feat(nodes): refactor parameter/primitive nodes

Refine concept of "parameter" nodes to "primitives":
- integer
- float
- string
- boolean
- image
- latents
- conditioning
- color

Each primitive has:
- A field definition, if it is not already python primitive value. The field is how this primitive value is passed between nodes. Collections are lists of the field in node definitions. ex: `ImageField` & `list[ImageField]`
- A single output class. ex: `ImageOutput`
- A collection output class. ex: `ImageCollectionOutput`
- A node, which functions to load or pass on the primitive value. ex: `ImageInvocation` (in this case, `ImageInvocation` replaces `LoadImage`)

Plus a number of related changes:
- Reorganize these into `primitives.py`
- Update all nodes and logic to use primitives
- Consolidate "prompt" outputs into "string" & "mask" into "image" (there's no reason for these to be different, the function identically)
- Update default graphs & tests
- Regen frontend types & minor frontend tidy related to changes

invokeai/app/invocations/baseinvocation.py

@@ -98,36 +98,49 @@ class UITypeHint(str, Enum):
     on adding a new field type, which involves client-side changes.
     """
 
+    # region Primitives
     Integer = "integer"
     Float = "float"
     Boolean = "boolean"
     String = "string"
-    Enum = "enum"
     Array = "array"
-    ImageField = "ImageField"
+    Image = "ImageField"
-    LatentsField = "LatentsField"
+    Latents = "LatentsField"
-    ConditioningField = "ConditioningField"
+    Conditioning = "ConditioningField"
-    ControlField = "ControlField"
+    Control = "ControlField"
-    MainModelField = "MainModelField"
+    Color = "ColorField"
-    SDXLMainModelField = "SDXLMainModelField"
-    SDXLRefinerModelField = "SDXLRefinerModelField"
-    ONNXModelField = "ONNXModelField"
-    VaeModelField = "VaeModelField"
-    LoRAModelField = "LoRAModelField"
-    ControlNetModelField = "ControlNetModelField"
-    UNetField = "UNetField"
-    VaeField = "VaeField"
-    ClipField = "ClipField"
-    ColorField = "ColorField"
     ImageCollection = "ImageCollection"
+    ConditioningCollection = "ConditioningCollection"
+    ColorCollection = "ColorCollection"
+    LatentsCollection = "LatentsCollection"
     IntegerCollection = "IntegerCollection"
     FloatCollection = "FloatCollection"
     StringCollection = "StringCollection"
     BooleanCollection = "BooleanCollection"
+    # endregion
+
+    # region Models
+    MainModel = "MainModelField"
+    SDXLMainModel = "SDXLMainModelField"
+    SDXLRefinerModel = "SDXLRefinerModelField"
+    ONNXModel = "ONNXModelField"
+    VaeModel = "VaeModelField"
+    LoRAModel = "LoRAModelField"
+    ControlNetModel = "ControlNetModelField"
+    UNet = "UNetField"
+    Vae = "VaeField"
+    CLIP = "ClipField"
+    # endregion
+
+    # region Iterate/Collect
     Collection = "Collection"
     CollectionItem = "CollectionItem"
-    Seed = "Seed"
+    # endregion
+
+    # region Misc
     FilePath = "FilePath"
+    Enum = "enum"
+    # endregion
 
 
 class UIComponent(str, Enum):

invokeai/app/invocations/collections.py

@@ -5,63 +5,10 @@ from typing import Literal
 import numpy as np
 from pydantic import validator
 
-from invokeai.app.models.image import ImageField
+from invokeai.app.invocations.primitives import ImageCollectionOutput, ImageField, IntegerCollectionOutput
 from invokeai.app.util.misc import SEED_MAX, get_random_seed
 
-from .baseinvocation import (
+from .baseinvocation import BaseInvocation, InputField, InvocationContext, UITypeHint, tags, title
-    BaseInvocation,
-    BaseInvocationOutput,
-    InputField,
-    InvocationContext,
-    OutputField,
-    UITypeHint,
-    tags,
-    title,
-)
-
-
-class IntCollectionOutput(BaseInvocationOutput):
-    """A collection of integers"""
-
-    type: Literal["int_collection_output"] = "int_collection_output"
-
-    # Outputs
-    collection: list[int] = OutputField(
-        default=[], description="The int collection", ui_type_hint=UITypeHint.IntegerCollection
-    )
-
-
-class FloatCollectionOutput(BaseInvocationOutput):
-    """A collection of floats"""
-
-    type: Literal["float_collection_output"] = "float_collection_output"
-
-    # Outputs
-    collection: list[float] = OutputField(
-        default=[], description="The float collection", ui_type_hint=UITypeHint.FloatCollection
-    )
-
-
-class StringCollectionOutput(BaseInvocationOutput):
-    """A collection of strings"""
-
-    type: Literal["string_collection_output"] = "string_collection_output"
-
-    # Outputs
-    collection: list[str] = OutputField(
-        default=[], description="The output strings", ui_type_hint=UITypeHint.StringCollection
-    )
-
-
-class ImageCollectionOutput(BaseInvocationOutput):
-    """A collection of images"""
-
-    type: Literal["image_collection_output"] = "image_collection_output"
-
-    # Outputs
-    collection: list[ImageField] = OutputField(
-        default=[], description="The output images", ui_type_hint=UITypeHint.ImageCollection
-    )
 
 
 @title("Integer Range")
@@ -82,8 +29,8 @@ class RangeInvocation(BaseInvocation):
             raise ValueError("stop must be greater than start")
         return v
 
-    def invoke(self, context: InvocationContext) -> IntCollectionOutput:
+    def invoke(self, context: InvocationContext) -> IntegerCollectionOutput:
-        return IntCollectionOutput(collection=list(range(self.start, self.stop, self.step)))
+        return IntegerCollectionOutput(collection=list(range(self.start, self.stop, self.step)))
 
 
 @title("Integer Range of Size")
@@ -98,8 +45,8 @@ class RangeOfSizeInvocation(BaseInvocation):
     size: int = InputField(default=1, description="The number of values")
     step: int = InputField(default=1, description="The step of the range")
 
-    def invoke(self, context: InvocationContext) -> IntCollectionOutput:
+    def invoke(self, context: InvocationContext) -> IntegerCollectionOutput:
-        return IntCollectionOutput(collection=list(range(self.start, self.start + self.size, self.step)))
+        return IntegerCollectionOutput(collection=list(range(self.start, self.start + self.size, self.step)))
 
 
 @title("Random Range")
@@ -120,9 +67,9 @@ class RandomRangeInvocation(BaseInvocation):
         default_factory=get_random_seed,
     )
 
-    def invoke(self, context: InvocationContext) -> IntCollectionOutput:
+    def invoke(self, context: InvocationContext) -> IntegerCollectionOutput:
         rng = np.random.default_rng(self.seed)
-        return IntCollectionOutput(collection=list(rng.integers(low=self.low, high=self.high, size=self.size)))
+        return IntegerCollectionOutput(collection=list(rng.integers(low=self.low, high=self.high, size=self.size)))
 
 
 @title("Image Collection")

invokeai/app/invocations/compel.py

@@ -5,7 +5,7 @@ from typing import List, Literal, Union
 import torch
 from compel import Compel, ReturnedEmbeddingsType
 from compel.prompt_parser import Blend, Conjunction, CrossAttentionControlSubstitute, FlattenedPrompt, Fragment
-from pydantic import BaseModel, Field
+from invokeai.app.invocations.primitives import ConditioningField, ConditioningOutput
 
 from invokeai.backend.stable_diffusion.diffusion.shared_invokeai_diffusion import (
     BasicConditioningInfo,
@@ -32,13 +32,6 @@ from .baseinvocation import (
 from .model import ClipField
 
 
-class ConditioningField(BaseModel):
-    conditioning_name: str = Field(description="The name of conditioning data")
-
-    class Config:
-        schema_extra = {"required": ["conditioning_name"]}
-
-
 @dataclass
 class ConditioningFieldData:
     conditionings: List[BasicConditioningInfo]
@@ -51,16 +44,6 @@ class ConditioningFieldData:
 #    PerpNeg = "perp_neg"
 
 
-class CompelOutput(BaseInvocationOutput):
-    """Compel parser output"""
-
-    # fmt: off
-    type: Literal["compel_output"] = "compel_output"
-
-    conditioning: ConditioningField = OutputField(description=FieldDescriptions.cond)
-    # fmt: on
-
-
 @title("Compel Prompt")
 @tags("prompt", "compel")
 class CompelInvocation(BaseInvocation):
@@ -80,7 +63,7 @@ class CompelInvocation(BaseInvocation):
     )
 
     @torch.no_grad()
-    def invoke(self, context: InvocationContext) -> CompelOutput:
+    def invoke(self, context: InvocationContext) -> ConditioningOutput:
         tokenizer_info = context.services.model_manager.get_model(
             **self.clip.tokenizer.dict(),
             context=context,
@@ -163,7 +146,7 @@ class CompelInvocation(BaseInvocation):
         conditioning_name = f"{context.graph_execution_state_id}_{self.id}_conditioning"
         context.services.latents.save(conditioning_name, conditioning_data)
 
-        return CompelOutput(
+        return ConditioningOutput(
             conditioning=ConditioningField(
                 conditioning_name=conditioning_name,
             ),
@@ -303,7 +286,7 @@ class SDXLCompelPromptInvocation(BaseInvocation, SDXLPromptInvocationBase):
     clip2: ClipField = InputField(description=FieldDescriptions.clip, input=Input.Connection)
 
     @torch.no_grad()
-    def invoke(self, context: InvocationContext) -> CompelOutput:
+    def invoke(self, context: InvocationContext) -> ConditioningOutput:
         c1, c1_pooled, ec1 = self.run_clip_compel(
             context, self.clip, self.prompt, False, "lora_te1_", zero_on_empty=True
         )
@@ -336,7 +319,7 @@ class SDXLCompelPromptInvocation(BaseInvocation, SDXLPromptInvocationBase):
         conditioning_name = f"{context.graph_execution_state_id}_{self.id}_conditioning"
         context.services.latents.save(conditioning_name, conditioning_data)
 
-        return CompelOutput(
+        return ConditioningOutput(
             conditioning=ConditioningField(
                 conditioning_name=conditioning_name,
             ),
@@ -361,7 +344,7 @@ class SDXLRefinerCompelPromptInvocation(BaseInvocation, SDXLPromptInvocationBase
     clip2: ClipField = InputField(description=FieldDescriptions.clip, input=Input.Connection)
 
     @torch.no_grad()
-    def invoke(self, context: InvocationContext) -> CompelOutput:
+    def invoke(self, context: InvocationContext) -> ConditioningOutput:
         # TODO: if there will appear lora for refiner - write proper prefix
         c2, c2_pooled, ec2 = self.run_clip_compel(context, self.clip2, self.style, True, "<NONE>", zero_on_empty=False)
 
@@ -384,7 +367,7 @@ class SDXLRefinerCompelPromptInvocation(BaseInvocation, SDXLPromptInvocationBase
         conditioning_name = f"{context.graph_execution_state_id}_{self.id}_conditioning"
         context.services.latents.save(conditioning_name, conditioning_data)
 
-        return CompelOutput(
+        return ConditioningOutput(
             conditioning=ConditioningField(
                 conditioning_name=conditioning_name,
             ),

invokeai/app/invocations/controlnet_image_processors.py

@@ -26,8 +26,11 @@ from controlnet_aux.util import HWC3, ade_palette
 from PIL import Image
 from pydantic import BaseModel, Field, validator
 
