name=self.scheduler,
-            seed=0,  # TODO: refactor this node
-        )
-
-        def torch2numpy(latent: torch.Tensor):
-            return latent.cpu().numpy()
-
-        def numpy2torch(latent, device):
-            return torch.from_numpy(latent).to(device)
-
-        def dispatch_progress(
-            self, context: InvocationContext, source_node_id: str, intermediate_state: PipelineIntermediateState
-        ) -> None:
-            stable_diffusion_step_callback(
-                context=context,
-                intermediate_state=intermediate_state,
-                node=self.model_dump(),
-                source_node_id=source_node_id,
-            )
-
-        scheduler.set_timesteps(self.steps)
-        latents = latents * np.float64(scheduler.init_noise_sigma)
-
-        extra_step_kwargs = {}
-        if "eta" in set(inspect.signature(scheduler.step).parameters.keys()):
-            extra_step_kwargs.update(
-                eta=0.0,
-            )
-
-        unet_info = context.services.model_manager.get_model(**self.unet.unet.model_dump())
-
-        with unet_info as unet:  # , ExitStack() as stack:
-            # loras = [(stack.enter_context(context.services.model_manager.get_model(**lora.dict(exclude={"weight"}))), lora.weight) for lora in self.unet.loras]
-            loras = [
-                (
-                    context.services.model_manager.get_model(**lora.model_dump(exclude={"weight"})).context.model,
-                    lora.weight,
-                )
-                for lora in self.unet.loras
-            ]
-
-            if loras:
-                unet.release_session()
-            with ONNXModelPatcher.apply_lora_unet(unet, loras):
-                # TODO:
-                _, _, h, w = latents.shape
-                unet.create_session(h, w)
-
-                timestep_dtype = next(
-                    (input.type for input in unet.session.get_inputs() if input.name == "timestep"), "tensor(float16)"
-                )
-                timestep_dtype = ORT_TO_NP_TYPE[timestep_dtype]
-                for i in tqdm(range(len(scheduler.timesteps))):
-                    t = scheduler.timesteps[i]
-                    # expand the latents if we are doing classifier free guidance
-                    latent_model_input = np.concatenate([latents] * 2) if do_classifier_free_guidance else latents
-                    latent_model_input = scheduler.scale_model_input(numpy2torch(latent_model_input, device), t)
-                    latent_model_input = latent_model_input.cpu().numpy()
-
-                    # predict the noise residual
-                    timestep = np.array([t], dtype=timestep_dtype)
-                    noise_pred = unet(sample=latent_model_input, timestep=timestep, encoder_hidden_states=prompt_embeds)
-                    noise_pred = noise_pred[0]
-
-                    # perform guidance
-                    if do_classifier_free_guidance:
-                        noise_pred_uncond, noise_pred_text = np.split(noise_pred, 2)
-                        noise_pred = noise_pred_uncond + self.cfg_scale * (noise_pred_text - noise_pred_uncond)
-
-                    # compute the previous noisy sample x_t -> x_t-1
-                    scheduler_output = scheduler.step(
-                        numpy2torch(noise_pred, device), t, numpy2torch(latents, device), **extra_step_kwargs
-                    )
-                    latents = torch2numpy(scheduler_output.prev_sample)
-
-                    state = PipelineIntermediateState(
-                        run_id="test", step=i, timestep=timestep, latents=scheduler_output.prev_sample
-                    )
-                    dispatch_progress(self, context=context, source_node_id=source_node_id, intermediate_state=state)
-
-                    # call the callback, if provided
-                    # if callback is not None and i % callback_steps == 0:
-                    #    callback(i, t, latents)
-
-        torch.cuda.empty_cache()
-
-        name = f"{context.graph_execution_state_id}__{self.id}"
-        context.services.latents.save(name, latents)
-        return build_latents_output(latents_name=name, latents=torch.from_numpy(latents))
-
-
-# Latent to image
-@invocation(
-    "l2i_onnx",
-    title="ONNX Latents to Image",
-    tags=["latents", "image", "vae", "onnx"],
-    category="image",
-    version="1.2.0",
-)
-class ONNXLatentsToImageInvocation(BaseInvocation, WithMetadata):
-    """Generates an image from latents."""
-
-    latents: LatentsField = InputField(
-        description=FieldDescriptions.denoised_latents,
-        input=Input.Connection,
-    )
-    vae: VaeField = InputField(
-        description=FieldDescriptions.vae,
-        input=Input.Connection,
-    )
-    # tiled: bool = InputField(default=False, description="Decode latents by overlaping tiles(less memory consumption)")
-
-    def invoke(self, context: InvocationContext) -> ImageOutput:
-        latents = context.services.latents.get(self.latents.latents_name)
-
-        if self.vae.vae.submodel != SubModelType.VaeDecoder:
-            raise Exception(f"Expected vae_decoder, found: {self.vae.vae.model_type}")
-
-        vae_info = context.services.model_manager.get_model(
-            **self.vae.vae.model_dump(),
-        )
-
-        # clear memory as vae decode can request a lot
-        torch.cuda.empty_cache()
-
-        with vae_info as vae:
-            vae.create_session()
-
-            # copied from
-            # https://github.com/huggingface/diffusers/blob/3ebbaf7c96801271f9e6c21400033b6aa5ffcf29/src/diffusers/pipelines/stable_diffusion/pipeline_onnx_stable_diffusion.py#L427
-            latents = 1 / 0.18215 * latents
-            # image = self.vae_decoder(latent_sample=latents)[0]
-            # it seems likes there is a strange result for using half-precision vae decoder if batchsize>1
-            image = np.concatenate([vae(latent_sample=latents[i : i + 1])[0] for i in range(latents.shape[0])])
-
-            image = np.clip(image / 2 + 0.5, 0, 1)
-            image = image.transpose((0, 2, 3, 1))
-            image = VaeImageProcessor.numpy_to_pil(image)[0]
-
-        torch.cuda.empty_cache()
-
-        image_dto = context.services.images.create(
-            image=image,
-            image_origin=ResourceOrigin.INTERNAL,
-            image_category=ImageCategory.GENERAL,
-            node_id=self.id,
-            session_id=context.graph_execution_state_id,
-            is_intermediate=self.is_intermediate,
-            metadata=self.metadata,
-            workflow=context.workflow,
-        )
-
-        return ImageOutput(
-            image=ImageField(image_name=image_dto.image_name),
-            width=image_dto.width,
-            height=image_dto.height,
-        )
-
-
-@invocation_output("model_loader_output_onnx")
-class ONNXModelLoaderOutput(BaseInvocationOutput):
-    """Model loader output"""
-
-    unet: UNetField = OutputField(default=None, description=FieldDescriptions.unet, title="UNet")
-    clip: ClipField = OutputField(default=None, description=FieldDescriptions.clip, title="CLIP")
-    vae_decoder: VaeField = OutputField(default=None, description=FieldDescriptions.vae, title="VAE Decoder")
-    vae_encoder: VaeField = OutputField(default=None, description=FieldDescriptions.vae, title="VAE Encoder")
-
-
-class OnnxModelField(BaseModel):
-    """Onnx model field"""
-
-    model_name: str = Field(description="Name of the model")
-    base_model: BaseModelType = Field(description="Base model")
-    model_type: ModelType = Field(description="Model Type")
-
-    model_config = ConfigDict(protected_namespaces=())
-
-
-@invocation("onnx_model_loader", title="ONNX Main Model", tags=["onnx", "model"], category="model", version="1.0.0")
-class OnnxModelLoaderInvocation(BaseInvocation):
-    """Loads a main model, outputting its submodels."""
-
-    model: OnnxModelField = InputField(
-        description=FieldDescriptions.onnx_main_model, input=Input.Direct, ui_type=UIType.ONNXModel
-    )
-
-    def invoke(self, context: InvocationContext) -> ONNXModelLoaderOutput:
-        base_model = self.model.base_model
-        model_name = self.model.model_name
-        model_type = ModelType.ONNX
-
-        # TODO: not found exceptions
-        if not context.services.model_manager.model_exists(
-            model_name=model_name,
-            base_model=base_model,
-            model_type=model_type,
-        ):
-            raise Exception(f"Unknown {base_model} {model_type} model: {model_name}")
-
-        """
-        if not context.services.model_manager.model_exists(
-            model_name=self.model_name,
-            model_type=SDModelType.Diffusers,
-            submodel=SDModelType.Tokenizer,
-        ):
-            raise Exception(
-                f"Failed to find tokenizer submodel in {self.model_name}! Check if model corrupted"
-            )
-
-        if not context.services.model_manager.model_exists(
-            model_name=self.model_name,
-            model_type=SDModelType.Diffusers,
-            submodel=SDModelType.TextEncoder,
-        ):
-            raise Exception(
-                f"Failed to find text_encoder submodel in {self.model_name}! Check if model corrupted"
-            )
-
-        if not context.services.model_manager.model_exists(
-            model_name=self.model_name,
-            model_type=SDModelType.Diffusers,
-            submodel=SDModelType.UNet,
-        ):
-            raise Exception(
-                f"Failed to find unet submodel from {self.model_name}! Check if model corrupted"
-            )
-        """
-
-        return ONNXModelLoaderOutput(
-            unet=UNetField(
-                unet=ModelInfo(
-                    model_name=model_name,
-                    base_model=base_model,
-                    model_type=model_type,
-                    submodel=SubModelType.UNet,
-                ),
-                scheduler=ModelInfo(
-                    model_name=model_name,
-                    base_model=base_model,
-                    model_type=model_type,
-                    submodel=SubModelType.Scheduler,
-                ),
-                loras=[],
-            ),
-            clip=ClipField(
-                tokenizer=ModelInfo(
-                    model_name=model_name,
-                    base_model=base_model,
-                    model_type=model_type,
-                    submodel=SubModelType.Tokenizer,
-                ),
-                text_encoder=ModelInfo(
-                    model_name=model_name,
-                    base_model=base_model,
-                    model_type=model_type,
-                    submodel=SubModelType.TextEncoder,
-                ),
-                loras=[],
-                skipped_layers=0,
-            ),
-            vae_decoder=VaeField(
-                vae=ModelInfo(
-                    model_name=model_name,
-                    base_model=base_model,
-                    model_type=model_type,
-                    submodel=SubModelType.VaeDecoder,
-                ),
-            ),
-            vae_encoder=VaeField(
-                vae=ModelInfo(
-                    model_name=model_name,
-                    base_model=base_model,
-                    model_type=model_type,
-                    submodel=SubModelType.VaeEncoder,
-                ),
-            ),
-        )

invokeai/app/invocations/param_easing.py

@@ -40,8 +40,10 @@ from easing_functions import (
 from matplotlib.ticker import MaxNLocator
 
 from invokeai.app.invocations.primitives import FloatCollectionOutput
+from invokeai.app.services.shared.invocation_context import InvocationContext
 
-from .baseinvocation import BaseInvocation, InputField, InvocationContext, invocation
+from .baseinvocation import BaseInvocation, invocation
+from .fields import InputField
 
 
 @invocation(
@@ -109,7 +111,7 @@ EASING_FUNCTION_KEYS = Literal[tuple(EASING_FUNCTIONS_MAP.keys())]
     title="Step Param Easing",
     tags=["step", "easing"],
     category="step",
-    version="1.0.0",
+    version="1.0.1",
 )
 class StepParamEasingInvocation(BaseInvocation):
     """Experimental per-step parameter easing for denoising steps"""
@@ -148,19 +150,19 @@ class StepParamEasingInvocation(BaseInvocation):
         postlist = list(num_poststeps * [self.post_end_value])
 
         if log_diagnostics:
-            context.services.logger.debug("start_step: " + str(start_step))
-            context.services.logger.debug("end_step: " + str(end_step))
-            context.services.logger.debug("num_easing_steps: " + str(num_easing_steps))
-            context.services.logger.debug("num_presteps: " + str(num_presteps))
-            context.services.logger.debug("num_poststeps: " + str(num_poststeps))
-            context.services.logger.debug("prelist size: " + str(len(prelist)))
-            context.services.logger.debug("postlist size: " + str(len(postlist)))
-            context.services.logger.debug("prelist: " + str(prelist))
-            context.services.logger.debug("postlist: " + str(postlist))
+            context.logger.debug("start_step: " + str(start_step))
+            context.logger.debug("end_step: " + str(end_step))
+            context.logger.debug("num_easing_steps: " + str(num_easing_steps))
+            context.logger.debug("num_presteps: " + str(num_presteps))
+            context.logger.debug("num_poststeps: " + str(num_poststeps))
+            context.logger.debug("prelist size: " + str(len(prelist)))
+            context.logger.debug("postlist size: " + str(len(postlist)))
+            context.logger.debug("prelist: " + str(prelist))
+            context.logger.debug("postlist: " + str(postlist))
 
         easing_class = EASING_FUNCTIONS_MAP[self.easing]
         if log_diagnostics:
-            context.services.logger.debug("easing class: " + str(easing_class))
+            context.logger.debug("easing class: " + str(easing_class))
         easing_list = []
         if self.mirror:  # "expected" mirroring
             # if number of steps is even, squeeze duration down to (number_of_steps)/2
@@ -171,7 +173,7 @@ class StepParamEasingInvocation(BaseInvocation):
 
             base_easing_duration = int(np.ceil(num_easing_steps / 2.0))
             if log_diagnostics:
-                context.services.logger.debug("base easing duration: " + str(base_easing_duration))
+                context.logger.debug("base easing duration: " + str(base_easing_duration))
             even_num_steps = num_easing_steps % 2 == 0  # even number of steps
             easing_function = easing_class(
                 start=self.start_value,
@@ -183,14 +185,14 @@ class StepParamEasingInvocation(BaseInvocation):
                 easing_val = easing_function.ease(step_index)
                 base_easing_vals.append(easing_val)
                 if log_diagnostics:
-                    context.services.logger.debug("step_index: " + str(step_index) + ", easing_val: " + str(easing_val))
+                    context.logger.debug("step_index: " + str(step_index) + ", easing_val: " + str(easing_val))
             if even_num_steps:
                 mirror_easing_vals = list(reversed(base_easing_vals))
             else:
                 mirror_easing_vals = list(reversed(base_easing_vals[0:-1]))
             if log_diagnostics:
-                context.services.logger.debug("base easing vals: " + str(base_easing_vals))
-                context.services.logger.debug("mirror easing vals: " + str(mirror_easing_vals))
+                context.logger.debug("base easing vals: " + str(base_easing_vals))
+                context.logger.debug("mirror easing vals: " + str(mirror_easing_vals))
             easing_list = base_easing_vals + mirror_easing_vals
 
         # FIXME: add alt_mirror option (alternative to default or mirror), or remove entirely
@@ -225,12 +227,12 @@ class StepParamEasingInvocation(BaseInvocation):
                 step_val = easing_function.ease(step_index)
                 easing_list.append(step_val)
                 if log_diagnostics:
-                    context.services.logger.debug("step_index: " + str(step_index) + ", easing_val: " + str(step_val))
+                    context.logger.debug("step_index: " + str(step_index) + ", easing_val: " + str(step_val))
 
         if log_diagnostics:
-            context.services.logger.debug("prelist size: " + str(len(prelist)))
-            context.services.logger.debug("easing_list size: " + str(len(easing_list)))
-            context.services.logger.debug("postlist size: " + str(len(postlist)))
+            context.logger.debug("prelist size: " + str(len(prelist)))
+            context.logger.debug("easing_list size: " + str(len(easing_list)))
+            context.logger.debug("postlist size: " + str(len(postlist)))
 
         param_list = prelist + easing_list + postlist
 

invokeai/app/invocations/primitives.py

@@ -1,20 +1,28 @@
 # Copyright (c) 2023 Kyle Schouviller (https://github.com/kyle0654)
 
-from typing import Optional, Tuple
+from typing import Optional
 
 import torch
-from pydantic import BaseModel, Field
 
-from invokeai.app.shared.fields import FieldDescriptions
+from invokeai.app.invocations.constants import LATENT_SCALE_FACTOR
+from invokeai.app.invocations.fields import (
+    ColorField,
+    ConditioningField,
+    DenoiseMaskField,
+    FieldDescriptions,
+    ImageField,
+    Input,
+    InputField,
+    LatentsField,
+    OutputField,
+    UIComponent,
+)
+from invokeai.app.services.images.images_common import ImageDTO
+from invokeai.app.services.shared.invocation_context import InvocationContext
 
 from .baseinvocation import (
     BaseInvocation,
     BaseInvocationOutput,
-    Input,
-    InputField,
-    InvocationContext,
-    OutputField,
-    UIComponent,
     invocation,
     invocation_output,
 )
@@ -221,18 +229,6 @@ class StringCollectionInvocation(BaseInvocation):
 # region Image
 
 
-class ImageField(BaseModel):
-    """An image primitive field"""
-
-    image_name: str = Field(description="The name of the image")
-
-
-class BoardField(BaseModel):
-    """A board primitive field"""
-
-    board_id: str = Field(description="The id of the board")
-
-
 @invocation_output("image_output")
 class ImageOutput(BaseInvocationOutput):
     """Base class for nodes that output a single image"""
@@ -241,6 +237,14 @@ class ImageOutput(BaseInvocationOutput):
     width: int = OutputField(description="The width of the image in pixels")
     height: int = OutputField(description="The height of the image in pixels")
 
+    @classmethod
+    def build(cls, image_dto: ImageDTO) -> "ImageOutput":
+        return cls(
+            image=ImageField(image_name=image_dto.image_name),
+            width=image_dto.width,
+            height=image_dto.height,
+        )
+
 
 @invocation_output("image_collection_output")
 class ImageCollectionOutput(BaseInvocationOutput):
@@ -251,16 +255,14 @@ class ImageCollectionOutput(BaseInvocationOutput):
     )
 
 
-@invocation("image", title="Image Primitive", tags=["primitives", "image"], category="primitives", version="1.0.0")
-class ImageInvocation(
-    BaseInvocation,
-):
+@invocation("image", title="Image Primitive", tags=["primitives", "image"], category="primitives", version="1.0.1")
+class ImageInvocation(BaseInvocation):
     """An image primitive value"""
 
     image: ImageField = InputField(description="The image to load")
 
     def invoke(self, context: InvocationContext) -> ImageOutput:
-        image = context.services.images.get_pil_image(self.image.image_name)
+        image = context.images.get_pil(self.image.image_name)
 
         return ImageOutput(
             image=ImageField(image_name=self.image.image_name),
@@ -290,42 +292,40 @@ class ImageCollectionInvocation(BaseInvocation):
 # region DenoiseMask
 
 
-class DenoiseMaskField(BaseModel):
-    """An inpaint mask field"""
-
-    mask_name: str = Field(description="The name of the mask image")
-    masked_latents_name: Optional[str] = Field(default=None, description="The name of the masked image latents")
-
-
 @invocation_output("denoise_mask_output")
 class DenoiseMaskOutput(BaseInvocationOutput):
     """Base class for nodes that output a single image"""
 
     denoise_mask: DenoiseMaskField = OutputField(description="Mask for denoise model run")
 
+    @classmethod
+    def build(cls, mask_name: str, masked_latents_name: Optional[str] = None) -> "DenoiseMaskOutput":
+        return cls(
+            denoise_mask=DenoiseMaskField(mask_name=mask_name, masked_latents_name=masked_latents_name),
+        )
+
 
