chore(ui): regen types

Crawled through a few custom nodes to figure out what I had missed.

docker/Dockerfile

@@ -18,8 +18,8 @@ ENV INVOKEAI_SRC=/opt/invokeai
 ENV VIRTUAL_ENV=/opt/venv/invokeai
 
 ENV PATH="$VIRTUAL_ENV/bin:$PATH"
-ARG TORCH_VERSION=2.1.0
-ARG TORCHVISION_VERSION=0.16
+ARG TORCH_VERSION=2.1.2
+ARG TORCHVISION_VERSION=0.16.2
 ARG GPU_DRIVER=cuda
 ARG TARGETPLATFORM="linux/amd64"
 # unused but available
@@ -35,7 +35,7 @@ RUN --mount=type=cache,target=/root/.cache/pip \
     if [ "$TARGETPLATFORM" = "linux/arm64" ] || [ "$GPU_DRIVER" = "cpu" ]; then \
         extra_index_url_arg="--extra-index-url https://download.pytorch.org/whl/cpu"; \
     elif [ "$GPU_DRIVER" = "rocm" ]; then \
-        extra_index_url_arg="--index-url https://download.pytorch.org/whl/rocm5.6"; \
+        extra_index_url_arg="--extra-index-url https://download.pytorch.org/whl/rocm5.6"; \
     else \
         extra_index_url_arg="--extra-index-url https://download.pytorch.org/whl/cu121"; \
     fi &&\
@@ -54,7 +54,7 @@ RUN --mount=type=cache,target=/root/.cache/pip \
     if [ "$GPU_DRIVER" = "cuda" ] && [ "$TARGETPLATFORM" = "linux/amd64" ]; then \
         pip install -e ".[xformers]"; \
     else \
-        pip install -e "."; \
+        pip install $extra_index_url_arg -e "."; \
     fi
 
 # #### Build the Web UI ------------------------------------

docker/run.sh

@@ -21,7 +21,7 @@ run() {
     printf "%s\n" "$build_args"
   fi
 
-  docker compose build $build_args
+  docker compose build $build_args $service_name
   unset build_args
 
   printf "%s\n" "starting service $service_name"

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:
 
-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.

docs/installation/020_INSTALL_MANUAL.md

@@ -230,13 +230,13 @@ manager, please follow these steps:
         === "local Webserver"
 
             ```bash
-            invokeai --web
+            invokeai-web
             ```
 
         === "Public Webserver"
 
             ```bash
-            invokeai --web --host 0.0.0.0
+            invokeai-web --host 0.0.0.0
             ```
 
         === "CLI"
@@ -402,4 +402,4 @@ environment variable INVOKEAI_ROOT to point to the installation directory.
 Note that if you run into problems with the Conda installation, the InvokeAI
 staff will **not** be able to help you out. Caveat Emptor!
 
-[dev-chat]: https://discord.com/channels/1020123559063990373/1049495067846524939
+[dev-chat]: https://discord.com/channels/1020123559063990373/1049495067846524939

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(
-                mask_name=mask_name,
-                masked_latents_name=masked_latents_name,
-            ),
+        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,
 )
@@ -69,13 +67,13 @@ 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(
-        noise=LatentsField(latents_name=latents_name, seed=seed),
-        width=latents.size()[3] * 8,
-        height=latents.size()[2] * 8,
-    )
+    @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] * LATENT_SCALE_FACTOR,
+            height=latents.size()[2] * LATENT_SCALE_FACTOR,
+        )
 
 
 @invocation(
@@ -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
@@ -154,7 +154,7 @@ class ImageService(ImageServiceABC):
             self.__invoker.services.logger.error("Image record not found")
             raise
         except Exception as e:
-            self.__invoker.services.logger.error("Problem getting image DTO")
+            self.__invoker.services.logger.error("Problem getting image metadata")
             raise e
 
     def get_workflow(self, image_name: str) -> Optional[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,
-                                workflow=queue_item.workflow,
-                            )
+                        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"
@@ -540,7 +537,7 @@ class Graph(BaseModel):
         except NodeNotFoundError:
             return False
 
-    def get_node(self, node_path: str) -> InvocationsUnion:
+    def get_node(self, node_path: str) -> BaseInvocation:
         """Gets a node from the graph using a node path."""
         # Materialized graphs may have nodes at the top level
         graph, node_id = self._get_graph_and_node(node_path)
@@ -891,7 +888,7 @@ class GraphExecutionState(BaseModel):
         # If next is still none, there's no next node, return None
         return next_node
 
-    def complete(self, node_id: str, output: InvocationOutputsUnion):
+    def complete(self, node_id: str, output: BaseInvocationOutput) -> None:
         """Marks a node as complete"""
 
         if node_id not in self.execution_graph.nodes:

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/model_management/seamless.py

@@ -1,10 +1,11 @@
 from __future__ import annotations
 
 from contextlib import contextmanager
-from typing import List, Union
+from typing import Callable, List, Union
 
 import torch.nn as nn
-from diffusers.models import AutoencoderKL, UNet2DConditionModel
+from diffusers.models.autoencoders.autoencoder_kl import AutoencoderKL
+from diffusers.models.unets.unet_2d_condition import UNet2DConditionModel
 
 
 def _conv_forward_asymmetric(self, input, weight, bias):
@@ -26,70 +27,51 @@ def _conv_forward_asymmetric(self, input, weight, bias):
 
 @contextmanager
 def set_seamless(model: Union[UNet2DConditionModel, AutoencoderKL], seamless_axes: List[str]):
+    # Callable: (input: Tensor, weight: Tensor, bias: Optional[Tensor]) -> Tensor
+    to_restore: list[tuple[nn.Conv2d | nn.ConvTranspose2d, Callable]] = []
     try:
-        to_restore = []
-
+        # Hard coded to skip down block layers, allowing for seamless tiling at the expense of prompt adherence
+        skipped_layers = 1
         for m_name, m in model.named_modules():
-            if isinstance(model, UNet2DConditionModel):
-                if ".attentions." in m_name:
+            if not isinstance(m, (nn.Conv2d, nn.ConvTranspose2d)):
+                continue
+
+            if isinstance(model, UNet2DConditionModel) and m_name.startswith("down_blocks.") and ".resnets." in m_name:
+                # down_blocks.1.resnets.1.conv1
+                _, block_num, _, resnet_num, submodule_name = m_name.split(".")
+                block_num = int(block_num)
+                resnet_num = int(resnet_num)
+
+                if block_num >= len(model.down_blocks) - skipped_layers:
                     continue
 
-                if ".resnets." in m_name:
-                    if ".conv2" in m_name:
-                        continue
-                    if ".conv_shortcut" in m_name:
-                        continue
-
-            """
-            if isinstance(model, UNet2DConditionModel):
-                if False and ".upsamplers." in m_name:
+                # Skip the second resnet (could be configurable)
+                if resnet_num > 0:
                     continue
 
-                if False and ".downsamplers." in m_name:
+                # Skip Conv2d layers (could be configurable)
+                if submodule_name == "conv2":
                     continue
 
-                if True and ".resnets." in m_name:
-                    if True and ".conv1" in m_name:
-                        if False and "down_blocks" in m_name:
-                            continue
-                        if False and "mid_block" in m_name:
-                            continue
-                        if False and "up_blocks" in m_name:
-                            continue
+            m.asymmetric_padding_mode = {}
+            m.asymmetric_padding = {}
+            m.asymmetric_padding_mode["x"] = "circular" if ("x" in seamless_axes) else "constant"
+            m.asymmetric_padding["x"] = (
+                m._reversed_padding_repeated_twice[0],
+                m._reversed_padding_repeated_twice[1],
+                0,
+                0,
+            )
+            m.asymmetric_padding_mode["y"] = "circular" if ("y" in seamless_axes) else "constant"
+            m.asymmetric_padding["y"] = (
+                0,
+                0,
+                m._reversed_padding_repeated_twice[2],
+                m._reversed_padding_repeated_twice[3],
+            )
 
-                    if True and ".conv2" in m_name:
-                        continue
-
-                    if True and ".conv_shortcut" in m_name:
-                        continue
-
-                if True and ".attentions." in m_name:
-                    continue
-
-                if False and m_name in ["conv_in", "conv_out"]:
-                    continue
-            """
-
-            if isinstance(m, (nn.Conv2d, nn.ConvTranspose2d)):
-                m.asymmetric_padding_mode = {}
-                m.asymmetric_padding = {}
-                m.asymmetric_padding_mode["x"] = "circular" if ("x" in seamless_axes) else "constant"
-                m.asymmetric_padding["x"] = (
-                    m._reversed_padding_repeated_twice[0],
-                    m._reversed_padding_repeated_twice[1],
-                    0,
-                    0,
-                )
-                m.asymmetric_padding_mode["y"] = "circular" if ("y" in seamless_axes) else "constant"
-                m.asymmetric_padding["y"] = (
-                    0,
-                    0,
-                    m._reversed_padding_repeated_twice[2],
-                    m._reversed_padding_repeated_twice[3],
-                )
-
-                to_restore.append((m, m._conv_forward))
-                m._conv_forward = _conv_forward_asymmetric.__get__(m, nn.Conv2d)
+            to_restore.append((m, m._conv_forward))
+            m._conv_forward = _conv_forward_asymmetric.__get__(m, nn.Conv2d)
 
         yield
 

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/package.json

@@ -52,6 +52,7 @@
     "@chakra-ui/react-use-size": "^2.1.0",
     "@dagrejs/graphlib": "^2.1.13",
     "@dnd-kit/core": "^6.1.0",
+    "@dnd-kit/sortable": "^8.0.0",
     "@dnd-kit/utilities": "^3.2.2",
     "@fontsource-variable/inter": "^5.0.16",
     "@invoke-ai/ui-library": "^0.0.18",

invokeai/frontend/web/pnpm-lock.yaml

@@ -22,6 +22,9 @@ dependencies:
   '@dnd-kit/core':
     specifier: ^6.1.0
     version: 6.1.0(react-dom@18.2.0)(react@18.2.0)
+  '@dnd-kit/sortable':
+    specifier: ^8.0.0
+    version: 8.0.0(@dnd-kit/core@6.1.0)(react@18.2.0)
   '@dnd-kit/utilities':
     specifier: ^3.2.2
     version: 3.2.2(react@18.2.0)
@@ -2884,6 +2887,18 @@ packages:
       tslib: 2.6.2
     dev: false
 