+from invokeai.app.invocations.primitives import ImageField, ImageOutput
+
+
 from ...backend.model_management import BaseModelType, ModelType
-from ..models.image import ImageCategory, ImageField, ResourceOrigin
+from ..models.image import ImageCategory, ResourceOrigin
 from .baseinvocation import (
     BaseInvocation,
     BaseInvocationOutput,
@@ -40,7 +43,6 @@ from .baseinvocation import (
     tags,
     title,
 )
-from ..models.image import ImageOutput
 
 
 CONTROLNET_MODE_VALUES = Literal["balanced", "more_prompt", "more_control", "unbalanced"]

invokeai/app/invocations/cv.py

@@ -5,10 +5,10 @@ from typing import Literal
 import cv2 as cv
 import numpy
 from PIL import Image, ImageOps
+from invokeai.app.invocations.primitives import ImageField, ImageOutput
 
-from invokeai.app.models.image import ImageCategory, ImageField, ResourceOrigin
+from invokeai.app.models.image import ImageCategory, ResourceOrigin
 from .baseinvocation import BaseInvocation, InputField, InvocationContext, tags, title
-from .image import ImageOutput
 
 
 @title("OpenCV Inpaint")

invokeai/app/invocations/image.py

@@ -8,34 +8,14 @@ import numpy
 from PIL import Image, ImageChops, ImageFilter, ImageOps
 
 from invokeai.app.invocations.metadata import CoreMetadata
+from invokeai.app.invocations.primitives import ImageField, ImageOutput
 from invokeai.backend.image_util.invisible_watermark import InvisibleWatermark
 from invokeai.backend.image_util.safety_checker import SafetyChecker
 
-from ..models.image import ImageCategory, ImageField, ImageOutput, MaskOutput, ResourceOrigin
+from ..models.image import ImageCategory, ResourceOrigin
 from .baseinvocation import BaseInvocation, FieldDescriptions, InputField, InvocationContext, tags, title
 
 
-@title("Load Image")
-@tags("image")
-class LoadImageInvocation(BaseInvocation):
-    """Load an image and provide it as output."""
-
-    # Metadata
-    type: Literal["load_image"] = "load_image"
-
-    # Inputs
-    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)
-
-        return ImageOutput(
-            image=ImageField(image_name=self.image.image_name),
-            width=image.width,
-            height=image.height,
-        )
-
-
 @title("Show Image")
 @tags("image")
 class ShowImageInvocation(BaseInvocation):
@@ -162,7 +142,7 @@ class MaskFromAlphaInvocation(BaseInvocation):
     image: ImageField = InputField(description="The image to create the mask from")
     invert: bool = InputField(default=False, description="Whether or not to invert the mask")
 
-    def invoke(self, context: InvocationContext) -> MaskOutput:
+    def invoke(self, context: InvocationContext) -> ImageOutput:
         image = context.services.images.get_pil_image(self.image.image_name)
 
         image_mask = image.split()[-1]
@@ -178,8 +158,8 @@ class MaskFromAlphaInvocation(BaseInvocation):
             is_intermediate=self.is_intermediate,
         )
 
-        return MaskOutput(
+        return ImageOutput(
-            mask=ImageField(image_name=image_dto.image_name),
+            image=ImageField(image_name=image_dto.image_name),
             width=image_dto.width,
             height=image_dto.height,
         )
@@ -608,7 +588,7 @@ class MaskEdgeInvocation(BaseInvocation):
         description="Second threshold for the hysteresis procedure in Canny edge detection"
     )
 
-    def invoke(self, context: InvocationContext) -> MaskOutput:
+    def invoke(self, context: InvocationContext) -> ImageOutput:
         mask = context.services.images.get_pil_image(self.image.image_name)
 
         npimg = numpy.asarray(mask, dtype=numpy.uint8)
@@ -633,8 +613,8 @@ class MaskEdgeInvocation(BaseInvocation):
             is_intermediate=self.is_intermediate,
         )
 
-        return MaskOutput(
+        return ImageOutput(
-            mask=ImageField(image_name=image_dto.image_name),
+            image=ImageField(image_name=image_dto.image_name),
             width=image_dto.width,
             height=image_dto.height,
         )

invokeai/app/invocations/infill.py

@@ -5,13 +5,13 @@ from typing import Literal, Optional, get_args
 import numpy as np
 import math
 from PIL import Image, ImageOps
+from invokeai.app.invocations.primitives import ImageField, ImageOutput, ColorField
 
-from invokeai.app.invocations.image import ImageOutput
 from invokeai.app.util.misc import SEED_MAX, get_random_seed
 from invokeai.backend.image_util.patchmatch import PatchMatch
 
-from ..models.image import ColorField, ImageCategory, ImageField, ResourceOrigin
+from ..models.image import ImageCategory, ResourceOrigin
-from .baseinvocation import BaseInvocation, InputField, InvocationContext, UITypeHint, title, tags
+from .baseinvocation import BaseInvocation, InputField, InvocationContext, title, tags
 
 
 def infill_methods() -> list[str]:

invokeai/app/invocations/latent.py

@@ -19,6 +19,13 @@ from pydantic import BaseModel, Field, validator
 from torchvision.transforms.functional import resize as tv_resize
 
 from invokeai.app.invocations.metadata import CoreMetadata
+from invokeai.app.invocations.primitives import (
+    ImageField,
+    ImageOutput,
+    LatentsField,
+    LatentsOutput,
+    build_latents_output,
+)
 from invokeai.app.util.controlnet_utils import prepare_control_image
 from invokeai.app.util.step_callback import stable_diffusion_step_callback
 from invokeai.backend.model_management.models import ModelType, SilenceWarnings
@@ -35,7 +42,7 @@ from ...backend.stable_diffusion.diffusers_pipeline import (
 from ...backend.stable_diffusion.diffusion.shared_invokeai_diffusion import PostprocessingSettings
 from ...backend.stable_diffusion.schedulers import SCHEDULER_MAP
 from ...backend.util.devices import choose_precision, choose_torch_device
-from ..models.image import ImageCategory, ImageField, ResourceOrigin
+from ..models.image import ImageCategory, ResourceOrigin
 from .baseinvocation import (
     BaseInvocation,
     BaseInvocationOutput,
@@ -50,43 +57,11 @@ from .baseinvocation import (
 )
 from .compel import ConditioningField
 from .controlnet_image_processors import ControlField
-from .image import ImageOutput
 from .model import ModelInfo, UNetField, VaeField
 
 DEFAULT_PRECISION = choose_precision(choose_torch_device())
 
 
-class LatentsField(BaseModel):
-    """A latents field used for passing latents between invocations"""
-
-    latents_name: str = Field(description="The name of the latents")
-    seed: Optional[int] = Field(default=None, description="Seed used to generate this latents")
-
-    class Config:
-        schema_extra = {"required": ["latents_name"]}
-
-
-class LatentsOutput(BaseInvocationOutput):
-    """Base class for invocations that output latents"""
-
-    type: Literal["latents_output"] = "latents_output"
-
-    # Inputs
-    latents: LatentsField = OutputField(
-        description=FieldDescriptions.latents,
-    )
-    width: int = OutputField(description=FieldDescriptions.width)
-    height: int = OutputField(description=FieldDescriptions.height)
-
-
-def build_latents_output(latents_name: str, latents: torch.Tensor, seed: Optional[int]):
-    return LatentsOutput(
-        latents=LatentsField(latents_name=latents_name, seed=seed),
-        width=latents.size()[3] * 8,
-        height=latents.size()[2] * 8,
-    )
-
-
 SAMPLER_NAME_VALUES = Literal[tuple(list(SCHEDULER_MAP.keys()))]
 
 

invokeai/app/invocations/math.py

@@ -4,30 +4,9 @@ from typing import Literal
 
 import numpy as np
 
-from .baseinvocation import (
+from invokeai.app.invocations.primitives import IntegerOutput
-    BaseInvocation,
-    BaseInvocationOutput,
-    FieldDescriptions,
-    InputField,
-    InvocationContext,
-    OutputField,
-    tags,
-    title,
-)
 
-
+from .baseinvocation import BaseInvocation, FieldDescriptions, InputField, InvocationContext, tags, title
-class IntOutput(BaseInvocationOutput):
-    """An integer output"""
-
-    type: Literal["int_output"] = "int_output"
-    a: int = OutputField(default=None, description="The output integer")
-
-
-class FloatOutput(BaseInvocationOutput):
-    """A float output"""
-
-    type: Literal["float_output"] = "float_output"
-    a: float = OutputField(default=None, description="The output float")
 
 
 @title("Add Integers")
@@ -41,8 +20,8 @@ class AddInvocation(BaseInvocation):
     a: int = InputField(default=0, description=FieldDescriptions.num_1)
     b: int = InputField(default=0, description=FieldDescriptions.num_2)
 
-    def invoke(self, context: InvocationContext) -> IntOutput:
+    def invoke(self, context: InvocationContext) -> IntegerOutput:
-        return IntOutput(a=self.a + self.b)
+        return IntegerOutput(a=self.a + self.b)
 
 
 @title("Subtract Integers")
@@ -56,8 +35,8 @@ class SubtractInvocation(BaseInvocation):
     a: int = InputField(default=0, description=FieldDescriptions.num_1)
     b: int = InputField(default=0, description=FieldDescriptions.num_2)
 
-    def invoke(self, context: InvocationContext) -> IntOutput:
+    def invoke(self, context: InvocationContext) -> IntegerOutput:
-        return IntOutput(a=self.a - self.b)
+        return IntegerOutput(a=self.a - self.b)
 
 
 @title("Multiply Integers")
@@ -71,8 +50,8 @@ class MultiplyInvocation(BaseInvocation):
     a: int = InputField(default=0, description=FieldDescriptions.num_1)
     b: int = InputField(default=0, description=FieldDescriptions.num_2)
 
-    def invoke(self, context: InvocationContext) -> IntOutput:
+    def invoke(self, context: InvocationContext) -> IntegerOutput:
-        return IntOutput(a=self.a * self.b)
+        return IntegerOutput(a=self.a * self.b)
 
 
 @title("Divide Integers")
@@ -86,8 +65,8 @@ class DivideInvocation(BaseInvocation):
     a: int = InputField(default=0, description=FieldDescriptions.num_1)
     b: int = InputField(default=0, description=FieldDescriptions.num_2)
 
-    def invoke(self, context: InvocationContext) -> IntOutput:
+    def invoke(self, context: InvocationContext) -> IntegerOutput:
-        return IntOutput(a=int(self.a / self.b))
+        return IntegerOutput(a=int(self.a / self.b))
 
 
 @title("Random Integer")
@@ -101,5 +80,5 @@ class RandomIntInvocation(BaseInvocation):
     low: int = InputField(default=0, description="The inclusive low value")
     high: int = InputField(default=np.iinfo(np.int32).max, description="The exclusive high value")
 
-    def invoke(self, context: InvocationContext) -> IntOutput:
+    def invoke(self, context: InvocationContext) -> IntegerOutput:
-        return IntOutput(a=np.random.randint(self.low, self.high))
+        return IntegerOutput(a=np.random.randint(self.low, self.high))

invokeai/app/invocations/metadata.py

@@ -7,6 +7,7 @@ from invokeai.app.invocations.baseinvocation import (
     BaseInvocationOutput,
     InputField,
     InvocationContext,
+    OutputField,
     tags,
     title,
 )
@@ -94,7 +95,7 @@ class MetadataAccumulatorOutput(BaseInvocationOutput):
 
     type: Literal["metadata_accumulator_output"] = "metadata_accumulator_output"
 
