Merge branch 'main' into feat/taesd

2026-02-08 19:25:19 -05:00 · 2023-09-11 22:11:25 +10:00
parent 7df67d077a 183f66c70c
commit 88db094cf2
146 changed files with 4092 additions and 8080 deletions
--- a/invokeai/app/api/routers/app_info.py
+++ b/invokeai/app/api/routers/app_info.py
@@ -1,19 +1,19 @@
 import typing
 from enum import Enum
+from pathlib import Path
+
 from fastapi import Body
 from fastapi.routing import APIRouter
-from pathlib import Path
 from pydantic import BaseModel, Field

+from invokeai.app.invocations.upscale import ESRGAN_MODELS
+from invokeai.backend.image_util.invisible_watermark import InvisibleWatermark
 from invokeai.backend.image_util.patchmatch import PatchMatch
 from invokeai.backend.image_util.safety_checker import SafetyChecker
-from invokeai.backend.image_util.invisible_watermark import InvisibleWatermark
-from invokeai.app.invocations.upscale import ESRGAN_MODELS
-
+from invokeai.backend.util.logging import logging
 from invokeai.version import __version__

 from ..dependencies import ApiDependencies
-from invokeai.backend.util.logging import logging


 class LogLevel(int, Enum):
@@ -55,7 +55,7 @@ async def get_version() -> AppVersion:

@app_router.get("/config", operation_id="get_config", status_code=200, response_model=AppConfig)
 async def get_config() -> AppConfig:
-    infill_methods = ["tile", "lama"]
+    infill_methods = ["tile", "lama", "cv2"]
    if PatchMatch.patchmatch_available():
        infill_methods.append("patchmatch")

--- a/invokeai/app/api_app.py
+++ b/invokeai/app/api_app.py
@@ -1,45 +1,47 @@
 # Copyright (c) 2022-2023 Kyle Schouviller (https://github.com/kyle0654) and the InvokeAI Team
-import asyncio
-import logging
-import socket
-from inspect import signature
-from pathlib import Path
-
-import uvicorn
-from fastapi import FastAPI
-from fastapi.middleware.cors import CORSMiddleware
-from fastapi.openapi.docs import get_redoc_html, get_swagger_ui_html
-from fastapi.openapi.utils import get_openapi
-from fastapi.staticfiles import StaticFiles
-from fastapi_events.handlers.local import local_handler
-from fastapi_events.middleware import EventHandlerASGIMiddleware
-from pydantic.schema import schema
-
 from .services.config import InvokeAIAppConfig
-from ..backend.util.logging import InvokeAILogger
-
-from invokeai.version.invokeai_version import __version__
-
-import invokeai.frontend.web as web_dir
-import mimetypes
-
-from .api.dependencies import ApiDependencies
-from .api.routers import sessions, models, images, boards, board_images, app_info
-from .api.sockets import SocketIO
-from .invocations.baseinvocation import BaseInvocation, _InputField, _OutputField, UIConfigBase
-
-import torch
-
-# noinspection PyUnresolvedReferences
-import invokeai.backend.util.hotfixes  # noqa: F401 (monkeypatching on import)
-
-if torch.backends.mps.is_available():
-    # noinspection PyUnresolvedReferences
-    import invokeai.backend.util.mps_fixes  # noqa: F401 (monkeypatching on import)
-

+# parse_args() must be called before any other imports. if it is not called first, consumers of the config
+# which are imported/used before parse_args() is called will get the default config values instead of the
+# values from the command line or config file.
 app_config = InvokeAIAppConfig.get_config()
 app_config.parse_args()
+
+if True:  # hack to make flake8 happy with imports coming after setting up the config
+    import asyncio
+    import logging
+    import mimetypes
+    import socket
+    from inspect import signature
+    from pathlib import Path
+
+    import torch
+    import uvicorn
+    from fastapi import FastAPI
+    from fastapi.middleware.cors import CORSMiddleware
+    from fastapi.openapi.docs import get_redoc_html, get_swagger_ui_html
+    from fastapi.openapi.utils import get_openapi
+    from fastapi.staticfiles import StaticFiles
+    from fastapi_events.handlers.local import local_handler
+    from fastapi_events.middleware import EventHandlerASGIMiddleware
+    from pydantic.schema import schema
+
+    # noinspection PyUnresolvedReferences
+    import invokeai.backend.util.hotfixes  # noqa: F401 (monkeypatching on import)
+    import invokeai.frontend.web as web_dir
+    from invokeai.version.invokeai_version import __version__
+
+    from ..backend.util.logging import InvokeAILogger
+    from .api.dependencies import ApiDependencies
+    from .api.routers import app_info, board_images, boards, images, models, sessions
+    from .api.sockets import SocketIO
+    from .invocations.baseinvocation import BaseInvocation, UIConfigBase, _InputField, _OutputField
+
+    if torch.backends.mps.is_available():
+        # noinspection PyUnresolvedReferences
+        import invokeai.backend.util.mps_fixes  # noqa: F401 (monkeypatching on import)
+
+
 logger = InvokeAILogger.getLogger(config=app_config)

 # fix for windows mimetypes registry entries being borked
--- a/invokeai/app/cli_app.py
+++ b/invokeai/app/cli_app.py
@@ -1,67 +1,64 @@
 # Copyright (c) 2022 Kyle Schouviller (https://github.com/kyle0654)

-import argparse
-import re
-import shlex
-import sys
-import time
-from typing import Union, get_type_hints, Optional
-
-from pydantic import BaseModel, ValidationError
-from pydantic.fields import Field
-
-# This should come early so that the logger can pick up its configuration options
 from .services.config import InvokeAIAppConfig
-from invokeai.backend.util.logging import InvokeAILogger
-from invokeai.version.invokeai_version import __version__
-
-
-from invokeai.app.services.board_image_record_storage import (
-    SqliteBoardImageRecordStorage,
-)
-from invokeai.app.services.board_images import (
-    BoardImagesService,
-    BoardImagesServiceDependencies,
-)
-from invokeai.app.services.board_record_storage import SqliteBoardRecordStorage
-from invokeai.app.services.boards import BoardService, BoardServiceDependencies
-from invokeai.app.services.image_record_storage import SqliteImageRecordStorage
-from invokeai.app.services.images import ImageService, ImageServiceDependencies
-from invokeai.app.services.resource_name import SimpleNameService
-from invokeai.app.services.urls import LocalUrlService
-from invokeai.app.services.invocation_stats import InvocationStatsService
-from .services.default_graphs import default_text_to_image_graph_id, create_system_graphs
-from .services.latent_storage import DiskLatentsStorage, ForwardCacheLatentsStorage
-
-from .cli.commands import BaseCommand, CliContext, ExitCli, SortedHelpFormatter, add_graph_parsers, add_parsers
-from .cli.completer import set_autocompleter
-from .invocations.baseinvocation import BaseInvocation
-from .services.events import EventServiceBase
-from .services.graph import (
-    Edge,
-    EdgeConnection,
-    GraphExecutionState,
-    GraphInvocation,
-    LibraryGraph,
-    are_connection_types_compatible,
-)
-from .services.image_file_storage import DiskImageFileStorage
-from .services.invocation_queue import MemoryInvocationQueue
-from .services.invocation_services import InvocationServices
-from .services.invoker import Invoker
-from .services.model_manager_service import ModelManagerService
-from .services.processor import DefaultInvocationProcessor
-from .services.sqlite import SqliteItemStorage
-
-import torch
-import invokeai.backend.util.hotfixes  # noqa: F401 (monkeypatching on import)
-
-if torch.backends.mps.is_available():
-    import invokeai.backend.util.mps_fixes  # noqa: F401 (monkeypatching on import)
-

+# parse_args() must be called before any other imports. if it is not called first, consumers of the config
+# which are imported/used before parse_args() is called will get the default config values instead of the
+# values from the command line or config file.
 config = InvokeAIAppConfig.get_config()
 config.parse_args()
+
+if True:  # hack to make flake8 happy with imports coming after setting up the config
+    import argparse
+    import re
+    import shlex
+    import sys
+    import time
+    from typing import Optional, Union, get_type_hints
+
+    import torch
+    from pydantic import BaseModel, ValidationError
+    from pydantic.fields import Field
+
+    import invokeai.backend.util.hotfixes  # noqa: F401 (monkeypatching on import)
+    from invokeai.app.services.board_image_record_storage import SqliteBoardImageRecordStorage
+    from invokeai.app.services.board_images import BoardImagesService, BoardImagesServiceDependencies
+    from invokeai.app.services.board_record_storage import SqliteBoardRecordStorage
+    from invokeai.app.services.boards import BoardService, BoardServiceDependencies
+    from invokeai.app.services.image_record_storage import SqliteImageRecordStorage
+    from invokeai.app.services.images import ImageService, ImageServiceDependencies
+    from invokeai.app.services.invocation_stats import InvocationStatsService
+    from invokeai.app.services.resource_name import SimpleNameService
+    from invokeai.app.services.urls import LocalUrlService
+    from invokeai.backend.util.logging import InvokeAILogger
+    from invokeai.version.invokeai_version import __version__
+
+    from .cli.commands import BaseCommand, CliContext, ExitCli, SortedHelpFormatter, add_graph_parsers, add_parsers
+    from .cli.completer import set_autocompleter
+    from .invocations.baseinvocation import BaseInvocation
+    from .services.default_graphs import create_system_graphs, default_text_to_image_graph_id
+    from .services.events import EventServiceBase
+    from .services.graph import (
+        Edge,
+        EdgeConnection,
+        GraphExecutionState,
+        GraphInvocation,
+        LibraryGraph,
+        are_connection_types_compatible,
+    )
+    from .services.image_file_storage import DiskImageFileStorage
+    from .services.invocation_queue import MemoryInvocationQueue
+    from .services.invocation_services import InvocationServices
+    from .services.invoker import Invoker
+    from .services.latent_storage import DiskLatentsStorage, ForwardCacheLatentsStorage
+    from .services.model_manager_service import ModelManagerService
+    from .services.processor import DefaultInvocationProcessor
+    from .services.sqlite import SqliteItemStorage
+
+    if torch.backends.mps.is_available():
+        import invokeai.backend.util.mps_fixes  # noqa: F401 (monkeypatching on import)
+
+
 logger = InvokeAILogger().getLogger(config=config)