+  /@dnd-kit/sortable@8.0.0(@dnd-kit/core@6.1.0)(react@18.2.0):
+    resolution: {integrity: sha512-U3jk5ebVXe1Lr7c2wU7SBZjcWdQP+j7peHJfCspnA81enlu88Mgd7CC8Q+pub9ubP7eKVETzJW+IBAhsqbSu/g==}
+    peerDependencies:
+      '@dnd-kit/core': ^6.1.0
+      react: '>=16.8.0'
+    dependencies:
+      '@dnd-kit/core': 6.1.0(react-dom@18.2.0)(react@18.2.0)
+      '@dnd-kit/utilities': 3.2.2(react@18.2.0)
+      react: 18.2.0
+      tslib: 2.6.2
+    dev: false
+
   /@dnd-kit/utilities@3.2.2(react@18.2.0):
     resolution: {integrity: sha512-+MKAJEOfaBe5SmV6t34p80MMKhjvUz0vRrvVJbPT0WElzaOJ/1xs+D+KDv+tD/NE5ujfrChEcshd4fLn0wpiqg==}
     peerDependencies:

invokeai/frontend/web/public/locales/de.json

@@ -69,7 +69,7 @@
         "random": "Zufall",
         "batch": "Stapel-Manager",
         "advanced": "Erweitert",
-        "unifiedCanvas": "Einheitliche Leinwand",
+        "unifiedCanvas": "Leinwand",
         "openInNewTab": "In einem neuem Tab öffnen",
         "statusProcessing": "wird bearbeitet",
         "linear": "Linear",
@@ -127,7 +127,7 @@
         "galleryImageResetSize": "Größe zurücksetzen",
         "gallerySettings": "Galerie-Einstellungen",
         "maintainAspectRatio": "Seitenverhältnis beibehalten",
-        "autoSwitchNewImages": "Automatisch zu neuen Bildern wechseln",
+        "autoSwitchNewImages": "Auto-Wechsel zu neuen Bildern",
         "singleColumnLayout": "Einspaltiges Layout",
         "allImagesLoaded": "Alle Bilder geladen",
         "loadMore": "Mehr laden",
@@ -226,7 +226,7 @@
         },
         "sendToImageToImage": {
             "title": "An Bild zu Bild senden",
-            "desc": "Aktuelles Bild an Bild zu Bild senden"
+            "desc": "Aktuelles Bild an Bild-zu-Bild senden"
         },
         "deleteImage": {
             "title": "Bild löschen",
@@ -258,7 +258,7 @@
         },
         "selectEraser": {
             "title": "Radiergummi auswählen",
-            "desc": "Wählt den Radiergummi für die Leinwand aus"
+            "desc": "Wählt den Radiergummi aus"
         },
         "decreaseBrushSize": {
             "title": "Pinselgröße verkleinern",
@@ -330,7 +330,7 @@
         },
         "downloadImage": {
             "title": "Bild herunterladen",
-            "desc": "Aktuelle Leinwand herunterladen"
+            "desc": "Aktuelles Bild herunterladen"
         },
         "undoStroke": {
             "title": "Pinselstrich rückgängig machen",
@@ -564,8 +564,8 @@
         "img2imgStrength": "Bild-zu-Bild-Stärke",
         "toggleLoopback": "Loopback umschalten",
         "sendTo": "Senden an",
-        "sendToImg2Img": "Senden an Bild zu Bild",
-        "sendToUnifiedCanvas": "Senden an Unified Canvas",
+        "sendToImg2Img": "Senden an Bild-zu-Bild",
+        "sendToUnifiedCanvas": "Senden an Leinwand",
         "copyImageToLink": "Bild-Link kopieren",
         "downloadImage": "Bild herunterladen",
         "openInViewer": "Im Viewer öffnen",
@@ -604,7 +604,9 @@
         "resetComplete": "Die Web-Oberfläche wurde zurückgesetzt.",
         "models": "Modelle",
         "useSlidersForAll": "Schieberegler für alle Optionen verwenden",
-        "showAdvancedOptions": "Erweiterte Optionen anzeigen"
+        "showAdvancedOptions": "Erweiterte Optionen anzeigen",
+        "alternateCanvasLayout": "Alternatives Leinwand-Layout",
+        "clearIntermediatesDesc1": "Das Löschen der Zwischenprodukte setzt Leinwand und ControlNet zurück."
     },
     "toast": {
         "tempFoldersEmptied": "Temp-Ordner geleert",
@@ -618,7 +620,7 @@
         "imageSavedToGallery": "Bild in die Galerie gespeichert",
         "canvasMerged": "Leinwand zusammengeführt",
         "sentToImageToImage": "Gesendet an Bild zu Bild",
-        "sentToUnifiedCanvas": "Gesendet an Unified Canvas",
+        "sentToUnifiedCanvas": "Gesendet an Leinwand",
         "parametersSet": "Parameter festlegen",
         "parametersNotSet": "Parameter nicht festgelegt",
         "parametersNotSetDesc": "Keine Metadaten für dieses Bild gefunden.",
@@ -635,7 +637,21 @@
         "metadataLoadFailed": "Metadaten konnten nicht geladen werden",
         "initialImageSet": "Ausgangsbild festgelegt",
         "initialImageNotSet": "Ausgangsbild nicht festgelegt",
-        "initialImageNotSetDesc": "Ausgangsbild konnte nicht geladen werden"
+        "initialImageNotSetDesc": "Ausgangsbild konnte nicht geladen werden",
+        "setCanvasInitialImage": "Ausgangsbild setzen",
+        "problemMergingCanvas": "Problem bei Verschmelzung der Leinwand",
+        "canvasCopiedClipboard": "Leinwand in Zwischenablage kopiert",
+        "canvasSentControlnetAssets": "Leinwand an ControlNet & Sammlung geschickt",
+        "problemDownloadingCanvasDesc": "Kann Basis-Layer nicht exportieren",
+        "canvasDownloaded": "Leinwand heruntergeladen",
+        "problemSavingCanvasDesc": "Kann Basis-Layer nicht exportieren",
+        "canvasSavedGallery": "Leinwand in Galerie gespeichert",
+        "problemMergingCanvasDesc": "Kann Basis-Layer nicht exportieren",
+        "problemSavingCanvas": "Problem beim Speichern der Leinwand",
+        "problemCopyingCanvas": "Problem beim Kopieren der Leinwand",
+        "problemCopyingCanvasDesc": "Kann Basis-Layer nicht exportieren",
+        "problemDownloadingCanvas": "Problem beim Herunterladen der Leinwand",
+        "setAsCanvasInitialImage": "Als Ausgangsbild gesetzt"
     },
     "tooltip": {
         "feature": {
@@ -648,7 +664,7 @@
             "faceCorrection": "Gesichtskorrektur mit GFPGAN oder Codeformer: Der Algorithmus erkennt Gesichter im Bild und korrigiert alle Fehler. Ein hoher Wert verändert das Bild stärker, was zu attraktiveren Gesichtern führt. Codeformer mit einer höheren Genauigkeit bewahrt das Originalbild auf Kosten einer stärkeren Gesichtskorrektur.",
             "imageToImage": "Bild zu Bild lädt ein beliebiges Bild als Ausgangsbild, aus dem dann zusammen mit dem Prompt ein neues Bild erzeugt wird. Je höher der Wert ist, desto stärker wird das Ergebnisbild verändert. Werte von 0,0 bis 1,0 sind möglich, der empfohlene Bereich ist .25-.75",
             "boundingBox": "Der Begrenzungsrahmen ist derselbe wie die Einstellungen für Breite und Höhe bei Text-zu-Bild oder Bild-zu-Bild. Es wird nur der Bereich innerhalb des Rahmens verarbeitet.",
-            "seamCorrection": "Steuert die Behandlung von sichtbaren Übergängen, die zwischen den erzeugten Bildern auf der Leinwand auftreten.",
+            "seamCorrection": "Behandlung von sichtbaren Übergängen, die zwischen den erzeugten Bildern auftreten.",
             "infillAndScaling": "Verwalten Sie Infill-Methoden (für maskierte oder gelöschte Bereiche der Leinwand) und Skalierung (nützlich für kleine Begrenzungsrahmengrößen)."
         }
     },
@@ -659,17 +675,17 @@
         "maskingOptions": "Maskierungsoptionen",
         "enableMask": "Maske aktivieren",
         "preserveMaskedArea": "Maskierten Bereich bewahren",
-        "clearMask": "Maske löschen",
+        "clearMask": "Maske löschen (Shift+C)",
         "brush": "Pinsel",
         "eraser": "Radierer",
         "fillBoundingBox": "Begrenzungsrahmen füllen",
         "eraseBoundingBox": "Begrenzungsrahmen löschen",
-        "colorPicker": "Farbpipette",
+        "colorPicker": "Pipette",
         "brushOptions": "Pinseloptionen",
         "brushSize": "Größe",
         "move": "Bewegen",
         "resetView": "Ansicht zurücksetzen",
-        "mergeVisible": "Sichtbare Zusammenführen",