-    metadata: CoreMetadata = Field(description="The core metadata for the image")
+    metadata: CoreMetadata = OutputField(description="The core metadata for the image")
 
 
 @title("Metadata Accumulator")

invokeai/app/invocations/model.py

@@ -365,7 +365,7 @@ class VaeLoaderInvocation(BaseInvocation):
 
     # Inputs
     vae_model: VAEModelField = InputField(
-        description=FieldDescriptions.vae_model, input=Input.Direct, ui_type_hint=UITypeHint.VaeModelField, title="VAE"
+        description=FieldDescriptions.vae_model, input=Input.Direct, ui_type_hint=UITypeHint.VaeModel, title="VAE"
     )
 
     def invoke(self, context: InvocationContext) -> VaeLoaderOutput:

invokeai/app/invocations/onnx.py

@@ -14,13 +14,14 @@ from pydantic import BaseModel, Field, validator
 from tqdm import tqdm
 
 from invokeai.app.invocations.metadata import CoreMetadata
+from invokeai.app.invocations.primitives import ConditioningField, ConditioningOutput, ImageField, ImageOutput
 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 ..models.image import ImageCategory, ImageField, ResourceOrigin
+from ..models.image import ImageCategory, ResourceOrigin
 from .baseinvocation import (
     BaseInvocation,
     BaseInvocationOutput,
@@ -34,9 +35,7 @@ from .baseinvocation import (
     tags,
     title,
 )
-from .compel import CompelOutput, ConditioningField
 from .controlnet_image_processors import ControlField
-from .image import ImageOutput
 from .latent import SAMPLER_NAME_VALUES, LatentsField, LatentsOutput, build_latents_output, get_scheduler
 from .model import ClipField, ModelInfo, UNetField, VaeField
 
@@ -66,7 +65,7 @@ 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) -> CompelOutput:
+    def invoke(self, context: InvocationContext) -> ConditioningOutput:
         tokenizer_info = context.services.model_manager.get_model(
             **self.clip.tokenizer.dict(),
         )
@@ -135,7 +134,7 @@ class ONNXPromptInvocation(BaseInvocation):
         # TODO: hacky but works ;D maybe rename latents somehow?
         context.services.latents.save(conditioning_name, (prompt_embeds, None))
 
-        return CompelOutput(
+        return ConditioningOutput(
             conditioning=ConditioningField(
                 conditioning_name=conditioning_name,
             ),
@@ -181,7 +180,7 @@ class ONNXTextToLatentsInvocation(BaseInvocation):
     control: Optional[Union[ControlField, list[ControlField]]] = InputField(
         default=None,
         description=FieldDescriptions.control,
-        ui_type_hint=UITypeHint.ControlField,
+        ui_type_hint=UITypeHint.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'")
@@ -417,7 +416,7 @@ class OnnxModelLoaderInvocation(BaseInvocation):
 
     # Inputs
     model: OnnxModelField = InputField(
-        description=FieldDescriptions.onnx_main_model, input=Input.Direct, ui_type_hint=UITypeHint.ONNXModelField
+        description=FieldDescriptions.onnx_main_model, input=Input.Direct, ui_type_hint=UITypeHint.ONNXModel
     )
 
     def invoke(self, context: InvocationContext) -> ONNXModelLoaderOutput:

invokeai/app/invocations/param_easing.py

@@ -42,9 +42,10 @@ from matplotlib.figure import Figure
 from matplotlib.ticker import MaxNLocator
 from pydantic import BaseModel, Field
 
+from invokeai.app.invocations.primitives import FloatCollectionOutput
+
 from ...backend.util.logging import InvokeAILogger
 from .baseinvocation import BaseInvocation, InputField, InvocationContext, tags, title
-from .collections import FloatCollectionOutput
 
 
 @title("Float Range")

invokeai/app/invocations/params.py

@@ -1,81 +0,0 @@
-# Copyright (c) 2023 Kyle Schouviller (https://github.com/kyle0654)
-
-from typing import Literal
-
-from invokeai.app.invocations.prompt import PromptOutput
-
-from .baseinvocation import (
-    BaseInvocation,
-    BaseInvocationOutput,
-    InputField,
-    InvocationContext,
-    OutputField,
-    tags,
-    title,
-)
-from .math import FloatOutput, IntOutput
-
-# Pass-through parameter nodes - used by subgraphs
-
-
-@title("Integer Parameter")
-@tags("integer")
-class ParamIntInvocation(BaseInvocation):
-    """An integer parameter"""
-
-    type: Literal["param_int"] = "param_int"
-
-    # Inputs
-    a: int = InputField(default=0, description="The integer value")
-
-    def invoke(self, context: InvocationContext) -> IntOutput:
-        return IntOutput(a=self.a)
-
-
-@title("Float Parameter")
-@tags("float")
-class ParamFloatInvocation(BaseInvocation):
-    """A float parameter"""
-
-    type: Literal["param_float"] = "param_float"
-
-    # Inputs
-    param: float = InputField(default=0.0, description="The float value")
-
-    def invoke(self, context: InvocationContext) -> FloatOutput:
-        return FloatOutput(a=self.param)
-
-
-class StringOutput(BaseInvocationOutput):
-    """A string output"""
-
-    type: Literal["string_output"] = "string_output"
-    text: str = OutputField(description="The output string")
-
-
-@title("String Parameter")
-@tags("string")
-class ParamStringInvocation(BaseInvocation):
-    """A string parameter"""
-
-    type: Literal["param_string"] = "param_string"
-
-    # Inputs
-    text: str = InputField(default="", description="The string value")
-
-    def invoke(self, context: InvocationContext) -> StringOutput:
-        return StringOutput(text=self.text)
-
-
-@title("Prompt Parameter")
-@tags("prompt")
-class ParamPromptInvocation(BaseInvocation):
-    """A prompt input parameter"""
-
-    type: Literal["param_prompt"] = "param_prompt"
-
-    # Inputs
-    prompt: str = InputField(default="", description="The prompt value")
-
-    def invoke(self, context: InvocationContext) -> PromptOutput:
-        return PromptOutput(prompt=self.prompt)

invokeai/app/invocations/primitives.py

@@ -0,0 +1,381 @@
+# Copyright (c) 2023 Kyle Schouviller (https://github.com/kyle0654)
+
+from typing import Literal, Optional, Tuple
+
+from pydantic import BaseModel, Field
+import torch
+
+from .baseinvocation import (
+    BaseInvocation,
+    BaseInvocationOutput,
+    FieldDescriptions,
+    Input,
+    InputField,
+    InvocationContext,
+    OutputField,
+    UIComponent,
+    UITypeHint,
+    tags,
+    title,
+)
+
+"""
+Primitives: Boolean, Integer, Float, String, Image, Latents, Conditioning, Color
+- primitive nodes
+- primitive outputs
+- primitive collection outputs
+"""
+
+# region Boolean
+
+
+class BooleanOutput(BaseInvocationOutput):
+    """Base class for nodes that output a single boolean"""
+
+    type: Literal["boolean_output"] = "boolean_output"
+    a: bool = OutputField(description="The output boolean")
+
+
+class BooleanCollectionOutput(BaseInvocationOutput):
+    """Base class for nodes that output a collection of booleans"""
+
+    type: Literal["boolean_collection_output"] = "boolean_collection_output"
+
+    # Outputs
+    collection: list[bool] = OutputField(
+        default_factory=list, description="The output boolean collection", ui_type_hint=UITypeHint.BooleanCollection
+    )
+
+
+@title("Boolean Primitive")
+@tags("boolean")
+class BooleanInvocation(BaseInvocation):
+    """A boolean primitive value"""
+
+    type: Literal["boolean"] = "boolean"
+
+    # Inputs
+    a: bool = InputField(default=False, description="The boolean value")
+
+    def invoke(self, context: InvocationContext) -> BooleanOutput:
+        return BooleanOutput(a=self.a)
+
+
+# endregion
+
+# region Integer
+
+
+class IntegerOutput(BaseInvocationOutput):
+    """Base class for nodes that output a single integer"""
+
+    type: Literal["integer_output"] = "integer_output"
+    a: int = OutputField(description="The output integer")
+
+
+class IntegerCollectionOutput(BaseInvocationOutput):
+    """Base class for nodes that output a collection of integers"""
+
+    type: Literal["integer_collection_output"] = "integer_collection_output"
+
+    # Outputs
+    collection: list[int] = OutputField(
+        default_factory=list, description="The int collection", ui_type_hint=UITypeHint.IntegerCollection
+    )
+
+
+@title("Integer Primitive")
+@tags("integer")
+class IntegerInvocation(BaseInvocation):
+    """An integer primitive value"""
+
+    type: Literal["integer"] = "integer"
+
+    # Inputs
+    a: int = InputField(default=0, description="The integer value")
+
+    def invoke(self, context: InvocationContext) -> IntegerOutput:
+        return IntegerOutput(a=self.a)
+
+
+# endregion
+
+# region Float
+
+
+class FloatOutput(BaseInvocationOutput):
+    """Base class for nodes that output a single float"""
+
+    type: Literal["float_output"] = "float_output"
+    a: float = OutputField(description="The output float")
+
+
+class FloatCollectionOutput(BaseInvocationOutput):
+    """Base class for nodes that output a collection of floats"""
+
+    type: Literal["float_collection_output"] = "float_collection_output"
+
+    # Outputs
+    collection: list[float] = OutputField(
+        default_factory=list, description="The float collection", ui_type_hint=UITypeHint.FloatCollection
+    )
+
+
+@title("Float Primitive")
+@tags("float")
+class FloatInvocation(BaseInvocation):
+    """A float primitive value"""
+
+    type: Literal["float"] = "float"
+
+    # Inputs
+    param: float = InputField(default=0.0, description="The float value")
+
+    def invoke(self, context: InvocationContext) -> FloatOutput:
+        return FloatOutput(a=self.param)
+
+
+# endregion
+
+# region String
+
+
+class StringOutput(BaseInvocationOutput):
+    """Base class for nodes that output a single string"""
+
+    type: Literal["string_output"] = "string_output"
+    text: str = OutputField(description="The output string")
+
+
+class StringCollectionOutput(BaseInvocationOutput):
+    """Base class for nodes that output a collection of strings"""
+
+    type: Literal["string_collection_output"] = "string_collection_output"
+
+    # Outputs
+    collection: list[str] = OutputField(
+        default_factory=list, description="The output strings", ui_type_hint=UITypeHint.StringCollection
+    )
+
+
+@title("String Primitive")
+@tags("string")
+class StringInvocation(BaseInvocation):
+    """A string primitive value"""
+
+    type: Literal["string"] = "string"
+
+    # Inputs
+    text: str = InputField(default="", description="The string value", ui_component=UIComponent.Textarea)
+
+    def invoke(self, context: InvocationContext) -> StringOutput:
+        return StringOutput(text=self.text)
+
+
+# endregion
+
+# region Image
+
+
+class ImageField(BaseModel):
+    """An image primitive field"""
+
+    image_name: str = Field(description="The name of the image")
+
+
+class ImageOutput(BaseInvocationOutput):
+    """Base class for nodes that output a single image"""
+
+    type: Literal["image_output"] = "image_output"
+    image: ImageField = OutputField(description="The output image")
+    width: int = OutputField(description="The width of the image in pixels")
+    height: int = OutputField(description="The height of the image in pixels")
+
+
+class ImageCollectionOutput(BaseInvocationOutput):
+    """Base class for nodes that output a collection of images"""
+
+    type: Literal["image_collection_output"] = "image_collection_output"
+
+    # Outputs
+    collection: list[ImageField] = OutputField(
+        default_factory=list, description="The output images", ui_type_hint=UITypeHint.ImageCollection
+    )
+
+
+@title("Image Primitive")
+@tags("image")
+class ImageInvocation(BaseInvocation):
+    """An image primitive value"""
+
+    # Metadata
+    type: Literal["image"] = "image"
+
+    # Inputs
+    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)
+
+        return ImageOutput(
+            image=ImageField(image_name=self.image.image_name),
+            width=image.width,
+            height=image.height,
+        )
+
+
+# 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")
+
+
+class LatentsOutput(BaseInvocationOutput):
+    """Base class for nodes that output a single latents tensor"""
+
+    type: Literal["latents_output"] = "latents_output"
+
+    latents: LatentsField = OutputField(
+        description=FieldDescriptions.latents,
+    )
+    width: int = OutputField(description=FieldDescriptions.width)
+    height: int = OutputField(description=FieldDescriptions.height)
+
+
+class LatentsCollectionOutput(BaseInvocationOutput):
+    """Base class for nodes that output a collection of latents tensors"""
+
+    type: Literal["latents_collection_output"] = "latents_collection_output"
+
+    latents: list[LatentsField] = OutputField(
+        default_factory=list,
+        description=FieldDescriptions.latents,
+        ui_type_hint=UITypeHint.LatentsCollection,
+    )
+
+
+@title("Latents Primitive")
+@tags("latents")
+class LatentsInvocation(BaseInvocation):
+    """A latents tensor primitive value"""
+
+    type: Literal["latents"] = "latents"
+
+    # Inputs
+    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)
+
+        return build_latents_output(self.latents.latents_name, latents)
+
+
+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)
+
+
+class ColorOutput(BaseInvocationOutput):
+    """Base class for nodes that output a single color"""
+
+    type: Literal["color_output"] = "color_output"
+    color: ColorField = OutputField(description="The output color")
+
+
+class ColorCollectionOutput(BaseInvocationOutput):
+    """Base class for nodes that output a collection of colors"""
+
+    type: Literal["color_collection_output"] = "color_collection_output"
+
+    # Outputs
+    collection: list[ColorField] = OutputField(
+        default_factory=list, description="The output colors", ui_type_hint=UITypeHint.ColorCollection
+    )
+
+
+@title("Color Primitive")
+@tags("color")
+class ColorInvocation(BaseInvocation):
+    """A color primitive value"""
+
+    type: Literal["color"] = "color"
+
+    # Inputs
+    color: ColorField = InputField(default=ColorField(r=0, g=0, b=0, a=255), description="The color value")
+
+    def invoke(self, context: InvocationContext) -> ColorOutput:
+        return ColorOutput(color=self.color)
+
+
+# endregion
+
+# region Conditioning
+
+
+class ConditioningField(BaseModel):
+    """A conditioning tensor primitive field"""
+
+    conditioning_name: str = Field(description="The name of conditioning tensor")
+
+
+class ConditioningOutput(BaseInvocationOutput):
+    """Base class for nodes that output a single conditioning tensor"""
+
+    type: Literal["conditioning_output"] = "conditioning_output"
+
+    conditioning: ConditioningField = OutputField(description=FieldDescriptions.cond)
+
+
+class ConditioningCollectionOutput(BaseInvocationOutput):
+    """Base class for nodes that output a collection of conditioning tensors"""
+
+    type: Literal["conditioning_collection_output"] = "conditioning_collection_output"
+
+    # Outputs
+    collection: list[ConditioningField] = OutputField(
+        default_factory=list,
+        description="The output conditioning tensors",
+        ui_type_hint=UITypeHint.ConditioningCollection,
+    )
+
+
+@title("Conditioning Primitive")
+@tags("conditioning")
+class ConditioningInvocation(BaseInvocation):
+    """A conditioning tensor primitive value"""
+
+    type: Literal["conditioning"] = "conditioning"
+
+    conditioning: ConditioningField = InputField(description=FieldDescriptions.cond, input=Input.Connection)
+
+    def invoke(self, context: InvocationContext) -> ConditioningOutput:
+        return ConditioningOutput(conditioning=self.conditioning)
+
+
+# endregion