--- a/invokeai/app/invocations/baseinvocation.py
+++ b/invokeai/app/invocations/baseinvocation.py
@@ -26,11 +26,18 @@ from typing import (
 from pydantic import BaseModel, Field, validator
 from pydantic.fields import Undefined, ModelField
 from pydantic.typing import NoArgAnyCallable
+import semver
+
+from invokeai.app.services.config.invokeai_config import InvokeAIAppConfig

 if TYPE_CHECKING:
    from ..services.invocation_services import InvocationServices


+class InvalidVersionError(ValueError):
+    pass
+
+
 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"
@@ -105,24 +112,39 @@ class UIType(str, Enum):
    """

    # region Primitives
-    Integer = "integer"
-    Float = "float"
    Boolean = "boolean"
-    String = "string"
-    Array = "array"
-    Image = "ImageField"
-    Latents = "LatentsField"
+    Color = "ColorField"
    Conditioning = "ConditioningField"
    Control = "ControlField"
-    Color = "ColorField"
-    ImageCollection = "ImageCollection"
-    ConditioningCollection = "ConditioningCollection"
-    ColorCollection = "ColorCollection"
-    LatentsCollection = "LatentsCollection"
-    IntegerCollection = "IntegerCollection"
-    FloatCollection = "FloatCollection"
-    StringCollection = "StringCollection"
+    Float = "float"
+    Image = "ImageField"
+    Integer = "integer"
+    Latents = "LatentsField"
+    String = "string"
+    # endregion
+
+    # region Collection Primitives
    BooleanCollection = "BooleanCollection"
+    ColorCollection = "ColorCollection"
+    ConditioningCollection = "ConditioningCollection"
+    ControlCollection = "ControlCollection"
+    FloatCollection = "FloatCollection"
+    ImageCollection = "ImageCollection"
+    IntegerCollection = "IntegerCollection"
+    LatentsCollection = "LatentsCollection"
+    StringCollection = "StringCollection"
+    # endregion
+
+    # region Polymorphic Primitives
+    BooleanPolymorphic = "BooleanPolymorphic"
+    ColorPolymorphic = "ColorPolymorphic"
+    ConditioningPolymorphic = "ConditioningPolymorphic"
+    ControlPolymorphic = "ControlPolymorphic"
+    FloatPolymorphic = "FloatPolymorphic"
+    ImagePolymorphic = "ImagePolymorphic"
+    IntegerPolymorphic = "IntegerPolymorphic"
+    LatentsPolymorphic = "LatentsPolymorphic"
+    StringPolymorphic = "StringPolymorphic"
    # endregion

    # region Models
@@ -176,6 +198,7 @@ class _InputField(BaseModel):
    ui_type: Optional[UIType]
    ui_component: Optional[UIComponent]
    ui_order: Optional[int]
+    item_default: Optional[Any]


 class _OutputField(BaseModel):
@@ -223,6 +246,7 @@ def InputField(
    ui_component: Optional[UIComponent] = None,
    ui_hidden: bool = False,
    ui_order: Optional[int] = None,
+    item_default: Optional[Any] = None,
    **kwargs: Any,
 ) -> Any:
    """
@@ -249,6 +273,11 @@ def InputField(
      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 bool item_default: [None] Specifies the default item value, if this is a collection input. \
+      Ignored for non-collection fields..
    """
    return Field(
        *args,
@@ -282,6 +311,7 @@ def InputField(
        ui_component=ui_component,
        ui_hidden=ui_hidden,
        ui_order=ui_order,
+        item_default=item_default,
        **kwargs,
    )

@@ -332,6 +362,8 @@ def OutputField(
      `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(
        *args,
@@ -376,6 +408,9 @@ class UIConfigBase(BaseModel):
    tags: Optional[list[str]] = Field(default_factory=None, description="The node's tags")
    title: Optional[str] = Field(default=None, description="The node's display name")
    category: Optional[str] = Field(default=None, description="The node's category")
+    version: Optional[str] = Field(
+        default=None, description='The node\'s version. Should be a valid semver string e.g. "1.0.0" or "3.8.13".'
+    )


 class InvocationContext:
@@ -437,6 +472,7 @@ class BaseInvocation(ABC, BaseModel):

    @classmethod
    def get_all_subclasses(cls):
+        app_config = InvokeAIAppConfig.get_config()
        subclasses = []
        toprocess = [cls]
        while len(toprocess) > 0:
@@ -444,7 +480,23 @@ class BaseInvocation(ABC, BaseModel):
            next_subclasses = next.__subclasses__()
            subclasses.extend(next_subclasses)
            toprocess.extend(next_subclasses)
-        return subclasses
+        allowed_invocations = []
+        for sc in subclasses:
+            is_in_allowlist = (
+                sc.__fields__.get("type").default in app_config.allow_nodes
+                if isinstance(app_config.allow_nodes, list)
+                else True
+            )
+
+            is_in_denylist = (
+                sc.__fields__.get("type").default in app_config.deny_nodes
+                if isinstance(app_config.deny_nodes, list)
+                else False
+            )
+
+            if is_in_allowlist and not is_in_denylist:
+                allowed_invocations.append(sc)
+        return allowed_invocations

    @classmethod
    def get_invocations(cls):
@@ -474,6 +526,8 @@ class BaseInvocation(ABC, BaseModel):
                schema["tags"] = uiconfig.tags
            if uiconfig and hasattr(uiconfig, "category"):
                schema["category"] = uiconfig.category
+            if uiconfig and hasattr(uiconfig, "version"):
+                schema["version"] = uiconfig.version
            if "required" not in schema or not isinstance(schema["required"], list):
                schema["required"] = list()
            schema["required"].extend(["type", "id"])
@@ -542,7 +596,11 @@ GenericBaseInvocation = TypeVar("GenericBaseInvocation", bound=BaseInvocation)


 def invocation(
-    invocation_type: str, title: Optional[str] = None, tags: Optional[list[str]] = None, category: Optional[str] = None
+    invocation_type: str,
+    title: Optional[str] = None,
+    tags: Optional[list[str]] = None,
+    category: Optional[str] = None,
+    version: Optional[str] = None,
 ) -> Callable[[Type[GenericBaseInvocation]], Type[GenericBaseInvocation]]:
    """
    Adds metadata to an invocation.
@@ -569,6 +627,12 @@ def invocation(
            cls.UIConfig.tags = tags
        if category is not None:
            cls.UIConfig.category = category
+        if version is not None:
+            try:
+                semver.Version.parse(version)
+            except ValueError as e:
+                raise InvalidVersionError(f'Invalid version string for node "{invocation_type}": "{version}"') from e
+            cls.UIConfig.version = version

        # Add the invocation type to the pydantic model of the invocation
        invocation_type_annotation = Literal[invocation_type]  # type: ignore
@@ -580,8 +644,9 @@ def invocation(
            config=cls.__config__,
        )
        cls.__fields__.update({"type": invocation_type_field})
-        cls.__annotations__.update({"type": invocation_type_annotation})
-
+        # to support 3.9, 3.10 and 3.11, as described in https://docs.python.org/3/howto/annotations.html
+        if annotations := cls.__dict__.get("__annotations__", None):
+            annotations.update({"type": invocation_type_annotation})
        return cls

    return wrapper
@@ -615,7 +680,10 @@ def invocation_output(
            config=cls.__config__,
        )
        cls.__fields__.update({"type": output_type_field})
-        cls.__annotations__.update({"type": output_type_annotation})
+
+        # to support 3.9, 3.10 and 3.11, as described in https://docs.python.org/3/howto/annotations.html
+        if annotations := cls.__dict__.get("__annotations__", None):
+            annotations.update({"type": output_type_annotation})

        return cls

--- a/invokeai/app/invocations/collections.py
+++ b/invokeai/app/invocations/collections.py
@@ -10,7 +10,9 @@ from invokeai.app.util.misc import SEED_MAX, get_random_seed
 from .baseinvocation import BaseInvocation, InputField, InvocationContext, invocation


-@invocation("range", title="Integer Range", tags=["collection", "integer", "range"], category="collections")
+@invocation(
+    "range", title="Integer Range", tags=["collection", "integer", "range"], category="collections", version="1.0.0"
+)
 class RangeInvocation(BaseInvocation):
    """Creates a range of numbers from start to stop with step"""

@@ -33,6 +35,7 @@ class RangeInvocation(BaseInvocation):
    title="Integer Range of Size",
    tags=["collection", "integer", "size", "range"],
    category="collections",
+    version="1.0.0",
 )
 class RangeOfSizeInvocation(BaseInvocation):
    """Creates a range from start to start + size with step"""
@@ -50,6 +53,7 @@ class RangeOfSizeInvocation(BaseInvocation):
    title="Random Range",
    tags=["range", "integer", "random", "collection"],
    category="collections",
+    version="1.0.0",
 )
 class RandomRangeInvocation(BaseInvocation):
    """Creates a collection of random numbers"""
--- a/invokeai/app/invocations/compel.py
+++ b/invokeai/app/invocations/compel.py
@@ -44,7 +44,7 @@ class ConditioningFieldData:
 #    PerpNeg = "perp_neg"


-@invocation("compel", title="Prompt", tags=["prompt", "compel"], category="conditioning")
+@invocation("compel", title="Prompt", tags=["prompt", "compel"], category="conditioning", version="1.0.0")
 class CompelInvocation(BaseInvocation):
    """Parse prompt using compel package to conditioning."""