+        "mergeVisible": "Sichtbare zusammenführen",
         "saveToGallery": "In Galerie speichern",
         "copyToClipboard": "In Zwischenablage kopieren",
         "downloadAsImage": "Als Bild herunterladen",
@@ -683,15 +699,15 @@
         "darkenOutsideSelection": "Außerhalb der Auswahl verdunkeln",
         "autoSaveToGallery": "Automatisch in Galerie speichern",
         "saveBoxRegionOnly": "Nur Auswahlbox speichern",
-        "limitStrokesToBox": "Striche auf Box beschränken",
-        "showCanvasDebugInfo": "Zusätzliche Informationen zur Leinwand anzeigen",
+        "limitStrokesToBox": "Striche auf Auswahl beschränken",
+        "showCanvasDebugInfo": "Zusätzliche Informationen anzeigen",
         "clearCanvasHistory": "Leinwand-Verlauf löschen",
         "clearHistory": "Verlauf löschen",
-        "clearCanvasHistoryMessage": "Wenn Sie den Verlauf der Leinwand löschen, bleibt die aktuelle Leinwand intakt, aber der Verlauf der Rückgängig- und Wiederherstellung wird unwiderruflich gelöscht.",
-        "clearCanvasHistoryConfirm": "Sind Sie sicher, dass Sie den Verlauf der Leinwand löschen möchten?",
+        "clearCanvasHistoryMessage": "Wenn Sie den Verlauf löschen, bleibt die aktuelle Leinwand intakt, aber der Verlauf der Rückgängig- und Wiederherstellung wird unwiderruflich gelöscht.",
+        "clearCanvasHistoryConfirm": "Sind Sie sicher, dass Sie den Verlauf löschen möchten?",
         "emptyTempImageFolder": "Temp-Image Ordner leeren",
         "emptyFolder": "Leerer Ordner",
-        "emptyTempImagesFolderMessage": "Wenn Sie den Ordner für temporäre Bilder leeren, wird auch der Unified Canvas vollständig zurückgesetzt. Dies umfasst den gesamten Verlauf der Rückgängig-/Wiederherstellungsvorgänge, die Bilder im Bereitstellungsbereich und die Leinwand-Basisebene.",
+        "emptyTempImagesFolderMessage": "Wenn Sie den Ordner für temporäre Bilder leeren, wird die Leinwand zurückgesetzt. Dies umfasst den gesamten Verlauf der Rückgängig-/Wiederherstellungsvorgänge, die Bilder im Bereitstellungsbereich und die Leinwand-Basisebene.",
         "emptyTempImagesFolderConfirm": "Sind Sie sicher, dass Sie den temporären Ordner leeren wollen?",
         "activeLayer": "Aktive Ebene",
         "canvasScale": "Leinwand Maßstab",
@@ -708,7 +724,7 @@
         "discardAll": "Alles verwerfen",
         "betaClear": "Löschen",
         "betaDarkenOutside": "Außen abdunkeln",
-        "betaLimitToBox": "Begrenzung auf das Feld",
+        "betaLimitToBox": "Auf Auswahl begrenzen",
         "betaPreserveMasked": "Maskiertes bewahren",
         "antialiasing": "Kantenglättung",
         "showResultsOn": "Zeige Ergebnisse (An)",
@@ -746,7 +762,7 @@
         "autoAddBoard": "Automatisches Hinzufügen zum Ordner",
         "topMessage": "Dieser Ordner enthält Bilder die in den folgenden Funktionen verwendet werden:",
         "move": "Bewegen",
-        "menuItemAutoAdd": "Automatisches Hinzufügen zu diesem Ordner",
+        "menuItemAutoAdd": "Auto-Hinzufügen zu diesem Ordner",
         "myBoard": "Meine Ordner",
         "searchBoard": "Ordner durchsuchen...",
         "noMatching": "Keine passenden Ordner",
@@ -826,7 +842,6 @@
         "pidi": "PIDI",
         "normalBae": "Normales BAE",
         "mlsdDescription": "Minimalistischer Liniensegmentdetektor",
-        "openPoseDescription": "Schätzung der menschlichen Pose mit Openpose",
         "control": "Kontrolle",
         "coarse": "Grob",
         "crop": "Zuschneiden",
@@ -839,10 +854,9 @@
         "lineartAnimeDescription": "Lineart-Verarbeitung im Anime-Stil",
         "minConfidence": "Minimales Vertrauen",
         "megaControl": "Mega-Kontrolle",
-        "autoConfigure": "Prozessor automatisch konfigurieren",
+        "autoConfigure": "Prozessor Auto-konfig",
         "normalBaeDescription": "Normale BAE-Verarbeitung",
         "noneDescription": "Es wurde keine Verarbeitung angewendet",
-        "openPose": "Openpose / \"Pose nutzen\"",
         "lineartAnime": "Lineart Anime / \"Strichzeichnung Anime\"",
         "mediapipeFaceDescription": "Gesichtserkennung mit Mediapipe",
         "canny": "\"Canny\"",
@@ -944,7 +958,7 @@
         "initImage": "Erstes Bild",
         "variations": "Seed-Gewichtungs-Paare",
         "vae": "VAE",
-        "workflow": "Arbeitsablauf",
+        "workflow": "Workflow",
         "scheduler": "Planer",
         "noRecallParameters": "Es wurden keine Parameter zum Abrufen gefunden",
         "recallParameters": "Parameter wiederherstellen"
@@ -1056,6 +1070,20 @@
                 "\"Per Bild\" wird einen einzigartigen Seed-Wert für jedes Bild verwenden. Dies bietet mehr Variationen."
             ],
             "heading": "Seed-Verhalten"
+        },
+        "dynamicPrompts": {
+            "paragraphs": [
+                "\"Dynamische Prompts\" übersetzt einen Prompt in mehrere.",
+                "Die Ausgangs-Syntax ist \"ein {roter|grüner|blauer} ball\". Das generiert 3 Prompts: \"ein roter ball\", \"ein grüner ball\" und \"ein blauer ball\".",
+                "Sie können die Syntax so oft verwenden, wie Sie in einem einzigen Prompt möchten, aber stellen Sie sicher, dass die Anzahl der Prompts zur Einstellung von \"Max Prompts\" passt."
+            ],
+            "heading": "Dynamische Prompts"
+        },
+        "controlNetWeight": {
+            "paragraphs": [
+                "Wie stark wird das ControlNet das generierte Bild beeinflussen wird."
+            ],
+            "heading": "Einfluss"
         }
     },
     "ui": {
@@ -1160,10 +1188,10 @@
         "outputFieldInInput": "Ausgabefeld im Eingang",
         "problemReadingWorkflow": "Problem beim Lesen des Arbeitsablaufs vom Bild",
         "reloadNodeTemplates": "Knoten-Vorlagen neu laden",
-        "newWorkflow": "Neuer Arbeitsablauf",
+        "newWorkflow": "Neuer Arbeitsablauf / Workflow",
         "newWorkflowDesc": "Einen neuen Arbeitsablauf erstellen?",
         "noFieldsLinearview": "Keine Felder zur linearen Ansicht hinzugefügt",
-        "clearWorkflow": "Arbeitsablauf löschen",
+        "clearWorkflow": "Workflow löschen",
         "clearWorkflowDesc": "Diesen Arbeitsablauf löschen und neu starten?",
         "noConnectionInProgress": "Es besteht keine Verbindung",
         "notes": "Anmerkungen",
@@ -1220,8 +1248,8 @@
         "stringDescription": "Zeichenfolgen (Strings) sind Text.",
         "fieldTypesMustMatch": "Feldtypen müssen übereinstimmen",
         "fitViewportNodes": "An Ansichtsgröße anpassen",
-        "missingCanvaInitMaskImages": "Fehlende Startbilder und Masken auf der Arbeitsfläche",
-        "missingCanvaInitImage": "Fehlendes Startbild auf der Arbeitsfläche",
+        "missingCanvaInitMaskImages": "Fehlende Startbilder und Masken auf der Leinwand",
+        "missingCanvaInitImage": "Fehlendes Startbild auf der Leinwand",
         "ipAdapterModelDescription": "IP-Adapter-Modellfeld",
         "latentsPolymorphicDescription": "Zwischen Nodes können Latents weitergegeben werden.",
         "loadingNodes": "Lade Nodes...",
@@ -1321,7 +1349,7 @@
         "workflows": "Arbeitsabläufe",
         "noSystemWorkflows": "Keine System-Arbeitsabläufe",
         "workflowName": "Arbeitsablauf-Name",
-        "workflowIsOpen": "Arbeitsablauf ist offen",
+        "workflowIsOpen": "Arbeitsablauf ist geöffnet",
         "saveWorkflowAs": "Arbeitsablauf speichern als",
         "searchWorkflows": "Suche Arbeitsabläufe",
         "newWorkflowCreated": "Neuer Arbeitsablauf erstellt",