invokeai/app/invocations/prompt.py

@@ -1,40 +1,13 @@
 from os.path import exists
-from typing import Literal, Optional
+from typing import Literal, Optional, Union
 
 import numpy as np
+from dynamicprompts.generators import CombinatorialPromptGenerator, RandomPromptGenerator
 from pydantic import validator
 
-from .baseinvocation import (
+from invokeai.app.invocations.primitives import StringCollectionOutput
-    BaseInvocation,
-    BaseInvocationOutput,
-    InputField,
-    InvocationContext,
-    OutputField,
-    UIComponent,
-    UITypeHint,
-    title,
-    tags,
-)
-from dynamicprompts.generators import RandomPromptGenerator, CombinatorialPromptGenerator
 
-
+from .baseinvocation import BaseInvocation, InputField, InvocationContext, UIComponent, UITypeHint, tags, title
-class PromptOutput(BaseInvocationOutput):
-    """Base class for invocations that output a prompt"""
-
-    type: Literal["prompt"] = "prompt"
-
-    prompt: str = OutputField(description="The output prompt")
-
-
-class PromptCollectionOutput(BaseInvocationOutput):
-    """Base class for invocations that output a collection of prompts"""
-
-    type: Literal["prompt_collection_output"] = "prompt_collection_output"
-
-    prompt_collection: list[str] = OutputField(
-        description="The output prompt collection", ui_type_hint=UITypeHint.StringCollection
-    )
-    count: int = OutputField(description="The size of the prompt collection")
 
 
 @title("Dynamic Prompt")
@@ -49,7 +22,7 @@ class DynamicPromptInvocation(BaseInvocation):
     max_prompts: int = InputField(default=1, description="The number of prompts to generate")
     combinatorial: bool = InputField(default=False, description="Whether to use the combinatorial generator")
 
-    def invoke(self, context: InvocationContext) -> PromptCollectionOutput:
+    def invoke(self, context: InvocationContext) -> StringCollectionOutput:
         if self.combinatorial:
             generator = CombinatorialPromptGenerator()
             prompts = generator.generate(self.prompt, max_prompts=self.max_prompts)
@@ -57,7 +30,7 @@ class DynamicPromptInvocation(BaseInvocation):
             generator = RandomPromptGenerator()
             prompts = generator.generate(self.prompt, num_images=self.max_prompts)
 
-        return PromptCollectionOutput(prompt_collection=prompts, count=len(prompts))
+        return StringCollectionOutput(collection=prompts)
 
 
 @title("Prompts from File")
@@ -70,10 +43,10 @@ class PromptsFromFileInvocation(BaseInvocation):
     # Inputs
     file_path: str = InputField(description="Path to prompt text file", ui_type_hint=UITypeHint.FilePath)
     pre_prompt: Optional[str] = InputField(
-        description="String to prepend to each prompt", ui_component=UIComponent.Textarea
+        default=None, description="String to prepend to each prompt", ui_component=UIComponent.Textarea
     )
     post_prompt: Optional[str] = InputField(
-        description="String to append to each prompt", ui_component=UIComponent.Textarea
+        default=None, description="String to append to each prompt", ui_component=UIComponent.Textarea
     )
     start_line: int = InputField(default=1, ge=1, description="Line in the file to start start from")
     max_prompts: int = InputField(default=1, ge=0, description="Max lines to read from file (0=all)")
@@ -84,7 +57,14 @@ class PromptsFromFileInvocation(BaseInvocation):
             raise ValueError(FileNotFoundError)
         return v
 
-    def promptsFromFile(self, file_path: str, pre_prompt: str, post_prompt: str, start_line: int, max_prompts: int):
+    def promptsFromFile(
+        self,
+        file_path: str,
+        pre_prompt: Union[str, None],
+        post_prompt: Union[str, None],
+        start_line: int,
+        max_prompts: int,
+    ):
         prompts = []
         start_line -= 1
         end_line = start_line + max_prompts
@@ -98,8 +78,8 @@ class PromptsFromFileInvocation(BaseInvocation):
                     break
         return prompts
 
-    def invoke(self, context: InvocationContext) -> PromptCollectionOutput:
+    def invoke(self, context: InvocationContext) -> StringCollectionOutput:
         prompts = self.promptsFromFile(
             self.file_path, self.pre_prompt, self.post_prompt, self.start_line, self.max_prompts
         )
-        return PromptCollectionOutput(prompt_collection=prompts, count=len(prompts))
+        return StringCollectionOutput(collection=prompts)

invokeai/app/invocations/sdxl.py

@@ -46,7 +46,7 @@ class SDXLModelLoaderInvocation(BaseInvocation):
 
     # Inputs
     model: MainModelField = InputField(
-        description=FieldDescriptions.sdxl_main_model, input=Input.Direct, ui_type_hint=UITypeHint.SDXLMainModelField
+        description=FieldDescriptions.sdxl_main_model, input=Input.Direct, ui_type_hint=UITypeHint.SDXLMainModel
     )
     # TODO: precision?
 
@@ -133,7 +133,7 @@ class SDXLRefinerModelLoaderInvocation(BaseInvocation):
     model: MainModelField = InputField(
         description=FieldDescriptions.sdxl_refiner_model,
         input=Input.Direct,
-        ui_type_hint=UITypeHint.SDXLRefinerModelField,
+        ui_type_hint=UITypeHint.SDXLRefinerModel,
     )
     # TODO: precision?
 

invokeai/app/invocations/upscale.py

@@ -7,11 +7,11 @@ import numpy as np
 from basicsr.archs.rrdbnet_arch import RRDBNet
 from PIL import Image
 from realesrgan import RealESRGANer
+from invokeai.app.invocations.primitives import ImageField, ImageOutput
 
-from invokeai.app.models.image import ImageCategory, ImageField, ResourceOrigin
+from invokeai.app.models.image import ImageCategory, ResourceOrigin
 
 from .baseinvocation import BaseInvocation, InputField, InvocationContext, title, tags
-from .image import ImageOutput
 
 # TODO: Populate this from disk?
 # TODO: Use model manager to load?

invokeai/app/models/image.py

@@ -1,30 +1,8 @@
 from enum import Enum
-from typing import Optional, Tuple, Literal
+
 from pydantic import BaseModel, Field
 
 from invokeai.app.util.metaenum import MetaEnum
-from ..invocations.baseinvocation import (
-    BaseInvocationOutput,
-)
-
-
-class ImageField(BaseModel):
-    """An image field used for passing image objects between invocations"""
-
-    image_name: str = Field(description="The name of the image")
-
-    class Config:
-        schema_extra = {"required": ["image_name"]}
-
-
-class ColorField(BaseModel):
-    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 ProgressImage(BaseModel):
@@ -35,39 +13,6 @@ class ProgressImage(BaseModel):
     dataURL: str = Field(description="The image data as a b64 data URL")
 