 # endregion
 
 # region Latents
 
 
-class LatentsField(BaseModel):
-    """A latents tensor primitive field"""
-
-    latents_name: str = Field(description="The name of the latents")
-    seed: Optional[int] = Field(default=None, description="Seed used to generate this latents")
-
-
 @invocation_output("latents_output")
 class LatentsOutput(BaseInvocationOutput):
     """Base class for nodes that output a single latents tensor"""
 
-    latents: LatentsField = OutputField(
-        description=FieldDescriptions.latents,
-    )
+    latents: LatentsField = OutputField(description=FieldDescriptions.latents)
     width: int = OutputField(description=FieldDescriptions.width)
     height: int = OutputField(description=FieldDescriptions.height)
 
+    @classmethod
+    def build(cls, latents_name: str, latents: torch.Tensor, seed: Optional[int] = None) -> "LatentsOutput":
+        return cls(
+            latents=LatentsField(latents_name=latents_name, seed=seed),
+            width=latents.size()[3] * LATENT_SCALE_FACTOR,
+            height=latents.size()[2] * LATENT_SCALE_FACTOR,
+        )
+
 
 @invocation_output("latents_collection_output")
 class LatentsCollectionOutput(BaseInvocationOutput):
@@ -337,7 +337,7 @@ class LatentsCollectionOutput(BaseInvocationOutput):
 
 
 @invocation(
-    "latents", title="Latents Primitive", tags=["primitives", "latents"], category="primitives", version="1.0.0"
+    "latents", title="Latents Primitive", tags=["primitives", "latents"], category="primitives", version="1.0.1"
 )
 class LatentsInvocation(BaseInvocation):
     """A latents tensor primitive value"""
@@ -345,9 +345,9 @@ class LatentsInvocation(BaseInvocation):
     latents: LatentsField = InputField(description="The latents tensor", input=Input.Connection)
 
     def invoke(self, context: InvocationContext) -> LatentsOutput:
-        latents = context.services.latents.get(self.latents.latents_name)
+        latents = context.tensors.load(self.latents.latents_name)
 
-        return build_latents_output(self.latents.latents_name, latents)
+        return LatentsOutput.build(self.latents.latents_name, latents)
 
 
 @invocation(
@@ -368,31 +368,11 @@ class LatentsCollectionInvocation(BaseInvocation):
         return LatentsCollectionOutput(collection=self.collection)
 
 
-def build_latents_output(latents_name: str, latents: torch.Tensor, seed: Optional[int] = None):
-    return LatentsOutput(
-        latents=LatentsField(latents_name=latents_name, seed=seed),
-        width=latents.size()[3] * 8,
-        height=latents.size()[2] * 8,
-    )
-
-
 # endregion
 
 # region Color
 
 
-class ColorField(BaseModel):
-    """A color primitive field"""
-
-    r: int = Field(ge=0, le=255, description="The red component")
-    g: int = Field(ge=0, le=255, description="The green component")
-    b: int = Field(ge=0, le=255, description="The blue component")
-    a: int = Field(ge=0, le=255, description="The alpha component")
-
-    def tuple(self) -> Tuple[int, int, int, int]:
-        return (self.r, self.g, self.b, self.a)
-
-
 @invocation_output("color_output")
 class ColorOutput(BaseInvocationOutput):
     """Base class for nodes that output a single color"""
@@ -424,18 +404,16 @@ class ColorInvocation(BaseInvocation):
 # region Conditioning
 
 
-class ConditioningField(BaseModel):
-    """A conditioning tensor primitive value"""
-
-    conditioning_name: str = Field(description="The name of conditioning tensor")
-
-
 @invocation_output("conditioning_output")
 class ConditioningOutput(BaseInvocationOutput):
     """Base class for nodes that output a single conditioning tensor"""
 
     conditioning: ConditioningField = OutputField(description=FieldDescriptions.cond)
 
+    @classmethod
+    def build(cls, conditioning_name: str) -> "ConditioningOutput":
+        return cls(conditioning=ConditioningField(conditioning_name=conditioning_name))
+
 
 @invocation_output("conditioning_collection_output")
 class ConditioningCollectionOutput(BaseInvocationOutput):

invokeai/app/invocations/prompt.py

@@ -6,8 +6,10 @@ from dynamicprompts.generators import CombinatorialPromptGenerator, RandomPrompt
 from pydantic import field_validator
 
 from invokeai.app.invocations.primitives import StringCollectionOutput
+from invokeai.app.services.shared.invocation_context import InvocationContext
 
-from .baseinvocation import BaseInvocation, InputField, InvocationContext, UIComponent, invocation
+from .baseinvocation import BaseInvocation, invocation
+from .fields import InputField, UIComponent
 
 
 @invocation(

invokeai/app/invocations/sdxl.py

@@ -1,14 +1,10 @@
-from invokeai.app.shared.fields import FieldDescriptions
+from invokeai.app.invocations.fields import FieldDescriptions, Input, InputField, OutputField, UIType
+from invokeai.app.services.shared.invocation_context import InvocationContext
 
 from ...backend.model_management import ModelType, SubModelType
 from .baseinvocation import (
     BaseInvocation,
     BaseInvocationOutput,
-    Input,
-    InputField,
-    InvocationContext,
-    OutputField,
-    UIType,
     invocation,
     invocation_output,
 )
@@ -34,7 +30,7 @@ class SDXLRefinerModelLoaderOutput(BaseInvocationOutput):
     vae: VaeField = OutputField(description=FieldDescriptions.vae, title="VAE")
 
 
-@invocation("sdxl_model_loader", title="SDXL Main Model", tags=["model", "sdxl"], category="model", version="1.0.0")
+@invocation("sdxl_model_loader", title="SDXL Main Model", tags=["model", "sdxl"], category="model", version="1.0.1")
 class SDXLModelLoaderInvocation(BaseInvocation):
     """Loads an sdxl base model, outputting its submodels."""
 
@@ -49,7 +45,7 @@ class SDXLModelLoaderInvocation(BaseInvocation):
         model_type = ModelType.Main
 
         # TODO: not found exceptions
-        if not context.services.model_manager.model_exists(
+        if not context.models.exists(
             model_name=model_name,
             base_model=base_model,
             model_type=model_type,
@@ -120,7 +116,7 @@ class SDXLModelLoaderInvocation(BaseInvocation):
     title="SDXL Refiner Model",
     tags=["model", "sdxl", "refiner"],
     category="model",
-    version="1.0.0",
+    version="1.0.1",
 )
 class SDXLRefinerModelLoaderInvocation(BaseInvocation):
     """Loads an sdxl refiner model, outputting its submodels."""
@@ -138,7 +134,7 @@ class SDXLRefinerModelLoaderInvocation(BaseInvocation):
         model_type = ModelType.Main
 
         # TODO: not found exceptions
-        if not context.services.model_manager.model_exists(
+        if not context.models.exists(
             model_name=model_name,
             base_model=base_model,
             model_type=model_type,

invokeai/app/invocations/strings.py

@@ -2,16 +2,15 @@
 
 import re
 
+from invokeai.app.services.shared.invocation_context import InvocationContext
+
 from .baseinvocation import (
     BaseInvocation,
     BaseInvocationOutput,
-    InputField,
-    InvocationContext,
-    OutputField,
-    UIComponent,
     invocation,
     invocation_output,
 )
+from .fields import InputField, OutputField, UIComponent
 from .primitives import StringOutput
 
 

invokeai/app/invocations/t2i_adapter.py

@@ -5,17 +5,13 @@ 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.controlnet_image_processors import CONTROLNET_RESIZE_VALUES
-from invokeai.app.invocations.primitives import ImageField
+from invokeai.app.invocations.fields import FieldDescriptions, ImageField, Input, InputField, OutputField
 from invokeai.app.invocations.util import validate_begin_end_step, validate_weights
-from invokeai.app.shared.fields import FieldDescriptions
+from invokeai.app.services.shared.invocation_context import InvocationContext
 from invokeai.backend.model_management.models.base import BaseModelType
 
 

invokeai/app/invocations/tiles.py

@@ -8,16 +8,12 @@ from invokeai.app.invocations.baseinvocation import (
     BaseInvocation,
     BaseInvocationOutput,
     Classification,
-    Input,
-    InputField,
-    InvocationContext,
-    OutputField,
-    WithMetadata,
     invocation,
     invocation_output,
 )
-from invokeai.app.invocations.primitives import ImageField, ImageOutput
-from invokeai.app.services.image_records.image_records_common import ImageCategory, ResourceOrigin
+from invokeai.app.invocations.fields import ImageField, Input, InputField, OutputField, WithBoard, WithMetadata
+from invokeai.app.invocations.primitives import ImageOutput
+from invokeai.app.services.shared.invocation_context import InvocationContext
 from invokeai.backend.tiles.tiles import (
     calc_tiles_even_split,
     calc_tiles_min_overlap,
@@ -236,7 +232,7 @@ BLEND_MODES = Literal["Linear", "Seam"]
     version="1.1.0",
     classification=Classification.Beta,
 )
-class MergeTilesToImageInvocation(BaseInvocation, WithMetadata):
+class MergeTilesToImageInvocation(BaseInvocation, WithMetadata, WithBoard):
     """Merge multiple tile images into a single image."""
 
     # Inputs
@@ -268,7 +264,7 @@ class MergeTilesToImageInvocation(BaseInvocation, WithMetadata):
         # existed in memory at an earlier point in the graph.
         tile_np_images: list[np.ndarray] = []
         for image in images:
-            pil_image = context.services.images.get_pil_image(image.image_name)
+            pil_image = context.images.get_pil(image.image_name)
             pil_image = pil_image.convert("RGB")
             tile_np_images.append(np.array(pil_image))
 
@@ -291,18 +287,5 @@ class MergeTilesToImageInvocation(BaseInvocation, WithMetadata):
         # Convert into a PIL image and save
         pil_image = Image.fromarray(np_image)
 
-        image_dto = context.services.images.create(
-            image=pil_image,
-            image_origin=ResourceOrigin.INTERNAL,
-            image_category=ImageCategory.GENERAL,
-            node_id=self.id,
-            session_id=context.graph_execution_state_id,
-            is_intermediate=self.is_intermediate,
-            metadata=self.metadata,
-            workflow=context.workflow,
-        )
-        return ImageOutput(
-            image=ImageField(image_name=image_dto.image_name),
-            width=image_dto.width,
-            height=image_dto.height,
-        )
+        image_dto = context.images.save(image=pil_image)
+        return ImageOutput.build(image_dto)

invokeai/app/invocations/upscale.py

@@ -8,13 +8,15 @@ import torch
 from PIL import Image
 from pydantic import ConfigDict
 
-from invokeai.app.invocations.primitives import ImageField, ImageOutput
-from invokeai.app.services.image_records.image_records_common import ImageCategory, ResourceOrigin
+from invokeai.app.invocations.fields import ImageField
+from invokeai.app.invocations.primitives import ImageOutput
+from invokeai.app.services.shared.invocation_context import InvocationContext
 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, invocation
+from .fields import InputField, WithBoard, WithMetadata
 
 # TODO: Populate this from disk?
 # TODO: Use model manager to load?
@@ -29,8 +31,8 @@ if choose_torch_device() == torch.device("mps"):
     from torch import mps
 
 
-@invocation("esrgan", title="Upscale (RealESRGAN)", tags=["esrgan", "upscale"], category="esrgan", version="1.3.0")
-class ESRGANInvocation(BaseInvocation, WithMetadata):
+@invocation("esrgan", title="Upscale (RealESRGAN)", tags=["esrgan", "upscale"], category="esrgan", version="1.3.1")
+class ESRGANInvocation(BaseInvocation, WithMetadata, WithBoard):
     """Upscales an image using RealESRGAN."""
 
     image: ImageField = InputField(description="The input image")
@@ -42,8 +44,8 @@ class ESRGANInvocation(BaseInvocation, WithMetadata):
     model_config = ConfigDict(protected_namespaces=())
 
     def invoke(self, context: InvocationContext) -> ImageOutput:
-        image = context.services.images.get_pil_image(self.image.image_name)
-        models_path = context.services.configuration.models_path
+        image = context.images.get_pil(self.image.image_name)
+        models_path = context.config.get().models_path
 
         rrdbnet_model = None
         netscale = None
@@ -87,7 +89,7 @@ class ESRGANInvocation(BaseInvocation, WithMetadata):
             netscale = 2
         else:
             msg = f"Invalid RealESRGAN model: {self.model_name}"
-            context.services.logger.error(msg)
+            context.logger.error(msg)
             raise ValueError(msg)
 
         esrgan_model_path = Path(f"core/upscaling/realesrgan/{self.model_name}")
@@ -110,19 +112,6 @@ class ESRGANInvocation(BaseInvocation, WithMetadata):
         if choose_torch_device() == torch.device("mps"):
             mps.empty_cache()
 
-        image_dto = context.services.images.create(
-            image=pil_image,
-            image_origin=ResourceOrigin.INTERNAL,
-            image_category=ImageCategory.GENERAL,
-            node_id=self.id,
-            session_id=context.graph_execution_state_id,
-            is_intermediate=self.is_intermediate,
-            metadata=self.metadata,
-            workflow=context.workflow,
-        )
+        image_dto = context.images.save(image=pil_image)
 
-        return ImageOutput(
-            image=ImageField(image_name=image_dto.image_name),
-            width=image_dto.width,
-            height=image_dto.height,
-        )
+        return ImageOutput.build(image_dto)

invokeai/app/services/events/events_base.py

@@ -11,7 +11,7 @@ from invokeai.app.services.session_queue.session_queue_common import (
     SessionQueueStatus,
 )
 from invokeai.app.util.misc import get_timestamp
-from invokeai.backend.model_management.model_manager import ModelInfo
+from invokeai.backend.model_management.model_manager import LoadedModelInfo
 from invokeai.backend.model_management.models.base import BaseModelType, ModelType, SubModelType
 
 
@@ -55,7 +55,7 @@ class EventServiceBase:
         queue_item_id: int,
         queue_batch_id: str,
         graph_execution_state_id: str,
-        node: dict,
+        node_id: str,
         source_node_id: str,
         progress_image: Optional[ProgressImage],
         step: int,
@@ -70,7 +70,7 @@ class EventServiceBase:
                 "queue_item_id": queue_item_id,
                 "queue_batch_id": queue_batch_id,
                 "graph_execution_state_id": graph_execution_state_id,
-                "node_id": node.get("id"),
+                "node_id": node_id,
                 "source_node_id": source_node_id,
                 "progress_image": progress_image.model_dump() if progress_image is not None else None,
                 "step": step,
@@ -201,7 +201,7 @@ class EventServiceBase:
         base_model: BaseModelType,
         model_type: ModelType,
         submodel: SubModelType,
-        model_info: ModelInfo,
+        loaded_model_info: LoadedModelInfo,
     ) -> None:
         """Emitted when a model is correctly loaded (returns model info)"""
         self.__emit_queue_event(
@@ -215,9 +215,9 @@ class EventServiceBase:
                 "base_model": base_model,
                 "model_type": model_type,
                 "submodel": submodel,
-                "hash": model_info.hash,
-                "location": str(model_info.location),
-                "precision": str(model_info.precision),
+                "hash": loaded_model_info.hash,
+                "location": str(loaded_model_info.location),
+                "precision": str(loaded_model_info.precision),
             },
         )
 

invokeai/app/services/image_files/image_files_base.py

@@ -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
 
 

invokeai/app/services/image_files/image_files_disk.py

@@ -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

invokeai/app/services/image_records/image_records_base.py

@@ -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

invokeai/app/services/image_records/image_records_sqlite.py

@@ -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
 

invokeai/app/services/images/images_base.py

@@ -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,

invokeai/app/services/images/images_default.py

@@ -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

invokeai/app/services/invocation_cache/invocation_cache_memory.py

@@ -37,7 +37,8 @@ class MemoryInvocationCache(InvocationCacheBase):
         if self._max_cache_size == 0:
             return
         self._invoker.services.images.on_deleted(self._delete_by_match)
-        self._invoker.services.latents.on_deleted(self._delete_by_match)
+        self._invoker.services.tensors.on_deleted(self._delete_by_match)
+        self._invoker.services.conditioning.on_deleted(self._delete_by_match)
 
     def get(self, key: Union[int, str]) -> Optional[BaseInvocationOutput]:
         with self._lock:

invokeai/app/services/invocation_processor/invocation_processor_default.py

@@ -5,11 +5,11 @@ from threading import BoundedSemaphore, Event, Thread
 from typing import Optional
 
 import invokeai.backend.util.logging as logger
-from invokeai.app.invocations.baseinvocation import InvocationContext
 from invokeai.app.services.invocation_queue.invocation_queue_common import InvocationQueueItem
 from invokeai.app.services.invocation_stats.invocation_stats_common import (
     GESStatsNotFoundError,
 )
+from invokeai.app.services.shared.invocation_context import InvocationContextData, build_invocation_context
 from invokeai.app.util.profiler import Profiler
 
 from ..invoker import Invoker
@@ -131,16 +131,20 @@ class DefaultInvocationProcessor(InvocationProcessorABC):
                         # which handles a few things:
                         # - nodes that require a value, but get it only from a connection
                         # - referencing the invocation cache instead of executing the node
-                        outputs = invocation.invoke_internal(
-                            InvocationContext(
-                                services=self.__invoker.services,
-                                graph_execution_state_id=graph_execution_state.id,
-                                queue_item_id=queue_item.session_queue_item_id,
-                                queue_id=queue_item.session_queue_id,
-                                queue_batch_id=queue_item.session_queue_batch_id,
+                        context_data = InvocationContextData(
+                            invocation=invocation,
+                            session_id=graph_id,
                             workflow=queue_item.workflow,
+                            source_node_id=source_node_id,
+                            queue_id=queue_item.session_queue_id,
+                            queue_item_id=queue_item.session_queue_item_id,
+                            batch_id=queue_item.session_queue_batch_id,
                         )
+                        context = build_invocation_context(
+                            services=self.__invoker.services,
+                            context_data=context_data,
                         )
+                        outputs = invocation.invoke_internal(context=context, services=self.__invoker.services)
 
                         # Check queue to see if this is canceled, and skip if so
                         if self.__invoker.services.queue.is_canceled(graph_execution_state.id):

invokeai/app/services/invocation_services.py

@@ -3,9 +3,15 @@ from __future__ import annotations
 
 from typing import TYPE_CHECKING
 
+from invokeai.app.services.object_serializer.object_serializer_base import ObjectSerializerBase
+
 if TYPE_CHECKING:
     from logging import Logger
 
+    import torch
+
+    from invokeai.backend.stable_diffusion.diffusion.conditioning_data import ConditioningFieldData
+
     from .board_image_records.board_image_records_base import BoardImageRecordStorageBase
     from .board_images.board_images_base import BoardImagesServiceABC
     from .board_records.board_records_base import BoardRecordStorageBase
@@ -21,7 +27,6 @@ if TYPE_CHECKING:
     from .invocation_queue.invocation_queue_base import InvocationQueueABC
     from .invocation_stats.invocation_stats_base import InvocationStatsServiceBase
     from .item_storage.item_storage_base import ItemStorageABC
-    from .latents_storage.latents_storage_base import LatentsStorageBase
     from .model_install import ModelInstallServiceBase
     from .model_manager.model_manager_base import ModelManagerServiceBase
     from .model_records import ModelRecordServiceBase
@@ -36,33 +41,6 @@ if TYPE_CHECKING:
 class InvocationServices:
     """Services that can be used by invocations"""
 