invokeai/frontend/web/public/locales/en.json

@@ -175,6 +175,7 @@
         "statusUpscaling": "Upscaling",
         "statusUpscalingESRGAN": "Upscaling (ESRGAN)",
         "template": "Template",
+        "toResolve": "To resolve",
         "training": "Training",
         "trainingDesc1": "A dedicated workflow for training your own embeddings and checkpoints using Textual Inversion and Dreambooth from the web interface.",
         "trainingDesc2": "InvokeAI already supports training custom embeddourings using Textual Inversion using the main script.",
@@ -900,6 +901,7 @@
         "doesNotExist": "does not exist",
         "downloadWorkflow": "Download Workflow JSON",
         "edge": "Edge",
+        "editMode": "Edit in Workflow Editor",
         "enum": "Enum",
         "enumDescription": "Enums are values that may be one of a number of options.",
         "executionStateCompleted": "Completed",
@@ -995,8 +997,10 @@
         "problemReadingMetadata": "Problem reading metadata from image",
         "problemReadingWorkflow": "Problem reading workflow from image",
         "problemSettingTitle": "Problem Setting Title",
+        "resetToDefaultValue": "Reset to default value",
         "reloadNodeTemplates": "Reload Node Templates",
         "removeLinearView": "Remove from Linear View",
+        "reorderLinearView": "Reorder Linear View",
         "newWorkflow": "New Workflow",
         "newWorkflowDesc": "Create a new workflow?",
         "newWorkflowDesc2": "Your current workflow has unsaved changes.",
@@ -1067,6 +1071,7 @@
         "vaeModelFieldDescription": "TODO",
         "validateConnections": "Validate Connections and Graph",
         "validateConnectionsHelp": "Prevent invalid connections from being made, and invalid graphs from being invoked",
+        "viewMode": "Use in Linear View",
         "unableToGetWorkflowVersion": "Unable to get workflow schema version",
         "unrecognizedWorkflowVersion": "Unrecognized workflow schema version {{version}}",
         "version": "Version",

invokeai/frontend/web/public/locales/it.json

@@ -795,7 +795,8 @@
         "workflowDeleted": "Flusso di lavoro eliminato",
         "problemRetrievingWorkflow": "Problema nel recupero del flusso di lavoro",
         "resetInitialImage": "Reimposta l'immagine iniziale",
-        "uploadInitialImage": "Carica l'immagine iniziale"
+        "uploadInitialImage": "Carica l'immagine iniziale",
+        "problemDownloadingImage": "Impossibile scaricare l'immagine"
     },
     "tooltip": {
         "feature": {
@@ -1134,7 +1135,10 @@
         "newWorkflow": "Nuovo flusso di lavoro",
         "newWorkflowDesc": "Creare un nuovo flusso di lavoro?",
         "newWorkflowDesc2": "Il flusso di lavoro attuale presenta modifiche non salvate.",
-        "unsupportedAnyOfLength": "unione di troppi elementi ({{count}})"
+        "unsupportedAnyOfLength": "unione di troppi elementi ({{count}})",
+        "clearWorkflowDesc": "Cancellare questo flusso di lavoro e avviarne uno nuovo?",
+        "clearWorkflow": "Cancella il flusso di lavoro",
+        "clearWorkflowDesc2": "Il tuo flusso di lavoro attuale presenta modifiche non salvate."
     },
     "boards": {
         "autoAddBoard": "Aggiungi automaticamente bacheca",
@@ -1191,7 +1195,6 @@
         "f": "F",
         "h": "A",
         "prompt": "Prompt",
-        "openPoseDescription": "Stima della posa umana utilizzando Openpose",
         "resizeMode": "Ridimensionamento",
         "weight": "Peso",
         "selectModel": "Seleziona un modello",
@@ -1672,7 +1675,9 @@
         "downloadWorkflow": "Salva su file",
         "uploadWorkflow": "Carica da file",
         "projectWorkflows": "Flussi di lavoro del progetto",
-        "noWorkflows": "Nessun flusso di lavoro"
+        "noWorkflows": "Nessun flusso di lavoro",
+        "workflowCleared": "Flusso di lavoro cancellato",
+        "saveWorkflowToProject": "Salva flusso di lavoro nel progetto"
     },
     "app": {
         "storeNotInitialized": "Il negozio non è inizializzato"

invokeai/frontend/web/public/locales/ja.json

@@ -555,7 +555,6 @@
         "balanced": "バランス",
         "prompt": "プロンプト",
         "depthMidasDescription": "Midasを使用して深度マップを生成",
-        "openPoseDescription": "Openposeを使用してポーズを推定",
         "control": "コントロール",
         "resizeMode": "リサイズモード",
         "weight": "重み",

invokeai/frontend/web/public/locales/ko.json

@@ -333,7 +333,6 @@
         "h": "H",
         "prompt": "프롬프트",
         "depthMidasDescription": "Midas를 사용하여 Depth map 생성하기",
-        "openPoseDescription": "Openpose를 이용한 사람 포즈 추정",
         "control": "Control",
         "resizeMode": "크기 조정 모드",
         "t2iEnabledControlNetDisabled": "$t(common.t2iAdapter) 사용 가능,$t(common.controlNet) 사용 불가능",
@@ -370,7 +369,6 @@
         "normalBaeDescription": "Normal BAE 처리",
         "noneDescription": "처리되지 않음",
         "saveControlImage": "Control Image 저장",
-        "openPose": "Openpose",
         "toggleControlNet": "해당 ControlNet으로 전환",
         "delete": "삭제",
         "controlAdapter_other": "Control Adapter(s)",

invokeai/frontend/web/public/locales/nl.json

@@ -1033,7 +1033,6 @@
         "prompt": "Prompt",
         "depthMidasDescription": "Genereer diepteblad via Midas",
         "controlnet": "$t(controlnet.controlAdapter_one) #{{number}} ($t(common.controlNet))",
-        "openPoseDescription": "Menselijke pose-benadering via Openpose",
         "control": "Controle",
         "resizeMode": "Modus schaling",
         "t2iEnabledControlNetDisabled": "$t(common.t2iAdapter) ingeschakeld, $t(common.controlNet)s uitgeschakeld",
@@ -1072,7 +1071,6 @@
         "normalBaeDescription": "Normale BAE-verwerking",
         "noneDescription": "Geen verwerking toegepast",
         "saveControlImage": "Bewaar controle-afbeelding",
-        "openPose": "Openpose",
         "toggleControlNet": "Zet deze ControlNet aan/uit",
         "delete": "Verwijder",
         "controlAdapter_one": "Control-adapter",

invokeai/frontend/web/public/locales/ru.json

@@ -1155,7 +1155,6 @@
         "resetControlImage": "Сбросить контрольное изображение",
         "prompt": "Запрос",
         "controlnet": "$t(controlnet.controlAdapter_one) №{{number}} $t(common.controlNet)",
-        "openPoseDescription": "Оценка позы человека с помощью Openpose",
         "resizeMode": "Режим изменения размера",
         "t2iEnabledControlNetDisabled": "$t(common.t2iAdapter) включен, $t(common.controlNet)s отключен",
         "weight": "Вес",

invokeai/frontend/web/public/locales/tr.json

@@ -259,7 +259,6 @@
         "mediapipeFace": "Mediapipe Yüz",
         "megaControl": "Aşırı Yönetim",
         "mlsd": "M-LSD",
-        "openPoseDescription": "Openpose kullanarak poz belirleme",
         "setControlImageDimensions": "Yönetim Görseli Boyutlarını En/Boydan Al",
         "pidi": "PIDI",
         "scribble": "çiziktirme",
@@ -273,7 +272,6 @@
         "mlsdDescription": "Minimalist Line Segment Detector (Kolay Çizgi Parçası Algılama)",
         "normalBae": "Normal BAE",
         "normalBaeDescription": "Normal BAE işleme",
-        "openPose": "Openpose",
         "resetControlImage": "Yönetim Görselini Kaldır",
         "enableIPAdapter": "IP Aracını Etkinleştir",
         "lineart": "Çizim",

invokeai/frontend/web/public/locales/zh_CN.json

@@ -1143,7 +1143,6 @@
         "balanced": "平衡",
         "prompt": "Prompt (提示词控制)",
         "depthMidasDescription": "使用 Midas 生成深度图",
-        "openPoseDescription": "使用 Openpose 进行人体姿态估计",
         "resizeMode": "缩放模式",
         "weight": "权重",
         "selectModel": "选择一个模型",
@@ -1207,7 +1206,6 @@
         "megaControl": "Mega Control (超级控制)",
         "depthZoe": "Depth (Zoe)",
         "colorMap": "Color",
-        "openPose": "Openpose",
         "controlAdapter_other": "Control Adapters",
         "lineartAnime": "Lineart Anime",
         "canny": "Canny",