 
-class ImageOutput(BaseInvocationOutput):
-    """Base class for invocations that output an image"""
-
-    # fmt: off
-    type: Literal["image_output"] = "image_output"
-    image:      ImageField = Field(default=None, description="The output image")
-    width:             int = Field(description="The width of the image in pixels")
-    height:            int = Field(description="The height of the image in pixels")
-    # fmt: on
-
-    class Config:
-        schema_extra = {"required": ["type", "image", "width", "height"]}
-
-
-class MaskOutput(BaseInvocationOutput):
-    """Base class for invocations that output a mask"""
-
-    # fmt: off
-    type: Literal["mask"] = "mask"
-    mask:      ImageField = Field(default=None, description="The output mask")
-    width:            int = Field(description="The width of the mask in pixels")
-    height:           int = Field(description="The height of the mask in pixels")
-    # fmt: on
-
-    class Config:
-        schema_extra = {
-            "required": [
-                "type",
-                "mask",
-            ]
-        }
-
-
 class ResourceOrigin(str, Enum, metaclass=MetaEnum):
     """The origin of a resource (eg image).
 

invokeai/app/services/default_graphs.py

@@ -2,7 +2,7 @@ from ..invocations.latent import LatentsToImageInvocation, DenoiseLatentsInvocat
 from ..invocations.image import ImageNSFWBlurInvocation
 from ..invocations.noise import NoiseInvocation
 from ..invocations.compel import CompelInvocation
-from ..invocations.params import ParamIntInvocation
+from ..invocations.primitives import IntegerInvocation
 from .graph import Edge, EdgeConnection, ExposedNodeInput, ExposedNodeOutput, Graph, LibraryGraph
 from .item_storage import ItemStorageABC
 
@@ -17,9 +17,9 @@ def create_text_to_image() -> LibraryGraph:
         description="Converts text to an image",
         graph=Graph(
             nodes={
-                "width": ParamIntInvocation(id="width", a=512),
+                "width": IntegerInvocation(id="width", a=512),
-                "height": ParamIntInvocation(id="height", a=512),
+                "height": IntegerInvocation(id="height", a=512),
-                "seed": ParamIntInvocation(id="seed", a=-1),
+                "seed": IntegerInvocation(id="seed", a=-1),
                 "3": NoiseInvocation(id="3"),
                 "4": CompelInvocation(id="4"),
                 "5": CompelInvocation(id="5"),

invokeai/frontend/web/src/services/api/guards.ts

@@ -3,8 +3,6 @@ import {
   GraphExecutionState,
   GraphInvocationOutput,
   ImageOutput,
-  MaskOutput,
-  PromptOutput,
   IterateInvocationOutput,
   CollectInvocationOutput,
   ImageField,
@@ -48,14 +46,6 @@ export const isLatentsOutput = (
   output: GraphExecutionState['results'][string]
 ): output is LatentsOutput => output?.type === 'latents_output';
 
-export const isMaskOutput = (
-  output: GraphExecutionState['results'][string]
-): output is MaskOutput => output?.type === 'mask';
-
-export const isPromptOutput = (
-  output: GraphExecutionState['results'][string]
-): output is PromptOutput => output?.type === 'prompt';
-
 export const isGraphOutput = (
   output: GraphExecutionState['results'][string]
 ): output is GraphInvocationOutput => output?.type === 'graph_output';