-    # TODO: Just forward-declared everything due to circular dependencies. Fix structure.
-    board_images: "BoardImagesServiceABC"
-    board_image_record_storage: "BoardImageRecordStorageBase"
-    boards: "BoardServiceABC"
-    board_records: "BoardRecordStorageBase"
-    configuration: "InvokeAIAppConfig"
-    events: "EventServiceBase"
-    graph_execution_manager: "ItemStorageABC[GraphExecutionState]"
-    images: "ImageServiceABC"
-    image_records: "ImageRecordStorageBase"
-    image_files: "ImageFileStorageBase"
-    latents: "LatentsStorageBase"
-    logger: "Logger"
-    model_manager: "ModelManagerServiceBase"
-    model_records: "ModelRecordServiceBase"
-    download_queue: "DownloadQueueServiceBase"
-    model_install: "ModelInstallServiceBase"
-    processor: "InvocationProcessorABC"
-    performance_statistics: "InvocationStatsServiceBase"
-    queue: "InvocationQueueABC"
-    session_queue: "SessionQueueBase"
-    session_processor: "SessionProcessorBase"
-    invocation_cache: "InvocationCacheBase"
-    names: "NameServiceBase"
-    urls: "UrlServiceBase"
-    workflow_records: "WorkflowRecordsStorageBase"
-
     def __init__(
         self,
         board_images: "BoardImagesServiceABC",
@@ -75,7 +53,6 @@ class InvocationServices:
         images: "ImageServiceABC",
         image_files: "ImageFileStorageBase",
         image_records: "ImageRecordStorageBase",
-        latents: "LatentsStorageBase",
         logger: "Logger",
         model_manager: "ModelManagerServiceBase",
         model_records: "ModelRecordServiceBase",
@@ -90,6 +67,8 @@ class InvocationServices:
         names: "NameServiceBase",
         urls: "UrlServiceBase",
         workflow_records: "WorkflowRecordsStorageBase",
+        tensors: "ObjectSerializerBase[torch.Tensor]",
+        conditioning: "ObjectSerializerBase[ConditioningFieldData]",
     ):
         self.board_images = board_images
         self.board_image_records = board_image_records
@@ -101,7 +80,6 @@ class InvocationServices:
         self.images = images
         self.image_files = image_files
         self.image_records = image_records
-        self.latents = latents
         self.logger = logger
         self.model_manager = model_manager
         self.model_records = model_records
@@ -116,3 +94,5 @@ class InvocationServices:
         self.names = names
         self.urls = urls
         self.workflow_records = workflow_records
+        self.tensors = tensors
+        self.conditioning = conditioning

invokeai/app/services/item_storage/item_storage_base.py

@@ -30,7 +30,7 @@ class ItemStorageABC(ABC, Generic[T]):
     @abstractmethod
     def set(self, item: T) -> None:
         """
-        Sets the item. The id will be extracted based on id_field.
+        Sets the item.
         :param item: the item to set
         """
         pass

invokeai/app/services/latents_storage/latents_storage_base.py

@@ -1,45 +0,0 @@
-# Copyright (c) 2023 Kyle Schouviller (https://github.com/kyle0654)
-
-from abc import ABC, abstractmethod
-from typing import Callable
-
-import torch
-
-
-class LatentsStorageBase(ABC):
-    """Responsible for storing and retrieving latents."""
-
-    _on_changed_callbacks: list[Callable[[torch.Tensor], None]]
-    _on_deleted_callbacks: list[Callable[[str], None]]
-
-    def __init__(self) -> None:
-        self._on_changed_callbacks = []
-        self._on_deleted_callbacks = []
-
-    @abstractmethod
-    def get(self, name: str) -> torch.Tensor:
-        pass
-
-    @abstractmethod
-    def save(self, name: str, data: torch.Tensor) -> None:
-        pass
-
-    @abstractmethod
-    def delete(self, name: str) -> None:
-        pass
-
-    def on_changed(self, on_changed: Callable[[torch.Tensor], None]) -> None:
-        """Register a callback for when an item is changed"""
-        self._on_changed_callbacks.append(on_changed)
-
-    def on_deleted(self, on_deleted: Callable[[str], None]) -> None:
-        """Register a callback for when an item is deleted"""
-        self._on_deleted_callbacks.append(on_deleted)
-
-    def _on_changed(self, item: torch.Tensor) -> None:
-        for callback in self._on_changed_callbacks:
-            callback(item)
-
-    def _on_deleted(self, item_id: str) -> None:
-        for callback in self._on_deleted_callbacks:
-            callback(item_id)

invokeai/app/services/latents_storage/latents_storage_disk.py

@@ -1,58 +0,0 @@
-# Copyright (c) 2023 Kyle Schouviller (https://github.com/kyle0654)
-
-from pathlib import Path
-from typing import Union
-
-import torch
-
-from invokeai.app.services.invoker import Invoker
-
-from .latents_storage_base import LatentsStorageBase
-
-
-class DiskLatentsStorage(LatentsStorageBase):
-    """Stores latents in a folder on disk without caching"""
-
-    __output_folder: Path
-
-    def __init__(self, output_folder: Union[str, Path]):
-        self.__output_folder = output_folder if isinstance(output_folder, Path) else Path(output_folder)
-        self.__output_folder.mkdir(parents=True, exist_ok=True)
-
-    def start(self, invoker: Invoker) -> None:
-        self._invoker = invoker
-        self._delete_all_latents()
-
-    def get(self, name: str) -> torch.Tensor:
-        latent_path = self.get_path(name)
-        return torch.load(latent_path)
-
-    def save(self, name: str, data: torch.Tensor) -> None:
-        self.__output_folder.mkdir(parents=True, exist_ok=True)
-        latent_path = self.get_path(name)
-        torch.save(data, latent_path)
-
-    def delete(self, name: str) -> None:
-        latent_path = self.get_path(name)
-        latent_path.unlink()
-
-    def get_path(self, name: str) -> Path:
-        return self.__output_folder / name
-
-    def _delete_all_latents(self) -> None:
-        """
-        Deletes all latents from disk.
-        Must be called after we have access to `self._invoker` (e.g. in `start()`).
-        """
-        deleted_latents_count = 0
-        freed_space = 0
-        for latents_file in Path(self.__output_folder).glob("*"):
-            if latents_file.is_file():
-                freed_space += latents_file.stat().st_size
-                deleted_latents_count += 1
-                latents_file.unlink()
-        if deleted_latents_count > 0:
-            freed_space_in_mb = round(freed_space / 1024 / 1024, 2)
-            self._invoker.services.logger.info(
-                f"Deleted {deleted_latents_count} latents files (freed {freed_space_in_mb}MB)"
-            )

invokeai/app/services/latents_storage/latents_storage_forward_cache.py

@@ -1,68 +0,0 @@
-# Copyright (c) 2023 Kyle Schouviller (https://github.com/kyle0654)
-
-from queue import Queue
-from typing import Dict, Optional
-
-import torch
-
-from invokeai.app.services.invoker import Invoker
-
-from .latents_storage_base import LatentsStorageBase
-
-
-class ForwardCacheLatentsStorage(LatentsStorageBase):
-    """Caches the latest N latents in memory, writing-thorugh to and reading from underlying storage"""
-
-    __cache: Dict[str, torch.Tensor]
-    __cache_ids: Queue
-    __max_cache_size: int
-    __underlying_storage: LatentsStorageBase
-
-    def __init__(self, underlying_storage: LatentsStorageBase, max_cache_size: int = 20):
-        super().__init__()
-        self.__underlying_storage = underlying_storage
-        self.__cache = {}
-        self.__cache_ids = Queue()
-        self.__max_cache_size = max_cache_size
-
-    def start(self, invoker: Invoker) -> None:
-        self._invoker = invoker
-        start_op = getattr(self.__underlying_storage, "start", None)
-        if callable(start_op):
-            start_op(invoker)
-
-    def stop(self, invoker: Invoker) -> None:
-        self._invoker = invoker
-        stop_op = getattr(self.__underlying_storage, "stop", None)
-        if callable(stop_op):
-            stop_op(invoker)
-
-    def get(self, name: str) -> torch.Tensor:
-        cache_item = self.__get_cache(name)
-        if cache_item is not None:
-            return cache_item
-
-        latent = self.__underlying_storage.get(name)
-        self.__set_cache(name, latent)
-        return latent
-
-    def save(self, name: str, data: torch.Tensor) -> None:
-        self.__underlying_storage.save(name, data)
-        self.__set_cache(name, data)
-        self._on_changed(data)
-
-    def delete(self, name: str) -> None:
-        self.__underlying_storage.delete(name)
-        if name in self.__cache:
-            del self.__cache[name]
-        self._on_deleted(name)
-
-    def __get_cache(self, name: str) -> Optional[torch.Tensor]:
-        return None if name not in self.__cache else self.__cache[name]
-
-    def __set_cache(self, name: str, data: torch.Tensor):
-        if name not in self.__cache:
-            self.__cache[name] = data
-            self.__cache_ids.put(name)
-            if self.__cache_ids.qsize() > self.__max_cache_size:
-                self.__cache.pop(self.__cache_ids.get())

invokeai/app/services/model_manager/model_manager_base.py

@@ -5,25 +5,23 @@ from __future__ import annotations
 from abc import ABC, abstractmethod
 from logging import Logger
 from pathlib import Path
-from typing import TYPE_CHECKING, Callable, List, Literal, Optional, Tuple, Union
+from typing import Callable, List, Literal, Optional, Tuple, Union
 
 from pydantic import Field
 
 from invokeai.app.services.config.config_default import InvokeAIAppConfig
+from invokeai.app.services.shared.invocation_context import InvocationContextData
 from invokeai.backend.model_management import (
     AddModelResult,
     BaseModelType,
+    LoadedModelInfo,
     MergeInterpolationMethod,
-    ModelInfo,
     ModelType,
     SchedulerPredictionType,
     SubModelType,
 )
 from invokeai.backend.model_management.model_cache import CacheStats
 
-if TYPE_CHECKING:
-    from invokeai.app.invocations.baseinvocation import BaseInvocation, InvocationContext
-
 
 class ModelManagerServiceBase(ABC):
     """Responsible for managing models on disk and in memory"""
@@ -49,9 +47,8 @@ class ModelManagerServiceBase(ABC):
         base_model: BaseModelType,
         model_type: ModelType,
         submodel: Optional[SubModelType] = None,
-        node: Optional[BaseInvocation] = None,
-        context: Optional[InvocationContext] = None,
-    ) -> ModelInfo:
+        context_data: Optional[InvocationContextData] = None,
+    ) -> LoadedModelInfo:
         """Retrieve the indicated model with name and type.
         submodel can be used to get a part (such as the vae)
         of a diffusers pipeline."""

invokeai/app/services/model_manager/model_manager_default.py

@@ -11,11 +11,13 @@ from pydantic import Field
 
 from invokeai.app.services.config.config_default import InvokeAIAppConfig
 from invokeai.app.services.invocation_processor.invocation_processor_common import CanceledException
+from invokeai.app.services.invoker import Invoker
+from invokeai.app.services.shared.invocation_context import InvocationContextData
 from invokeai.backend.model_management import (
     AddModelResult,
     BaseModelType,
+    LoadedModelInfo,
     MergeInterpolationMethod,
-    ModelInfo,
     ModelManager,
     ModelMerger,
     ModelNotFoundException,
@@ -30,7 +32,7 @@ from invokeai.backend.util import choose_precision, choose_torch_device
 from .model_manager_base import ModelManagerServiceBase
 
 if TYPE_CHECKING:
-    from invokeai.app.invocations.baseinvocation import InvocationContext
+    pass
 
 
 # simple implementation
@@ -86,47 +88,50 @@ class ModelManagerService(ModelManagerServiceBase):
         )
         logger.info("Model manager service initialized")
 
+    def start(self, invoker: Invoker) -> None:
+        self._invoker: Optional[Invoker] = invoker
+
     def get_model(
         self,
         model_name: str,
         base_model: BaseModelType,
         model_type: ModelType,
         submodel: Optional[SubModelType] = None,
-        context: Optional[InvocationContext] = None,
-    ) -> ModelInfo:
+        context_data: Optional[InvocationContextData] = None,
+    ) -> LoadedModelInfo:
         """
         Retrieve the indicated model. submodel can be used to get a
         part (such as the vae) of a diffusers mode.
         """
 
         # we can emit model loading events if we are executing with access to the invocation context
-        if context:
+        if context_data is not None:
             self._emit_load_event(
-                context=context,
+                context_data=context_data,
                 model_name=model_name,
                 base_model=base_model,
                 model_type=model_type,
                 submodel=submodel,
             )
 
-        model_info = self.mgr.get_model(
+        loaded_model_info = self.mgr.get_model(
             model_name,
             base_model,
             model_type,
             submodel,
         )
 
-        if context:
+        if context_data is not None:
             self._emit_load_event(
-                context=context,
+                context_data=context_data,
                 model_name=model_name,
                 base_model=base_model,
                 model_type=model_type,
                 submodel=submodel,
-                model_info=model_info,
+                loaded_model_info=loaded_model_info,
             )
 
-        return model_info
+        return loaded_model_info
 
     def model_exists(
         self,
@@ -263,34 +268,37 @@ class ModelManagerService(ModelManagerServiceBase):
 
     def _emit_load_event(
         self,
-        context: InvocationContext,
+        context_data: InvocationContextData,
         model_name: str,
         base_model: BaseModelType,
         model_type: ModelType,
         submodel: Optional[SubModelType] = None,
-        model_info: Optional[ModelInfo] = None,
+        loaded_model_info: Optional[LoadedModelInfo] = None,
     ):
-        if context.services.queue.is_canceled(context.graph_execution_state_id):
+        if self._invoker is None:
+            return
+
+        if self._invoker.services.queue.is_canceled(context_data.session_id):
             raise CanceledException()
 
-        if model_info:
-            context.services.events.emit_model_load_completed(
-                queue_id=context.queue_id,
-                queue_item_id=context.queue_item_id,
-                queue_batch_id=context.queue_batch_id,
-                graph_execution_state_id=context.graph_execution_state_id,
+        if loaded_model_info:
+            self._invoker.services.events.emit_model_load_completed(
+                queue_id=context_data.queue_id,
+                queue_item_id=context_data.queue_item_id,
+                queue_batch_id=context_data.batch_id,
+                graph_execution_state_id=context_data.session_id,
                 model_name=model_name,
                 base_model=base_model,
                 model_type=model_type,
                 submodel=submodel,
-                model_info=model_info,
+                loaded_model_info=loaded_model_info,
             )
         else:
-            context.services.events.emit_model_load_started(
-                queue_id=context.queue_id,
-                queue_item_id=context.queue_item_id,
-                queue_batch_id=context.queue_batch_id,
-                graph_execution_state_id=context.graph_execution_state_id,
+            self._invoker.services.events.emit_model_load_started(
+                queue_id=context_data.queue_id,
+                queue_item_id=context_data.queue_item_id,
+                queue_batch_id=context_data.batch_id,
+                graph_execution_state_id=context_data.session_id,
                 model_name=model_name,
                 base_model=base_model,
                 model_type=model_type,

invokeai/app/services/object_serializer/object_serializer_base.py

@@ -0,0 +1,44 @@
+from abc import ABC, abstractmethod
+from typing import Callable, Generic, TypeVar
+
+T = TypeVar("T")
+
+
+class ObjectSerializerBase(ABC, Generic[T]):
+    """Saves and loads arbitrary python objects."""
+
+    def __init__(self) -> None:
+        self._on_deleted_callbacks: list[Callable[[str], None]] = []
+
+    @abstractmethod
+    def load(self, name: str) -> T:
+        """
+        Loads the object.
+        :param name: The name of the object to load.
+        :raises ObjectNotFoundError: if the object is not found
+        """
+        pass
+
+    @abstractmethod
+    def save(self, obj: T) -> str:
+        """
+        Saves the object, returning its name.
+        :param obj: The object to save.
+        """
+        pass
+
+    @abstractmethod
+    def delete(self, name: str) -> None:
+        """
+        Deletes the object, if it exists.
+        :param name: The name of the object to delete.
+        """
+        pass
+
+    def on_deleted(self, on_deleted: Callable[[str], None]) -> None:
+        """Register a callback for when an object is deleted"""
+        self._on_deleted_callbacks.append(on_deleted)
+
+    def _on_deleted(self, name: str) -> None:
+        for callback in self._on_deleted_callbacks:
+            callback(name)

invokeai/app/services/object_serializer/object_serializer_common.py

@@ -0,0 +1,5 @@
+class ObjectNotFoundError(KeyError):
+    """Raised when an object is not found while loading"""
+
+    def __init__(self, name: str) -> None:
+        super().__init__(f"Object with name {name} not found")

invokeai/app/services/object_serializer/object_serializer_disk.py

@@ -0,0 +1,85 @@
+import tempfile
+import typing
+from dataclasses import dataclass
+from pathlib import Path
+from typing import TYPE_CHECKING, Optional, TypeVar
+
+import torch
+
+from invokeai.app.services.object_serializer.object_serializer_base import ObjectSerializerBase
+from invokeai.app.services.object_serializer.object_serializer_common import ObjectNotFoundError
+from invokeai.app.util.misc import uuid_string
+
+if TYPE_CHECKING:
+    from invokeai.app.services.invoker import Invoker
+
+
+T = TypeVar("T")
+
+
+@dataclass
+class DeleteAllResult:
+    deleted_count: int
+    freed_space_bytes: float
+
+
+class ObjectSerializerDisk(ObjectSerializerBase[T]):
+    """Disk-backed storage for arbitrary python objects. Serialization is handled by `torch.save` and `torch.load`.
+
+    :param output_dir: The folder where the serialized objects will be stored
+    :param ephemeral: If True, objects will be stored in a temporary directory inside the given output_dir and cleaned up on exit
+    """
+
+    def __init__(self, output_dir: Path, ephemeral: bool = False):
+        super().__init__()
+        self._ephemeral = ephemeral
+        self._base_output_dir = output_dir
+        self._base_output_dir.mkdir(parents=True, exist_ok=True)
+        # Must specify `ignore_cleanup_errors` to avoid fatal errors during cleanup on Windows
+        self._tempdir = (
+            tempfile.TemporaryDirectory(dir=self._base_output_dir, ignore_cleanup_errors=True) if ephemeral else None
+        )
+        self._output_dir = Path(self._tempdir.name) if self._tempdir else self._base_output_dir
+        self.__obj_class_name: Optional[str] = None
+
+    def load(self, name: str) -> T:
+        file_path = self._get_path(name)
+        try:
+            return torch.load(file_path)  # pyright: ignore [reportUnknownMemberType]
+        except FileNotFoundError as e:
+            raise ObjectNotFoundError(name) from e
+
+    def save(self, obj: T) -> str:
+        name = self._new_name()
+        file_path = self._get_path(name)
+        torch.save(obj, file_path)  # pyright: ignore [reportUnknownMemberType]
+        return name
+
+    def delete(self, name: str) -> None:
+        file_path = self._get_path(name)
+        file_path.unlink()
+
+    @property
+    def _obj_class_name(self) -> str:
+        if not self.__obj_class_name:
+            # `__orig_class__` is not available in the constructor for some technical, undoubtedly very pythonic reason
+            self.__obj_class_name = typing.get_args(self.__orig_class__)[0].__name__  # pyright: ignore [reportUnknownMemberType, reportAttributeAccessIssue]
+        return self.__obj_class_name
+
+    def _get_path(self, name: str) -> Path:
+        return self._output_dir / name
+
+    def _new_name(self) -> str:
+        return f"{self._obj_class_name}_{uuid_string()}"
+
+    def _tempdir_cleanup(self) -> None:
+        """Calls `cleanup` on the temporary directory, if it exists."""
+        if self._tempdir:
+            self._tempdir.cleanup()
+
+    def __del__(self) -> None:
+        # In case the service is not properly stopped, clean up the temporary directory when the class instance is GC'd.
+        self._tempdir_cleanup()
+
+    def stop(self, invoker: "Invoker") -> None:
+        self._tempdir_cleanup()