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

@@ -2,7 +2,7 @@ import type { UnknownAction } from '@reduxjs/toolkit';
 import { isAnyGraphBuilt } from 'features/nodes/store/actions';
 import { nodeTemplatesBuilt } from 'features/nodes/store/nodeTemplatesSlice';
 import { cloneDeep } from 'lodash-es';
-import { receivedOpenAPISchema } from 'services/api/thunks/schema';
+import { appInfoApi } from 'services/api/endpoints/appInfo';
 import type { Graph } from 'services/api/types';
 import { socketGeneratorProgress } from 'services/events/actions';
 
@@ -18,7 +18,7 @@ export const actionSanitizer = <A extends UnknownAction>(action: A): A => {
     }
   }
 
-  if (receivedOpenAPISchema.fulfilled.match(action)) {
+  if (appInfoApi.endpoints.getOpenAPISchema.matchFulfilled(action)) {
     return {
       ...action,
       payload: '<OpenAPI schema omitted>',

invokeai/frontend/web/src/app/store/middleware/listenerMiddleware/index.ts

@@ -23,6 +23,7 @@ import { addControlNetImageProcessedListener } from './listeners/controlNetImage
 import { addEnqueueRequestedCanvasListener } from './listeners/enqueueRequestedCanvas';
 import { addEnqueueRequestedLinear } from './listeners/enqueueRequestedLinear';
 import { addEnqueueRequestedNodes } from './listeners/enqueueRequestedNodes';
+import { addGetOpenAPISchemaListener } from './listeners/getOpenAPISchema';
 import {
   addImageAddedToBoardFulfilledListener,
   addImageAddedToBoardRejectedListener,
@@ -47,7 +48,6 @@ import { addInitialImageSelectedListener } from './listeners/initialImageSelecte
 import { addModelSelectedListener } from './listeners/modelSelected';
 import { addModelsLoadedListener } from './listeners/modelsLoaded';
 import { addDynamicPromptsListener } from './listeners/promptChanged';
-import { addReceivedOpenAPISchemaListener } from './listeners/receivedOpenAPISchema';
 import { addSocketConnectedEventListener as addSocketConnectedListener } from './listeners/socketio/socketConnected';
 import { addSocketDisconnectedEventListener as addSocketDisconnectedListener } from './listeners/socketio/socketDisconnected';
 import { addGeneratorProgressEventListener as addGeneratorProgressListener } from './listeners/socketio/socketGeneratorProgress';
@@ -150,7 +150,7 @@ addImageRemovedFromBoardRejectedListener();
 addBoardIdSelectedListener();
 
 // Node schemas
-addReceivedOpenAPISchemaListener();
+addGetOpenAPISchemaListener();
 
 // Workflows
 addWorkflowLoadRequestedListener();

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

@@ -3,18 +3,18 @@ import { parseify } from 'common/util/serialize';
 import { nodeTemplatesBuilt } from 'features/nodes/store/nodeTemplatesSlice';
 import { parseSchema } from 'features/nodes/util/schema/parseSchema';
 import { size } from 'lodash-es';
-import { receivedOpenAPISchema } from 'services/api/thunks/schema';
+import { appInfoApi } from 'services/api/endpoints/appInfo';
 
 import { startAppListening } from '..';
 
-export const addReceivedOpenAPISchemaListener = () => {
+export const addGetOpenAPISchemaListener = () => {
   startAppListening({
-    actionCreator: receivedOpenAPISchema.fulfilled,
+    matcher: appInfoApi.endpoints.getOpenAPISchema.matchFulfilled,
     effect: (action, { dispatch, getState }) => {
       const log = logger('system');
       const schemaJSON = action.payload;
 
-      log.debug({ schemaJSON }, 'Received OpenAPI schema');
+      log.debug({ schemaJSON: parseify(schemaJSON) }, 'Received OpenAPI schema');
       const { nodesAllowlist, nodesDenylist } = getState().config;
 
       const nodeTemplates = parseSchema(schemaJSON, nodesAllowlist, nodesDenylist);
@@ -26,10 +26,14 @@ export const addReceivedOpenAPISchemaListener = () => {
   });
 
   startAppListening({
-    actionCreator: receivedOpenAPISchema.rejected,
+    matcher: appInfoApi.endpoints.getOpenAPISchema.matchRejected,
     effect: (action) => {
-      const log = logger('system');
-      log.error({ error: parseify(action.error) }, 'Problem retrieving OpenAPI Schema');
+      // If action.meta.condition === true, the request was canceled/skipped because another request was in flight or
+      // the value was already in the cache. We don't want to log these errors.
+      if (!action.meta.condition) {
+        const log = logger('system');
+        log.error({ error: parseify(action.error) }, 'Problem retrieving OpenAPI Schema');
+      }
     },
   });
 };

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

@@ -1,10 +1,9 @@
 import { logger } from 'app/logging/logger';
 import { $baseUrl } from 'app/store/nanostores/baseUrl';
-import { isEqual, size } from 'lodash-es';
+import { isEqual } from 'lodash-es';
 import { atom } from 'nanostores';
 import { api } from 'services/api';
 import { queueApi, selectQueueStatus } from 'services/api/endpoints/queue';
-import { receivedOpenAPISchema } from 'services/api/thunks/schema';
 import { socketConnected } from 'services/events/actions';
 
 import { startAppListening } from '../..';
@@ -77,17 +76,4 @@ export const addSocketConnectedEventListener = () => {
       }
     },
   });
-
-  startAppListening({
-    actionCreator: socketConnected,
-    effect: async (action, { dispatch, getState }) => {
-      const { nodeTemplates, config } = getState();
-      // We only want to re-fetch the schema if we don't have any node templates
-      if (!size(nodeTemplates.templates) && !config.disabledTabs.includes('nodes')) {
-        // This request is a createAsyncThunk - resetting API state as in the above listener
-        // will not trigger this request, so we need to manually do it.
-        dispatch(receivedOpenAPISchema());
-      }
-    },
-  });
 };

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/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

@@ -6,7 +6,6 @@ import { WorkflowMigrationError, WorkflowVersionError } from 'features/nodes/typ
 import { validateWorkflow } from 'features/nodes/util/workflow/validateWorkflow';
 import { addToast } from 'features/system/store/systemSlice';
 import { makeToast } from 'features/system/util/makeToast';
-import { setActiveTab } from 'features/ui/store/uiSlice';
 import { t } from 'i18next';
 import { z } from 'zod';
 import { fromZodError } from 'zod-validation-error';
@@ -53,7 +52,6 @@ export const addWorkflowLoadRequestedListener = () => {
           });
         }
 
-        dispatch(setActiveTab('nodes'));
         requestAnimationFrame(() => {
           $flow.get()?.fitView();
         });

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

@@ -1,22 +1,28 @@
+import { useStore } from '@nanostores/react';
 import { useAppToaster } from 'app/components/Toaster';
+import { $authToken } from 'app/store/nanostores/authToken';
 import { useAppDispatch } from 'app/store/storeHooks';
 import { imageDownloaded } from 'features/gallery/store/actions';
 import { useCallback } from 'react';
 import { useTranslation } from 'react-i18next';
 
-import { useImageUrlToBlob } from './useImageUrlToBlob';
-
 export const useDownloadImage = () => {
   const toaster = useAppToaster();
   const { t } = useTranslation();
-  const imageUrlToBlob = useImageUrlToBlob();
   const dispatch = useAppDispatch();
+  const authToken = useStore($authToken);
 
   const downloadImage = useCallback(
     async (image_url: string, image_name: string) => {
       try {
-        const blob = await imageUrlToBlob(image_url);
-
+        const requestOpts = authToken
+          ? {
+              headers: {
+                Authorization: `Bearer ${authToken}`,
+              },
+            }
+          : {};
+        const blob = await fetch(image_url, requestOpts).then((resp) => resp.blob());
         if (!blob) {
           throw new Error('Unable to create Blob');
         }
@@ -40,7 +46,7 @@ export const useDownloadImage = () => {
         });
       }
     },
-    [t, toaster, imageUrlToBlob, dispatch]
+    [t, toaster, dispatch, authToken]
   );
 
   return { downloadImage };

invokeai/frontend/web/src/features/dnd/components/DndSortable.tsx

@@ -0,0 +1,23 @@
+import type { DragEndEvent } from '@dnd-kit/core';
+import { SortableContext, verticalListSortingStrategy } from '@dnd-kit/sortable';
+import type { PropsWithChildren } from 'react';
+import { memo } from 'react';
+
+import { DndContextTypesafe } from './DndContextTypesafe';
+
+type Props = PropsWithChildren & {
+  items: string[];
+  onDragEnd(event: DragEndEvent): void;
+};
+
+const DndSortable = (props: Props) => {
+  return (
+    <DndContextTypesafe onDragEnd={props.onDragEnd}>
+      <SortableContext items={props.items} strategy={verticalListSortingStrategy}>
+        {props.children}
+      </SortableContext>
+    </DndContextTypesafe>
+  );
+};
+
+export default memo(DndSortable);

invokeai/frontend/web/src/features/nodes/components/NodeEditor.tsx

@@ -1,7 +1,6 @@
 import 'reactflow/dist/style.css';
 