invokeai/frontend/web/src/services/api/schema.d.ts

@@ -548,6 +548,69 @@ export type components = {
        */
       file: Blob;
     };
+    /**
+     * BooleanCollectionOutput 
+     * @description Base class for nodes that output a collection of booleans
+     */
+    BooleanCollectionOutput: {
+      /**
+       * Type 
+       * @default boolean_collection_output 
+       * @enum {string}
+       */
+      type?: "boolean_collection_output";
+      /**
+       * Collection 
+       * @description The output boolean collection
+       */
+      collection?: (boolean)[];
+    };
+    /**
+     * Boolean Primitive 
+     * @description A boolean primitive value
+     */
+    BooleanInvocation: {
+      /**
+       * Id 
+       * @description The id of this node. Must be unique among all nodes.
+       */
+      id: string;
+      /**
+       * Is Intermediate 
+       * @description Whether or not this node is an intermediate node. 
+       * @default false
+       */
+      is_intermediate?: boolean;
+      /**
+       * Type 
+       * @default bool 
+       * @enum {string}
+       */
+      type: "bool";
+      /**
+       * A 
+       * @description The boolean value 
+       * @default false
+       */
+      a?: boolean;
+    };
+    /**
+     * BooleanOutput 
+     * @description Base class for nodes that output a single boolean
+     */
+    BooleanOutput: {
+      /**
+       * Type 
+       * @default boolean_output 
+       * @enum {string}
+       */
+      type?: "boolean_output";
+      /**
+       * A 
+       * @description The output boolean
+       */
+      a: boolean;
+    };
     /**
      * Canny Processor 
      * @description Canny edge detection for ControlNet
@@ -712,6 +775,23 @@ export type components = {
        */
       collection: (unknown)[];
     };
+    /**
+     * ColorCollectionOutput 
+     * @description Base class for nodes that output a collection of colors
+     */
+    ColorCollectionOutput: {
+      /**
+       * Type 
+       * @default color_collection_output 
+       * @enum {string}
+       */
+      type?: "color_collection_output";
+      /**
+       * Collection 
+       * @description The output colors
+       */
+      collection?: (components["schemas"]["ColorField"])[];
+    };
     /**
      * Color Correct 
      * @description Shifts the colors of a target image to match the reference image, optionally
@@ -757,7 +837,10 @@ export type components = {
        */
       mask_blur_radius?: number;
     };
-    /** ColorField */
+    /**
+     * ColorField 
+     * @description A color primitive field
+     */
     ColorField: {
       /**
        * R 
@@ -780,6 +863,57 @@ export type components = {
        */
       a: number;
     };
+    /**
+     * Color Primitive 
+     * @description A color primitive value
+     */
+    ColorInvocation: {
+      /**
+       * Id 
+       * @description The id of this node. Must be unique among all nodes.
+       */
+      id: string;
+      /**
+       * Is Intermediate 
+       * @description Whether or not this node is an intermediate node. 
+       * @default false
+       */
+      is_intermediate?: boolean;
+      /**
+       * Type 
+       * @default color 
+       * @enum {string}
+       */
+      type: "color";
+      /**
+       * Color 
+       * @description The color value 
+       * @default {
+       *   "r": 0,
+       *   "g": 0,
+       *   "b": 0,
+       *   "a": 255
+       * }
+       */
+      color?: components["schemas"]["ColorField"];
+    };
+    /**
+     * ColorOutput 
+     * @description Base class for nodes that output a single color
+     */
+    ColorOutput: {
+      /**
+       * Type 
+       * @default color_output 
+       * @enum {string}
+       */
+      type?: "color_output";
+      /**
+       * Color 
+       * @description The output color
+       */
+      color: components["schemas"]["ColorField"];
+    };
     /**
      * Compel Prompt 
      * @description Parse prompt using compel package to conditioning.
@@ -815,30 +949,78 @@ export type components = {
       clip?: components["schemas"]["ClipField"];
     };
     /**
-     * CompelOutput 
+     * ConditioningCollectionOutput 
-     * @description Compel parser output
+     * @description Base class for nodes that output a collection of conditioning tensors
      */
-    CompelOutput: {
+    ConditioningCollectionOutput: {
       /**
        * Type 
-       * @default compel_output 
+       * @default conditioning_collection_output 
        * @enum {string}
        */
-      type?: "compel_output";
+      type?: "conditioning_collection_output";
+      /**
+       * Collection 
+       * @description The output conditioning tensors
+       */
+      collection?: (components["schemas"]["ConditioningField"])[];
+    };
+    /**
+     * ConditioningField 
+     * @description A conditioning tensor primitive field
+     */
+    ConditioningField: {
+      /**
+       * Conditioning Name 
+       * @description The name of conditioning tensor
+       */
+      conditioning_name: string;
+    };
+    /**
+     * Conditioning 
+     * @description A conditioning tensor primitive value
+     */
+    ConditioningInvocation: {
+      /**
+       * Id 
+       * @description The id of this node. Must be unique among all nodes.
+       */
+      id: string;
+      /**
+       * Is Intermediate 
+       * @description Whether or not this node is an intermediate node. 
+       * @default false
+       */
+      is_intermediate?: boolean;
+      /**
+       * Type 
+       * @default conditioning 
+       * @enum {string}
+       */
+      type: "conditioning";
+      /**
+       * Conditioning 
+       * @description Conditioning tensor
+       */
+      conditioning?: components["schemas"]["ConditioningField"];
+    };
+    /**
+     * ConditioningOutput 
+     * @description Base class for nodes that output a single conditioning tensor
+     */
+    ConditioningOutput: {
+      /**
+       * Type 
+       * @default conditioning_output 
+       * @enum {string}
+       */
+      type?: "conditioning_output";
       /**
        * Conditioning 
        * @description Conditioning tensor
        */
       conditioning: components["schemas"]["ConditioningField"];
     };
-    /** ConditioningField */
-    ConditioningField: {
-      /**
-       * Conditioning Name 
-       * @description The name of conditioning data
-       */
-      conditioning_name: string;
-    };
     /**
      * Content Shuffle Processor 
      * @description Applies content shuffle processing to image
@@ -1507,7 +1689,7 @@ export type components = {
     };
     /**
      * FloatCollectionOutput 
-     * @description A collection of floats
+     * @description Base class for nodes that output a collection of floats
      */
     FloatCollectionOutput: {
       /**
@@ -1518,11 +1700,39 @@ export type components = {
       type?: "float_collection_output";
       /**
        * Collection 
-       * @description The float collection 
+       * @description The float collection
-       * @default []
        */
       collection?: (number)[];
     };
+    /**
+     * Float Primitive 
+     * @description A float primitive value
+     */
+    FloatInvocation: {
+      /**
+       * Id 
+       * @description The id of this node. Must be unique among all nodes.
+       */
+      id: string;
+      /**
+       * Is Intermediate 
+       * @description Whether or not this node is an intermediate node. 
+       * @default false
+       */
+      is_intermediate?: boolean;
+      /**
+       * Type 
+       * @default float 
+       * @enum {string}
+       */
+      type: "float";
+      /**
+       * Param 
+       * @description The float value 
+       * @default 0
+       */
+      param?: number;
+    };
     /**
      * Float Range 
      * @description Creates a range
@@ -1566,7 +1776,7 @@ export type components = {
     };
     /**
      * FloatOutput 
-     * @description A float output
+     * @description Base class for nodes that output a single float
      */
     FloatOutput: {
       /**
@@ -1579,7 +1789,7 @@ export type components = {
        * A 
        * @description The output float
        */
-      a?: number;
+      a: number;
     };
     /** Graph */
     Graph: {
@@ -1593,7 +1803,7 @@ export type components = {
        * @description The nodes in this graph
        */
       nodes?: {
-        [key: string]: (components["schemas"]["ControlNetInvocation"] | components["schemas"]["ImageProcessorInvocation"] | components["schemas"]["MainModelLoaderInvocation"] | components["schemas"]["LoraLoaderInvocation"] | components["schemas"]["SDXLLoraLoaderInvocation"] | components["schemas"]["VaeLoaderInvocation"] | components["schemas"]["MetadataAccumulatorInvocation"] | components["schemas"]["SDXLModelLoaderInvocation"] | components["schemas"]["SDXLRefinerModelLoaderInvocation"] | components["schemas"]["CompelInvocation"] | components["schemas"]["SDXLCompelPromptInvocation"] | components["schemas"]["SDXLRefinerCompelPromptInvocation"] | components["schemas"]["ClipSkipInvocation"] | components["schemas"]["LoadImageInvocation"] | components["schemas"]["ShowImageInvocation"] | components["schemas"]["ImageCropInvocation"] | components["schemas"]["ImagePasteInvocation"] | components["schemas"]["MaskFromAlphaInvocation"] | components["schemas"]["ImageMultiplyInvocation"] | components["schemas"]["ImageChannelInvocation"] | components["schemas"]["ImageConvertInvocation"] | components["schemas"]["ImageBlurInvocation"] | components["schemas"]["ImageResizeInvocation"] | components["schemas"]["ImageScaleInvocation"] | components["schemas"]["ImageLerpInvocation"] | components["schemas"]["ImageInverseLerpInvocation"] | components["schemas"]["ImageNSFWBlurInvocation"] | components["schemas"]["ImageWatermarkInvocation"] | components["schemas"]["MaskEdgeInvocation"] | components["schemas"]["MaskCombineInvocation"] | components["schemas"]["ColorCorrectInvocation"] | components["schemas"]["ImageHueAdjustmentInvocation"] | components["schemas"]["ImageLuminosityAdjustmentInvocation"] | components["schemas"]["ImageSaturationAdjustmentInvocation"] | components["schemas"]["DenoiseLatentsInvocation"] | components["schemas"]["LatentsToImageInvocation"] | components["schemas"]["ResizeLatentsInvocation"] | components["schemas"]["ScaleLatentsInvocation"] | components["schemas"]["ImageToLatentsInvocation"] | components["schemas"]["ONNXPromptInvocation"] | components["schemas"]["ONNXTextToLatentsInvocation"] | components["schemas"]["ONNXLatentsToImageInvocation"] | components["schemas"]["OnnxModelLoaderInvocation"] | components["schemas"]["DynamicPromptInvocation"] | components["schemas"]["PromptsFromFileInvocation"] | components["schemas"]["AddInvocation"] | components["schemas"]["SubtractInvocation"] | components["schemas"]["MultiplyInvocation"] | components["schemas"]["DivideInvocation"] | components["schemas"]["RandomIntInvocation"] | components["schemas"]["ParamIntInvocation"] | components["schemas"]["ParamFloatInvocation"] | components["schemas"]["ParamStringInvocation"] | components["schemas"]["ParamPromptInvocation"] | components["schemas"]["CvInpaintInvocation"] | components["schemas"]["RangeInvocation"] | components["schemas"]["RangeOfSizeInvocation"] | components["schemas"]["RandomRangeInvocation"] | components["schemas"]["ImageCollectionInvocation"] | components["schemas"]["FloatLinearRangeInvocation"] | components["schemas"]["StepParamEasingInvocation"] | components["schemas"]["NoiseInvocation"] | components["schemas"]["ESRGANInvocation"] | components["schemas"]["InfillColorInvocation"] | components["schemas"]["InfillTileInvocation"] | components["schemas"]["InfillPatchMatchInvocation"] | components["schemas"]["GraphInvocation"] | components["schemas"]["IterateInvocation"] | components["schemas"]["CollectInvocation"] | components["schemas"]["CannyImageProcessorInvocation"] | components["schemas"]["HedImageProcessorInvocation"] | components["schemas"]["LineartImageProcessorInvocation"] | components["schemas"]["LineartAnimeImageProcessorInvocation"] | components["schemas"]["OpenposeImageProcessorInvocation"] | components["schemas"]["MidasDepthImageProcessorInvocation"] | components["schemas"]["NormalbaeImageProcessorInvocation"] | components["schemas"]["MlsdImageProcessorInvocation"] | components["schemas"]["PidiImageProcessorInvocation"] | components["schemas"]["ContentShuffleImageProcessorInvocation"] | components["schemas"]["ZoeDepthImageProcessorInvocation"] | components["schemas"]["MediapipeFaceProcessorInvocation"] | components["schemas"]["LeresImageProcessorInvocation"] | components["schemas"]["TileResamplerProcessorInvocation"] | components["schemas"]["SegmentAnythingProcessorInvocation"]) | undefined;