@@ -267,6 +267,7 @@ class SDXLPromptInvocationBase:
    title="SDXL Prompt",
    tags=["sdxl", "compel", "prompt"],
    category="conditioning",
+    version="1.0.0",
 )
 class SDXLCompelPromptInvocation(BaseInvocation, SDXLPromptInvocationBase):
    """Parse prompt using compel package to conditioning."""
@@ -351,6 +352,7 @@ class SDXLCompelPromptInvocation(BaseInvocation, SDXLPromptInvocationBase):
    title="SDXL Refiner Prompt",
    tags=["sdxl", "compel", "prompt"],
    category="conditioning",
+    version="1.0.0",
 )
 class SDXLRefinerCompelPromptInvocation(BaseInvocation, SDXLPromptInvocationBase):
    """Parse prompt using compel package to conditioning."""
@@ -403,7 +405,7 @@ class ClipSkipInvocationOutput(BaseInvocationOutput):
    clip: ClipField = OutputField(default=None, description=FieldDescriptions.clip, title="CLIP")


-@invocation("clip_skip", title="CLIP Skip", tags=["clipskip", "clip", "skip"], category="conditioning")
+@invocation("clip_skip", title="CLIP Skip", tags=["clipskip", "clip", "skip"], category="conditioning", version="1.0.0")
 class ClipSkipInvocation(BaseInvocation):
    """Skip layers in clip text_encoder model."""

--- a/invokeai/app/invocations/controlnet_image_processors.py
+++ b/invokeai/app/invocations/controlnet_image_processors.py
@@ -95,14 +95,12 @@ class ControlOutput(BaseInvocationOutput):
    control: ControlField = OutputField(description=FieldDescriptions.control)


-@invocation("controlnet", title="ControlNet", tags=["controlnet"], category="controlnet")
+@invocation("controlnet", title="ControlNet", tags=["controlnet"], category="controlnet", version="1.0.0")
 class ControlNetInvocation(BaseInvocation):
    """Collects ControlNet info to pass to other nodes"""

    image: ImageField = InputField(description="The control image")
-    control_model: ControlNetModelField = InputField(
-        default="lllyasviel/sd-controlnet-canny", description=FieldDescriptions.controlnet_model, input=Input.Direct
-    )
+    control_model: ControlNetModelField = InputField(description=FieldDescriptions.controlnet_model, input=Input.Direct)
    control_weight: Union[float, List[float]] = InputField(
        default=1.0, description="The weight given to the ControlNet", ui_type=UIType.Float
    )
@@ -129,7 +127,9 @@ class ControlNetInvocation(BaseInvocation):
        )


-@invocation("image_processor", title="Base Image Processor", tags=["controlnet"], category="controlnet")
+@invocation(
+    "image_processor", title="Base Image Processor", tags=["controlnet"], category="controlnet", version="1.0.0"
+)
 class ImageProcessorInvocation(BaseInvocation):
    """Base class for invocations that preprocess images for ControlNet"""

@@ -173,6 +173,7 @@ class ImageProcessorInvocation(BaseInvocation):
    title="Canny Processor",
    tags=["controlnet", "canny"],
    category="controlnet",
+    version="1.0.0",
 )
 class CannyImageProcessorInvocation(ImageProcessorInvocation):
    """Canny edge detection for ControlNet"""
@@ -195,6 +196,7 @@ class CannyImageProcessorInvocation(ImageProcessorInvocation):
    title="HED (softedge) Processor",
    tags=["controlnet", "hed", "softedge"],
    category="controlnet",
+    version="1.0.0",
 )
 class HedImageProcessorInvocation(ImageProcessorInvocation):
    """Applies HED edge detection to image"""
@@ -223,6 +225,7 @@ class HedImageProcessorInvocation(ImageProcessorInvocation):
    title="Lineart Processor",
    tags=["controlnet", "lineart"],
    category="controlnet",
+    version="1.0.0",
 )
 class LineartImageProcessorInvocation(ImageProcessorInvocation):
    """Applies line art processing to image"""
@@ -244,6 +247,7 @@ class LineartImageProcessorInvocation(ImageProcessorInvocation):
    title="Lineart Anime Processor",
    tags=["controlnet", "lineart", "anime"],
    category="controlnet",
+    version="1.0.0",
 )
 class LineartAnimeImageProcessorInvocation(ImageProcessorInvocation):
    """Applies line art anime processing to image"""
@@ -266,6 +270,7 @@ class LineartAnimeImageProcessorInvocation(ImageProcessorInvocation):
    title="Openpose Processor",
    tags=["controlnet", "openpose", "pose"],
    category="controlnet",
+    version="1.0.0",
 )
 class OpenposeImageProcessorInvocation(ImageProcessorInvocation):
    """Applies Openpose processing to image"""
@@ -290,6 +295,7 @@ class OpenposeImageProcessorInvocation(ImageProcessorInvocation):
    title="Midas Depth Processor",
    tags=["controlnet", "midas"],
    category="controlnet",
+    version="1.0.0",
 )
 class MidasDepthImageProcessorInvocation(ImageProcessorInvocation):
    """Applies Midas depth processing to image"""
@@ -316,6 +322,7 @@ class MidasDepthImageProcessorInvocation(ImageProcessorInvocation):
    title="Normal BAE Processor",
    tags=["controlnet"],
    category="controlnet",
+    version="1.0.0",
 )
 class NormalbaeImageProcessorInvocation(ImageProcessorInvocation):
    """Applies NormalBae processing to image"""
@@ -331,7 +338,9 @@ class NormalbaeImageProcessorInvocation(ImageProcessorInvocation):
        return processed_image


-@invocation("mlsd_image_processor", title="MLSD Processor", tags=["controlnet", "mlsd"], category="controlnet")
+@invocation(
+    "mlsd_image_processor", title="MLSD Processor", tags=["controlnet", "mlsd"], category="controlnet", version="1.0.0"
+)
 class MlsdImageProcessorInvocation(ImageProcessorInvocation):
    """Applies MLSD processing to image"""

@@ -352,7 +361,9 @@ class MlsdImageProcessorInvocation(ImageProcessorInvocation):
        return processed_image


-@invocation("pidi_image_processor", title="PIDI Processor", tags=["controlnet", "pidi"], category="controlnet")
+@invocation(
+    "pidi_image_processor", title="PIDI Processor", tags=["controlnet", "pidi"], category="controlnet", version="1.0.0"
+)
 class PidiImageProcessorInvocation(ImageProcessorInvocation):
    """Applies PIDI processing to image"""

@@ -378,6 +389,7 @@ class PidiImageProcessorInvocation(ImageProcessorInvocation):
    title="Content Shuffle Processor",
    tags=["controlnet", "contentshuffle"],
    category="controlnet",
+    version="1.0.0",
 )
 class ContentShuffleImageProcessorInvocation(ImageProcessorInvocation):
    """Applies content shuffle processing to image"""
@@ -407,6 +419,7 @@ class ContentShuffleImageProcessorInvocation(ImageProcessorInvocation):
    title="Zoe (Depth) Processor",
    tags=["controlnet", "zoe", "depth"],
    category="controlnet",
+    version="1.0.0",
 )
 class ZoeDepthImageProcessorInvocation(ImageProcessorInvocation):
    """Applies Zoe depth processing to image"""
@@ -422,6 +435,7 @@ class ZoeDepthImageProcessorInvocation(ImageProcessorInvocation):
    title="Mediapipe Face Processor",
    tags=["controlnet", "mediapipe", "face"],
    category="controlnet",
+    version="1.0.0",
 )
 class MediapipeFaceProcessorInvocation(ImageProcessorInvocation):
    """Applies mediapipe face processing to image"""
@@ -444,6 +458,7 @@ class MediapipeFaceProcessorInvocation(ImageProcessorInvocation):
    title="Leres (Depth) Processor",
    tags=["controlnet", "leres", "depth"],
    category="controlnet",
+    version="1.0.0",
 )
 class LeresImageProcessorInvocation(ImageProcessorInvocation):
    """Applies leres processing to image"""
@@ -472,6 +487,7 @@ class LeresImageProcessorInvocation(ImageProcessorInvocation):
    title="Tile Resample Processor",
    tags=["controlnet", "tile"],
    category="controlnet",
+    version="1.0.0",
 )
 class TileResamplerProcessorInvocation(ImageProcessorInvocation):
    """Tile resampler processor"""
@@ -511,6 +527,7 @@ class TileResamplerProcessorInvocation(ImageProcessorInvocation):
    title="Segment Anything Processor",
    tags=["controlnet", "segmentanything"],
    category="controlnet",
+    version="1.0.0",
 )
 class SegmentAnythingProcessorInvocation(ImageProcessorInvocation):
    """Applies segment anything processing to image"""
--- a/invokeai/app/invocations/cv.py
+++ b/invokeai/app/invocations/cv.py
@@ -10,12 +10,7 @@ from invokeai.app.models.image import ImageCategory, ResourceOrigin
 from .baseinvocation import BaseInvocation, InputField, InvocationContext, invocation


-@invocation(
-    "cv_inpaint",
-    title="OpenCV Inpaint",
-    tags=["opencv", "inpaint"],
-    category="inpaint",
-)
+@invocation("cv_inpaint", title="OpenCV Inpaint", tags=["opencv", "inpaint"], category="inpaint", version="1.0.0")
 class CvInpaintInvocation(BaseInvocation):
    """Simple inpaint using opencv."""

--- a/invokeai/app/invocations/image.py
+++ b/invokeai/app/invocations/image.py
@@ -16,7 +16,7 @@ from ..models.image import ImageCategory, ResourceOrigin
 from .baseinvocation import BaseInvocation, FieldDescriptions, InputField, InvocationContext, invocation


-@invocation("show_image", title="Show Image", tags=["image"], category="image")
+@invocation("show_image", title="Show Image", tags=["image"], category="image", version="1.0.0")
 class ShowImageInvocation(BaseInvocation):
    """Displays a provided image using the OS image viewer, and passes it forward in the pipeline."""

@@ -36,7 +36,7 @@ class ShowImageInvocation(BaseInvocation):
        )


-@invocation("blank_image", title="Blank Image", tags=["image"], category="image")
+@invocation("blank_image", title="Blank Image", tags=["image"], category="image", version="1.0.0")
 class BlankImageInvocation(BaseInvocation):
    """Creates a blank image and forwards it to the pipeline"""

@@ -65,7 +65,7 @@ class BlankImageInvocation(BaseInvocation):
        )


-@invocation("img_crop", title="Crop Image", tags=["image", "crop"], category="image")
+@invocation("img_crop", title="Crop Image", tags=["image", "crop"], category="image", version="1.0.0")
 class ImageCropInvocation(BaseInvocation):
    """Crops an image to a specified box. The box can be outside of the image."""