 import { Flex } from '@invoke-ai/ui-library';
-import { useAppSelector } from 'app/store/storeHooks';
 import { IAINoContentFallback } from 'common/components/IAIImageFallback';
 import TopPanel from 'features/nodes/components/flow/panels/TopPanel/TopPanel';
 import { SaveWorkflowAsDialog } from 'features/workflowLibrary/components/SaveWorkflowAsDialog/SaveWorkflowAsDialog';
@@ -11,6 +10,7 @@ import type { CSSProperties } from 'react';
 import { memo } from 'react';
 import { useTranslation } from 'react-i18next';
 import { MdDeviceHub } from 'react-icons/md';
+import { useGetOpenAPISchemaQuery } from 'services/api/endpoints/appInfo';
 
 import AddNodePopover from './flow/AddNodePopover/AddNodePopover';
 import { Flow } from './flow/Flow';
@@ -40,7 +40,7 @@ const exit: AnimationProps['exit'] = {
 };
 
 const NodeEditor = () => {
-  const isReady = useAppSelector((s) => s.nodes.isReady);
+  const { data, isLoading } = useGetOpenAPISchemaQuery();
   const { t } = useTranslation();
   return (
     <Flex
@@ -53,7 +53,7 @@ const NodeEditor = () => {
       justifyContent="center"
     >
       <AnimatePresence>
-        {isReady && (
+        {data && (
           <motion.div initial={initial} animate={animate} exit={exit} style={isReadyMotionStyles}>
             <Flow />
             <AddNodePopover />
@@ -65,7 +65,7 @@ const NodeEditor = () => {
         )}
       </AnimatePresence>
       <AnimatePresence>
-        {!isReady && (
+        {isLoading && (
           <motion.div initial={initial} animate={animate} exit={exit} style={notIsReadyMotionStyles}>
             <Flex
               layerStyle="first"

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

@@ -1,6 +1,7 @@
 import { IconButton } from '@invoke-ai/ui-library';
 import { createSelector } from '@reduxjs/toolkit';
 import { useAppDispatch, useAppSelector } from 'app/store/storeHooks';
+import { useFieldValue } from 'features/nodes/hooks/useFieldValue';
 import {
   selectWorkflowSlice,
   workflowExposedFieldAdded,
@@ -18,7 +19,7 @@ type Props = {
 const FieldLinearViewToggle = ({ nodeId, fieldName }: Props) => {
   const dispatch = useAppDispatch();
   const { t } = useTranslation();
-
+  const value = useFieldValue(nodeId, fieldName);
   const selectIsExposed = useMemo(
     () =>
       createSelector(selectWorkflowSlice, (workflow) => {
@@ -30,8 +31,8 @@ const FieldLinearViewToggle = ({ nodeId, fieldName }: Props) => {
   const isExposed = useAppSelector(selectIsExposed);
 
   const handleExposeField = useCallback(() => {
-    dispatch(workflowExposedFieldAdded({ nodeId, fieldName }));
-  }, [dispatch, fieldName, nodeId]);
+    dispatch(workflowExposedFieldAdded({ nodeId, fieldName, value }));
+  }, [dispatch, fieldName, nodeId, value]);
 
   const handleUnexposeField = useCallback(() => {
     dispatch(workflowExposedFieldRemoved({ nodeId, fieldName }));

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

@@ -1,12 +1,15 @@
+import { useSortable } from '@dnd-kit/sortable';
+import { CSS } from '@dnd-kit/utilities';
 import { Flex, Icon, IconButton, Spacer, Tooltip } from '@invoke-ai/ui-library';
 import { useAppDispatch } from 'app/store/storeHooks';
 import NodeSelectionOverlay from 'common/components/NodeSelectionOverlay';
+import { useFieldOriginalValue } from 'features/nodes/hooks/useFieldOriginalValue';
 import { useMouseOverNode } from 'features/nodes/hooks/useMouseOverNode';
 import { workflowExposedFieldRemoved } from 'features/nodes/store/workflowSlice';
 import { HANDLE_TOOLTIP_OPEN_DELAY } from 'features/nodes/types/constants';
 import { memo, useCallback } from 'react';
 import { useTranslation } from 'react-i18next';
-import { PiInfoBold, PiTrashSimpleBold } from 'react-icons/pi';
+import { PiArrowCounterClockwiseBold, PiDotsSixVerticalBold, PiInfoBold, PiTrashSimpleBold } from 'react-icons/pi';
 
 import EditableFieldTitle from './EditableFieldTitle';
 import FieldTooltipContent from './FieldTooltipContent';
@@ -19,46 +22,79 @@ type Props = {
 
 const LinearViewField = ({ nodeId, fieldName }: Props) => {
   const dispatch = useAppDispatch();
+  const { isValueChanged, onReset } = useFieldOriginalValue(nodeId, fieldName);
   const { isMouseOverNode, handleMouseOut, handleMouseOver } = useMouseOverNode(nodeId);
   const { t } = useTranslation();
+
   const handleRemoveField = useCallback(() => {
     dispatch(workflowExposedFieldRemoved({ nodeId, fieldName }));
   }, [dispatch, fieldName, nodeId]);
 
+  const { attributes, listeners, setNodeRef, transform, transition } = useSortable({ id: `${nodeId}.${fieldName}` });
+
+  const style = {
+    transform: CSS.Translate.toString(transform),
+    transition,
+  };
+
   return (
     <Flex
       onMouseEnter={handleMouseOver}
       onMouseLeave={handleMouseOut}
       layerStyle="second"
+      alignItems="center"
       position="relative"
       borderRadius="base"
       w="full"
       p={4}
-      flexDir="column"
+      paddingLeft={0}
+      ref={setNodeRef}
+      style={style}
     >
-      <Flex>
-        <EditableFieldTitle nodeId={nodeId} fieldName={fieldName} kind="input" />
-        <Spacer />
-        <Tooltip
-          label={<FieldTooltipContent nodeId={nodeId} fieldName={fieldName} kind="input" />}
-          openDelay={HANDLE_TOOLTIP_OPEN_DELAY}
-          placement="top"
-        >
-          <Flex h="full" alignItems="center">
-            <Icon fontSize="sm" color="base.300" as={PiInfoBold} />
-          </Flex>
-        </Tooltip>
-        <IconButton
-          aria-label={t('nodes.removeLinearView')}
-          tooltip={t('nodes.removeLinearView')}
-          variant="ghost"
-          size="sm"
-          onClick={handleRemoveField}
-          icon={<PiTrashSimpleBold />}
-        />
+      <IconButton
+        aria-label={t('nodes.reorderLinearView')}
+        variant="ghost"
+        icon={<PiDotsSixVerticalBold />}
+        {...listeners}
+        {...attributes}
+        mx={2}
+        height="full"
+      />
+      <Flex flexDir="column" w="full">
+        <Flex alignItems="center">
+          <EditableFieldTitle nodeId={nodeId} fieldName={fieldName} kind="input" />
+          <Spacer />
+          {isValueChanged && (
+            <IconButton
+              aria-label={t('nodes.resetToDefaultValue')}
+              tooltip={t('nodes.resetToDefaultValue')}
+              variant="ghost"
+              size="sm"
+              onClick={onReset}
+              icon={<PiArrowCounterClockwiseBold />}
+            />
+          )}
+          <Tooltip
+            label={<FieldTooltipContent nodeId={nodeId} fieldName={fieldName} kind="input" />}
+            openDelay={HANDLE_TOOLTIP_OPEN_DELAY}
+            placement="top"
+          >
+            <Flex h="full" alignItems="center">
+              <Icon fontSize="sm" color="base.300" as={PiInfoBold} />
+            </Flex>
+          </Tooltip>
+          <IconButton
+            aria-label={t('nodes.removeLinearView')}
+            tooltip={t('nodes.removeLinearView')}
+            variant="ghost"
+            size="sm"
+            onClick={handleRemoveField}
+            icon={<PiTrashSimpleBold />}
+          />
+        </Flex>
+        <InputFieldRenderer nodeId={nodeId} fieldName={fieldName} />
+        <NodeSelectionOverlay isSelected={false} isHovered={isMouseOverNode} />
       </Flex>
-      <InputFieldRenderer nodeId={nodeId} fieldName={fieldName} />
-      <NodeSelectionOverlay isSelected={false} isHovered={isMouseOverNode} />
     </Flex>
   );
 };

invokeai/frontend/web/src/features/nodes/components/flow/panels/TopPanel/TopPanel.tsx

@@ -1,25 +1,23 @@
 import { Flex, Spacer } from '@invoke-ai/ui-library';
+import { useAppSelector } from 'app/store/storeHooks';
 import AddNodeButton from 'features/nodes/components/flow/panels/TopPanel/AddNodeButton';
 import ClearFlowButton from 'features/nodes/components/flow/panels/TopPanel/ClearFlowButton';
 import SaveWorkflowButton from 'features/nodes/components/flow/panels/TopPanel/SaveWorkflowButton';
 import UpdateNodesButton from 'features/nodes/components/flow/panels/TopPanel/UpdateNodesButton';
-import WorkflowName from 'features/nodes/components/flow/panels/TopPanel/WorkflowName';
-import WorkflowLibraryButton from 'features/workflowLibrary/components/WorkflowLibraryButton';
+import { WorkflowName } from 'features/nodes/components/sidePanel/WorkflowName';
 import WorkflowLibraryMenu from 'features/workflowLibrary/components/WorkflowLibraryMenu/WorkflowLibraryMenu';
 import { memo } from 'react';
 
 const TopCenterPanel = () => {
+  const name = useAppSelector((s) => s.workflow.name);
   return (
     <Flex gap={2} top={2} left={2} right={2} position="absolute" alignItems="flex-start" pointerEvents="none">
-      <Flex flexDir="column" gap="2">
-        <Flex gap="2">
-          <AddNodeButton />
-          <WorkflowLibraryButton />
-        </Flex>
+      <Flex gap="2">
+        <AddNodeButton />
         <UpdateNodesButton />
       </Flex>
       <Spacer />
-      <WorkflowName />
+      {!!name.length && <WorkflowName />}
       <Spacer />
       <ClearFlowButton />
       <SaveWorkflowButton />

invokeai/frontend/web/src/features/nodes/components/flow/panels/TopPanel/UpdateNodesButton.tsx