+        [key: string]: (components["schemas"]["BooleanInvocation"] | components["schemas"]["IntegerInvocation"] | components["schemas"]["FloatInvocation"] | components["schemas"]["StringInvocation"] | components["schemas"]["ImageInvocation"] | components["schemas"]["LatentsInvocation"] | components["schemas"]["ColorInvocation"] | components["schemas"]["ConditioningInvocation"] | components["schemas"]["ControlNetInvocation"] | components["schemas"]["ImageProcessorInvocation"] | components["schemas"]["MainModelLoaderInvocation"] | components["schemas"]["LoraLoaderInvocation"] | components["schemas"]["SDXLLoraLoaderInvocation"] | components["schemas"]["VaeLoaderInvocation"] | components["schemas"]["MetadataAccumulatorInvocation"] | components["schemas"]["SDXLModelLoaderInvocation"] | components["schemas"]["SDXLRefinerModelLoaderInvocation"] | components["schemas"]["CompelInvocation"] | components["schemas"]["SDXLCompelPromptInvocation"] | components["schemas"]["SDXLRefinerCompelPromptInvocation"] | components["schemas"]["ClipSkipInvocation"] | components["schemas"]["DenoiseLatentsInvocation"] | components["schemas"]["LatentsToImageInvocation"] | components["schemas"]["ResizeLatentsInvocation"] | components["schemas"]["ScaleLatentsInvocation"] | components["schemas"]["ImageToLatentsInvocation"] | components["schemas"]["ONNXPromptInvocation"] | components["schemas"]["ONNXTextToLatentsInvocation"] | components["schemas"]["ONNXLatentsToImageInvocation"] | components["schemas"]["OnnxModelLoaderInvocation"] | components["schemas"]["ShowImageInvocation"] | components["schemas"]["ImageCropInvocation"] | components["schemas"]["ImagePasteInvocation"] | components["schemas"]["MaskFromAlphaInvocation"] | components["schemas"]["ImageMultiplyInvocation"] | components["schemas"]["ImageChannelInvocation"] | components["schemas"]["ImageConvertInvocation"] | components["schemas"]["ImageBlurInvocation"] | components["schemas"]["ImageResizeInvocation"] | components["schemas"]["ImageScaleInvocation"] | components["schemas"]["ImageLerpInvocation"] | components["schemas"]["ImageInverseLerpInvocation"] | components["schemas"]["ImageNSFWBlurInvocation"] | components["schemas"]["ImageWatermarkInvocation"] | components["schemas"]["MaskEdgeInvocation"] | components["schemas"]["MaskCombineInvocation"] | components["schemas"]["ColorCorrectInvocation"] | components["schemas"]["ImageHueAdjustmentInvocation"] | components["schemas"]["ImageLuminosityAdjustmentInvocation"] | components["schemas"]["ImageSaturationAdjustmentInvocation"] | components["schemas"]["DynamicPromptInvocation"] | components["schemas"]["PromptsFromFileInvocation"] | components["schemas"]["CvInpaintInvocation"] | components["schemas"]["FloatLinearRangeInvocation"] | components["schemas"]["StepParamEasingInvocation"] | components["schemas"]["AddInvocation"] | components["schemas"]["SubtractInvocation"] | components["schemas"]["MultiplyInvocation"] | components["schemas"]["DivideInvocation"] | components["schemas"]["RandomIntInvocation"] | components["schemas"]["NoiseInvocation"] | components["schemas"]["RangeInvocation"] | components["schemas"]["RangeOfSizeInvocation"] | components["schemas"]["RandomRangeInvocation"] | components["schemas"]["ImageCollectionInvocation"] | components["schemas"]["ESRGANInvocation"] | components["schemas"]["InfillColorInvocation"] | components["schemas"]["InfillTileInvocation"] | components["schemas"]["InfillPatchMatchInvocation"] | components["schemas"]["GraphInvocation"] | components["schemas"]["IterateInvocation"] | components["schemas"]["CollectInvocation"] | components["schemas"]["CannyImageProcessorInvocation"] | components["schemas"]["HedImageProcessorInvocation"] | components["schemas"]["LineartImageProcessorInvocation"] | components["schemas"]["LineartAnimeImageProcessorInvocation"] | components["schemas"]["OpenposeImageProcessorInvocation"] | components["schemas"]["MidasDepthImageProcessorInvocation"] | components["schemas"]["NormalbaeImageProcessorInvocation"] | components["schemas"]["MlsdImageProcessorInvocation"] | components["schemas"]["PidiImageProcessorInvocation"] | components["schemas"]["ContentShuffleImageProcessorInvocation"] | components["schemas"]["ZoeDepthImageProcessorInvocation"] | components["schemas"]["MediapipeFaceProcessorInvocation"] | components["schemas"]["LeresImageProcessorInvocation"] | components["schemas"]["TileResamplerProcessorInvocation"] | components["schemas"]["SegmentAnythingProcessorInvocation"]) | undefined;
       };
       /**
        * Edges 
@@ -1636,7 +1846,7 @@ export type components = {
        * @description The results of node executions
        */
       results: {
-        [key: string]: (components["schemas"]["ImageOutput"] | components["schemas"]["MaskOutput"] | components["schemas"]["ControlOutput"] | components["schemas"]["ModelLoaderOutput"] | components["schemas"]["LoraLoaderOutput"] | components["schemas"]["SDXLLoraLoaderOutput"] | components["schemas"]["VaeLoaderOutput"] | components["schemas"]["MetadataAccumulatorOutput"] | components["schemas"]["SDXLModelLoaderOutput"] | components["schemas"]["SDXLRefinerModelLoaderOutput"] | components["schemas"]["CompelOutput"] | components["schemas"]["ClipSkipInvocationOutput"] | components["schemas"]["LatentsOutput"] | components["schemas"]["ONNXModelLoaderOutput"] | components["schemas"]["PromptOutput"] | components["schemas"]["PromptCollectionOutput"] | components["schemas"]["IntOutput"] | components["schemas"]["FloatOutput"] | components["schemas"]["StringOutput"] | components["schemas"]["IntCollectionOutput"] | components["schemas"]["FloatCollectionOutput"] | components["schemas"]["StringCollectionOutput"] | components["schemas"]["ImageCollectionOutput"] | components["schemas"]["NoiseOutput"] | components["schemas"]["GraphInvocationOutput"] | components["schemas"]["IterateInvocationOutput"] | components["schemas"]["CollectInvocationOutput"]) | undefined;
+        [key: string]: (components["schemas"]["BooleanOutput"] | components["schemas"]["BooleanCollectionOutput"] | components["schemas"]["IntegerOutput"] | components["schemas"]["IntegerCollectionOutput"] | components["schemas"]["FloatOutput"] | components["schemas"]["FloatCollectionOutput"] | components["schemas"]["StringOutput"] | components["schemas"]["StringCollectionOutput"] | components["schemas"]["ImageOutput"] | components["schemas"]["ImageCollectionOutput"] | components["schemas"]["LatentsOutput"] | components["schemas"]["LatentsCollectionOutput"] | components["schemas"]["ColorOutput"] | components["schemas"]["ColorCollectionOutput"] | components["schemas"]["ConditioningOutput"] | components["schemas"]["ConditioningCollectionOutput"] | components["schemas"]["ControlOutput"] | components["schemas"]["ModelLoaderOutput"] | components["schemas"]["LoraLoaderOutput"] | components["schemas"]["SDXLLoraLoaderOutput"] | components["schemas"]["VaeLoaderOutput"] | components["schemas"]["MetadataAccumulatorOutput"] | components["schemas"]["SDXLModelLoaderOutput"] | components["schemas"]["SDXLRefinerModelLoaderOutput"] | components["schemas"]["ClipSkipInvocationOutput"] | components["schemas"]["ONNXModelLoaderOutput"] | components["schemas"]["NoiseOutput"] | components["schemas"]["GraphInvocationOutput"] | components["schemas"]["IterateInvocationOutput"] | components["schemas"]["CollectInvocationOutput"]) | undefined;
       };
       /**
        * Errors 
@@ -1870,7 +2080,7 @@ export type components = {
     };
     /**
      * ImageCollectionOutput 
-     * @description A collection of images
+     * @description Base class for nodes that output a collection of images
      */
     ImageCollectionOutput: {
       /**
@@ -1881,8 +2091,7 @@ export type components = {
       type?: "image_collection_output";
       /**
        * Collection 
-       * @description The output images 
+       * @description The output images
-       * @default []
        */
       collection?: (components["schemas"]["ImageField"])[];
     };
@@ -2045,7 +2254,7 @@ export type components = {
     };
     /**
      * ImageField 
-     * @description An image field used for passing image objects between invocations
+     * @description An image primitive field
      */
     ImageField: {
       /**
@@ -2128,6 +2337,34 @@ export type components = {
        */
       max?: number;
     };
+    /**
+     * Image 
+     * @description An image primitive value
+     */
+    ImageInvocation: {
+      /**
+       * Id 
+       * @description The id of this node. Must be unique among all nodes.
+       */
+      id: string;
+      /**
+       * Is Intermediate 
+       * @description Whether or not this node is an intermediate node. 
+       * @default false
+       */
+      is_intermediate?: boolean;
+      /**
+       * Type 
+       * @default image 
+       * @enum {string}
+       */
+      type: "image";
+      /**
+       * Image 
+       * @description The image to load
+       */
+      image?: components["schemas"]["ImageField"];
+    };
     /**
      * Lerp Image 
      * @description Linear interpolation of all pixels of an image
@@ -2286,7 +2523,7 @@ export type components = {
     };
     /**
      * ImageOutput 
-     * @description Base class for invocations that output an image
+     * @description Base class for nodes that output a single image
      */
     ImageOutput: {
       /**
@@ -2294,7 +2531,7 @@ export type components = {
        * @default image_output 
        * @enum {string}
        */
-      type: "image_output";
+      type?: "image_output";
       /**
        * Image 
        * @description The output image
@@ -2746,10 +2983,10 @@ export type components = {
       seed?: number;
     };
     /**
-     * IntCollectionOutput 
+     * IntegerCollectionOutput 
-     * @description A collection of integers
+     * @description Base class for nodes that output a collection of integers
      */
-    IntCollectionOutput: {
+    IntegerCollectionOutput: {
       /**
        * Type 
        * @default int_collection_output 
@@ -2758,16 +2995,44 @@ export type components = {
       type?: "int_collection_output";
       /**
        * Collection 
-       * @description The int collection 
+       * @description The int collection
-       * @default []
        */
       collection?: (number)[];
     };
     /**
-     * IntOutput 
+     * Integer Primitive 
-     * @description An integer output
+     * @description An integer primitive value
      */
-    IntOutput: {
+    IntegerInvocation: {
+      /**
+       * Id 
+       * @description The id of this node. Must be unique among all nodes.
+       */
+      id: string;
+      /**
+       * Is Intermediate 
+       * @description Whether or not this node is an intermediate node. 
+       * @default false
+       */
+      is_intermediate?: boolean;
+      /**
+       * Type 
+       * @default int 
+       * @enum {string}
+       */
+      type: "int";
+      /**
+       * A 
+       * @description The integer value 
+       * @default 0
+       */
+      a?: number;
+    };
+    /**
+     * IntegerOutput 
+     * @description Base class for nodes that output a single integer
+     */
+    IntegerOutput: {
       /**
        * Type 
        * @default int_output 
@@ -2778,7 +3043,7 @@ export type components = {
        * A 
        * @description The output integer
        */
-      a?: number;
+      a: number;
     };
     /**
      * IterateInvocation 
@@ -2831,9 +3096,26 @@ export type components = {
        */
       item?: unknown;
     };
+    /**
+     * LatentsCollectionOutput 
+     * @description Base class for nodes that output a collection of latents tensors
+     */
+    LatentsCollectionOutput: {
+      /**
+       * Type 
+       * @default latents_collection_output 
+       * @enum {string}
+       */
+      type?: "latents_collection_output";
+      /**
+       * Latents 
+       * @description Latents tensor
+       */
+      latents?: (components["schemas"]["LatentsField"])[];
+    };
     /**
      * LatentsField 
-     * @description A latents field used for passing latents between invocations
+     * @description A latents tensor primitive field
      */
     LatentsField: {
       /**
@@ -2847,9 +3129,37 @@ export type components = {
        */
       seed?: number;
     };
+    /**
+     * Latents 
+     * @description A latents tensor primitive value
+     */
+    LatentsInvocation: {
+      /**
+       * Id 
+       * @description The id of this node. Must be unique among all nodes.
+       */
+      id: string;
+      /**
+       * Is Intermediate 
+       * @description Whether or not this node is an intermediate node. 
+       * @default false
+       */
+      is_intermediate?: boolean;
+      /**
+       * Type 
+       * @default latents 
+       * @enum {string}
+       */
+      type: "latents";
+      /**
+       * Latents 
+       * @description The latents tensor
+       */
+      latents?: components["schemas"]["LatentsField"];
+    };
     /**
      * LatentsOutput 
-     * @description Base class for invocations that output latents
+     * @description Base class for nodes that output a single latents tensor
      */
     LatentsOutput: {
       /**
@@ -3120,34 +3430,6 @@ export type components = {
      * @enum {string}
      */
     LoRAModelFormat: "lycoris" | "diffusers";
-    /**
-     * Load Image 
-     * @description Load an image and provide it as output.
-     */
-    LoadImageInvocation: {
-      /**
-       * Id 
-       * @description The id of this node. Must be unique among all nodes.
-       */
-      id: string;
-      /**
-       * Is Intermediate 
-       * @description Whether or not this node is an intermediate node. 
-       * @default false
-       */
-      is_intermediate?: boolean;
-      /**
-       * Type 
-       * @default load_image 
-       * @enum {string}
-       */
-      type: "load_image";
-      /**
-       * Image 
-       * @description The image to load
-       */
-      image?: components["schemas"]["ImageField"];
-    };
     /**
      * LogLevel 
      * @description An enumeration. 
@@ -3397,33 +3679,6 @@ export type components = {
        */
       invert?: boolean;
     };
-    /**
-     * MaskOutput 
-     * @description Base class for invocations that output a mask
-     */
-    MaskOutput: {
-      /**
-       * Type 
-       * @default mask 
-       * @enum {string}
-       */
-      type: "mask";
-      /**
-       * Mask 
-       * @description The output mask
-       */
-      mask: components["schemas"]["ImageField"];
-      /**
-       * Width 
-       * @description The width of the mask in pixels
-       */
-      width?: number;
-      /**
-       * Height 
-       * @description The height of the mask in pixels
-       */
-      height?: number;
-    };
     /**
      * Mediapipe Face Processor 
      * @description Applies mediapipe face processing to image
@@ -4342,122 +4597,6 @@ export type components = {
        */
       total: number;
     };
-    /**
-     * Float Parameter 
-     * @description A float parameter
-     */
-    ParamFloatInvocation: {
-      /**
-       * Id 
-       * @description The id of this node. Must be unique among all nodes.
-       */
-      id: string;
-      /**
-       * Is Intermediate 
-       * @description Whether or not this node is an intermediate node. 
-       * @default false
-       */
-      is_intermediate?: boolean;
-      /**
-       * Type 
-       * @default param_float 
-       * @enum {string}
-       */
-      type: "param_float";
-      /**
-       * Param 
-       * @description The float value 
-       * @default 0
-       */
-      param?: number;
-    };
-    /**
-     * Integer Parameter 
-     * @description An integer parameter
-     */
-    ParamIntInvocation: {
-      /**
-       * Id 
-       * @description The id of this node. Must be unique among all nodes.
-       */
-      id: string;