@@ -98,7 +98,7 @@ class ImageCropInvocation(BaseInvocation):
        )


-@invocation("img_paste", title="Paste Image", tags=["image", "paste"], category="image")
+@invocation("img_paste", title="Paste Image", tags=["image", "paste"], category="image", version="1.0.0")
 class ImagePasteInvocation(BaseInvocation):
    """Pastes an image into another image."""

@@ -146,7 +146,7 @@ class ImagePasteInvocation(BaseInvocation):
        )


-@invocation("tomask", title="Mask from Alpha", tags=["image", "mask"], category="image")
+@invocation("tomask", title="Mask from Alpha", tags=["image", "mask"], category="image", version="1.0.0")
 class MaskFromAlphaInvocation(BaseInvocation):
    """Extracts the alpha channel of an image as a mask."""

@@ -177,7 +177,7 @@ class MaskFromAlphaInvocation(BaseInvocation):
        )


-@invocation("img_mul", title="Multiply Images", tags=["image", "multiply"], category="image")
+@invocation("img_mul", title="Multiply Images", tags=["image", "multiply"], category="image", version="1.0.0")
 class ImageMultiplyInvocation(BaseInvocation):
    """Multiplies two images together using `PIL.ImageChops.multiply()`."""

@@ -210,7 +210,7 @@ class ImageMultiplyInvocation(BaseInvocation):
 IMAGE_CHANNELS = Literal["A", "R", "G", "B"]


-@invocation("img_chan", title="Extract Image Channel", tags=["image", "channel"], category="image")
+@invocation("img_chan", title="Extract Image Channel", tags=["image", "channel"], category="image", version="1.0.0")
 class ImageChannelInvocation(BaseInvocation):
    """Gets a channel from an image."""

@@ -242,7 +242,7 @@ class ImageChannelInvocation(BaseInvocation):
 IMAGE_MODES = Literal["L", "RGB", "RGBA", "CMYK", "YCbCr", "LAB", "HSV", "I", "F"]


-@invocation("img_conv", title="Convert Image Mode", tags=["image", "convert"], category="image")
+@invocation("img_conv", title="Convert Image Mode", tags=["image", "convert"], category="image", version="1.0.0")
 class ImageConvertInvocation(BaseInvocation):
    """Converts an image to a different mode."""

@@ -271,7 +271,7 @@ class ImageConvertInvocation(BaseInvocation):
        )


-@invocation("img_blur", title="Blur Image", tags=["image", "blur"], category="image")
+@invocation("img_blur", title="Blur Image", tags=["image", "blur"], category="image", version="1.0.0")
 class ImageBlurInvocation(BaseInvocation):
    """Blurs an image"""

@@ -325,7 +325,7 @@ PIL_RESAMPLING_MAP = {
 }


-@invocation("img_resize", title="Resize Image", tags=["image", "resize"], category="image")
+@invocation("img_resize", title="Resize Image", tags=["image", "resize"], category="image", version="1.0.0")
 class ImageResizeInvocation(BaseInvocation):
    """Resizes an image to specific dimensions"""

@@ -365,7 +365,7 @@ class ImageResizeInvocation(BaseInvocation):
        )


-@invocation("img_scale", title="Scale Image", tags=["image", "scale"], category="image")
+@invocation("img_scale", title="Scale Image", tags=["image", "scale"], category="image", version="1.0.0")
 class ImageScaleInvocation(BaseInvocation):
    """Scales an image by a factor"""

@@ -406,7 +406,7 @@ class ImageScaleInvocation(BaseInvocation):
        )


-@invocation("img_lerp", title="Lerp Image", tags=["image", "lerp"], category="image")
+@invocation("img_lerp", title="Lerp Image", tags=["image", "lerp"], category="image", version="1.0.0")
 class ImageLerpInvocation(BaseInvocation):
    """Linear interpolation of all pixels of an image"""

@@ -439,7 +439,7 @@ class ImageLerpInvocation(BaseInvocation):
        )


-@invocation("img_ilerp", title="Inverse Lerp Image", tags=["image", "ilerp"], category="image")
+@invocation("img_ilerp", title="Inverse Lerp Image", tags=["image", "ilerp"], category="image", version="1.0.0")
 class ImageInverseLerpInvocation(BaseInvocation):
    """Inverse linear interpolation of all pixels of an image"""

@@ -472,7 +472,7 @@ class ImageInverseLerpInvocation(BaseInvocation):
        )


-@invocation("img_nsfw", title="Blur NSFW Image", tags=["image", "nsfw"], category="image")
+@invocation("img_nsfw", title="Blur NSFW Image", tags=["image", "nsfw"], category="image", version="1.0.0")
 class ImageNSFWBlurInvocation(BaseInvocation):
    """Add blur to NSFW-flagged images"""

@@ -517,7 +517,9 @@ class ImageNSFWBlurInvocation(BaseInvocation):
        return caution.resize((caution.width // 2, caution.height // 2))


-@invocation("img_watermark", title="Add Invisible Watermark", tags=["image", "watermark"], category="image")
+@invocation(
+    "img_watermark", title="Add Invisible Watermark", tags=["image", "watermark"], category="image", version="1.0.0"
+)
 class ImageWatermarkInvocation(BaseInvocation):
    """Add an invisible watermark to an image"""

@@ -548,7 +550,7 @@ class ImageWatermarkInvocation(BaseInvocation):
        )


-@invocation("mask_edge", title="Mask Edge", tags=["image", "mask", "inpaint"], category="image")
+@invocation("mask_edge", title="Mask Edge", tags=["image", "mask", "inpaint"], category="image", version="1.0.0")
 class MaskEdgeInvocation(BaseInvocation):
    """Applies an edge mask to an image"""

@@ -561,7 +563,7 @@ class MaskEdgeInvocation(BaseInvocation):
    )

    def invoke(self, context: InvocationContext) -> ImageOutput:
-        mask = context.services.images.get_pil_image(self.image.image_name)
+        mask = context.services.images.get_pil_image(self.image.image_name).convert("L")

        npimg = numpy.asarray(mask, dtype=numpy.uint8)
        npgradient = numpy.uint8(255 * (1.0 - numpy.floor(numpy.abs(0.5 - numpy.float32(npimg) / 255.0) * 2.0)))
@@ -593,7 +595,9 @@ class MaskEdgeInvocation(BaseInvocation):
        )


-@invocation("mask_combine", title="Combine Masks", tags=["image", "mask", "multiply"], category="image")
+@invocation(
+    "mask_combine", title="Combine Masks", tags=["image", "mask", "multiply"], category="image", version="1.0.0"
+)
 class MaskCombineInvocation(BaseInvocation):
    """Combine two masks together by multiplying them using `PIL.ImageChops.multiply()`."""

@@ -623,7 +627,7 @@ class MaskCombineInvocation(BaseInvocation):
        )


-@invocation("color_correct", title="Color Correct", tags=["image", "color"], category="image")
+@invocation("color_correct", title="Color Correct", tags=["image", "color"], category="image", version="1.0.0")
 class ColorCorrectInvocation(BaseInvocation):
    """
    Shifts the colors of a target image to match the reference image, optionally
@@ -696,8 +700,13 @@ class ColorCorrectInvocation(BaseInvocation):
        # Blur the mask out (into init image) by specified amount
        if self.mask_blur_radius > 0:
            nm = numpy.asarray(pil_init_mask, dtype=numpy.uint8)
+            inverted_nm = 255 - nm
+            dilation_size = int(round(self.mask_blur_radius) + 20)
+            dilating_kernel = cv2.getStructuringElement(cv2.MORPH_ELLIPSE, (dilation_size, dilation_size))
+            inverted_dilated_nm = cv2.dilate(inverted_nm, dilating_kernel)
+            dilated_nm = 255 - inverted_dilated_nm
            nmd = cv2.erode(
-                nm,
+                dilated_nm,
                kernel=numpy.ones((3, 3), dtype=numpy.uint8),
                iterations=int(self.mask_blur_radius / 2),
            )
@@ -728,7 +737,7 @@ class ColorCorrectInvocation(BaseInvocation):
        )


-@invocation("img_hue_adjust", title="Adjust Image Hue", tags=["image", "hue"], category="image")
+@invocation("img_hue_adjust", title="Adjust Image Hue", tags=["image", "hue"], category="image", version="1.0.0")
 class ImageHueAdjustmentInvocation(BaseInvocation):
    """Adjusts the Hue of an image."""