@@ -25,6 +25,7 @@ const UpdateNodesButton = () => {
       icon={<PiWarningBold />}
       onClick={handleClickUpdateNodes}
       pointerEvents="auto"
+      colorScheme="warning"
     />
   );
 };

invokeai/frontend/web/src/features/nodes/components/flow/panels/TopPanel/WorkflowName.tsx

@@ -1,15 +0,0 @@
-import { Text } from '@invoke-ai/ui-library';
-import { useAppSelector } from 'app/store/storeHooks';
-import { memo } from 'react';
-
-const TopCenterPanel = () => {
-  const name = useAppSelector((s) => s.workflow.name);
-
-  return (
-    <Text m={2} fontSize="lg" userSelect="none" noOfLines={1} wordBreak="break-all" fontWeight="semibold" opacity={0.8}>
-      {name}
-    </Text>
-  );
-};
-
-export default memo(TopCenterPanel);

invokeai/frontend/web/src/features/nodes/components/flow/panels/TopRightPanel/ReloadSchemaButton.tsx

@@ -1,17 +1,16 @@
 import { Button } from '@invoke-ai/ui-library';
-import { useAppDispatch } from 'app/store/storeHooks';
 import { memo, useCallback } from 'react';
 import { useTranslation } from 'react-i18next';
 import { PiArrowsClockwiseBold } from 'react-icons/pi';
-import { receivedOpenAPISchema } from 'services/api/thunks/schema';
+import { useLazyGetOpenAPISchemaQuery } from 'services/api/endpoints/appInfo';
 
 const ReloadNodeTemplatesButton = () => {
   const { t } = useTranslation();
-  const dispatch = useAppDispatch();
+  const [_getOpenAPISchema] = useLazyGetOpenAPISchemaQuery();
 
   const handleReloadSchema = useCallback(() => {
-    dispatch(receivedOpenAPISchema());
-  }, [dispatch]);
+    _getOpenAPISchema();
+  }, [_getOpenAPISchema]);
 
   return (
     <Button

invokeai/frontend/web/src/features/nodes/components/flow/panels/TopRightPanel/TopRightPanel.tsx

@@ -1,15 +0,0 @@
-import { Flex } from '@invoke-ai/ui-library';
-import WorkflowLibraryButton from 'features/workflowLibrary/components/WorkflowLibraryButton';
-import WorkflowLibraryMenu from 'features/workflowLibrary/components/WorkflowLibraryMenu/WorkflowLibraryMenu';
-import { memo } from 'react';
-
-const TopRightPanel = () => {
-  return (
-    <Flex gap={2} position="absolute" top={2} insetInlineEnd={2}>
-      <WorkflowLibraryButton />
-      <WorkflowLibraryMenu />
-    </Flex>
-  );
-};
-
-export default memo(TopRightPanel);

invokeai/frontend/web/src/features/nodes/components/sidePanel/ModeToggle.tsx

@@ -0,0 +1,43 @@
+import { Flex, IconButton } from '@invoke-ai/ui-library';
+import { useAppDispatch, useAppSelector } from 'app/store/storeHooks';
+import { workflowModeChanged } from 'features/nodes/store/workflowSlice';
+import { useCallback } from 'react';
+import { useTranslation } from 'react-i18next';
+import { PiEyeBold, PiPencilBold } from 'react-icons/pi';
+
+export const ModeToggle = () => {
+  const dispatch = useAppDispatch();
+  const mode = useAppSelector((s) => s.workflow.mode);
+  const { t } = useTranslation();
+
+  const onClickEdit = useCallback(() => {
+    dispatch(workflowModeChanged('edit'));
+  }, [dispatch]);
+
+  const onClickView = useCallback(() => {
+    dispatch(workflowModeChanged('view'));
+  }, [dispatch]);
+
+  return (
+    <Flex justifyContent="flex-end">
+      {mode === 'view' && (
+        <IconButton
+          aria-label={t('nodes.editMode')}
+          tooltip={t('nodes.editMode')}
+          onClick={onClickEdit}
+          icon={<PiPencilBold />}
+          colorScheme="invokeBlue"
+        />
+      )}
+      {mode === 'edit' && (
+        <IconButton
+          aria-label={t('nodes.viewMode')}
+          tooltip={t('nodes.viewMode')}
+          onClick={onClickView}
+          icon={<PiEyeBold />}
+          colorScheme="invokeBlue"
+        />
+      )}
+    </Flex>
+  );
+};

invokeai/frontend/web/src/features/nodes/components/sidePanel/NodeEditorPanelGroup.tsx

@@ -1,22 +1,37 @@
 import 'reactflow/dist/style.css';
 
 import { Flex } from '@invoke-ai/ui-library';
+import { createMemoizedSelector } from 'app/store/createMemoizedSelector';
+import { useAppSelector } from 'app/store/storeHooks';
+import { selectWorkflowSlice } from 'features/nodes/store/workflowSlice';
 import QueueControls from 'features/queue/components/QueueControls';
 import ResizeHandle from 'features/ui/components/tabs/ResizeHandle';
 import { usePanelStorage } from 'features/ui/hooks/usePanelStorage';
+import WorkflowLibraryButton from 'features/workflowLibrary/components/WorkflowLibraryButton';
 import type { CSSProperties } from 'react';
 import { memo, useCallback, useRef } from 'react';
 import type { ImperativePanelGroupHandle } from 'react-resizable-panels';
 import { Panel, PanelGroup } from 'react-resizable-panels';
 
 import InspectorPanel from './inspector/InspectorPanel';
+import { WorkflowViewMode } from './viewMode/WorkflowViewMode';
 import WorkflowPanel from './workflow/WorkflowPanel';
+import { WorkflowMenu } from './WorkflowMenu';
+import { WorkflowName } from './WorkflowName';
 
 const panelGroupStyles: CSSProperties = { height: '100%', width: '100%' };
 
+const selector = createMemoizedSelector(selectWorkflowSlice, (workflow) => {
+  return {
+    mode: workflow.mode,
+  };
+});
+
 const NodeEditorPanelGroup = () => {
+  const { mode } = useAppSelector(selector);
   const panelGroupRef = useRef<ImperativePanelGroupHandle>(null);
   const panelStorage = usePanelStorage();
+
   const handleDoubleClickHandle = useCallback(() => {
     if (!panelGroupRef.current) {
       return;
@@ -27,22 +42,33 @@ const NodeEditorPanelGroup = () => {
   return (
     <Flex w="full" h="full" gap={2} flexDir="column">
       <QueueControls />
-      <PanelGroup
-        ref={panelGroupRef}
-        id="workflow-panel-group"
-        autoSaveId="workflow-panel-group"
-        direction="vertical"
-        style={panelGroupStyles}
-        storage={panelStorage}
-      >
-        <Panel id="workflow" collapsible minSize={25}>
-          <WorkflowPanel />
-        </Panel>
-        <ResizeHandle orientation="horizontal" onDoubleClick={handleDoubleClickHandle} />
-        <Panel id="inspector" collapsible minSize={25}>
-          <InspectorPanel />
-        </Panel>
-      </PanelGroup>
+      <Flex w="full" justifyContent="space-between" alignItems="center" gap="4" padding={1}>
+        <Flex justifyContent="space-between" alignItems="center" gap="4">
+          <WorkflowLibraryButton />
+          <WorkflowName />
+        </Flex>
+        <WorkflowMenu />
+      </Flex>
+
+      {mode === 'view' && <WorkflowViewMode />}
+      {mode === 'edit' && (
+        <PanelGroup
+          ref={panelGroupRef}
+          id="workflow-panel-group"
+          autoSaveId="workflow-panel-group"
+          direction="vertical"
+          style={panelGroupStyles}
+          storage={panelStorage}
+        >
+          <Panel id="workflow" collapsible minSize={25}>
+            <WorkflowPanel />
+          </Panel>
+          <ResizeHandle orientation="horizontal" onDoubleClick={handleDoubleClickHandle} />
+          <Panel id="inspector" collapsible minSize={25}>
+            <InspectorPanel />
+          </Panel>
+        </PanelGroup>
+      )}
     </Flex>
   );
 };