invokeai/app/services/object_serializer/object_serializer_forward_cache.py

@@ -0,0 +1,65 @@
+from queue import Queue
+from typing import TYPE_CHECKING, Optional, TypeVar
+
+from invokeai.app.services.object_serializer.object_serializer_base import ObjectSerializerBase
+
+T = TypeVar("T")
+
+if TYPE_CHECKING:
+    from invokeai.app.services.invoker import Invoker
+
+
+class ObjectSerializerForwardCache(ObjectSerializerBase[T]):
+    """
+    Provides a LRU cache for an instance of `ObjectSerializerBase`.
+    Saving an object to the cache always writes through to the underlying storage.
+    """
+
+    def __init__(self, underlying_storage: ObjectSerializerBase[T], max_cache_size: int = 20):
+        super().__init__()
+        self._underlying_storage = underlying_storage
+        self._cache: dict[str, T] = {}
+        self._cache_ids = Queue[str]()
+        self._max_cache_size = max_cache_size
+
+    def start(self, invoker: "Invoker") -> None:
+        self._invoker = invoker
+        start_op = getattr(self._underlying_storage, "start", None)
+        if callable(start_op):
+            start_op(invoker)
+
+    def stop(self, invoker: "Invoker") -> None:
+        self._invoker = invoker
+        stop_op = getattr(self._underlying_storage, "stop", None)
+        if callable(stop_op):
+            stop_op(invoker)
+
+    def load(self, name: str) -> T:
+        cache_item = self._get_cache(name)
+        if cache_item is not None:
+            return cache_item
+
+        obj = self._underlying_storage.load(name)
+        self._set_cache(name, obj)
+        return obj
+
+    def save(self, obj: T) -> str:
+        name = self._underlying_storage.save(obj)
+        self._set_cache(name, obj)
+        return name
+
+    def delete(self, name: str) -> None:
+        self._underlying_storage.delete(name)
+        if name in self._cache:
+            del self._cache[name]
+        self._on_deleted(name)
+
+    def _get_cache(self, name: str) -> Optional[T]:
+        return None if name not in self._cache else self._cache[name]
+
+    def _set_cache(self, name: str, data: T):
+        if name not in self._cache:
+            self._cache[name] = data
+            self._cache_ids.put(name)
+            if self._cache_ids.qsize() > self._max_cache_size:
+                self._cache.pop(self._cache_ids.get())

invokeai/app/services/shared/graph.py

@@ -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.services.shared.invocation_context import InvocationContext
 from invokeai.app.util.misc import uuid_string
 
 # in 3.10 this would be "from types import NoneType"

invokeai/app/services/shared/invocation_context.py

@@ -0,0 +1,409 @@
+from dataclasses import dataclass
+from typing import TYPE_CHECKING, Optional
+
+from PIL.Image import Image
+from torch import Tensor
+
+from invokeai.app.invocations.fields import MetadataField, WithBoard, WithMetadata
+from invokeai.app.services.boards.boards_common import BoardDTO
+from invokeai.app.services.config.config_default import InvokeAIAppConfig
+from invokeai.app.services.image_records.image_records_common import ImageCategory, ResourceOrigin
+from invokeai.app.services.images.images_common import ImageDTO
+from invokeai.app.services.invocation_services import InvocationServices
+from invokeai.app.services.workflow_records.workflow_records_common import WorkflowWithoutID
+from invokeai.app.util.step_callback import stable_diffusion_step_callback
+from invokeai.backend.model_management.model_manager import LoadedModelInfo
+from invokeai.backend.model_management.models.base import BaseModelType, ModelType, SubModelType
+from invokeai.backend.stable_diffusion.diffusers_pipeline import PipelineIntermediateState
+from invokeai.backend.stable_diffusion.diffusion.conditioning_data import ConditioningFieldData
+
+if TYPE_CHECKING:
+    from invokeai.app.invocations.baseinvocation import BaseInvocation
+
+"""
+The InvocationContext provides access to various services and data about the current invocation.
+
+We do not provide the invocation services directly, as their methods are both dangerous and
+inconvenient to use.
+
+For example:
+- The `images` service allows nodes to delete or unsafely modify existing images.
+- The `configuration` service allows nodes to change the app's config at runtime.
+- The `events` service allows nodes to emit arbitrary events.
+
+Wrapping these services provides a simpler and safer interface for nodes to use.
+
+When a node executes, a fresh `InvocationContext` is built for it, ensuring nodes cannot interfere
+with each other.
+
+Many of the wrappers have the same signature as the methods they wrap. This allows us to write
+user-facing docstrings and not need to go and update the internal services to match.
+
+Note: The docstrings are in weird places, but that's where they must be to get IDEs to see them.
+"""
+
+
+@dataclass
+class InvocationContextData:
+    invocation: "BaseInvocation"
+    """The invocation that is being executed."""
+    session_id: str
+    """The session that is being executed."""
+    queue_id: str
+    """The queue in which the session is being executed."""
+    source_node_id: str
+    """The ID of the node from which the currently executing invocation was prepared."""
+    queue_item_id: int
+    """The ID of the queue item that is being executed."""
+    batch_id: str
+    """The ID of the batch that is being executed."""
+    workflow: Optional[WorkflowWithoutID] = None
+    """The workflow associated with this queue item, if any."""
+
+
+class InvocationContextInterface:
+    def __init__(self, services: InvocationServices, context_data: InvocationContextData) -> None:
+        self._services = services
+        self._context_data = context_data
+
+
+class BoardsInterface(InvocationContextInterface):
+    def create(self, board_name: str) -> BoardDTO:
+        """
+        Creates a board.
+
+        :param board_name: The name of the board to create.
+        """
+        return self._services.boards.create(board_name)
+
+    def get_dto(self, board_id: str) -> BoardDTO:
+        """
+        Gets a board DTO.
+
+        :param board_id: The ID of the board to get.
+        """
+        return self._services.boards.get_dto(board_id)
+
+    def get_all(self) -> list[BoardDTO]:
+        """
+        Gets all boards.
+        """
+        return self._services.boards.get_all()
+
+    def add_image_to_board(self, board_id: str, image_name: str) -> None:
+        """
+        Adds an image to a board.
+
+        :param board_id: The ID of the board to add the image to.
+        :param image_name: The name of the image to add to the board.
+        """
+        return self._services.board_images.add_image_to_board(board_id, image_name)
+
+    def get_all_image_names_for_board(self, board_id: str) -> list[str]:
+        """
+        Gets all image names for a board.
+
+        :param board_id: The ID of the board to get the image names for.
+        """
+        return self._services.board_images.get_all_board_image_names_for_board(board_id)
+
+
+class LoggerInterface(InvocationContextInterface):
+    def debug(self, message: str) -> None:
+        """
+        Logs a debug message.
+
+        :param message: The message to log.
+        """
+        self._services.logger.debug(message)
+
+    def info(self, message: str) -> None:
+        """
+        Logs an info message.
+
+        :param message: The message to log.
+        """
+        self._services.logger.info(message)
+
+    def warning(self, message: str) -> None:
+        """
+        Logs a warning message.
+
+        :param message: The message to log.
+        """
+        self._services.logger.warning(message)
+
+    def error(self, message: str) -> None:
+        """
+        Logs an error message.
+
+        :param message: The message to log.
+        """
+        self._services.logger.error(message)
+
+
+class ImagesInterface(InvocationContextInterface):
+    def save(
+        self,
+        image: Image,
+        board_id: Optional[str] = None,
+        image_category: ImageCategory = ImageCategory.GENERAL,
+        metadata: Optional[MetadataField] = None,
+    ) -> ImageDTO:
+        """
+        Saves an image, returning its DTO.
+
+        If the current queue item has a workflow or metadata, it is automatically saved with the image.
+
+        :param image: The image to save, as a PIL image.
+        :param board_id: The board ID to add the image to, if it should be added. It the invocation \
+            inherits from `WithBoard`, that board will be used automatically. **Use this only if \
+            you want to override or provide a board manually!**
+        :param image_category: The category of the image. Only the GENERAL category is added \
+            to the gallery.
+        :param metadata: The metadata to save with the image, if it should have any. If the \
+            invocation inherits from `WithMetadata`, that metadata will be used automatically. \
+            **Use this only if you want to override or provide metadata manually!**
+        """
+
+        # If `metadata` is provided directly, use that. Else, use the metadata provided by `WithMetadata`, falling back to None.
+        metadata_ = None
+        if metadata:
+            metadata_ = metadata
+        elif isinstance(self._context_data.invocation, WithMetadata):
+            metadata_ = self._context_data.invocation.metadata
+
+        # If `board_id` is provided directly, use that. Else, use the board provided by `WithBoard`, falling back to None.
+        board_id_ = None
+        if board_id:
+            board_id_ = board_id
+        elif isinstance(self._context_data.invocation, WithBoard) and self._context_data.invocation.board:
+            board_id_ = self._context_data.invocation.board.board_id
+
+        return self._services.images.create(
+            image=image,
+            is_intermediate=self._context_data.invocation.is_intermediate,
+            image_category=image_category,
+            board_id=board_id_,
+            metadata=metadata_,
+            image_origin=ResourceOrigin.INTERNAL,
+            workflow=self._context_data.workflow,
+            session_id=self._context_data.session_id,
+            node_id=self._context_data.invocation.id,
+        )
+
+    def get_pil(self, image_name: str) -> Image:
+        """
+        Gets an image as a PIL Image object.
+
+        :param image_name: The name of the image to get.
+        """
+        return self._services.images.get_pil_image(image_name)
+
+    def get_metadata(self, image_name: str) -> Optional[MetadataField]:
+        """
+        Gets an image's metadata, if it has any.
+
+        :param image_name: The name of the image to get the metadata for.
+        """
+        return self._services.images.get_metadata(image_name)
+
+    def get_dto(self, image_name: str) -> ImageDTO:
+        """
+        Gets an image as an ImageDTO object.
+
+        :param image_name: The name of the image to get.
+        """
+        return self._services.images.get_dto(image_name)
+
+
+class TensorsInterface(InvocationContextInterface):
+    def save(self, tensor: Tensor) -> str:
+        """
+        Saves a tensor, returning its name.
+
+        :param tensor: The tensor to save.
+        """
+
+        name = self._services.tensors.save(obj=tensor)
+        return name
+
+    def load(self, name: str) -> Tensor:
+        """
+        Loads a tensor by name.
+
+        :param name: The name of the tensor to load.
+        """
+        return self._services.tensors.load(name)
+
+
+class ConditioningInterface(InvocationContextInterface):
+    def save(self, conditioning_data: ConditioningFieldData) -> str:
+        """
+        Saves a conditioning data object, returning its name.
+
+        :param conditioning_context_data: The conditioning data to save.
+        """
+
+        name = self._services.conditioning.save(obj=conditioning_data)
+        return name
+
+    def load(self, name: str) -> ConditioningFieldData:
+        """
+        Loads conditioning data by name.
+
+        :param name: The name of the conditioning data to load.
+        """
+
+        return self._services.conditioning.load(name)
+
+
+class ModelsInterface(InvocationContextInterface):
+    def exists(self, model_name: str, base_model: BaseModelType, model_type: ModelType) -> bool:
+        """
+        Checks if a model exists.
+
+        :param model_name: The name of the model to check.
+        :param base_model: The base model of the model to check.
+        :param model_type: The type of the model to check.
+        """
+        return self._services.model_manager.model_exists(model_name, base_model, model_type)
+
+    def load(
+        self, model_name: str, base_model: BaseModelType, model_type: ModelType, submodel: Optional[SubModelType] = None
+    ) -> LoadedModelInfo:
+        """
+        Loads a model.
+
+        :param model_name: The name of the model to get.
+        :param base_model: The base model of the model to get.
+        :param model_type: The type of the model to get.
+        :param submodel: The submodel of the model to get.
+        :returns: An object representing the loaded model.
+        """
+
+        # The model manager emits events as it loads the model. It needs the context data to build
+        # the event payloads.
+
+        return self._services.model_manager.get_model(
+            model_name, base_model, model_type, submodel, context_data=self._context_data
+        )
+
+    def get_info(self, model_name: str, base_model: BaseModelType, model_type: ModelType) -> dict:
+        """
+        Gets a model's info, an dict-like object.
+
+        :param model_name: The name of the model to get.
+        :param base_model: The base model of the model to get.
+        :param model_type: The type of the model to get.
+        """
+        return self._services.model_manager.model_info(model_name, base_model, model_type)
+
+
+class ConfigInterface(InvocationContextInterface):
+    def get(self) -> InvokeAIAppConfig:
+        """Gets the app's config."""
+
+        return self._services.configuration.get_config()
+
+
+class UtilInterface(InvocationContextInterface):
+    def sd_step_callback(self, intermediate_state: PipelineIntermediateState, base_model: BaseModelType) -> None:
+        """
+        The step callback emits a progress event with the current step, the total number of
+        steps, a preview image, and some other internal metadata.
+
+        This should be called after each denoising step.
+
+        :param intermediate_state: The intermediate state of the diffusion pipeline.
+        :param base_model: The base model for the current denoising step.
+        """
+
+        # The step callback needs access to the events and the invocation queue services, but this
+        # represents a dangerous level of access.
+        #
+        # We wrap the step callback so that nodes do not have direct access to these services.
+
+        stable_diffusion_step_callback(
+            context_data=self._context_data,
+            intermediate_state=intermediate_state,
+            base_model=base_model,
+            invocation_queue=self._services.queue,
+            events=self._services.events,
+        )
+
+
+class InvocationContext:
+    """
+    The `InvocationContext` provides access to various services and data for the current invocation.
+    """
+
+    def __init__(
+        self,
+        images: ImagesInterface,
+        tensors: TensorsInterface,
+        conditioning: ConditioningInterface,
+        models: ModelsInterface,
+        logger: LoggerInterface,
+        config: ConfigInterface,
+        util: UtilInterface,
+        boards: BoardsInterface,
+        context_data: InvocationContextData,
+        services: InvocationServices,
+    ) -> None:
+        self.images = images
+        """Provides methods to save, get and update images and their metadata."""
+        self.tensors = tensors
+        """Provides methods to save and get tensors, including image, noise, masks, and masked images."""
+        self.conditioning = conditioning
+        """Provides methods to save and get conditioning data."""
+        self.models = models
+        """Provides methods to check if a model exists, get a model, and get a model's info."""
+        self.logger = logger
+        """Provides access to the app logger."""
+        self.config = config
+        """Provides access to the app's config."""
+        self.util = util
+        """Provides utility methods."""
+        self.boards = boards
+        """Provides methods to interact with boards."""
+        self._data = context_data
+        """Provides data about the current queue item and invocation. This is an internal API and may change without warning."""
+        self._services = services
+        """Provides access to the full application services. This is an internal API and may change without warning."""
+
+
+def build_invocation_context(
+    services: InvocationServices,
+    context_data: InvocationContextData,
+) -> InvocationContext:
+    """
+    Builds the invocation context for a specific invocation execution.
+
+    :param invocation_services: The invocation services to wrap.
+    :param invocation_context_data: The invocation context data.
+    """
+
+    logger = LoggerInterface(services=services, context_data=context_data)
+    images = ImagesInterface(services=services, context_data=context_data)
+    tensors = TensorsInterface(services=services, context_data=context_data)
+    models = ModelsInterface(services=services, context_data=context_data)
+    config = ConfigInterface(services=services, context_data=context_data)
+    util = UtilInterface(services=services, context_data=context_data)
+    conditioning = ConditioningInterface(services=services, context_data=context_data)
+    boards = BoardsInterface(services=services, context_data=context_data)
+
+    ctx = InvocationContext(
+        images=images,
+        logger=logger,
+        config=config,
+        tensors=tensors,
+        models=models,
+        context_data=context_data,
+        util=util,
+        conditioning=conditioning,
+        services=services,
+        boards=boards,
+    )
+
+    return ctx

invokeai/app/shared/fields.py

@@ -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."

invokeai/app/shared/models.py

@@ -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):

invokeai/app/util/step_callback.py

@@ -1,3 +1,5 @@
+from typing import TYPE_CHECKING
+
 import torch
 from PIL import Image
 
@@ -6,7 +8,11 @@ from invokeai.app.services.invocation_processor.invocation_processor_common impo
 from ...backend.model_management.models import BaseModelType
 from ...backend.stable_diffusion import PipelineIntermediateState
 from ...backend.util.util import image_to_dataURL
-from ..invocations.baseinvocation import InvocationContext
+
+if TYPE_CHECKING:
+    from invokeai.app.services.events.events_base import EventServiceBase
+    from invokeai.app.services.invocation_queue.invocation_queue_base import InvocationQueueABC
+    from invokeai.app.services.shared.invocation_context import InvocationContextData
 
 
 def sample_to_lowres_estimated_image(samples, latent_rgb_factors, smooth_matrix=None):
@@ -25,13 +31,13 @@ def sample_to_lowres_estimated_image(samples, latent_rgb_factors, smooth_matrix=
 
 
 def stable_diffusion_step_callback(
-    context: InvocationContext,
+    context_data: "InvocationContextData",
     intermediate_state: PipelineIntermediateState,
-    node: dict,
-    source_node_id: str,
     base_model: BaseModelType,
-):
-    if context.services.queue.is_canceled(context.graph_execution_state_id):
+    invocation_queue: "InvocationQueueABC",
+    events: "EventServiceBase",
+) -> None:
+    if invocation_queue.is_canceled(context_data.session_id):
         raise CanceledException
 
     # Some schedulers report not only the noisy latents at the current timestep,
@@ -108,13 +114,13 @@ def stable_diffusion_step_callback(
 
     dataURL = image_to_dataURL(image, image_format="JPEG")
 