@@ -769,38 +778,95 @@ class ImageHueAdjustmentInvocation(BaseInvocation):
        )


+COLOR_CHANNELS = Literal[
+    "Red (RGBA)",
+    "Green (RGBA)",
+    "Blue (RGBA)",
+    "Alpha (RGBA)",
+    "Cyan (CMYK)",
+    "Magenta (CMYK)",
+    "Yellow (CMYK)",
+    "Black (CMYK)",
+    "Hue (HSV)",
+    "Saturation (HSV)",
+    "Value (HSV)",
+    "Luminosity (LAB)",
+    "A (LAB)",
+    "B (LAB)",
+    "Y (YCbCr)",
+    "Cb (YCbCr)",
+    "Cr (YCbCr)",
+]
+
+CHANNEL_FORMATS = {
+    "Red (RGBA)": ("RGBA", 0),
+    "Green (RGBA)": ("RGBA", 1),
+    "Blue (RGBA)": ("RGBA", 2),
+    "Alpha (RGBA)": ("RGBA", 3),
+    "Cyan (CMYK)": ("CMYK", 0),
+    "Magenta (CMYK)": ("CMYK", 1),
+    "Yellow (CMYK)": ("CMYK", 2),
+    "Black (CMYK)": ("CMYK", 3),
+    "Hue (HSV)": ("HSV", 0),
+    "Saturation (HSV)": ("HSV", 1),
+    "Value (HSV)": ("HSV", 2),
+    "Luminosity (LAB)": ("LAB", 0),
+    "A (LAB)": ("LAB", 1),
+    "B (LAB)": ("LAB", 2),
+    "Y (YCbCr)": ("YCbCr", 0),
+    "Cb (YCbCr)": ("YCbCr", 1),
+    "Cr (YCbCr)": ("YCbCr", 2),
+}
+
+
@invocation(
-    "img_luminosity_adjust",
-    title="Adjust Image Luminosity",
-    tags=["image", "luminosity", "hsl"],
+    "img_channel_offset",
+    title="Offset Image Channel",
+    tags=[
+        "image",
+        "offset",
+        "red",
+        "green",
+        "blue",
+        "alpha",
+        "cyan",
+        "magenta",
+        "yellow",
+        "black",
+        "hue",
+        "saturation",
+        "luminosity",
+        "value",
+    ],
    category="image",
+    version="1.0.0",
 )
-class ImageLuminosityAdjustmentInvocation(BaseInvocation):
-    """Adjusts the Luminosity (Value) of an image."""
+class ImageChannelOffsetInvocation(BaseInvocation):
+    """Add or subtract a value from a specific color channel of an image."""

    image: ImageField = InputField(description="The image to adjust")
-    luminosity: float = InputField(
-        default=1.0, ge=0, le=1, description="The factor by which to adjust the luminosity (value)"
-    )
+    channel: COLOR_CHANNELS = InputField(description="Which channel to adjust")
+    offset: int = InputField(default=0, ge=-255, le=255, description="The amount to adjust the channel by")

    def invoke(self, context: InvocationContext) -> ImageOutput:
        pil_image = context.services.images.get_pil_image(self.image.image_name)

-        # Convert PIL image to OpenCV format (numpy array), note color channel
-        # ordering is changed from RGB to BGR
-        image = numpy.array(pil_image.convert("RGB"))[:, :, ::-1]
+        # extract the channel and mode from the input and reference tuple
+        mode = CHANNEL_FORMATS[self.channel][0]
+        channel_number = CHANNEL_FORMATS[self.channel][1]

-        # Convert image to HSV color space
-        hsv_image = cv2.cvtColor(image, cv2.COLOR_BGR2HSV)
+        # Convert PIL image to new format
+        converted_image = numpy.array(pil_image.convert(mode)).astype(int)
+        image_channel = converted_image[:, :, channel_number]

-        # Adjust the luminosity (value)
-        hsv_image[:, :, 2] = numpy.clip(hsv_image[:, :, 2] * self.luminosity, 0, 255)
+        # Adjust the value, clipping to 0..255
+        image_channel = numpy.clip(image_channel + self.offset, 0, 255)

-        # Convert image back to BGR color space
-        image = cv2.cvtColor(hsv_image, cv2.COLOR_HSV2BGR)
+        # Put the channel back into the image
+        converted_image[:, :, channel_number] = image_channel

-        # Convert back to PIL format and to original color mode
-        pil_image = Image.fromarray(image[:, :, ::-1], "RGB").convert("RGBA")
+        # Convert back to RGBA format and output
+        pil_image = Image.fromarray(converted_image.astype(numpy.uint8), mode=mode).convert("RGBA")

        image_dto = context.services.images.create(
            image=pil_image,
@@ -822,35 +888,60 @@ class ImageLuminosityAdjustmentInvocation(BaseInvocation):


@invocation(
-    "img_saturation_adjust",
-    title="Adjust Image Saturation",
-    tags=["image", "saturation", "hsl"],
+    "img_channel_multiply",
+    title="Multiply Image Channel",
+    tags=[
+        "image",
+        "invert",
+        "scale",
+        "multiply",
+        "red",
+        "green",
+        "blue",
+        "alpha",
+        "cyan",
+        "magenta",
+        "yellow",
+        "black",
+        "hue",
+        "saturation",
+        "luminosity",
+        "value",
+    ],
    category="image",
+    version="1.0.0",
 )
-class ImageSaturationAdjustmentInvocation(BaseInvocation):
-    """Adjusts the Saturation of an image."""
+class ImageChannelMultiplyInvocation(BaseInvocation):
+    """Scale a specific color channel of an image."""

    image: ImageField = InputField(description="The image to adjust")
-    saturation: float = InputField(default=1.0, ge=0, le=1, description="The factor by which to adjust the saturation")
+    channel: COLOR_CHANNELS = InputField(description="Which channel to adjust")
+    scale: float = InputField(default=1.0, ge=0.0, description="The amount to scale the channel by.")
+    invert_channel: bool = InputField(default=False, description="Invert the channel after scaling")

    def invoke(self, context: InvocationContext) -> ImageOutput:
        pil_image = context.services.images.get_pil_image(self.image.image_name)

-        # Convert PIL image to OpenCV format (numpy array), note color channel
-        # ordering is changed from RGB to BGR
-        image = numpy.array(pil_image.convert("RGB"))[:, :, ::-1]
+        # extract the channel and mode from the input and reference tuple
+        mode = CHANNEL_FORMATS[self.channel][0]
+        channel_number = CHANNEL_FORMATS[self.channel][1]

-        # Convert image to HSV color space
-        hsv_image = cv2.cvtColor(image, cv2.COLOR_BGR2HSV)
+        # Convert PIL image to new format
+        converted_image = numpy.array(pil_image.convert(mode)).astype(float)
+        image_channel = converted_image[:, :, channel_number]

-        # Adjust the saturation
-        hsv_image[:, :, 1] = numpy.clip(hsv_image[:, :, 1] * self.saturation, 0, 255)
+        # Adjust the value, clipping to 0..255
+        image_channel = numpy.clip(image_channel * self.scale, 0, 255)

-        # Convert image back to BGR color space
-        image = cv2.cvtColor(hsv_image, cv2.COLOR_HSV2BGR)
+        # Invert the channel if requested
+        if self.invert_channel:
+            image_channel = 255 - image_channel

-        # Convert back to PIL format and to original color mode
-        pil_image = Image.fromarray(image[:, :, ::-1], "RGB").convert("RGBA")
+        # Put the channel back into the image
+        converted_image[:, :, channel_number] = image_channel
+
+        # Convert back to RGBA format and output
+        pil_image = Image.fromarray(converted_image.astype(numpy.uint8), mode=mode).convert("RGBA")

        image_dto = context.services.images.create(
            image=pil_image,
--- a/invokeai/app/invocations/infill.py
+++ b/invokeai/app/invocations/infill.py
@@ -8,19 +8,17 @@ from PIL import Image, ImageOps

 from invokeai.app.invocations.primitives import ColorField, ImageField, ImageOutput
 from invokeai.app.util.misc import SEED_MAX, get_random_seed
+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 ..models.image import ImageCategory, ResourceOrigin
 from .baseinvocation import BaseInvocation, InputField, InvocationContext, invocation
+from .image import PIL_RESAMPLING_MAP, PIL_RESAMPLING_MODES


 def infill_methods() -> list[str]:
-    methods = [
-        "tile",
-        "solid",
-        "lama",
-    ]
+    methods = ["tile", "solid", "lama", "cv2"]
    if PatchMatch.patchmatch_available():
        methods.insert(0, "patchmatch")
    return methods
@@ -49,6 +47,10 @@ def infill_patchmatch(im: Image.Image) -> Image.Image:
    return im_patched


+def infill_cv2(im: Image.Image) -> Image.Image:
+    return cv2_inpaint(im)
+
+
 def get_tile_images(image: np.ndarray, width=8, height=8):
    _nrows, _ncols, depth = image.shape
    _strides = image.strides
@@ -116,7 +118,7 @@ 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")
+@invocation("infill_rgba", title="Solid Color Infill", tags=["image", "inpaint"], category="inpaint", version="1.0.0")
 class InfillColorInvocation(BaseInvocation):
    """Infills transparent areas of an image with a solid color"""

@@ -151,7 +153,7 @@ class InfillColorInvocation(BaseInvocation):
        )


-@invocation("infill_tile", title="Tile Infill", tags=["image", "inpaint"], category="inpaint")
+@invocation("infill_tile", title="Tile Infill", tags=["image", "inpaint"], category="inpaint", version="1.0.0")
 class InfillTileInvocation(BaseInvocation):
    """Infills transparent areas of an image with tiles of the image"""

@@ -187,20 +189,42 @@ class InfillTileInvocation(BaseInvocation):
        )


-@invocation("infill_patchmatch", title="PatchMatch Infill", tags=["image", "inpaint"], category="inpaint")
+@invocation(
+    "infill_patchmatch", title="PatchMatch Infill", tags=["image", "inpaint"], category="inpaint", version="1.0.0"
+)
 class InfillPatchMatchInvocation(BaseInvocation):
    """Infills transparent areas of an image using the PatchMatch algorithm"""

    image: ImageField = InputField(description="The image to infill")
+    downscale: float = InputField(default=2.0, gt=0, description="Run patchmatch on downscaled image to speedup infill")
+    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)
+        image = context.services.images.get_pil_image(self.image.image_name).convert("RGBA")
+
+        resample_mode = PIL_RESAMPLING_MAP[self.resample_mode]
+
+        infill_image = image.copy()
+        width = int(image.width / self.downscale)
+        height = int(image.height / self.downscale)
+        infill_image = infill_image.resize(
+            (width, height),
+            resample=resample_mode,
+        )

        if PatchMatch.patchmatch_available():
-            infilled = infill_patchmatch(image.copy())
+            infilled = infill_patchmatch(infill_image)
        else:
            raise ValueError("PatchMatch is not available on this system")

+        infilled = infilled.resize(
+            (image.width, image.height),
+            resample=resample_mode,
+        )
+
+        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,
@@ -218,7 +242,7 @@ class InfillPatchMatchInvocation(BaseInvocation):
        )


-@invocation("infill_lama", title="LaMa Infill", tags=["image", "inpaint"], category="inpaint")
+@invocation("infill_lama", title="LaMa Infill", tags=["image", "inpaint"], category="inpaint", version="1.0.0")
 class LaMaInfillInvocation(BaseInvocation):
    """Infills transparent areas of an image using the LaMa model"""

@@ -243,3 +267,30 @@ class LaMaInfillInvocation(BaseInvocation):
            width=image_dto.width,
            height=image_dto.height,
        )
+
+
+@invocation("infill_cv2", title="CV2 Infill", tags=["image", "inpaint"], category="inpaint")
+class CV2InfillInvocation(BaseInvocation):
+    """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)
+
+        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,
+        )
+
+        return ImageOutput(
+            image=ImageField(image_name=image_dto.image_name),
+            width=image_dto.width,
+            height=image_dto.height,
+        )
--- a/invokeai/app/invocations/latent.py
+++ b/invokeai/app/invocations/latent.py
@@ -76,7 +76,7 @@ class SchedulerOutput(BaseInvocationOutput):
    scheduler: SAMPLER_NAME_VALUES = OutputField(description=FieldDescriptions.scheduler, ui_type=UIType.Scheduler)


-@invocation("scheduler", title="Scheduler", tags=["scheduler"], category="latents")
+@invocation("scheduler", title="Scheduler", tags=["scheduler"], category="latents", version="1.0.0")
 class SchedulerInvocation(BaseInvocation):
    """Selects a scheduler."""