invokeai/frontend/web/src/features/nodes/components/sidePanel/WorkflowMenu.tsx

@@ -0,0 +1,26 @@
+import { Flex } from '@invoke-ai/ui-library';
+import { createMemoizedSelector } from 'app/store/createMemoizedSelector';
+import { useAppSelector } from 'app/store/storeHooks';
+import SaveWorkflowButton from 'features/nodes/components/flow/panels/TopPanel/SaveWorkflowButton';
+import { selectWorkflowSlice } from 'features/nodes/store/workflowSlice';
+import { NewWorkflowButton } from 'features/workflowLibrary/components/NewWorkflowButton';
+
+import { ModeToggle } from './ModeToggle';
+
+const selector = createMemoizedSelector(selectWorkflowSlice, (workflow) => {
+  return {
+    mode: workflow.mode,
+  };
+});
+
+export const WorkflowMenu = () => {
+  const { mode } = useAppSelector(selector);
+
+  return (
+    <Flex gap="2" alignItems="center">
+      {mode === 'edit' && <SaveWorkflowButton />}
+      <NewWorkflowButton />
+      <ModeToggle />
+    </Flex>
+  );
+};

invokeai/frontend/web/src/features/nodes/components/sidePanel/WorkflowName.tsx

@@ -0,0 +1,37 @@
+import { Flex, Icon, Text, Tooltip } from '@invoke-ai/ui-library';
+import { useAppSelector } from 'app/store/storeHooks';
+import { useTranslation } from 'react-i18next';
+import { PiDotOutlineFill } from 'react-icons/pi';
+
+import WorkflowInfoTooltipContent from './viewMode/WorkflowInfoTooltipContent';
+import { WorkflowWarning } from './viewMode/WorkflowWarning';
+
+export const WorkflowName = () => {
+  const { name, isTouched, mode } = useAppSelector((s) => s.workflow);
+  const { t } = useTranslation();
+
+  return (
+    <Flex gap="1" alignItems="center">
+      {name.length ? (
+        <Tooltip label={<WorkflowInfoTooltipContent />} placement="top">
+          <Text fontSize="lg" userSelect="none" noOfLines={1} wordBreak="break-all" fontWeight="semibold">
+            {name}
+          </Text>
+        </Tooltip>
+      ) : (
+        <Text fontSize="lg" fontStyle="italic" fontWeight="semibold">
+          {t('workflows.unnamedWorkflow')}
+        </Text>
+      )}
+
+      {isTouched && mode === 'edit' && (
+        <Tooltip label="Workflow has unsaved changes">
+          <Flex>
+            <Icon as={PiDotOutlineFill} boxSize="20px" sx={{ color: 'invokeYellow.500' }} />
+          </Flex>
+        </Tooltip>
+      )}
+      <WorkflowWarning />
+    </Flex>
+  );
+};

invokeai/frontend/web/src/features/nodes/components/sidePanel/viewMode/WorkflowField.tsx

@@ -0,0 +1,53 @@
+import { Flex, FormLabel, Icon, IconButton, Spacer, Tooltip } from '@invoke-ai/ui-library';
+import FieldTooltipContent from 'features/nodes/components/flow/nodes/Invocation/fields/FieldTooltipContent';
+import InputFieldRenderer from 'features/nodes/components/flow/nodes/Invocation/fields/InputFieldRenderer';
+import { useFieldLabel } from 'features/nodes/hooks/useFieldLabel';
+import { useFieldOriginalValue } from 'features/nodes/hooks/useFieldOriginalValue';
+import { useFieldTemplateTitle } from 'features/nodes/hooks/useFieldTemplateTitle';
+import { HANDLE_TOOLTIP_OPEN_DELAY } from 'features/nodes/types/constants';
+import { t } from 'i18next';
+import { memo } from 'react';
+import { PiArrowCounterClockwiseBold, PiInfoBold } from 'react-icons/pi';
+
+type Props = {
+  nodeId: string;
+  fieldName: string;
+};
+
+const WorkflowField = ({ nodeId, fieldName }: Props) => {
+  const label = useFieldLabel(nodeId, fieldName);
+  const fieldTemplateTitle = useFieldTemplateTitle(nodeId, fieldName, 'input');
+  const { isValueChanged, onReset } = useFieldOriginalValue(nodeId, fieldName);
+
+  return (
+    <Flex layerStyle="second" position="relative" borderRadius="base" w="full" p={4} gap="2" flexDir="column">
+      <Flex alignItems="center">
+        <FormLabel fontSize="sm">{label || fieldTemplateTitle}</FormLabel>
+
+        <Spacer />
+        {isValueChanged && (
+          <IconButton
+            aria-label={t('nodes.resetToDefaultValue')}
+            tooltip={t('nodes.resetToDefaultValue')}
+            variant="ghost"
+            size="sm"
+            onClick={onReset}
+            icon={<PiArrowCounterClockwiseBold />}
+          />
+        )}
+        <Tooltip
+          label={<FieldTooltipContent nodeId={nodeId} fieldName={fieldName} kind="input" />}
+          openDelay={HANDLE_TOOLTIP_OPEN_DELAY}
+          placement="top"
+        >
+          <Flex h="24px" alignItems="center">
+            <Icon fontSize="md" color="base.300" as={PiInfoBold} />
+          </Flex>
+        </Tooltip>
+      </Flex>
+      <InputFieldRenderer nodeId={nodeId} fieldName={fieldName} />
+    </Flex>
+  );
+};
+
+export default memo(WorkflowField);

invokeai/frontend/web/src/features/nodes/components/sidePanel/viewMode/WorkflowInfoTooltipContent.tsx

@@ -0,0 +1,68 @@
+import { Box, Flex, Text } from '@invoke-ai/ui-library';
+import { createMemoizedSelector } from 'app/store/createMemoizedSelector';
+import { useAppSelector } from 'app/store/storeHooks';
+import { selectWorkflowSlice } from 'features/nodes/store/workflowSlice';
+import { memo } from 'react';
+import { useTranslation } from 'react-i18next';
+
+const selector = createMemoizedSelector(selectWorkflowSlice, (workflow) => {
+  return {
+    name: workflow.name,
+    description: workflow.description,
+    notes: workflow.notes,
+    author: workflow.author,
+    tags: workflow.tags,
+  };
+});
+
+const WorkflowInfoTooltipContent = () => {
+  const { name, description, notes, author, tags } = useAppSelector(selector);
+  const { t } = useTranslation();
+
+  return (
+    <Flex flexDir="column" gap="2">
+      {!!name.length && (
+        <Box>
+          <Text fontWeight="semibold">{t('nodes.workflowName')}</Text>
+          <Text opacity={0.7} fontStyle="oblique 5deg">
+            {name}
+          </Text>
+        </Box>
+      )}
+      {!!author.length && (
+        <Box>
+          <Text fontWeight="semibold">{t('nodes.workflowAuthor')}</Text>
+          <Text opacity={0.7} fontStyle="oblique 5deg">
+            {author}
+          </Text>
+        </Box>
+      )}
+      {!!tags.length && (
+        <Box>
+          <Text fontWeight="semibold">{t('nodes.workflowTags')}</Text>
+          <Text opacity={0.7} fontStyle="oblique 5deg">
+            {tags}
+          </Text>
+        </Box>
+      )}
+      {!!description.length && (
+        <Box>
+          <Text fontWeight="semibold">{t('nodes.workflowDescription')}</Text>
+          <Text opacity={0.7} fontStyle="oblique 5deg">
+            {description}
+          </Text>
+        </Box>
+      )}
+      {!!notes.length && (
+        <Box>
+          <Text fontWeight="semibold">{t('nodes.workflowNotes')}</Text>
+          <Text opacity={0.7} fontStyle="oblique 5deg">
+            {notes}
+          </Text>
+        </Box>
+      )}
+    </Flex>
+  );
+};
+
+export default memo(WorkflowInfoTooltipContent);

invokeai/frontend/web/src/features/nodes/components/sidePanel/viewMode/WorkflowViewMode.tsx

@@ -0,0 +1,39 @@
+import { Box, Flex } from '@invoke-ai/ui-library';
+import { createMemoizedSelector } from 'app/store/createMemoizedSelector';
+import { useAppSelector } from 'app/store/storeHooks';
+import { IAINoContentFallback } from 'common/components/IAIImageFallback';
+import ScrollableContent from 'common/components/OverlayScrollbars/ScrollableContent';
+import { selectWorkflowSlice } from 'features/nodes/store/workflowSlice';
+import { t } from 'i18next';
+import { useGetOpenAPISchemaQuery } from 'services/api/endpoints/appInfo';
+
+import WorkflowField from './WorkflowField';
+
+const selector = createMemoizedSelector(selectWorkflowSlice, (workflow) => {
+  return {
+    fields: workflow.exposedFields,
+    name: workflow.name,
+  };
+});
+
+export const WorkflowViewMode = () => {
+  const { isLoading } = useGetOpenAPISchemaQuery();
+  const { fields } = useAppSelector(selector);
+  return (
+    <Box position="relative" w="full" h="full">
+      <ScrollableContent>
+        <Flex position="relative" flexDir="column" alignItems="flex-start" p={1} gap={2} h="full" w="full">
+          {isLoading ? (
+            <IAINoContentFallback label={t('nodes.loadingNodes')} icon={null} />
+          ) : fields.length ? (
+            fields.map(({ nodeId, fieldName }) => (
+              <WorkflowField key={`${nodeId}.${fieldName}`} nodeId={nodeId} fieldName={fieldName} />
+            ))
+          ) : (
+            <IAINoContentFallback label={t('nodes.noFieldsLinearview')} icon={null} />
+          )}
+        </Flex>
+      </ScrollableContent>
+    </Box>
+  );
+};