-    context.services.events.emit_generator_progress(
-        queue_id=context.queue_id,
-        queue_item_id=context.queue_item_id,
-        queue_batch_id=context.queue_batch_id,
-        graph_execution_state_id=context.graph_execution_state_id,
-        node=node,
-        source_node_id=source_node_id,
+    events.emit_generator_progress(
+        queue_id=context_data.queue_id,
+        queue_item_id=context_data.queue_item_id,
+        queue_batch_id=context_data.batch_id,
+        graph_execution_state_id=context_data.session_id,
+        node_id=context_data.invocation.id,
+        source_node_id=context_data.source_node_id,
         progress_image=ProgressImage(width=width, height=height, dataURL=dataURL),
         step=intermediate_state.step,
         order=intermediate_state.order,

invokeai/backend/__init__.py

@@ -1,5 +1,12 @@
 """
 Initialization file for invokeai.backend
 """
-from .model_management import BaseModelType, ModelCache, ModelInfo, ModelManager, ModelType, SubModelType  # noqa: F401
+from .model_management import (  # noqa: F401
+    BaseModelType,
+    LoadedModelInfo,
+    ModelCache,
+    ModelManager,
+    ModelType,
+    SubModelType,
+)
 from .model_management.models import SilenceWarnings  # noqa: F401

invokeai/backend/model_management/__init__.py

@@ -3,7 +3,7 @@
 Initialization file for invokeai.backend.model_management
 """
 # This import must be first
-from .model_manager import AddModelResult, ModelInfo, ModelManager, SchedulerPredictionType
+from .model_manager import AddModelResult, LoadedModelInfo, ModelManager, SchedulerPredictionType
 from .lora import ModelPatcher, ONNXModelPatcher
 from .model_cache import ModelCache
 

invokeai/backend/model_management/model_manager.py

@@ -271,7 +271,7 @@ CONFIG_FILE_VERSION = "3.0.0"
 
 
 @dataclass
-class ModelInfo:
+class LoadedModelInfo:
     context: ModelLocker
     name: str
     base_model: BaseModelType
@@ -450,7 +450,7 @@ class ModelManager(object):
         base_model: BaseModelType,
         model_type: ModelType,
         submodel_type: Optional[SubModelType] = None,
-    ) -> ModelInfo:
+    ) -> LoadedModelInfo:
         """Given a model named identified in models.yaml, return
         an ModelInfo object describing it.
         :param model_name: symbolic name of the model in models.yaml
@@ -508,7 +508,7 @@ class ModelManager(object):
 
         model_hash = "<NO_HASH>"  # TODO:
 
-        return ModelInfo(
+        return LoadedModelInfo(
             context=model_context,
             name=model_name,
             base_model=base_model,

invokeai/backend/stable_diffusion/diffusion/conditioning_data.py

@@ -32,6 +32,11 @@ class BasicConditioningInfo:
         return self
 
 
+@dataclass
+class ConditioningFieldData:
+    conditionings: List[BasicConditioningInfo]
+
+
 @dataclass
 class SDXLConditioningInfo(BasicConditioningInfo):
     pooled_embeds: torch.Tensor

invokeai/backend/tiles/tiles.py

@@ -3,7 +3,7 @@ from typing import Union
 
 import numpy as np
 
-from invokeai.app.invocations.latent import LATENT_SCALE_FACTOR
+from invokeai.app.invocations.constants import LATENT_SCALE_FACTOR
 from invokeai.backend.tiles.utils import TBLR, Tile, paste, seam_blend
 
 

invokeai/backend/util/test_utils.py

@@ -7,7 +7,7 @@ import torch
 
 from invokeai.app.services.config.config_default import InvokeAIAppConfig
 from invokeai.backend.install.model_install_backend import ModelInstall
-from invokeai.backend.model_management.model_manager import ModelInfo
+from invokeai.backend.model_management.model_manager import LoadedModelInfo
 from invokeai.backend.model_management.models.base import BaseModelType, ModelNotFoundException, ModelType, SubModelType
 
 
@@ -34,8 +34,8 @@ def install_and_load_model(
     base_model: BaseModelType,
     model_type: ModelType,
     submodel_type: Optional[SubModelType] = None,
-) -> ModelInfo:
-    """Install a model if it is not already installed, then get the ModelInfo for that model.
+) -> LoadedModelInfo:
+    """Install a model if it is not already installed, then get the LoadedModelInfo for that model.
 
     This is intended as a utility function for tests.
 
@@ -49,9 +49,9 @@ def install_and_load_model(
         submodel_type (Optional[SubModelType]): The submodel type, forwarded to ModelManager.get_model(...).
 
     Returns:
-        ModelInfo
+        LoadedModelInfo
     """
-    # If the requested model is already installed, return its ModelInfo.
+    # If the requested model is already installed, return its LoadedModelInfo.
     with contextlib.suppress(ModelNotFoundException):
         return model_installer.mgr.get_model(model_name, base_model, model_type, submodel_type)
 

invokeai/frontend/web/config/common.mts

@@ -1,12 +0,0 @@
-import react from '@vitejs/plugin-react-swc';
-import { visualizer } from 'rollup-plugin-visualizer';
-import type { PluginOption, UserConfig } from 'vite';
-import eslint from 'vite-plugin-eslint';
-import tsconfigPaths from 'vite-tsconfig-paths';
-
-export const commonPlugins: UserConfig['plugins'] = [
-  react(),
-  eslint(),
-  tsconfigPaths(),
-  visualizer() as unknown as PluginOption,
-];

invokeai/frontend/web/config/vite.app.config.mts

@@ -1,33 +0,0 @@
-import type { UserConfig } from 'vite';
-
-import { commonPlugins } from './common.mjs';
-
-export const appConfig: UserConfig = {
-  base: './',
-  plugins: [...commonPlugins],
-  build: {
-    chunkSizeWarningLimit: 1500,
-  },
-  server: {
-    // Proxy HTTP requests to the flask server
-    proxy: {
-      // Proxy socket.io to the nodes socketio server
-      '/ws/socket.io': {
-        target: 'ws://127.0.0.1:9090',
-        ws: true,
-      },
-      // Proxy openapi schema definiton
-      '/openapi.json': {
-        target: 'http://127.0.0.1:9090/openapi.json',
-        rewrite: (path) => path.replace(/^\/openapi.json/, ''),
-        changeOrigin: true,
-      },
-      // proxy nodes api
-      '/api/v1': {
-        target: 'http://127.0.0.1:9090/api/v1',
-        rewrite: (path) => path.replace(/^\/api\/v1/, ''),
-        changeOrigin: true,
-      },
-    },
-  },
-};

invokeai/frontend/web/config/vite.package.config.mts

@@ -1,46 +0,0 @@
-import path from 'path';
-import type { UserConfig } from 'vite';
-import cssInjectedByJsPlugin from 'vite-plugin-css-injected-by-js';
-import dts from 'vite-plugin-dts';
-
-import { commonPlugins } from './common.mjs';
-
-export const packageConfig: UserConfig = {
-  base: './',
-  plugins: [
-    ...commonPlugins,
-    dts({
-      insertTypesEntry: true,
-    }),
-    cssInjectedByJsPlugin(),
-  ],
-  build: {
-    cssCodeSplit: true,
-    lib: {
-      entry: path.resolve(__dirname, '../src/index.ts'),
-      name: 'InvokeAIUI',
-      fileName: (format) => `invoke-ai-ui.${format}.js`,
-    },
-    rollupOptions: {
-      external: ['react', 'react-dom', '@emotion/react', '@chakra-ui/react', '@invoke-ai/ui-library'],
-      output: {
-        globals: {
-          react: 'React',
-          'react-dom': 'ReactDOM',
-          '@emotion/react': 'EmotionReact',
-          '@invoke-ai/ui-library': 'UiLibrary',
-        },
-      },
-    },
-  },
-  resolve: {
-    alias: {
-      app: path.resolve(__dirname, '../src/app'),
-      assets: path.resolve(__dirname, '../src/assets'),
-      common: path.resolve(__dirname, '../src/common'),
-      features: path.resolve(__dirname, '../src/features'),
-      services: path.resolve(__dirname, '../src/services'),
-      theme: path.resolve(__dirname, '../src/theme'),
-    },
-  },
-};

invokeai/frontend/web/package.json

@@ -33,7 +33,9 @@
     "preinstall": "npx only-allow pnpm",
     "storybook": "storybook dev -p 6006",
     "build-storybook": "storybook build",
-    "unimported": "npx unimported"
+    "unimported": "npx unimported",
+    "test": "vitest",
+    "test:no-watch": "vitest --no-watch"
   },
   "madge": {
     "excludeRegExp": [
@@ -157,7 +159,8 @@
     "vite-plugin-css-injected-by-js": "^3.3.1",
     "vite-plugin-dts": "^3.7.1",
     "vite-plugin-eslint": "^1.8.1",
-    "vite-tsconfig-paths": "^4.3.1"
+    "vite-tsconfig-paths": "^4.3.1",
+    "vitest": "^1.2.2"
   },
   "pnpm": {
     "patchedDependencies": {

invokeai/frontend/web/pnpm-lock.yaml

@@ -215,7 +215,7 @@ devDependencies:
     version: 7.6.10(react-dom@18.2.0)(react@18.2.0)(typescript@5.3.3)(vite@5.0.12)
   '@storybook/test':
     specifier: ^7.6.10
-    version: 7.6.10
+    version: 7.6.10(vitest@1.2.2)
   '@storybook/theming':
     specifier: ^7.6.10
     version: 7.6.10(react-dom@18.2.0)(react@18.2.0)
@@ -318,6 +318,9 @@ devDependencies:
   vite-tsconfig-paths:
     specifier: ^4.3.1
     version: 4.3.1(typescript@5.3.3)(vite@5.0.12)
+  vitest:
+    specifier: ^1.2.2
+    version: 1.2.2(@types/node@20.11.5)
 
 packages:
 
@@ -5464,7 +5467,7 @@ packages:
       - supports-color
     dev: true
 
-  /@storybook/test@7.6.10:
+  /@storybook/test@7.6.10(vitest@1.2.2):
     resolution: {integrity: sha512-dn/T+HcWOBlVh3c74BHurp++BaqBoQgNbSIaXlYDpJoZ+DzNIoEQVsWFYm5gCbtKK27iFd4n52RiQI3f6Vblqw==}
     dependencies:
       '@storybook/client-logger': 7.6.10
@@ -5472,7 +5475,7 @@ packages:
       '@storybook/instrumenter': 7.6.10
       '@storybook/preview-api': 7.6.10
       '@testing-library/dom': 9.3.4
-      '@testing-library/jest-dom': 6.2.0
+      '@testing-library/jest-dom': 6.2.0(vitest@1.2.2)
       '@testing-library/user-event': 14.3.0(@testing-library/dom@9.3.4)
       '@types/chai': 4.3.11
       '@vitest/expect': 0.34.7
@@ -5652,7 +5655,7 @@ packages:
       pretty-format: 27.5.1
     dev: true
 
-  /@testing-library/jest-dom@6.2.0:
+  /@testing-library/jest-dom@6.2.0(vitest@1.2.2):
     resolution: {integrity: sha512-+BVQlJ9cmEn5RDMUS8c2+TU6giLvzaHZ8sU/x0Jj7fk+6/46wPdwlgOPcpxS17CjcanBi/3VmGMqVr2rmbUmNw==}
     engines: {node: '>=14', npm: '>=6', yarn: '>=1'}
     peerDependencies:
@@ -5678,6 +5681,7 @@ packages:
       dom-accessibility-api: 0.6.3
       lodash: 4.17.21
       redent: 3.0.0
+      vitest: 1.2.2(@types/node@20.11.5)
     dev: true
 
   /@testing-library/user-event@14.3.0(@testing-library/dom@9.3.4):
@@ -6490,12 +6494,42 @@ packages:
       chai: 4.4.1
     dev: true
 
+  /@vitest/expect@1.2.2:
+    resolution: {integrity: sha512-3jpcdPAD7LwHUUiT2pZTj2U82I2Tcgg2oVPvKxhn6mDI2On6tfvPQTjAI4628GUGDZrCm4Zna9iQHm5cEexOAg==}
+    dependencies:
+      '@vitest/spy': 1.2.2
+      '@vitest/utils': 1.2.2
+      chai: 4.4.1
+    dev: true
+
+  /@vitest/runner@1.2.2:
+    resolution: {integrity: sha512-JctG7QZ4LSDXr5CsUweFgcpEvrcxOV1Gft7uHrvkQ+fsAVylmWQvnaAr/HDp3LAH1fztGMQZugIheTWjaGzYIg==}
+    dependencies:
+      '@vitest/utils': 1.2.2
+      p-limit: 5.0.0
+      pathe: 1.1.2
+    dev: true
+
+  /@vitest/snapshot@1.2.2:
+    resolution: {integrity: sha512-SmGY4saEw1+bwE1th6S/cZmPxz/Q4JWsl7LvbQIky2tKE35US4gd0Mjzqfr84/4OD0tikGWaWdMja/nWL5NIPA==}
+    dependencies:
+      magic-string: 0.30.5
+      pathe: 1.1.2
+      pretty-format: 29.7.0
+    dev: true
+
   /@vitest/spy@0.34.7:
     resolution: {integrity: sha512-NMMSzOY2d8L0mcOt4XcliDOS1ISyGlAXuQtERWVOoVHnKwmG+kKhinAiGw3dTtMQWybfa89FG8Ucg9tiC/FhTQ==}
     dependencies:
       tinyspy: 2.2.0
     dev: true
 
+  /@vitest/spy@1.2.2:
+    resolution: {integrity: sha512-k9Gcahssw8d7X3pSLq3e3XEu/0L78mUkCjivUqCQeXJm9clfXR/Td8+AP+VC1O6fKPIDLcHDTAmBOINVuv6+7g==}
+    dependencies:
+      tinyspy: 2.2.0
+    dev: true
+
   /@vitest/utils@0.34.7:
     resolution: {integrity: sha512-ziAavQLpCYS9sLOorGrFFKmy2gnfiNU0ZJ15TsMz/K92NAPS/rp9K4z6AJQQk5Y8adCy4Iwpxy7pQumQ/psnRg==}
     dependencies:
@@ -6504,6 +6538,15 @@ packages:
       pretty-format: 29.7.0
     dev: true
 
+  /@vitest/utils@1.2.2:
+    resolution: {integrity: sha512-WKITBHLsBHlpjnDQahr+XK6RE7MiAsgrIkr0pGhQ9ygoxBfUeG0lUG5iLlzqjmKSlBv3+j5EGsriBzh+C3Tq9g==}
+    dependencies:
+      diff-sequences: 29.6.3
+      estree-walker: 3.0.3
+      loupe: 2.3.7
+      pretty-format: 29.7.0
+    dev: true
+
   /@volar/language-core@1.11.1:
     resolution: {integrity: sha512-dOcNn3i9GgZAcJt43wuaEykSluAuOkQgzni1cuxLxTV0nJKanQztp7FxyswdRILaKH+P2XZMPRp2S4MV/pElCw==}
     dependencies:
@@ -7184,6 +7227,11 @@ packages:
     engines: {node: '>=0.4.0'}
     dev: true
 
+  /acorn-walk@8.3.2:
+    resolution: {integrity: sha512-cjkyv4OtNCIeqhHrfS81QWXoCBPExR/J62oyEqepVw8WaQeSqpW2uhuLPh1m9eWhDuOo/jUXVTlifvesOWp/4A==}
+    engines: {node: '>=0.4.0'}
+    dev: true
+
   /acorn@7.4.1:
     resolution: {integrity: sha512-nQyp0o1/mNdbTO1PO6kHkwSrmgZ0MT/jCCpNiwbUjGoRN4dlBhqJtoQuCnEOKzgTVwg0ZWiCoQy6SxMebQVh8A==}
     engines: {node: '>=0.4.0'}
@@ -7661,6 +7709,11 @@ packages:
     engines: {node: '>= 0.8'}
     dev: true
 
+  /cac@6.7.14:
+    resolution: {integrity: sha512-b6Ilus+c3RrdDk+JhLKUAQfzzgLEPy6wcXqS7f/xe1EETvsDP6GORG7SFuOs6cID5YkqchW/LXZbX5bc8j7ZcQ==}
+    engines: {node: '>=8'}
+    dev: true
+
   /call-bind@1.0.5:
     resolution: {integrity: sha512-C3nQxfFZxFRVoJoGKKI8y3MOEo129NQ+FgQ08iye+Mk4zNZZGdjfs06bVTr+DBSlA66Q2VEcMki/cUCP4SercQ==}
     dependencies:
@@ -9173,6 +9226,12 @@ packages:
     resolution: {integrity: sha512-Rfkk/Mp/DL7JVje3u18FxFujQlTNR2q6QfMSMB7AvCBx91NGj/ba3kCfza0f6dVDbw7YlRf/nDrn7pQrCCyQ/w==}
     dev: true
 
+  /estree-walker@3.0.3:
+    resolution: {integrity: sha512-7RUKfXgSMMkzt6ZuXmqapOurLGPPfgj6l9uRZ7lRGolvk0y2yocc35LdcxKC5PQZdn2DMqioAQ2NoWcrTKmm6g==}
+    dependencies:
+      '@types/estree': 1.0.5
+    dev: true
+
   /esutils@2.0.3:
     resolution: {integrity: sha512-kVscqXk4OCp68SZ0dkgEKVi6/8ij300KBWTJq32P/dYeWTSwK41WyTxalN1eRmA5Z9UU/LX9D7FWSmV9SAYx6g==}
     engines: {node: '>=0.10.0'}
@@ -10547,6 +10606,10 @@ packages:
     hasBin: true
     dev: true
 
+  /jsonc-parser@3.2.1:
+    resolution: {integrity: sha512-AilxAyFOAcK5wA1+LeaySVBrHsGQvUFCDWXKpZjzaL0PqW+xfBOttn8GNtWKFWqneyMZj41MWF9Kl6iPWLwgOA==}
+    dev: true
+
   /jsondiffpatch@0.6.0:
     resolution: {integrity: sha512-3QItJOXp2AP1uv7waBkao5nCvhEv+QmJAd38Ybq7wNI74Q+BBmnLn4EDKz6yI9xGAIQoUF87qHt+kc1IVxB4zQ==}
     engines: {node: ^18.0.0 || >=20.0.0}
@@ -10648,6 +10711,14 @@ packages:
     engines: {node: ^12.20.0 || ^14.13.1 || >=16.0.0}
     dev: true
 
+  /local-pkg@0.5.0:
+    resolution: {integrity: sha512-ok6z3qlYyCDS4ZEU27HaU6x/xZa9Whf8jD4ptH5UZTQYZVYeb9bnZ3ojVhiJNLiXK1Hfc0GNbLXcmZ5plLDDBg==}
+    engines: {node: '>=14'}
+    dependencies:
+      mlly: 1.5.0
+      pkg-types: 1.0.3
+    dev: true
+
   /locate-path@3.0.0:
     resolution: {integrity: sha512-7AO748wWnIhNqAuaty2ZWHkQHRSNfPVIsPIfwEOWO22AmaoVrWavlOcMR5nzTLNYvp36X220/maaRsrec1G65A==}
     engines: {node: '>=6'}
@@ -10986,6 +11057,15 @@ packages:
     hasBin: true
     dev: true
 