@@ -88,7 +88,9 @@ class SchedulerInvocation(BaseInvocation):
        return SchedulerOutput(scheduler=self.scheduler)


-@invocation("create_denoise_mask", title="Create Denoise Mask", tags=["mask", "denoise"], category="latents")
+@invocation(
+    "create_denoise_mask", title="Create Denoise Mask", tags=["mask", "denoise"], category="latents", version="1.0.0"
+)
 class CreateDenoiseMaskInvocation(BaseInvocation):
    """Creates mask for denoising model run."""

@@ -188,6 +190,7 @@ def get_scheduler(
    title="Denoise Latents",
    tags=["latents", "denoise", "txt2img", "t2i", "t2l", "img2img", "i2i", "l2l"],
    category="latents",
+    version="1.0.0",
 )
 class DenoiseLatentsInvocation(BaseInvocation):
    """Denoises noisy latents to decodable images"""
@@ -210,12 +213,14 @@ class DenoiseLatentsInvocation(BaseInvocation):
    )
    unet: UNetField = InputField(description=FieldDescriptions.unet, input=Input.Connection, title="UNet", ui_order=2)
    control: Union[ControlField, list[ControlField]] = InputField(
-        default=None, description=FieldDescriptions.control, input=Input.Connection, ui_order=5
+        default=None,
+        description=FieldDescriptions.control,
+        input=Input.Connection,
+        ui_order=5,
    )
    latents: Optional[LatentsField] = InputField(description=FieldDescriptions.latents, input=Input.Connection)
    denoise_mask: Optional[DenoiseMaskField] = InputField(
-        default=None,
-        description=FieldDescriptions.mask,
+        default=None, description=FieldDescriptions.mask, input=Input.Connection, ui_order=6
    )

    @validator("cfg_scale")
@@ -319,7 +324,7 @@ class DenoiseLatentsInvocation(BaseInvocation):
        context: InvocationContext,
        # really only need model for dtype and device
        model: StableDiffusionGeneratorPipeline,
-        control_input: List[ControlField],
+        control_input: Union[ControlField, List[ControlField]],
        latents_shape: List[int],
        exit_stack: ExitStack,
        do_classifier_free_guidance: bool = True,
@@ -544,7 +549,9 @@ class DenoiseLatentsInvocation(BaseInvocation):
        return build_latents_output(latents_name=name, latents=result_latents, seed=seed)


-@invocation("l2i", title="Latents to Image", tags=["latents", "image", "vae", "l2i"], category="latents")
+@invocation(
+    "l2i", title="Latents to Image", tags=["latents", "image", "vae", "l2i"], category="latents", version="1.0.0"
+)
 class LatentsToImageInvocation(BaseInvocation):
    """Generates an image from latents."""

@@ -650,7 +657,7 @@ class LatentsToImageInvocation(BaseInvocation):
 LATENTS_INTERPOLATION_MODE = Literal["nearest", "linear", "bilinear", "bicubic", "trilinear", "area", "nearest-exact"]


-@invocation("lresize", title="Resize Latents", tags=["latents", "resize"], category="latents")
+@invocation("lresize", title="Resize Latents", tags=["latents", "resize"], category="latents", version="1.0.0")
 class ResizeLatentsInvocation(BaseInvocation):
    """Resizes latents to explicit width/height (in pixels). Provided dimensions are floor-divided by 8."""

@@ -694,7 +701,7 @@ class ResizeLatentsInvocation(BaseInvocation):
        return build_latents_output(latents_name=name, latents=resized_latents, seed=self.latents.seed)


-@invocation("lscale", title="Scale Latents", tags=["latents", "resize"], category="latents")
+@invocation("lscale", title="Scale Latents", tags=["latents", "resize"], category="latents", version="1.0.0")
 class ScaleLatentsInvocation(BaseInvocation):
    """Scales latents by a given factor."""

@@ -730,7 +737,9 @@ class ScaleLatentsInvocation(BaseInvocation):
        return build_latents_output(latents_name=name, latents=resized_latents, seed=self.latents.seed)


-@invocation("i2l", title="Image to Latents", tags=["latents", "image", "vae", "i2l"], category="latents")
+@invocation(
+    "i2l", title="Image to Latents", tags=["latents", "image", "vae", "i2l"], category="latents", version="1.0.0"
+)
 class ImageToLatentsInvocation(BaseInvocation):
    """Encodes an image into latents."""

@@ -830,7 +839,7 @@ class ImageToLatentsInvocation(BaseInvocation):
        return vae.encode(image_tensor).latents


-@invocation("lblend", title="Blend Latents", tags=["latents", "blend"], category="latents")
+@invocation("lblend", title="Blend Latents", tags=["latents", "blend"], category="latents", version="1.0.0")
 class BlendLatentsInvocation(BaseInvocation):
    """Blend two latents using a given alpha. Latents must have same size."""

--- a/invokeai/app/invocations/math.py
+++ b/invokeai/app/invocations/math.py
@@ -7,7 +7,7 @@ from invokeai.app.invocations.primitives import IntegerOutput
 from .baseinvocation import BaseInvocation, FieldDescriptions, InputField, InvocationContext, invocation


-@invocation("add", title="Add Integers", tags=["math", "add"], category="math")
+@invocation("add", title="Add Integers", tags=["math", "add"], category="math", version="1.0.0")
 class AddInvocation(BaseInvocation):
    """Adds two numbers"""

@@ -18,7 +18,7 @@ class AddInvocation(BaseInvocation):
        return IntegerOutput(value=self.a + self.b)


-@invocation("sub", title="Subtract Integers", tags=["math", "subtract"], category="math")
+@invocation("sub", title="Subtract Integers", tags=["math", "subtract"], category="math", version="1.0.0")
 class SubtractInvocation(BaseInvocation):
    """Subtracts two numbers"""

@@ -29,7 +29,7 @@ class SubtractInvocation(BaseInvocation):
        return IntegerOutput(value=self.a - self.b)


-@invocation("mul", title="Multiply Integers", tags=["math", "multiply"], category="math")
+@invocation("mul", title="Multiply Integers", tags=["math", "multiply"], category="math", version="1.0.0")
 class MultiplyInvocation(BaseInvocation):
    """Multiplies two numbers"""

@@ -40,7 +40,7 @@ class MultiplyInvocation(BaseInvocation):
        return IntegerOutput(value=self.a * self.b)


-@invocation("div", title="Divide Integers", tags=["math", "divide"], category="math")
+@invocation("div", title="Divide Integers", tags=["math", "divide"], category="math", version="1.0.0")
 class DivideInvocation(BaseInvocation):
    """Divides two numbers"""

@@ -51,7 +51,7 @@ class DivideInvocation(BaseInvocation):
        return IntegerOutput(value=int(self.a / self.b))


-@invocation("rand_int", title="Random Integer", tags=["math", "random"], category="math")
+@invocation("rand_int", title="Random Integer", tags=["math", "random"], category="math", version="1.0.0")
 class RandomIntInvocation(BaseInvocation):
    """Outputs a single random integer."""

--- a/invokeai/app/invocations/metadata.py
+++ b/invokeai/app/invocations/metadata.py
@@ -98,7 +98,9 @@ class MetadataAccumulatorOutput(BaseInvocationOutput):
    metadata: CoreMetadata = OutputField(description="The core metadata for the image")


-@invocation("metadata_accumulator", title="Metadata Accumulator", tags=["metadata"], category="metadata")
+@invocation(
+    "metadata_accumulator", title="Metadata Accumulator", tags=["metadata"], category="metadata", version="1.0.0"
+)
 class MetadataAccumulatorInvocation(BaseInvocation):
    """Outputs a Core Metadata Object"""

--- a/invokeai/app/invocations/model.py
+++ b/invokeai/app/invocations/model.py
@@ -73,7 +73,7 @@ class LoRAModelField(BaseModel):
    base_model: BaseModelType = Field(description="Base model")


-@invocation("main_model_loader", title="Main Model", tags=["model"], category="model")
+@invocation("main_model_loader", title="Main Model", tags=["model"], category="model", version="1.0.0")
 class MainModelLoaderInvocation(BaseInvocation):
    """Loads a main model, outputting its submodels."""

@@ -173,7 +173,7 @@ class LoraLoaderOutput(BaseInvocationOutput):
    clip: Optional[ClipField] = OutputField(default=None, description=FieldDescriptions.clip, title="CLIP")


-@invocation("lora_loader", title="LoRA", tags=["model"], category="model")
+@invocation("lora_loader", title="LoRA", tags=["model"], category="model", version="1.0.0")
 class LoraLoaderInvocation(BaseInvocation):
    """Apply selected lora to unet and text_encoder."""