+  /mlly@1.5.0:
+    resolution: {integrity: sha512-NPVQvAY1xr1QoVeG0cy8yUYC7FQcOx6evl/RjT1wL5FvzPnzOysoqB/jmx/DhssT2dYa8nxECLAaFI/+gVLhDQ==}
+    dependencies:
+      acorn: 8.11.3
+      pathe: 1.1.2
+      pkg-types: 1.0.3
+      ufo: 1.3.2
+    dev: true
+
   /module-definition@3.4.0:
     resolution: {integrity: sha512-XxJ88R1v458pifaSkPNLUTdSPNVGMP2SXVncVmApGO+gAfrLANiYe6JofymCzVceGOMwQE2xogxBSc8uB7XegA==}
     engines: {node: '>=6.0'}
@@ -11380,6 +11460,13 @@ packages:
       yocto-queue: 0.1.0
     dev: true
 
+  /p-limit@5.0.0:
+    resolution: {integrity: sha512-/Eaoq+QyLSiXQ4lyYV23f14mZRQcXnxfHrN0vCai+ak9G0pp9iEQukIIZq5NccEvwRB8PUnZT0KsOoDCINS1qQ==}
+    engines: {node: '>=18'}
+    dependencies:
+      yocto-queue: 1.0.0
+    dev: true
+
   /p-locate@3.0.0:
     resolution: {integrity: sha512-x+12w/To+4GFfgJhBEpiDcLozRJGegY+Ei7/z0tSLkMmxGZNybVMSfWj9aJn8Z5Fc7dBUNJOOVgPv2H7IwulSQ==}
     engines: {node: '>=6'}
@@ -11550,6 +11637,14 @@ packages:
       find-up: 5.0.0
     dev: true
 
+  /pkg-types@1.0.3:
+    resolution: {integrity: sha512-nN7pYi0AQqJnoLPC9eHFQ8AcyaixBUOwvqc5TDnIKCMEE6I0y8P7OKA7fPexsXGCGxQDl/cmrLAp26LhcwxZ4A==}
+    dependencies:
+      jsonc-parser: 3.2.1
+      mlly: 1.5.0
+      pathe: 1.1.2
+    dev: true
+
   /pluralize@8.0.0:
     resolution: {integrity: sha512-Nc3IT5yHzflTfbjgqWcCPpo7DaKy4FnpB0l/zCAW0Tc7jxAiuqSxHasntB3D7887LSrA93kDJ9IXovxJYxyLCA==}
     engines: {node: '>=4'}
@@ -12850,6 +12945,10 @@ packages:
       object-inspect: 1.13.1
     dev: true
 
+  /siginfo@2.0.0:
+    resolution: {integrity: sha512-ybx0WO1/8bSBLEWXZvEd7gMW3Sn3JFlW3TvX1nREbDLRNQNaeNN8WK0meBwPdAaOI7TtRRRJn/Es1zhrrCHu7g==}
+    dev: true
+
   /signal-exit@3.0.7:
     resolution: {integrity: sha512-wnD2ZE+l+SPC/uoS0vXeE9L1+0wuaMqKlfz9AMUo38JsyLSBWSFcHR1Rri62LZc12vLr1gb3jl7iwQhgwpAbGQ==}
     dev: true
@@ -12968,6 +13067,10 @@ packages:
       stackframe: 1.3.4
     dev: false
 
+  /stackback@0.0.2:
+    resolution: {integrity: sha512-1XMJE5fQo1jGH6Y/7ebnwPOBEkIEnT4QF32d5R1+VXdXveM0IBMJt8zfaxX1P3QhVwrYe+576+jkANtSS2mBbw==}
+    dev: true
+
   /stackframe@1.3.4:
     resolution: {integrity: sha512-oeVtt7eWQS+Na6F//S4kJ2K2VbRlS9D43mAlMyVpVWovy9o+jfgH8O9agzANzaiLjclA0oYzUXEM4PurhSUChw==}
     dev: false
@@ -12992,6 +13095,10 @@ packages:
     engines: {node: '>= 0.8'}
     dev: true
 
+  /std-env@3.7.0:
+    resolution: {integrity: sha512-JPbdCEQLj1w5GilpiHAx3qJvFndqybBysA3qUOnznweH4QbNYUsW/ea8QzSrnh0vNsezMMw5bcVool8lM0gwzg==}
+    dev: true
+
   /stop-iteration-iterator@1.0.0:
     resolution: {integrity: sha512-iCGQj+0l0HOdZ2AEeBADlsRC+vsnDsZsbdSiH1yNSjcfKM7fdpCMfqAL/dwF5BLiw/XhRft/Wax6zQbhq2BcjQ==}
     engines: {node: '>= 0.4'}
@@ -13161,6 +13268,12 @@ packages:
     engines: {node: '>=8'}
     dev: true
 
+  /strip-literal@1.3.0:
+    resolution: {integrity: sha512-PugKzOsyXpArk0yWmUwqOZecSO0GH0bPoctLcqNDH9J04pVW3lflYE0ujElBGTloevcxF5MofAOZ7C5l2b+wLg==}
+    dependencies:
+      acorn: 8.11.3
+    dev: true
+
   /stylis@4.2.0:
     resolution: {integrity: sha512-Orov6g6BB1sDfYgzWfTHDOxamtX1bE/zo104Dh9e6fqJ3PooipYyfJ0pUmrZO2wAvO8YbEyeFrkV91XTsGMSrw==}
     dev: false
@@ -13311,6 +13424,15 @@ packages:
   /tiny-invariant@1.3.1:
     resolution: {integrity: sha512-AD5ih2NlSssTCwsMznbvwMZpJ1cbhkGd2uueNxzv2jDlEeZdU04JQfRnggJQ8DrcVBGjAsCKwFBbDlVNtEMlzw==}
 
+  /tinybench@2.6.0:
+    resolution: {integrity: sha512-N8hW3PG/3aOoZAN5V/NSAEDz0ZixDSSt5b/a05iqtpgfLWMSVuCo7w0k2vVvEjdrIoeGqZzweX2WlyioNIHchA==}
+    dev: true
+
+  /tinypool@0.8.2:
+    resolution: {integrity: sha512-SUszKYe5wgsxnNOVlBYO6IC+8VGWdVGZWAqUxp3UErNBtptZvWbwyUOyzNL59zigz2rCA92QiL3wvG+JDSdJdQ==}
+    engines: {node: '>=14.0.0'}
+    dev: true
+
   /tinyspy@2.2.0:
     resolution: {integrity: sha512-d2eda04AN/cPOR89F7Xv5bK/jrQEhmcLFe6HFldoeO9AJtps+fqEnh486vnT/8y4bw38pSyxDcTCAq+Ks2aJTg==}
     engines: {node: '>=14.0.0'}
@@ -13828,6 +13950,27 @@ packages:
     engines: {node: '>= 0.8'}
     dev: true
 
+  /vite-node@1.2.2(@types/node@20.11.5):
+    resolution: {integrity: sha512-1as4rDTgVWJO3n1uHmUYqq7nsFgINQ9u+mRcXpjeOMJUmviqNKjcZB7UfRZrlM7MjYXMKpuWp5oGkjaFLnjawg==}
+    engines: {node: ^18.0.0 || >=20.0.0}
+    hasBin: true
+    dependencies:
+      cac: 6.7.14
+      debug: 4.3.4
+      pathe: 1.1.2
+      picocolors: 1.0.0
+      vite: 5.0.12(@types/node@20.11.5)
+    transitivePeerDependencies:
+      - '@types/node'
+      - less
+      - lightningcss
+      - sass
+      - stylus
+      - sugarss
+      - supports-color
+      - terser
+    dev: true
+
   /vite-plugin-css-injected-by-js@3.3.1(vite@5.0.12):
     resolution: {integrity: sha512-PjM/X45DR3/V1K1fTRs8HtZHEQ55kIfdrn+dzaqNBFrOYO073SeSNCxp4j7gSYhV9NffVHaEnOL4myoko0ePAg==}
     peerDependencies:
@@ -13926,6 +14069,63 @@ packages:
       fsevents: 2.3.3
     dev: true
 
+  /vitest@1.2.2(@types/node@20.11.5):
+    resolution: {integrity: sha512-d5Ouvrnms3GD9USIK36KG8OZ5bEvKEkITFtnGv56HFaSlbItJuYr7hv2Lkn903+AvRAgSixiamozUVfORUekjw==}
+    engines: {node: ^18.0.0 || >=20.0.0}
+    hasBin: true
+    peerDependencies:
+      '@edge-runtime/vm': '*'
+      '@types/node': ^18.0.0 || >=20.0.0
+      '@vitest/browser': ^1.0.0
+      '@vitest/ui': ^1.0.0
+      happy-dom: '*'
+      jsdom: '*'
+    peerDependenciesMeta:
+      '@edge-runtime/vm':
+        optional: true
+      '@types/node':
+        optional: true
+      '@vitest/browser':
+        optional: true
+      '@vitest/ui':
+        optional: true
+      happy-dom:
+        optional: true
+      jsdom:
+        optional: true
+    dependencies:
+      '@types/node': 20.11.5
+      '@vitest/expect': 1.2.2
+      '@vitest/runner': 1.2.2
+      '@vitest/snapshot': 1.2.2
+      '@vitest/spy': 1.2.2
+      '@vitest/utils': 1.2.2
+      acorn-walk: 8.3.2
+      cac: 6.7.14
+      chai: 4.4.1
+      debug: 4.3.4
+      execa: 8.0.1
+      local-pkg: 0.5.0
+      magic-string: 0.30.5
+      pathe: 1.1.2
+      picocolors: 1.0.0
+      std-env: 3.7.0
+      strip-literal: 1.3.0
+      tinybench: 2.6.0
+      tinypool: 0.8.2
+      vite: 5.0.12(@types/node@20.11.5)
+      vite-node: 1.2.2(@types/node@20.11.5)
+      why-is-node-running: 2.2.2
+    transitivePeerDependencies:
+      - less
+      - lightningcss
+      - sass
+      - stylus
+      - sugarss
+      - supports-color
+      - terser
+    dev: true
+
   /void-elements@3.1.0:
     resolution: {integrity: sha512-Dhxzh5HZuiHQhbvTW9AMetFfBHDMYpo23Uo9btPXgdYP+3T5S+p+jgNy7spra+veYhBP2dCSgxR/i2Y02h5/6w==}
     engines: {node: '>=0.10.0'}
@@ -14049,6 +14249,15 @@ packages:
       isexe: 2.0.0
     dev: true
 
+  /why-is-node-running@2.2.2:
+    resolution: {integrity: sha512-6tSwToZxTOcotxHeA+qGCq1mVzKR3CwcJGmVcY+QE8SHy6TnpFnh8PAvPNHYr7EcuVeG0QSMxtYCuO1ta/G/oA==}
+    engines: {node: '>=8'}
+    hasBin: true
+    dependencies:
+      siginfo: 2.0.0
+      stackback: 0.0.2
+    dev: true
+
   /wordwrap@1.0.0:
     resolution: {integrity: sha512-gvVzJFlPycKc5dZN4yPkP8w7Dc37BtP1yczEneOb4uq34pXZcvrtRTmWV8W+Ume+XCxKgbjM+nevkyFPMybd4Q==}
     dev: true
@@ -14189,6 +14398,11 @@ packages:
     engines: {node: '>=10'}
     dev: true
 
+  /yocto-queue@1.0.0:
+    resolution: {integrity: sha512-9bnSc/HEW2uRy67wc+T8UwauLuPJVn28jb+GtJY16iiKWyvmYJRXVT4UamsAEGQfPohgr2q4Tq0sQbQlxTfi1g==}
+    engines: {node: '>=12.20'}
+    dev: true
+
   /z-schema@5.0.5:
     resolution: {integrity: sha512-D7eujBWkLa3p2sIpJA0d1pr7es+a7m0vFAnZLlCEKq/Ij2k0MLi9Br2UPxoxdYystm5K1yeBGzub0FlYUEWj2Q==}
     engines: {node: '>=8.0.0'}

invokeai/frontend/web/src/app/store/middleware/devtools/actionSanitizer.ts

@@ -1,6 +1,6 @@
 import type { UnknownAction } from '@reduxjs/toolkit';
 import { isAnyGraphBuilt } from 'features/nodes/store/actions';
-import { nodeTemplatesBuilt } from 'features/nodes/store/nodeTemplatesSlice';
+import { nodeTemplatesBuilt } from 'features/nodes/store/nodesSlice';
 import { cloneDeep } from 'lodash-es';
 import { appInfoApi } from 'services/api/endpoints/appInfo';
 import type { Graph } from 'services/api/types';

invokeai/frontend/web/src/app/store/middleware/listenerMiddleware/listeners/getOpenAPISchema.ts

@@ -1,6 +1,6 @@
 import { logger } from 'app/logging/logger';
 import { parseify } from 'common/util/serialize';
-import { nodeTemplatesBuilt } from 'features/nodes/store/nodeTemplatesSlice';
+import { nodeTemplatesBuilt } from 'features/nodes/store/nodesSlice';
 import { parseSchema } from 'features/nodes/util/schema/parseSchema';
 import { size } from 'lodash-es';
 import { appInfoApi } from 'services/api/endpoints/appInfo';

invokeai/frontend/web/src/app/store/middleware/listenerMiddleware/listeners/socketio/socketInvocationComplete.ts

@@ -4,7 +4,7 @@ import { addImageToStagingArea } from 'features/canvas/store/canvasSlice';
 import { boardIdSelected, galleryViewChanged, imageSelected } from 'features/gallery/store/gallerySlice';
 import { IMAGE_CATEGORIES } from 'features/gallery/store/types';
 import { isImageOutput } from 'features/nodes/types/common';
-import { LINEAR_UI_OUTPUT, nodeIDDenyList } from 'features/nodes/util/graph/constants';
+import { CANVAS_OUTPUT } from 'features/nodes/util/graph/constants';
 import { boardsApi } from 'services/api/endpoints/boards';
 import { imagesApi } from 'services/api/endpoints/images';
 import { imagesAdapter } from 'services/api/util';
@@ -24,10 +24,9 @@ export const addInvocationCompleteEventListener = () => {
       const { data } = action.payload;
       log.debug({ data: parseify(data) }, `Invocation complete (${action.payload.data.node.type})`);
 
-      const { result, node, queue_batch_id, source_node_id } = data;
-
+      const { result, node, queue_batch_id } = data;
       // This complete event has an associated image output
-      if (isImageOutput(result) && !nodeTypeDenylist.includes(node.type) && !nodeIDDenyList.includes(source_node_id)) {
+      if (isImageOutput(result) && !nodeTypeDenylist.includes(node.type)) {
         const { image_name } = result.image;
         const { canvas, gallery } = getState();
 
@@ -42,7 +41,7 @@ export const addInvocationCompleteEventListener = () => {
         imageDTORequest.unsubscribe();
 
         // Add canvas images to the staging area
-        if (canvas.batchIds.includes(queue_batch_id) && [LINEAR_UI_OUTPUT].includes(data.source_node_id)) {
+        if (canvas.batchIds.includes(queue_batch_id) && data.source_node_id === CANVAS_OUTPUT) {
           dispatch(addImageToStagingArea(imageDTO));
         }
 

invokeai/frontend/web/src/app/store/middleware/listenerMiddleware/listeners/updateAllNodesRequested.ts

@@ -15,8 +15,7 @@ export const addUpdateAllNodesRequestedListener = () => {
     actionCreator: updateAllNodesRequested,
     effect: (action, { dispatch, getState }) => {
       const log = logger('nodes');
-      const nodes = getState().nodes.nodes;
-      const templates = getState().nodeTemplates.templates;
+      const { nodes, templates } = getState().nodes;
 
       let unableToUpdateCount = 0;
 

invokeai/frontend/web/src/app/store/middleware/listenerMiddleware/listeners/upscaleRequested.ts

@@ -39,16 +39,12 @@ export const addUpscaleRequestedListener = () => {
         return;
       }
 
-      const { esrganModelName } = state.postprocessing;
-      const { autoAddBoardId } = state.gallery;
-
       const enqueueBatchArg: BatchConfig = {
         prepend: true,
         batch: {
           graph: buildAdHocUpscaleGraph({
             image_name,
-            esrganModelName,
-            autoAddBoardId,
+            state,
           }),
           runs: 1,
         },

invokeai/frontend/web/src/app/store/middleware/listenerMiddleware/listeners/workflowLoadRequested.ts

@@ -18,7 +18,7 @@ export const addWorkflowLoadRequestedListener = () => {
     effect: (action, { dispatch, getState }) => {
       const log = logger('nodes');
       const { workflow, asCopy } = action.payload;
-      const nodeTemplates = getState().nodeTemplates.templates;
+      const nodeTemplates = getState().nodes.templates;
 
       try {
         const { workflow: validatedWorkflow, warnings } = validateWorkflow(workflow, nodeTemplates);

invokeai/frontend/web/src/app/store/store.ts

@@ -16,7 +16,6 @@ import { hrfPersistConfig, hrfSlice } from 'features/hrf/store/hrfSlice';
 import { loraPersistConfig, loraSlice } from 'features/lora/store/loraSlice';
 import { modelManagerPersistConfig, modelManagerSlice } from 'features/modelManager/store/modelManagerSlice';
 import { nodesPersistConfig, nodesSlice } from 'features/nodes/store/nodesSlice';
-import { nodesTemplatesSlice } from 'features/nodes/store/nodeTemplatesSlice';
 import { workflowPersistConfig, workflowSlice } from 'features/nodes/store/workflowSlice';
 import { generationPersistConfig, generationSlice } from 'features/parameters/store/generationSlice';
 import { postprocessingPersistConfig, postprocessingSlice } from 'features/parameters/store/postprocessingSlice';
@@ -46,7 +45,6 @@ const allReducers = {
   [gallerySlice.name]: gallerySlice.reducer,
   [generationSlice.name]: generationSlice.reducer,
   [nodesSlice.name]: nodesSlice.reducer,
-  [nodesTemplatesSlice.name]: nodesTemplatesSlice.reducer,
   [postprocessingSlice.name]: postprocessingSlice.reducer,
   [systemSlice.name]: systemSlice.reducer,
   [configSlice.name]: configSlice.reducer,

invokeai/frontend/web/src/app/store/storeHooks.ts

@@ -1,7 +1,8 @@
 import type { AppThunkDispatch, RootState } from 'app/store/store';
 import type { TypedUseSelectorHook } from 'react-redux';
-import { useDispatch, useSelector } from 'react-redux';
+import { useDispatch, useSelector, useStore } from 'react-redux';
 
 // Use throughout your app instead of plain `useDispatch` and `useSelector`
 export const useAppDispatch = () => useDispatch<AppThunkDispatch>();
 export const useAppSelector: TypedUseSelectorHook<RootState> = useSelector;
+export const useAppStore = () => useStore<RootState>();

invokeai/frontend/web/src/app/store/util.ts

@@ -0,0 +1,2 @@
+export const EMPTY_ARRAY = [];
+export const EMPTY_OBJECT = {};

invokeai/frontend/web/src/common/hooks/useIsReadyToEnqueue.ts

@@ -8,7 +8,6 @@ import { isControlNetOrT2IAdapter } from 'features/controlAdapters/store/types';
 import { selectDynamicPromptsSlice } from 'features/dynamicPrompts/store/dynamicPromptsSlice';
 import { getShouldProcessPrompt } from 'features/dynamicPrompts/util/getShouldProcessPrompt';
 import { selectNodesSlice } from 'features/nodes/store/nodesSlice';
-import { selectNodeTemplatesSlice } from 'features/nodes/store/nodeTemplatesSlice';
 import { isInvocationNode } from 'features/nodes/types/invocation';
 import { selectGenerationSlice } from 'features/parameters/store/generationSlice';
 import { selectSystemSlice } from 'features/system/store/systemSlice';
@@ -23,11 +22,10 @@ const selector = createMemoizedSelector(
     selectGenerationSlice,
     selectSystemSlice,
     selectNodesSlice,
-    selectNodeTemplatesSlice,
     selectDynamicPromptsSlice,
     activeTabNameSelector,
   ],
-  (controlAdapters, generation, system, nodes, nodeTemplates, dynamicPrompts, activeTabName) => {
+  (controlAdapters, generation, system, nodes, dynamicPrompts, activeTabName) => {
     const { initialImage, model, positivePrompt } = generation;
 
     const { isConnected } = system;
@@ -54,7 +52,7 @@ const selector = createMemoizedSelector(
             return;
           }
 
-          const nodeTemplate = nodeTemplates.templates[node.data.type];
+          const nodeTemplate = nodes.templates[node.data.type];
 
           if (!nodeTemplate) {
             // Node type not found

invokeai/frontend/web/src/features/nodes/components/flow/AddNodePopover/AddNodePopover.tsx

@@ -7,8 +7,12 @@ import { createMemoizedSelector } from 'app/store/createMemoizedSelector';
 import { useAppDispatch, useAppSelector } from 'app/store/storeHooks';
 import type { SelectInstance } from 'chakra-react-select';
 import { useBuildNode } from 'features/nodes/hooks/useBuildNode';
-import { addNodePopoverClosed, addNodePopoverOpened, nodeAdded } from 'features/nodes/store/nodesSlice';
-import { selectNodeTemplatesSlice } from 'features/nodes/store/nodeTemplatesSlice';
+import {
+  addNodePopoverClosed,
+  addNodePopoverOpened,
+  nodeAdded,
+  selectNodesSlice,
+} from 'features/nodes/store/nodesSlice';
 import { validateSourceAndTargetTypes } from 'features/nodes/store/util/validateSourceAndTargetTypes';
 import { filter, map, memoize, some } from 'lodash-es';
 import type { KeyboardEventHandler } from 'react';
@@ -54,10 +58,10 @@ const AddNodePopover = () => {
   const fieldFilter = useAppSelector((s) => s.nodes.connectionStartFieldType);
   const handleFilter = useAppSelector((s) => s.nodes.connectionStartParams?.handleType);
 
-  const selector = createMemoizedSelector(selectNodeTemplatesSlice, (nodeTemplates) => {
+  const selector = createMemoizedSelector(selectNodesSlice, (nodes) => {
     // If we have a connection in progress, we need to filter the node choices
     const filteredNodeTemplates = fieldFilter
-      ? filter(nodeTemplates.templates, (template) => {
+      ? filter(nodes.templates, (template) => {
           const handles = handleFilter === 'source' ? template.inputs : template.outputs;
 
           return some(handles, (handle) => {
@@ -67,7 +71,7 @@ const AddNodePopover = () => {
             return validateSourceAndTargetTypes(sourceType, targetType);
           });
         })
-      : map(nodeTemplates.templates);
+      : map(nodes.templates);
 
     const options: ComboboxOption[] = map(filteredNodeTemplates, (template) => {
       return {

invokeai/frontend/web/src/features/nodes/components/flow/edges/util/makeEdgeSelector.ts

@@ -1,10 +1,17 @@
 import { createMemoizedSelector } from 'app/store/createMemoizedSelector';
 import { colorTokenToCssVar } from 'common/util/colorTokenToCssVar';
 import { selectNodesSlice } from 'features/nodes/store/nodesSlice';
+import { selectFieldOutputTemplate } from 'features/nodes/store/selectors';
 import { isInvocationNode } from 'features/nodes/types/invocation';
 
 import { getFieldColor } from './getEdgeColor';
 
+const defaultReturnValue = {
+  isSelected: false,
+  shouldAnimate: false,
+  stroke: colorTokenToCssVar('base.500'),
+};
+
 export const makeEdgeSelector = (
   source: string,
   sourceHandleId: string | null | undefined,
@@ -12,14 +19,19 @@ export const makeEdgeSelector = (
   targetHandleId: string | null | undefined,
   selected?: boolean
 ) =>
-  createMemoizedSelector(selectNodesSlice, (nodes) => {
+  createMemoizedSelector(selectNodesSlice, (nodes): { isSelected: boolean; shouldAnimate: boolean; stroke: string } => {
     const sourceNode = nodes.nodes.find((node) => node.id === source);
     const targetNode = nodes.nodes.find((node) => node.id === target);
 
     const isInvocationToInvocationEdge = isInvocationNode(sourceNode) && isInvocationNode(targetNode);
 
-    const isSelected = sourceNode?.selected || targetNode?.selected || selected;
-    const sourceType = isInvocationToInvocationEdge ? sourceNode?.data?.outputs[sourceHandleId || '']?.type : undefined;
+    const isSelected = Boolean(sourceNode?.selected || targetNode?.selected || selected);
+    if (!sourceNode || !sourceHandleId) {
+      return defaultReturnValue;
+    }
+
+    const outputFieldTemplate = selectFieldOutputTemplate(nodes, sourceNode.id, sourceHandleId);
+    const sourceType = isInvocationToInvocationEdge ? outputFieldTemplate?.type : undefined;
 
     const stroke = sourceType && nodes.shouldColorEdges ? getFieldColor(sourceType) : colorTokenToCssVar('base.500');
 

invokeai/frontend/web/src/features/nodes/components/flow/nodes/Invocation/InvocationNodeCollapsedHandles.tsx

@@ -1,6 +1,5 @@
 import { useChakraThemeTokens } from 'common/hooks/useChakraThemeTokens';
-import { useNodeData } from 'features/nodes/hooks/useNodeData';
-import { isInvocationNodeData } from 'features/nodes/types/invocation';
+import { useNodeTemplate } from 'features/nodes/hooks/useNodeTemplate';
 import { map } from 'lodash-es';
 import type { CSSProperties } from 'react';
 import { memo, useMemo } from 'react';
@@ -13,7 +12,7 @@ interface Props {
 const hiddenHandleStyles: CSSProperties = { visibility: 'hidden' };
 
 const InvocationNodeCollapsedHandles = ({ nodeId }: Props) => {
-  const data = useNodeData(nodeId);
+  const template = useNodeTemplate(nodeId);
   const { base600 } = useChakraThemeTokens();
 
   const dummyHandleStyles: CSSProperties = useMemo(
@@ -37,7 +36,7 @@ const InvocationNodeCollapsedHandles = ({ nodeId }: Props) => {
     [dummyHandleStyles]
   );
 
-  if (!isInvocationNodeData(data)) {
+  if (!template) {
     return null;
   }
 
@@ -45,14 +44,14 @@ const InvocationNodeCollapsedHandles = ({ nodeId }: Props) => {
     <>
       <Handle
         type="target"
-        id={`${data.id}-collapsed-target`}
+        id={`${nodeId}-collapsed-target`}
         isConnectable={false}
         position={Position.Left}
         style={collapsedTargetStyles}
       />
-      {map(data.inputs, (input) => (
+      {map(template.inputs, (input) => (
         <Handle
-          key={`${data.id}-${input.name}-collapsed-input-handle`}
+          key={`${nodeId}-${input.name}-collapsed-input-handle`}
           type="target"
           id={input.name}
           isConnectable={false}
@@ -62,14 +61,14 @@ const InvocationNodeCollapsedHandles = ({ nodeId }: Props) => {
       ))}
       <Handle
         type="source"
-        id={`${data.id}-collapsed-source`}
+        id={`${nodeId}-collapsed-source`}
         isConnectable={false}
         position={Position.Right}
         style={collapsedSourceStyles}
       />
-      {map(data.outputs, (output) => (
+      {map(template.outputs, (output) => (
         <Handle
-          key={`${data.id}-${output.name}-collapsed-output-handle`}
+          key={`${nodeId}-${output.name}-collapsed-output-handle`}
           type="source"
           id={output.name}
           isConnectable={false}

invokeai/frontend/web/src/features/nodes/components/flow/nodes/Invocation/InvocationNodeWrapper.tsx

@@ -1,7 +1,7 @@
 import { createSelector } from '@reduxjs/toolkit';
 import { useAppSelector } from 'app/store/storeHooks';
 import InvocationNode from 'features/nodes/components/flow/nodes/Invocation/InvocationNode';
-import { selectNodeTemplatesSlice } from 'features/nodes/store/nodeTemplatesSlice';
+import { selectNodesSlice } from 'features/nodes/store/nodesSlice';
 import type { InvocationNodeData } from 'features/nodes/types/invocation';
 import { memo, useMemo } from 'react';
 import type { NodeProps } from 'reactflow';
@@ -13,7 +13,7 @@ const InvocationNodeWrapper = (props: NodeProps<InvocationNodeData>) => {
   const { id: nodeId, type, isOpen, label } = data;
 
   const hasTemplateSelector = useMemo(
-    () => createSelector(selectNodeTemplatesSlice, (nodeTemplates) => Boolean(nodeTemplates.templates[type])),
+    () => createSelector(selectNodesSlice, (nodes) => Boolean(nodes.templates[type])),
     [type]
   );
 

invokeai/frontend/web/src/features/nodes/components/flow/nodes/Invocation/fields/EditableFieldTitle.tsx

@@ -22,7 +22,7 @@ import FieldTooltipContent from './FieldTooltipContent';
 interface Props {
   nodeId: string;
   fieldName: string;
-  kind: 'input' | 'output';
+  kind: 'inputs' | 'outputs';
   isMissingInput?: boolean;
   withTooltip?: boolean;
 }
@@ -58,7 +58,7 @@ const EditableFieldTitle = forwardRef((props: Props, ref) => {
 
   return (
     <Tooltip
-      label={withTooltip ? <FieldTooltipContent nodeId={nodeId} fieldName={fieldName} kind="input" /> : undefined}
+      label={withTooltip ? <FieldTooltipContent nodeId={nodeId} fieldName={fieldName} kind="inputs" /> : undefined}
       openDelay={HANDLE_TOOLTIP_OPEN_DELAY}
     >
       <Editable

invokeai/frontend/web/src/features/nodes/components/flow/nodes/Invocation/fields/FieldTitle.tsx

@@ -6,7 +6,7 @@ import { memo } from 'react';
 interface Props {
   nodeId: string;
   fieldName: string;
-  kind: 'input' | 'output';
+  kind: 'inputs' | 'outputs';
   isMissingInput?: boolean;
 }
 

invokeai/frontend/web/src/features/nodes/components/flow/nodes/Invocation/fields/FieldTooltipContent.tsx

@@ -1,5 +1,5 @@
 import { Flex, Text } from '@invoke-ai/ui-library';
-import { useFieldInstance } from 'features/nodes/hooks/useFieldData';
+import { useFieldInputInstance } from 'features/nodes/hooks/useFieldInputInstance';
 import { useFieldTemplate } from 'features/nodes/hooks/useFieldTemplate';
 import { useFieldTypeName } from 'features/nodes/hooks/usePrettyFieldType';
 import { isFieldInputInstance, isFieldInputTemplate } from 'features/nodes/types/field';
@@ -9,11 +9,11 @@ import { useTranslation } from 'react-i18next';
 interface Props {
   nodeId: string;
   fieldName: string;
-  kind: 'input' | 'output';
+  kind: 'inputs' | 'outputs';
 }
 
 const FieldTooltipContent = ({ nodeId, fieldName, kind }: Props) => {
-  const field = useFieldInstance(nodeId, fieldName);
+  const field = useFieldInputInstance(nodeId, fieldName);
   const fieldTemplate = useFieldTemplate(nodeId, fieldName, kind);
   const isInputTemplate = isFieldInputTemplate(fieldTemplate);
   const fieldTypeName = useFieldTypeName(fieldTemplate?.type);

invokeai/frontend/web/src/features/nodes/components/flow/nodes/Invocation/fields/InputField.tsx

@@ -25,7 +25,7 @@ const InputField = ({ nodeId, fieldName }: Props) => {
   const [isHovered, setIsHovered] = useState(false);
 
   const { isConnected, isConnectionInProgress, isConnectionStartField, connectionError, shouldDim } =
-    useConnectionState({ nodeId, fieldName, kind: 'input' });
+    useConnectionState({ nodeId, fieldName, kind: 'inputs' });
 
   const isMissingInput = useMemo(() => {
     if (!fieldTemplate) {
@@ -76,7 +76,7 @@ const InputField = ({ nodeId, fieldName }: Props) => {
           <EditableFieldTitle
             nodeId={nodeId}
             fieldName={fieldName}
-            kind="input"
+            kind="inputs"
             isMissingInput={isMissingInput}
             withTooltip
           />
@@ -101,7 +101,7 @@ const InputField = ({ nodeId, fieldName }: Props) => {
             <EditableFieldTitle
               nodeId={nodeId}
               fieldName={fieldName}
-              kind="input"
+              kind="inputs"
               isMissingInput={isMissingInput}
               withTooltip
             />

invokeai/frontend/web/src/features/nodes/components/flow/nodes/Invocation/fields/InputFieldRenderer.tsx

@@ -1,6 +1,5 @@
-import { Box, Text } from '@invoke-ai/ui-library';
-import { useFieldInstance } from 'features/nodes/hooks/useFieldData';
-import { useFieldTemplate } from 'features/nodes/hooks/useFieldTemplate';
+import { useFieldInputInstance } from 'features/nodes/hooks/useFieldInputInstance';
+import { useFieldInputTemplate } from 'features/nodes/hooks/useFieldInputTemplate';
 import {
   isBoardFieldInputInstance,
   isBoardFieldInputTemplate,
@@ -38,7 +37,6 @@ import {
   isVAEModelFieldInputTemplate,
 } from 'features/nodes/types/field';
 import { memo } from 'react';
-import { useTranslation } from 'react-i18next';
 
 import BoardFieldInputComponent from './inputs/BoardFieldInputComponent';
 import BooleanFieldInputComponent from './inputs/BooleanFieldInputComponent';
@@ -63,17 +61,8 @@ type InputFieldProps = {
 };
 
 const InputFieldRenderer = ({ nodeId, fieldName }: InputFieldProps) => {
-  const { t } = useTranslation();
-  const fieldInstance = useFieldInstance(nodeId, fieldName);
-  const fieldTemplate = useFieldTemplate(nodeId, fieldName, 'input');
-
-  if (fieldTemplate?.fieldKind === 'output') {
-    return (
-      <Box p={2}>
-        {t('nodes.outputFieldInInput')}: {fieldInstance?.type.name}
-      </Box>
-    );
-  }
+  const fieldInstance = useFieldInputInstance(nodeId, fieldName);
+  const fieldTemplate = useFieldInputTemplate(nodeId, fieldName);
 
   if (isStringFieldInputInstance(fieldInstance) && isStringFieldInputTemplate(fieldTemplate)) {
     return <StringFieldInputComponent nodeId={nodeId} field={fieldInstance} fieldTemplate={fieldTemplate} />;
@@ -141,18 +130,10 @@ const InputFieldRenderer = ({ nodeId, fieldName }: InputFieldProps) => {
     return <SchedulerFieldInputComponent nodeId={nodeId} field={fieldInstance} fieldTemplate={fieldTemplate} />;
   }
 
-  if (fieldInstance && fieldTemplate) {
+  if (fieldTemplate) {
     // Fallback for when there is no component for the type
     return null;
   }
-
-  return (
-    <Box p={1}>
-      <Text fontSize="sm" fontWeight="semibold" color="error.300">
-        {t('nodes.unknownFieldType', { type: fieldInstance?.type.name })}
-      </Text>
-    </Box>
-  );
 };
 
 export default memo(InputFieldRenderer);

invokeai/frontend/web/src/features/nodes/components/flow/nodes/Invocation/fields/LinearViewField.tsx

@@ -62,7 +62,7 @@ const LinearViewField = ({ nodeId, fieldName }: Props) => {
       />
       <Flex flexDir="column" w="full">
         <Flex alignItems="center">
-          <EditableFieldTitle nodeId={nodeId} fieldName={fieldName} kind="input" />
+          <EditableFieldTitle nodeId={nodeId} fieldName={fieldName} kind="inputs" />
           <Spacer />
           {isValueChanged && (
             <IconButton
@@ -75,7 +75,7 @@ const LinearViewField = ({ nodeId, fieldName }: Props) => {
             />
           )}
           <Tooltip
-            label={<FieldTooltipContent nodeId={nodeId} fieldName={fieldName} kind="input" />}
+            label={<FieldTooltipContent nodeId={nodeId} fieldName={fieldName} kind="inputs" />}
             openDelay={HANDLE_TOOLTIP_OPEN_DELAY}
             placement="top"
           >

invokeai/frontend/web/src/features/nodes/components/flow/nodes/Invocation/fields/OutputField.tsx

@@ -1,6 +1,5 @@
 import { Flex, FormControl, FormLabel, Tooltip } from '@invoke-ai/ui-library';
 import { useConnectionState } from 'features/nodes/hooks/useConnectionState';
-import { useFieldOutputInstance } from 'features/nodes/hooks/useFieldOutputInstance';
 import { useFieldOutputTemplate } from 'features/nodes/hooks/useFieldOutputTemplate';
 import { HANDLE_TOOLTIP_OPEN_DELAY } from 'features/nodes/types/constants';
 import type { PropsWithChildren } from 'react';
@@ -18,18 +17,17 @@ interface Props {
 const OutputField = ({ nodeId, fieldName }: Props) => {
   const { t } = useTranslation();
   const fieldTemplate = useFieldOutputTemplate(nodeId, fieldName);
-  const fieldInstance = useFieldOutputInstance(nodeId, fieldName);
 
   const { isConnected, isConnectionInProgress, isConnectionStartField, connectionError, shouldDim } =
-    useConnectionState({ nodeId, fieldName, kind: 'output' });
+    useConnectionState({ nodeId, fieldName, kind: 'outputs' });
 
-  if (!fieldTemplate || !fieldInstance) {
+  if (!fieldTemplate) {
     return (
       <OutputFieldWrapper shouldDim={shouldDim}>
         <FormControl alignItems="stretch" justifyContent="space-between" gap={2} h="full" w="full">
           <FormLabel display="flex" alignItems="center" h="full" color="error.300" mb={0} px={1} gap={2}>
             {t('nodes.unknownOutput', {
-              name: fieldTemplate?.title ?? fieldName,
+              name: fieldName,
             })}
           </FormLabel>
         </FormControl>
@@ -40,7 +38,7 @@ const OutputField = ({ nodeId, fieldName }: Props) => {
   return (
     <OutputFieldWrapper shouldDim={shouldDim}>
       <Tooltip
-        label={<FieldTooltipContent nodeId={nodeId} fieldName={fieldName} kind="output" />}
+        label={<FieldTooltipContent nodeId={nodeId} fieldName={fieldName} kind="outputs" />}
         openDelay={HANDLE_TOOLTIP_OPEN_DELAY}
         placement="top"
         shouldWrapChildren

invokeai/frontend/web/src/features/nodes/components/sidePanel/inspector/InspectorDetailsTab.tsx