@@ -244,7 +244,7 @@ class SDXLLoraLoaderOutput(BaseInvocationOutput):
    clip2: Optional[ClipField] = OutputField(default=None, description=FieldDescriptions.clip, title="CLIP 2")


-@invocation("sdxl_lora_loader", title="SDXL LoRA", tags=["lora", "model"], category="model")
+@invocation("sdxl_lora_loader", title="SDXL LoRA", tags=["lora", "model"], category="model", version="1.0.0")
 class SDXLLoraLoaderInvocation(BaseInvocation):
    """Apply selected lora to unet and text_encoder."""

@@ -338,7 +338,7 @@ class VaeLoaderOutput(BaseInvocationOutput):
    vae: VaeField = OutputField(description=FieldDescriptions.vae, title="VAE")


-@invocation("vae_loader", title="VAE", tags=["vae", "model"], category="model")
+@invocation("vae_loader", title="VAE", tags=["vae", "model"], category="model", version="1.0.0")
 class VaeLoaderInvocation(BaseInvocation):
    """Loads a VAE model, outputting a VaeLoaderOutput"""

@@ -376,7 +376,7 @@ class SeamlessModeOutput(BaseInvocationOutput):
    vae: Optional[VaeField] = OutputField(description=FieldDescriptions.vae, title="VAE")


-@invocation("seamless", title="Seamless", tags=["seamless", "model"], category="model")
+@invocation("seamless", title="Seamless", tags=["seamless", "model"], category="model", version="1.0.0")
 class SeamlessModeInvocation(BaseInvocation):
    """Applies the seamless transformation to the Model UNet and VAE."""

--- a/invokeai/app/invocations/noise.py
+++ b/invokeai/app/invocations/noise.py
@@ -78,7 +78,7 @@ def build_noise_output(latents_name: str, latents: torch.Tensor, seed: int):
    )


-@invocation("noise", title="Noise", tags=["latents", "noise"], category="latents")
+@invocation("noise", title="Noise", tags=["latents", "noise"], category="latents", version="1.0.0")
 class NoiseInvocation(BaseInvocation):
    """Generates latent noise."""

--- a/invokeai/app/invocations/onnx.py
+++ b/invokeai/app/invocations/onnx.py
@@ -56,7 +56,7 @@ ORT_TO_NP_TYPE = {
 PRECISION_VALUES = Literal[tuple(list(ORT_TO_NP_TYPE.keys()))]


-@invocation("prompt_onnx", title="ONNX Prompt (Raw)", tags=["prompt", "onnx"], category="conditioning")
+@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)
@@ -143,6 +143,7 @@ class ONNXPromptInvocation(BaseInvocation):
    title="ONNX Text to Latents",
    tags=["latents", "inference", "txt2img", "onnx"],
    category="latents",
+    version="1.0.0",
 )
 class ONNXTextToLatentsInvocation(BaseInvocation):
    """Generates latents from conditionings."""
@@ -319,6 +320,7 @@ class ONNXTextToLatentsInvocation(BaseInvocation):
    title="ONNX Latents to Image",
    tags=["latents", "image", "vae", "onnx"],
    category="image",
+    version="1.0.0",
 )
 class ONNXLatentsToImageInvocation(BaseInvocation):
    """Generates an image from latents."""
@@ -403,7 +405,7 @@ class OnnxModelField(BaseModel):
    model_type: ModelType = Field(description="Model Type")


-@invocation("onnx_model_loader", title="ONNX Main Model", tags=["onnx", "model"], category="model")
+@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."""

--- a/invokeai/app/invocations/param_easing.py
+++ b/invokeai/app/invocations/param_easing.py
@@ -45,7 +45,7 @@ from invokeai.app.invocations.primitives import FloatCollectionOutput
 from .baseinvocation import BaseInvocation, InputField, InvocationContext, invocation


-@invocation("float_range", title="Float Range", tags=["math", "range"], category="math")
+@invocation("float_range", title="Float Range", tags=["math", "range"], category="math", version="1.0.0")
 class FloatLinearRangeInvocation(BaseInvocation):
    """Creates a range"""

@@ -96,7 +96,7 @@ EASING_FUNCTION_KEYS = Literal[tuple(list(EASING_FUNCTIONS_MAP.keys()))]


 # actually I think for now could just use CollectionOutput (which is list[Any]
-@invocation("step_param_easing", title="Step Param Easing", tags=["step", "easing"], category="step")
+@invocation("step_param_easing", title="Step Param Easing", tags=["step", "easing"], category="step", version="1.0.0")
 class StepParamEasingInvocation(BaseInvocation):
    """Experimental per-step parameter easing for denoising steps"""

--- a/invokeai/app/invocations/primitives.py
+++ b/invokeai/app/invocations/primitives.py
@@ -14,7 +14,6 @@ from .baseinvocation import (
    InvocationContext,
    OutputField,
    UIComponent,
-    UIType,
    invocation,
    invocation_output,
 )
@@ -40,10 +39,14 @@ class BooleanOutput(BaseInvocationOutput):
 class BooleanCollectionOutput(BaseInvocationOutput):
    """Base class for nodes that output a collection of booleans"""

-    collection: list[bool] = OutputField(description="The output boolean collection", ui_type=UIType.BooleanCollection)
+    collection: list[bool] = OutputField(
+        description="The output boolean collection",
+    )


-@invocation("boolean", title="Boolean Primitive", tags=["primitives", "boolean"], category="primitives")
+@invocation(
+    "boolean", title="Boolean Primitive", tags=["primitives", "boolean"], category="primitives", version="1.0.0"
+)
 class BooleanInvocation(BaseInvocation):
    """A boolean primitive value"""

@@ -58,13 +61,12 @@ class BooleanInvocation(BaseInvocation):
    title="Boolean Collection Primitive",
    tags=["primitives", "boolean", "collection"],
    category="primitives",
+    version="1.0.0",
 )
 class BooleanCollectionInvocation(BaseInvocation):
    """A collection of boolean primitive values"""

-    collection: list[bool] = InputField(
-        default_factory=list, description="The collection of boolean values", ui_type=UIType.BooleanCollection
-    )
+    collection: list[bool] = InputField(default_factory=list, description="The collection of boolean values")

    def invoke(self, context: InvocationContext) -> BooleanCollectionOutput:
        return BooleanCollectionOutput(collection=self.collection)
@@ -86,10 +88,14 @@ class IntegerOutput(BaseInvocationOutput):
 class IntegerCollectionOutput(BaseInvocationOutput):
    """Base class for nodes that output a collection of integers"""

-    collection: list[int] = OutputField(description="The int collection", ui_type=UIType.IntegerCollection)
+    collection: list[int] = OutputField(
+        description="The int collection",
+    )


-@invocation("integer", title="Integer Primitive", tags=["primitives", "integer"], category="primitives")
+@invocation(
+    "integer", title="Integer Primitive", tags=["primitives", "integer"], category="primitives", version="1.0.0"
+)
 class IntegerInvocation(BaseInvocation):
    """An integer primitive value"""

@@ -104,13 +110,12 @@ class IntegerInvocation(BaseInvocation):
    title="Integer Collection Primitive",
    tags=["primitives", "integer", "collection"],
    category="primitives",
+    version="1.0.0",
 )
 class IntegerCollectionInvocation(BaseInvocation):
    """A collection of integer primitive values"""

-    collection: list[int] = InputField(
-        default_factory=list, description="The collection of integer values", ui_type=UIType.IntegerCollection
-    )
+    collection: list[int] = InputField(default_factory=list, description="The collection of integer values")

    def invoke(self, context: InvocationContext) -> IntegerCollectionOutput:
        return IntegerCollectionOutput(collection=self.collection)
@@ -132,10 +137,12 @@ class FloatOutput(BaseInvocationOutput):
 class FloatCollectionOutput(BaseInvocationOutput):
    """Base class for nodes that output a collection of floats"""

-    collection: list[float] = OutputField(description="The float collection", ui_type=UIType.FloatCollection)
+    collection: list[float] = OutputField(
+        description="The float collection",
+    )


-@invocation("float", title="Float Primitive", tags=["primitives", "float"], category="primitives")
+@invocation("float", title="Float Primitive", tags=["primitives", "float"], category="primitives", version="1.0.0")
 class FloatInvocation(BaseInvocation):
    """A float primitive value"""

@@ -150,13 +157,12 @@ class FloatInvocation(BaseInvocation):
    title="Float Collection Primitive",
    tags=["primitives", "float", "collection"],
    category="primitives",
+    version="1.0.0",
 )
 class FloatCollectionInvocation(BaseInvocation):
    """A collection of float primitive values"""

-    collection: list[float] = InputField(
-        default_factory=list, description="The collection of float values", ui_type=UIType.FloatCollection
-    )
+    collection: list[float] = InputField(default_factory=list, description="The collection of float values")

    def invoke(self, context: InvocationContext) -> FloatCollectionOutput:
        return FloatCollectionOutput(collection=self.collection)
@@ -178,10 +184,12 @@ class StringOutput(BaseInvocationOutput):
 class StringCollectionOutput(BaseInvocationOutput):
    """Base class for nodes that output a collection of strings"""

-    collection: list[str] = OutputField(description="The output strings", ui_type=UIType.StringCollection)
+    collection: list[str] = OutputField(
+        description="The output strings",
+    )


-@invocation("string", title="String Primitive", tags=["primitives", "string"], category="primitives")
+@invocation("string", title="String Primitive", tags=["primitives", "string"], category="primitives", version="1.0.0")
 class StringInvocation(BaseInvocation):
    """A string primitive value"""

@@ -196,13 +204,12 @@ class StringInvocation(BaseInvocation):
    title="String Collection Primitive",
    tags=["primitives", "string", "collection"],
    category="primitives",
+    version="1.0.0",
 )
 class StringCollectionInvocation(BaseInvocation):
    """A collection of string primitive values"""

-    collection: list[str] = InputField(
-        default_factory=list, description="The collection of string values", ui_type=UIType.StringCollection
-    )
+    collection: list[str] = InputField(default_factory=list, description="The collection of string values")

    def invoke(self, context: InvocationContext) -> StringCollectionOutput:
        return StringCollectionOutput(collection=self.collection)
@@ -232,10 +239,12 @@ class ImageOutput(BaseInvocationOutput):
 class ImageCollectionOutput(BaseInvocationOutput):
    """Base class for nodes that output a collection of images"""

-    collection: list[ImageField] = OutputField(description="The output images", ui_type=UIType.ImageCollection)
+    collection: list[ImageField] = OutputField(
+        description="The output images",
+    )


-@invocation("image", title="Image Primitive", tags=["primitives", "image"], category="primitives")
+@invocation("image", title="Image Primitive", tags=["primitives", "image"], category="primitives", version="1.0.0")
 class ImageInvocation(BaseInvocation):
    """An image primitive value"""

@@ -256,13 +265,12 @@ class ImageInvocation(BaseInvocation):
    title="Image Collection Primitive",
    tags=["primitives", "image", "collection"],
    category="primitives",
+    version="1.0.0",
 )
 class ImageCollectionInvocation(BaseInvocation):
    """A collection of image primitive values"""

-    collection: list[ImageField] = InputField(
-        default_factory=list, description="The collection of image values", ui_type=UIType.ImageCollection
-    )
+    collection: list[ImageField] = InputField(description="The collection of image values")

    def invoke(self, context: InvocationContext) -> ImageCollectionOutput:
        return ImageCollectionOutput(collection=self.collection)
@@ -316,11 +324,12 @@ class LatentsCollectionOutput(BaseInvocationOutput):

    collection: list[LatentsField] = OutputField(
        description=FieldDescriptions.latents,
-        ui_type=UIType.LatentsCollection,
    )


-@invocation("latents", title="Latents Primitive", tags=["primitives", "latents"], category="primitives")
+@invocation(
+    "latents", title="Latents Primitive", tags=["primitives", "latents"], category="primitives", version="1.0.0"
+)
 class LatentsInvocation(BaseInvocation):
    """A latents tensor primitive value"""

@@ -337,12 +346,13 @@ class LatentsInvocation(BaseInvocation):
    title="Latents Collection Primitive",
    tags=["primitives", "latents", "collection"],
    category="primitives",
+    version="1.0.0",
 )
 class LatentsCollectionInvocation(BaseInvocation):
    """A collection of latents tensor primitive values"""

    collection: list[LatentsField] = InputField(
-        description="The collection of latents tensors", ui_type=UIType.LatentsCollection
+        description="The collection of latents tensors",
    )

    def invoke(self, context: InvocationContext) -> LatentsCollectionOutput:
@@ -385,10 +395,12 @@ class ColorOutput(BaseInvocationOutput):
 class ColorCollectionOutput(BaseInvocationOutput):
    """Base class for nodes that output a collection of colors"""

-    collection: list[ColorField] = OutputField(description="The output colors", ui_type=UIType.ColorCollection)
+    collection: list[ColorField] = OutputField(
+        description="The output colors",
+    )


-@invocation("color", title="Color Primitive", tags=["primitives", "color"], category="primitives")
+@invocation("color", title="Color Primitive", tags=["primitives", "color"], category="primitives", version="1.0.0")
 class ColorInvocation(BaseInvocation):
    """A color primitive value"""

@@ -422,7 +434,6 @@ class ConditioningCollectionOutput(BaseInvocationOutput):

    collection: list[ConditioningField] = OutputField(
        description="The output conditioning tensors",
-        ui_type=UIType.ConditioningCollection,
    )


@@ -431,6 +442,7 @@ class ConditioningCollectionOutput(BaseInvocationOutput):
    title="Conditioning Primitive",
    tags=["primitives", "conditioning"],
    category="primitives",
+    version="1.0.0",
 )
 class ConditioningInvocation(BaseInvocation):
    """A conditioning tensor primitive value"""
@@ -446,6 +458,7 @@ class ConditioningInvocation(BaseInvocation):
    title="Conditioning Collection Primitive",
    tags=["primitives", "conditioning", "collection"],
    category="primitives",
+    version="1.0.0",
 )
 class ConditioningCollectionInvocation(BaseInvocation):
    """A collection of conditioning tensor primitive values"""
@@ -453,7 +466,6 @@ class ConditioningCollectionInvocation(BaseInvocation):
    collection: list[ConditioningField] = InputField(
        default_factory=list,
        description="The collection of conditioning tensors",
-        ui_type=UIType.ConditioningCollection,
    )

    def invoke(self, context: InvocationContext) -> ConditioningCollectionOutput:
--- a/invokeai/app/invocations/prompt.py
+++ b/invokeai/app/invocations/prompt.py
@@ -10,7 +10,7 @@ from invokeai.app.invocations.primitives import StringCollectionOutput
 from .baseinvocation import BaseInvocation, InputField, InvocationContext, UIComponent, invocation


-@invocation("dynamic_prompt", title="Dynamic Prompt", tags=["prompt", "collection"], category="prompt")
+@invocation("dynamic_prompt", title="Dynamic Prompt", tags=["prompt", "collection"], category="prompt", version="1.0.0")
 class DynamicPromptInvocation(BaseInvocation):
    """Parses a prompt using adieyal/dynamicprompts' random or combinatorial generator"""

@@ -29,7 +29,7 @@ class DynamicPromptInvocation(BaseInvocation):
        return StringCollectionOutput(collection=prompts)


-@invocation("prompt_from_file", title="Prompts from File", tags=["prompt", "file"], category="prompt")
+@invocation("prompt_from_file", title="Prompts from File", tags=["prompt", "file"], category="prompt", version="1.0.0")
 class PromptsFromFileInvocation(BaseInvocation):
    """Loads prompts from a text file"""

--- a/invokeai/app/invocations/sdxl.py
+++ b/invokeai/app/invocations/sdxl.py
@@ -33,7 +33,7 @@ class SDXLRefinerModelLoaderOutput(BaseInvocationOutput):
    vae: VaeField = OutputField(description=FieldDescriptions.vae, title="VAE")


-@invocation("sdxl_model_loader", title="SDXL Main Model", tags=["model", "sdxl"], category="model")
+@invocation("sdxl_model_loader", title="SDXL Main Model", tags=["model", "sdxl"], category="model", version="1.0.0")
 class SDXLModelLoaderInvocation(BaseInvocation):
    """Loads an sdxl base model, outputting its submodels."""

@@ -119,6 +119,7 @@ class SDXLModelLoaderInvocation(BaseInvocation):
    title="SDXL Refiner Model",
    tags=["model", "sdxl", "refiner"],
    category="model",
+    version="1.0.0",
 )
 class SDXLRefinerModelLoaderInvocation(BaseInvocation):
    """Loads an sdxl refiner model, outputting its submodels."""
--- a/invokeai/app/invocations/upscale.py
+++ b/invokeai/app/invocations/upscale.py
@@ -23,7 +23,7 @@ ESRGAN_MODELS = Literal[
 ]


-@invocation("esrgan", title="Upscale (RealESRGAN)", tags=["esrgan", "upscale"], category="esrgan")
+@invocation("esrgan", title="Upscale (RealESRGAN)", tags=["esrgan", "upscale"], category="esrgan", version="1.0.0")
 class ESRGANInvocation(BaseInvocation):
    """Upscales an image using RealESRGAN."""

--- a/invokeai/app/services/config/base.py
+++ b/invokeai/app/services/config/base.py
@@ -42,7 +42,9 @@ class InvokeAISettings(BaseSettings):

    def parse_args(self, argv: list = sys.argv[1:]):
        parser = self.get_parser()
-        opt = parser.parse_args(argv)
+        opt, unknown_opts = parser.parse_known_args(argv)
+        if len(unknown_opts) > 0:
+            print("Unknown args:", unknown_opts)
        for name in self.__fields__:
            if name not in self._excluded():
                value = getattr(opt, name)
--- a/invokeai/app/services/config/invokeai_config.py
+++ b/invokeai/app/services/config/invokeai_config.py
@@ -254,6 +254,10 @@ class InvokeAIAppConfig(InvokeAISettings):
    attention_slice_size: Literal[tuple(["auto", "balanced", "max", 1, 2, 3, 4, 5, 6, 7, 8])] = Field(default="auto", description='Slice size, valid when attention_type=="sliced"', category="Generation", )
    force_tiled_decode: bool = Field(default=False, description="Whether to enable tiled VAE decode (reduces memory consumption with some performance penalty)", category="Generation",)

+    # NODES
+    allow_nodes         : Optional[List[str]] = Field(default=None, description="List of nodes to allow. Omit to allow all.", category="Nodes")
+    deny_nodes          : Optional[List[str]] = Field(default=None, description="List of nodes to deny. Omit to deny none.", category="Nodes")
+
    # DEPRECATED FIELDS - STILL HERE IN ORDER TO OBTAN VALUES FROM PRE-3.1 CONFIG FILES
    always_use_cpu      : bool = Field(default=False, description="If true, use the CPU for rendering even if a GPU is available.", category='Memory/Performance')
    free_gpu_mem        : Optional[bool] = Field(default=None, description="If true, purge model from GPU after each generation.", category='Memory/Performance')
--- a/invokeai/app/services/graph.py
+++ b/invokeai/app/services/graph.py
@@ -112,6 +112,10 @@ def are_connection_types_compatible(from_type: Any, to_type: Any) -> bool:
        if to_type in get_args(from_type):
            return True

+        # allow int -> float, pydantic will cast for us
+        if from_type is int and to_type is float:
+            return True
+
        # if not issubclass(from_type, to_type):
        if not is_union_subtype(from_type, to_type):
            return False
@@ -178,7 +182,7 @@ class IterateInvocationOutput(BaseInvocationOutput):


 # TODO: Fill this out and move to invocations
-@invocation("iterate")
+@invocation("iterate", version="1.0.0")
 class IterateInvocation(BaseInvocation):
    """Iterates over a list of items"""

@@ -199,7 +203,7 @@ class CollectInvocationOutput(BaseInvocationOutput):
    )


-@invocation("collect")
+@invocation("collect", version="1.0.0")
 class CollectInvocation(BaseInvocation):
    """Collects values into a collection"""