@@ -6,19 +6,18 @@ import ScrollableContent from 'common/components/OverlayScrollbars/ScrollableCon
 import NotesTextarea from 'features/nodes/components/flow/nodes/Invocation/NotesTextarea';
 import { useNodeNeedsUpdate } from 'features/nodes/hooks/useNodeNeedsUpdate';
 import { selectNodesSlice } from 'features/nodes/store/nodesSlice';
-import { selectNodeTemplatesSlice } from 'features/nodes/store/nodeTemplatesSlice';
 import { isInvocationNode } from 'features/nodes/types/invocation';
 import { memo } from 'react';
 import { useTranslation } from 'react-i18next';
 
 import EditableNodeTitle from './details/EditableNodeTitle';
 
-const selector = createMemoizedSelector(selectNodesSlice, selectNodeTemplatesSlice, (nodes, nodeTemplates) => {
+const selector = createMemoizedSelector(selectNodesSlice, (nodes) => {
   const lastSelectedNodeId = nodes.selectedNodes[nodes.selectedNodes.length - 1];
 
   const lastSelectedNode = nodes.nodes.find((node) => node.id === lastSelectedNodeId);
 
-  const lastSelectedNodeTemplate = lastSelectedNode ? nodeTemplates.templates[lastSelectedNode.data.type] : undefined;
+  const lastSelectedNodeTemplate = lastSelectedNode ? nodes.templates[lastSelectedNode.data.type] : undefined;
 
   if (!isInvocationNode(lastSelectedNode) || !lastSelectedNodeTemplate) {
     return;

invokeai/frontend/web/src/features/nodes/components/sidePanel/inspector/InspectorOutputsTab.tsx

@@ -5,7 +5,6 @@ import { IAINoContentFallback } from 'common/components/IAIImageFallback';
 import ScrollableContent from 'common/components/OverlayScrollbars/ScrollableContent';
 import DataViewer from 'features/gallery/components/ImageMetadataViewer/DataViewer';
 import { selectNodesSlice } from 'features/nodes/store/nodesSlice';
-import { selectNodeTemplatesSlice } from 'features/nodes/store/nodeTemplatesSlice';
 import { isInvocationNode } from 'features/nodes/types/invocation';
 import { memo } from 'react';
 import { useTranslation } from 'react-i18next';
@@ -14,12 +13,12 @@ import type { AnyResult } from 'services/events/types';
 
 import ImageOutputPreview from './outputs/ImageOutputPreview';
 
-const selector = createMemoizedSelector(selectNodesSlice, selectNodeTemplatesSlice, (nodes, nodeTemplates) => {
+const selector = createMemoizedSelector(selectNodesSlice, (nodes) => {
   const lastSelectedNodeId = nodes.selectedNodes[nodes.selectedNodes.length - 1];
 
   const lastSelectedNode = nodes.nodes.find((node) => node.id === lastSelectedNodeId);
 
-  const lastSelectedNodeTemplate = lastSelectedNode ? nodeTemplates.templates[lastSelectedNode.data.type] : undefined;
+  const lastSelectedNodeTemplate = lastSelectedNode ? nodes.templates[lastSelectedNode.data.type] : undefined;
 
   const nes = nodes.nodeExecutionStates[lastSelectedNodeId ?? '__UNKNOWN_NODE__'];
 

invokeai/frontend/web/src/features/nodes/components/sidePanel/inspector/InspectorTemplateTab.tsx

@@ -3,16 +3,15 @@ import { useAppSelector } from 'app/store/storeHooks';
 import { IAINoContentFallback } from 'common/components/IAIImageFallback';
 import DataViewer from 'features/gallery/components/ImageMetadataViewer/DataViewer';
 import { selectNodesSlice } from 'features/nodes/store/nodesSlice';
-import { selectNodeTemplatesSlice } from 'features/nodes/store/nodeTemplatesSlice';
 import { memo } from 'react';
 import { useTranslation } from 'react-i18next';
 
-const selector = createMemoizedSelector(selectNodesSlice, selectNodeTemplatesSlice, (nodes, nodeTemplates) => {
+const selector = createMemoizedSelector(selectNodesSlice, (nodes) => {
   const lastSelectedNodeId = nodes.selectedNodes[nodes.selectedNodes.length - 1];
 
   const lastSelectedNode = nodes.nodes.find((node) => node.id === lastSelectedNodeId);
 
-  const lastSelectedNodeTemplate = lastSelectedNode ? nodeTemplates.templates[lastSelectedNode.data.type] : undefined;
+  const lastSelectedNodeTemplate = lastSelectedNode ? nodes.templates[lastSelectedNode.data.type] : undefined;
 
   return {
     template: lastSelectedNodeTemplate,

invokeai/frontend/web/src/features/nodes/hooks/useAnyOrDirectInputFieldNames.ts

@@ -1,26 +1,22 @@
 import { createMemoizedSelector } from 'app/store/createMemoizedSelector';
 import { useAppSelector } from 'app/store/storeHooks';
+import { EMPTY_ARRAY } from 'app/store/util';
 import { selectNodesSlice } from 'features/nodes/store/nodesSlice';
-import { selectNodeTemplatesSlice } from 'features/nodes/store/nodeTemplatesSlice';
-import { isInvocationNode } from 'features/nodes/types/invocation';
+import { selectNodeTemplate } from 'features/nodes/store/selectors';
 import { getSortedFilteredFieldNames } from 'features/nodes/util/node/getSortedFilteredFieldNames';
 import { TEMPLATE_BUILDER_MAP } from 'features/nodes/util/schema/buildFieldInputTemplate';
 import { keys, map } from 'lodash-es';
 import { useMemo } from 'react';
 
-export const useAnyOrDirectInputFieldNames = (nodeId: string) => {
+export const useAnyOrDirectInputFieldNames = (nodeId: string): string[] => {
   const selector = useMemo(
     () =>
-      createMemoizedSelector(selectNodesSlice, selectNodeTemplatesSlice, (nodes, nodeTemplates) => {
-        const node = nodes.nodes.find((node) => node.id === nodeId);
-        if (!isInvocationNode(node)) {
-          return [];
+      createMemoizedSelector(selectNodesSlice, (nodes) => {
+        const template = selectNodeTemplate(nodes, nodeId);
+        if (!template) {
+          return EMPTY_ARRAY;
         }
-        const nodeTemplate = nodeTemplates.templates[node.data.type];
-        if (!nodeTemplate) {
-          return [];
-        }
-        const fields = map(nodeTemplate.inputs).filter(
+        const fields = map(template.inputs).filter(
           (field) =>
             (['any', 'direct'].includes(field.input) || field.type.isCollectionOrScalar) &&
             keys(TEMPLATE_BUILDER_MAP).includes(field.type.name)

invokeai/frontend/web/src/features/nodes/hooks/useBuildNode.ts

@@ -13,7 +13,7 @@ export const SHARED_NODE_PROPERTIES: Partial<Node> = {
 };
 
 export const useBuildNode = () => {
-  const nodeTemplates = useAppSelector((s) => s.nodeTemplates.templates);
+  const nodeTemplates = useAppSelector((s) => s.nodes.templates);
 
   const flow = useReactFlow();
 

invokeai/frontend/web/src/features/nodes/hooks/useConnectionInputFieldNames.ts

@@ -1,28 +1,24 @@
 import { createMemoizedSelector } from 'app/store/createMemoizedSelector';
 import { useAppSelector } from 'app/store/storeHooks';
+import { EMPTY_ARRAY } from 'app/store/util';
 import { selectNodesSlice } from 'features/nodes/store/nodesSlice';
-import { selectNodeTemplatesSlice } from 'features/nodes/store/nodeTemplatesSlice';
-import { isInvocationNode } from 'features/nodes/types/invocation';
+import { selectNodeTemplate } from 'features/nodes/store/selectors';
 import { getSortedFilteredFieldNames } from 'features/nodes/util/node/getSortedFilteredFieldNames';
 import { TEMPLATE_BUILDER_MAP } from 'features/nodes/util/schema/buildFieldInputTemplate';
 import { keys, map } from 'lodash-es';
 import { useMemo } from 'react';
 
-export const useConnectionInputFieldNames = (nodeId: string) => {
+export const useConnectionInputFieldNames = (nodeId: string): string[] => {
   const selector = useMemo(
     () =>
-      createMemoizedSelector(selectNodesSlice, selectNodeTemplatesSlice, (nodes, nodeTemplates) => {
-        const node = nodes.nodes.find((node) => node.id === nodeId);
-        if (!isInvocationNode(node)) {
-          return [];
-        }
-        const nodeTemplate = nodeTemplates.templates[node.data.type];
-        if (!nodeTemplate) {
-          return [];
+      createMemoizedSelector(selectNodesSlice, (nodes) => {
+        const template = selectNodeTemplate(nodes, nodeId);
+        if (!template) {
+          return EMPTY_ARRAY;
         }
 
         // get the visible fields
-        const fields = map(nodeTemplate.inputs).filter(
+        const fields = map(template.inputs).filter(
           (field) =>
             (field.input === 'connection' && !field.type.isCollectionOrScalar) ||
             !keys(TEMPLATE_BUILDER_MAP).includes(field.type.name)

invokeai/frontend/web/src/features/nodes/hooks/useConnectionState.ts

@@ -14,7 +14,7 @@ const selectIsConnectionInProgress = createSelector(
 export type UseConnectionStateProps = {
   nodeId: string;
   fieldName: string;
-  kind: 'input' | 'output';
+  kind: 'inputs' | 'outputs';
 };
 
 export const useConnectionState = ({ nodeId, fieldName, kind }: UseConnectionStateProps) => {
@@ -26,8 +26,8 @@ export const useConnectionState = ({ nodeId, fieldName, kind }: UseConnectionSta
         Boolean(
           nodes.edges.filter((edge) => {
             return (
-              (kind === 'input' ? edge.target : edge.source) === nodeId &&
-              (kind === 'input' ? edge.targetHandle : edge.sourceHandle) === fieldName
+              (kind === 'inputs' ? edge.target : edge.source) === nodeId &&
+              (kind === 'inputs' ? edge.targetHandle : edge.sourceHandle) === fieldName
             );
           }).length
         )
@@ -36,7 +36,7 @@ export const useConnectionState = ({ nodeId, fieldName, kind }: UseConnectionSta
   );
 
   const selectConnectionError = useMemo(
-    () => makeConnectionErrorSelector(nodeId, fieldName, kind === 'input' ? 'target' : 'source', fieldType),
+    () => makeConnectionErrorSelector(nodeId, fieldName, kind === 'inputs' ? 'target' : 'source', fieldType),
     [nodeId, fieldName, kind, fieldType]
   );
 
@@ -46,7 +46,7 @@ export const useConnectionState = ({ nodeId, fieldName, kind }: UseConnectionSta
         Boolean(
           nodes.connectionStartParams?.nodeId === nodeId &&
             nodes.connectionStartParams?.handleId === fieldName &&
-            nodes.connectionStartParams?.handleType === { input: 'target', output: 'source' }[kind]
+            nodes.connectionStartParams?.handleType === { inputs: 'target', outputs: 'source' }[kind]
         )
       ),
     [fieldName, kind, nodeId]

invokeai/frontend/web/src/features/nodes/hooks/useDoNodeVersionsMatch.ts

@@ -2,23 +2,19 @@ import { createSelector } from '@reduxjs/toolkit';
 import { useAppSelector } from 'app/store/storeHooks';
 import { compareVersions } from 'compare-versions';
 import { selectNodesSlice } from 'features/nodes/store/nodesSlice';
-import { selectNodeTemplatesSlice } from 'features/nodes/store/nodeTemplatesSlice';
-import { isInvocationNode } from 'features/nodes/types/invocation';
+import { selectNodeData, selectNodeTemplate } from 'features/nodes/store/selectors';
 import { useMemo } from 'react';
 
-export const useDoNodeVersionsMatch = (nodeId: string) => {
+export const useDoNodeVersionsMatch = (nodeId: string): boolean => {
   const selector = useMemo(
     () =>
-      createSelector(selectNodesSlice, selectNodeTemplatesSlice, (nodes, nodeTemplates) => {
-        const node = nodes.nodes.find((node) => node.id === nodeId);
-        if (!isInvocationNode(node)) {
+      createSelector(selectNodesSlice, (nodes) => {
+        const data = selectNodeData(nodes, nodeId);
+        const template = selectNodeTemplate(nodes, nodeId);
+        if (!template?.version || !data?.version) {
           return false;
         }
-        const nodeTemplate = nodeTemplates.templates[node?.data.type ?? ''];
-        if (!nodeTemplate?.version || !node.data?.version) {
-          return false;
-        }
-        return compareVersions(nodeTemplate.version, node.data.version) === 0;
+        return compareVersions(template.version, data.version) === 0;
       }),
     [nodeId]
   );

invokeai/frontend/web/src/features/nodes/hooks/useDoesInputHaveValue.ts

@@ -1,18 +1,18 @@
 import { createMemoizedSelector } from 'app/store/createMemoizedSelector';
 import { useAppSelector } from 'app/store/storeHooks';
 import { selectNodesSlice } from 'features/nodes/store/nodesSlice';
-import { isInvocationNode } from 'features/nodes/types/invocation';
+import { selectNodeData } from 'features/nodes/store/selectors';
 import { useMemo } from 'react';
 
-export const useDoesInputHaveValue = (nodeId: string, fieldName: string) => {
+export const useDoesInputHaveValue = (nodeId: string, fieldName: string): boolean => {
   const selector = useMemo(
     () =>
       createMemoizedSelector(selectNodesSlice, (nodes) => {
-        const node = nodes.nodes.find((node) => node.id === nodeId);
-        if (!isInvocationNode(node)) {
-          return;
+        const data = selectNodeData(nodes, nodeId);
+        if (!data) {
+          return false;
         }
-        return node?.data.inputs[fieldName]?.value !== undefined;
+        return data.inputs[fieldName]?.value !== undefined;
       }),
     [fieldName, nodeId]
   );

invokeai/frontend/web/src/features/nodes/hooks/useFieldData.ts

@@ -1,23 +0,0 @@
-import { createMemoizedSelector } from 'app/store/createMemoizedSelector';
-import { useAppSelector } from 'app/store/storeHooks';
-import { selectNodesSlice } from 'features/nodes/store/nodesSlice';
-import { isInvocationNode } from 'features/nodes/types/invocation';
-import { useMemo } from 'react';
-
-export const useFieldInstance = (nodeId: string, fieldName: string) => {
-  const selector = useMemo(
-    () =>
-      createMemoizedSelector(selectNodesSlice, (nodes) => {
-        const node = nodes.nodes.find((node) => node.id === nodeId);
-        if (!isInvocationNode(node)) {
-          return;
-        }
-        return node?.data.inputs[fieldName];
-      }),
-    [fieldName, nodeId]
-  );
-
-  const fieldData = useAppSelector(selector);
-
-  return fieldData;
-};

invokeai/frontend/web/src/features/nodes/hooks/useFieldInputInstance.ts

@@ -1,23 +1,20 @@
 import { createMemoizedSelector } from 'app/store/createMemoizedSelector';
 import { useAppSelector } from 'app/store/storeHooks';
 import { selectNodesSlice } from 'features/nodes/store/nodesSlice';
-import { isInvocationNode } from 'features/nodes/types/invocation';
+import { selectFieldInputInstance } from 'features/nodes/store/selectors';
+import type { FieldInputInstance } from 'features/nodes/types/field';
 import { useMemo } from 'react';
 
-export const useFieldInputInstance = (nodeId: string, fieldName: string) => {
+export const useFieldInputInstance = (nodeId: string, fieldName: string): FieldInputInstance | null => {
   const selector = useMemo(
     () =>
       createMemoizedSelector(selectNodesSlice, (nodes) => {
-        const node = nodes.nodes.find((node) => node.id === nodeId);
-        if (!isInvocationNode(node)) {
-          return;
-        }
-        return node.data.inputs[fieldName];
+        return selectFieldInputInstance(nodes, nodeId, fieldName);
       }),
     [fieldName, nodeId]
   );
 
-  const fieldTemplate = useAppSelector(selector);
+  const fieldData = useAppSelector(selector);
 
-  return fieldTemplate;
+  return fieldData;
 };

invokeai/frontend/web/src/features/nodes/hooks/useFieldInputKind.ts

@@ -1,21 +1,16 @@
 import { createSelector } from '@reduxjs/toolkit';
 import { useAppSelector } from 'app/store/storeHooks';
 import { selectNodesSlice } from 'features/nodes/store/nodesSlice';
-import { selectNodeTemplatesSlice } from 'features/nodes/store/nodeTemplatesSlice';
-import { isInvocationNode } from 'features/nodes/types/invocation';
+import { selectFieldInputTemplate } from 'features/nodes/store/selectors';
+import type { FieldInput } from 'features/nodes/types/field';
 import { useMemo } from 'react';
 
 export const useFieldInputKind = (nodeId: string, fieldName: string) => {
   const selector = useMemo(
     () =>
-      createSelector(selectNodesSlice, selectNodeTemplatesSlice, (nodes, nodeTemplates) => {
-        const node = nodes.nodes.find((node) => node.id === nodeId);
-        if (!isInvocationNode(node)) {
-          return;
-        }
-        const nodeTemplate = nodeTemplates.templates[node?.data.type ?? ''];
-        const fieldTemplate = nodeTemplate?.inputs[fieldName];
-        return fieldTemplate?.input;
+      createSelector(selectNodesSlice, (nodes): FieldInput | null => {
+        const template = selectFieldInputTemplate(nodes, nodeId, fieldName);
+        return template?.input ?? null;
       }),
     [fieldName, nodeId]
   );

invokeai/frontend/web/src/features/nodes/hooks/useFieldInputTemplate.ts

@@ -1,20 +1,15 @@
 import { createMemoizedSelector } from 'app/store/createMemoizedSelector';
 import { useAppSelector } from 'app/store/storeHooks';
 import { selectNodesSlice } from 'features/nodes/store/nodesSlice';
-import { selectNodeTemplatesSlice } from 'features/nodes/store/nodeTemplatesSlice';
-import { isInvocationNode } from 'features/nodes/types/invocation';
+import { selectFieldInputTemplate } from 'features/nodes/store/selectors';
+import type { FieldInputTemplate } from 'features/nodes/types/field';
 import { useMemo } from 'react';
 
-export const useFieldInputTemplate = (nodeId: string, fieldName: string) => {
+export const useFieldInputTemplate = (nodeId: string, fieldName: string): FieldInputTemplate | null => {
   const selector = useMemo(
     () =>
-      createMemoizedSelector(selectNodesSlice, selectNodeTemplatesSlice, (nodes, nodeTemplates) => {
-        const node = nodes.nodes.find((node) => node.id === nodeId);
-        if (!isInvocationNode(node)) {
-          return;
-        }
-        const nodeTemplate = nodeTemplates.templates[node?.data.type ?? ''];
-        return nodeTemplate?.inputs[fieldName];
+      createMemoizedSelector(selectNodesSlice, (nodes) => {
+        return selectFieldInputTemplate(nodes, nodeId, fieldName);
       }),
     [fieldName, nodeId]
   );