-      /**
-       * Is Intermediate 
-       * @description Whether or not this node is an intermediate node. 
-       * @default false
-       */
-      is_intermediate?: boolean;
-      /**
-       * Type 
-       * @default param_int 
-       * @enum {string}
-       */
-      type: "param_int";
-      /**
-       * A 
-       * @description The integer value 
-       * @default 0
-       */
-      a?: number;
-    };
-    /**
-     * Prompt Parameter 
-     * @description A prompt input parameter
-     */
-    ParamPromptInvocation: {
-      /**
-       * Id 
-       * @description The id of this node. Must be unique among all nodes.
-       */
-      id: string;
-      /**
-       * Is Intermediate 
-       * @description Whether or not this node is an intermediate node. 
-       * @default false
-       */
-      is_intermediate?: boolean;
-      /**
-       * Type 
-       * @default param_prompt 
-       * @enum {string}
-       */
-      type: "param_prompt";
-      /**
-       * Prompt 
-       * @description The prompt value 
-       * @default
-       */
-      prompt?: string;
-    };
-    /**
-     * String Parameter 
-     * @description A string parameter
-     */
-    ParamStringInvocation: {
-      /**
-       * Id 
-       * @description The id of this node. Must be unique among all nodes.
-       */
-      id: string;
-      /**
-       * Is Intermediate 
-       * @description Whether or not this node is an intermediate node. 
-       * @default false
-       */
-      is_intermediate?: boolean;
-      /**
-       * Type 
-       * @default param_string 
-       * @enum {string}
-       */
-      type: "param_string";
-      /**
-       * Text 
-       * @description The string value 
-       * @default
-       */
-      text?: string;
-    };
     /**
      * PIDI Processor 
      * @description Applies PIDI processing to image
@@ -4510,45 +4649,6 @@ export type components = {
        */
       scribble?: boolean;
     };
-    /**
-     * PromptCollectionOutput 
-     * @description Base class for invocations that output a collection of prompts
-     */
-    PromptCollectionOutput: {
-      /**
-       * Type 
-       * @default prompt_collection_output 
-       * @enum {string}
-       */
-      type?: "prompt_collection_output";
-      /**
-       * Prompt Collection 
-       * @description The output prompt collection
-       */
-      prompt_collection: (string)[];
-      /**
-       * Count 
-       * @description The size of the prompt collection
-       */
-      count: number;
-    };
-    /**
-     * PromptOutput 
-     * @description Base class for invocations that output a prompt
-     */
-    PromptOutput: {
-      /**
-       * Type 
-       * @default prompt 
-       * @enum {string}
-       */
-      type?: "prompt";
-      /**
-       * Prompt 
-       * @description The output prompt
-       */
-      prompt: string;
-    };
     /**
      * Prompts from File 
      * @description Loads prompts from a text file
@@ -5499,7 +5599,7 @@ export type components = {
     };
     /**
      * StringCollectionOutput 
-     * @description A collection of strings
+     * @description Base class for nodes that output a collection of strings
      */
     StringCollectionOutput: {
       /**
@@ -5510,14 +5610,42 @@ export type components = {
       type?: "string_collection_output";
       /**
        * Collection 
-       * @description The output strings 
+       * @description The output strings
-       * @default []
        */
       collection?: (string)[];
     };
+    /**
+     * String Primitive 
+     * @description A string primitive value
+     */
+    StringInvocation: {
+      /**
+       * Id 
+       * @description The id of this node. Must be unique among all nodes.
+       */
+      id: string;
+      /**
+       * Is Intermediate 
+       * @description Whether or not this node is an intermediate node. 
+       * @default false
+       */
+      is_intermediate?: boolean;
+      /**
+       * Type 
+       * @default string 
+       * @enum {string}
+       */
+      type: "string";
+      /**
+       * Text 
+       * @description The string value 
+       * @default
+       */
+      text?: string;
+    };
     /**
      * StringOutput 
-     * @description A string output
+     * @description Base class for nodes that output a single string
      */
     StringOutput: {
       /**
@@ -5815,7 +5943,7 @@ export type components = {
      * If a field should be provided a data type that does not exactly match the python type of the field,     use this to provide the type that should be used instead. See the node development docs for detail     on adding a new field type, which involves client-side changes. 
      * @enum {string}
      */
-    UITypeHint: "integer" | "float" | "boolean" | "string" | "enum" | "array" | "ImageField" | "LatentsField" | "ConditioningField" | "ControlField" | "MainModelField" | "SDXLMainModelField" | "SDXLRefinerModelField" | "ONNXModelField" | "VaeModelField" | "LoRAModelField" | "ControlNetModelField" | "UNetField" | "VaeField" | "ClipField" | "ColorField" | "ImageCollection" | "IntegerCollection" | "FloatCollection" | "StringCollection" | "BooleanCollection" | "Collection" | "CollectionItem" | "Seed" | "FilePath";
+    UITypeHint: "integer" | "float" | "boolean" | "string" | "array" | "ImageField" | "LatentsField" | "ConditioningField" | "ControlField" | "ColorField" | "ImageCollection" | "ConditioningCollection" | "ColorCollection" | "LatentsCollection" | "IntegerCollection" | "FloatCollection" | "StringCollection" | "BooleanCollection" | "MainModelField" | "SDXLMainModelField" | "SDXLRefinerModelField" | "ONNXModelField" | "VaeModelField" | "LoRAModelField" | "ControlNetModelField" | "UNetField" | "VaeField" | "ClipField" | "Collection" | "CollectionItem" | "FilePath" | "enum";
     /**
      * UIComponent 
      * @description The type of UI component to use for a field, used to override the default components, which are     inferred from the field type. 
@@ -5988,7 +6116,7 @@ export type operations = {
     };
     requestBody: {
       content: {
-        "application/json": components["schemas"]["ControlNetInvocation"] | components["schemas"]["ImageProcessorInvocation"] | components["schemas"]["MainModelLoaderInvocation"] | components["schemas"]["LoraLoaderInvocation"] | components["schemas"]["SDXLLoraLoaderInvocation"] | components["schemas"]["VaeLoaderInvocation"] | components["schemas"]["MetadataAccumulatorInvocation"] | components["schemas"]["SDXLModelLoaderInvocation"] | components["schemas"]["SDXLRefinerModelLoaderInvocation"] | components["schemas"]["CompelInvocation"] | components["schemas"]["SDXLCompelPromptInvocation"] | components["schemas"]["SDXLRefinerCompelPromptInvocation"] | components["schemas"]["ClipSkipInvocation"] | components["schemas"]["LoadImageInvocation"] | components["schemas"]["ShowImageInvocation"] | components["schemas"]["ImageCropInvocation"] | components["schemas"]["ImagePasteInvocation"] | components["schemas"]["MaskFromAlphaInvocation"] | components["schemas"]["ImageMultiplyInvocation"] | components["schemas"]["ImageChannelInvocation"] | components["schemas"]["ImageConvertInvocation"] | components["schemas"]["ImageBlurInvocation"] | components["schemas"]["ImageResizeInvocation"] | components["schemas"]["ImageScaleInvocation"] | components["schemas"]["ImageLerpInvocation"] | components["schemas"]["ImageInverseLerpInvocation"] | components["schemas"]["ImageNSFWBlurInvocation"] | components["schemas"]["ImageWatermarkInvocation"] | components["schemas"]["MaskEdgeInvocation"] | components["schemas"]["MaskCombineInvocation"] | components["schemas"]["ColorCorrectInvocation"] | components["schemas"]["ImageHueAdjustmentInvocation"] | components["schemas"]["ImageLuminosityAdjustmentInvocation"] | components["schemas"]["ImageSaturationAdjustmentInvocation"] | components["schemas"]["DenoiseLatentsInvocation"] | components["schemas"]["LatentsToImageInvocation"] | components["schemas"]["ResizeLatentsInvocation"] | components["schemas"]["ScaleLatentsInvocation"] | components["schemas"]["ImageToLatentsInvocation"] | components["schemas"]["ONNXPromptInvocation"] | components["schemas"]["ONNXTextToLatentsInvocation"] | components["schemas"]["ONNXLatentsToImageInvocation"] | components["schemas"]["OnnxModelLoaderInvocation"] | components["schemas"]["DynamicPromptInvocation"] | components["schemas"]["PromptsFromFileInvocation"] | components["schemas"]["AddInvocation"] | components["schemas"]["SubtractInvocation"] | components["schemas"]["MultiplyInvocation"] | components["schemas"]["DivideInvocation"] | components["schemas"]["RandomIntInvocation"] | components["schemas"]["ParamIntInvocation"] | components["schemas"]["ParamFloatInvocation"] | components["schemas"]["ParamStringInvocation"] | components["schemas"]["ParamPromptInvocation"] | components["schemas"]["CvInpaintInvocation"] | components["schemas"]["RangeInvocation"] | components["schemas"]["RangeOfSizeInvocation"] | components["schemas"]["RandomRangeInvocation"] | components["schemas"]["ImageCollectionInvocation"] | components["schemas"]["FloatLinearRangeInvocation"] | components["schemas"]["StepParamEasingInvocation"] | components["schemas"]["NoiseInvocation"] | components["schemas"]["ESRGANInvocation"] | components["schemas"]["InfillColorInvocation"] | components["schemas"]["InfillTileInvocation"] | components["schemas"]["InfillPatchMatchInvocation"] | components["schemas"]["GraphInvocation"] | components["schemas"]["IterateInvocation"] | components["schemas"]["CollectInvocation"] | components["schemas"]["CannyImageProcessorInvocation"] | components["schemas"]["HedImageProcessorInvocation"] | components["schemas"]["LineartImageProcessorInvocation"] | components["schemas"]["LineartAnimeImageProcessorInvocation"] | components["schemas"]["OpenposeImageProcessorInvocation"] | components["schemas"]["MidasDepthImageProcessorInvocation"] | components["schemas"]["NormalbaeImageProcessorInvocation"] | components["schemas"]["MlsdImageProcessorInvocation"] | components["schemas"]["PidiImageProcessorInvocation"] | components["schemas"]["ContentShuffleImageProcessorInvocation"] | components["schemas"]["ZoeDepthImageProcessorInvocation"] | components["schemas"]["MediapipeFaceProcessorInvocation"] | components["schemas"]["LeresImageProcessorInvocation"] | components["schemas"]["TileResamplerProcessorInvocation"] | components["schemas"]["SegmentAnythingProcessorInvocation"];
+        "application/json": components["schemas"]["BooleanInvocation"] | components["schemas"]["IntegerInvocation"] | components["schemas"]["FloatInvocation"] | components["schemas"]["StringInvocation"] | components["schemas"]["ImageInvocation"] | components["schemas"]["LatentsInvocation"] | components["schemas"]["ColorInvocation"] | components["schemas"]["ConditioningInvocation"] | components["schemas"]["ControlNetInvocation"] | components["schemas"]["ImageProcessorInvocation"] | components["schemas"]["MainModelLoaderInvocation"] | components["schemas"]["LoraLoaderInvocation"] | components["schemas"]["SDXLLoraLoaderInvocation"] | components["schemas"]["VaeLoaderInvocation"] | components["schemas"]["MetadataAccumulatorInvocation"] | components["schemas"]["SDXLModelLoaderInvocation"] | components["schemas"]["SDXLRefinerModelLoaderInvocation"] | components["schemas"]["CompelInvocation"] | components["schemas"]["SDXLCompelPromptInvocation"] | components["schemas"]["SDXLRefinerCompelPromptInvocation"] | components["schemas"]["ClipSkipInvocation"] | components["schemas"]["DenoiseLatentsInvocation"] | components["schemas"]["LatentsToImageInvocation"] | components["schemas"]["ResizeLatentsInvocation"] | components["schemas"]["ScaleLatentsInvocation"] | components["schemas"]["ImageToLatentsInvocation"] | components["schemas"]["ONNXPromptInvocation"] | components["schemas"]["ONNXTextToLatentsInvocation"] | components["schemas"]["ONNXLatentsToImageInvocation"] | components["schemas"]["OnnxModelLoaderInvocation"] | components["schemas"]["ShowImageInvocation"] | components["schemas"]["ImageCropInvocation"] | components["schemas"]["ImagePasteInvocation"] | components["schemas"]["MaskFromAlphaInvocation"] | components["schemas"]["ImageMultiplyInvocation"] | components["schemas"]["ImageChannelInvocation"] | components["schemas"]["ImageConvertInvocation"] | components["schemas"]["ImageBlurInvocation"] | components["schemas"]["ImageResizeInvocation"] | components["schemas"]["ImageScaleInvocation"] | components["schemas"]["ImageLerpInvocation"] | components["schemas"]["ImageInverseLerpInvocation"] | components["schemas"]["ImageNSFWBlurInvocation"] | components["schemas"]["ImageWatermarkInvocation"] | components["schemas"]["MaskEdgeInvocation"] | components["schemas"]["MaskCombineInvocation"] | components["schemas"]["ColorCorrectInvocation"] | components["schemas"]["ImageHueAdjustmentInvocation"] | components["schemas"]["ImageLuminosityAdjustmentInvocation"] | components["schemas"]["ImageSaturationAdjustmentInvocation"] | components["schemas"]["DynamicPromptInvocation"] | components["schemas"]["PromptsFromFileInvocation"] | components["schemas"]["CvInpaintInvocation"] | components["schemas"]["FloatLinearRangeInvocation"] | components["schemas"]["StepParamEasingInvocation"] | components["schemas"]["AddInvocation"] | components["schemas"]["SubtractInvocation"] | components["schemas"]["MultiplyInvocation"] | components["schemas"]["DivideInvocation"] | components["schemas"]["RandomIntInvocation"] | components["schemas"]["NoiseInvocation"] | components["schemas"]["RangeInvocation"] | components["schemas"]["RangeOfSizeInvocation"] | components["schemas"]["RandomRangeInvocation"] | components["schemas"]["ImageCollectionInvocation"] | components["schemas"]["ESRGANInvocation"] | components["schemas"]["InfillColorInvocation"] | components["schemas"]["InfillTileInvocation"] | components["schemas"]["InfillPatchMatchInvocation"] | components["schemas"]["GraphInvocation"] | components["schemas"]["IterateInvocation"] | components["schemas"]["CollectInvocation"] | components["schemas"]["CannyImageProcessorInvocation"] | components["schemas"]["HedImageProcessorInvocation"] | components["schemas"]["LineartImageProcessorInvocation"] | components["schemas"]["LineartAnimeImageProcessorInvocation"] | components["schemas"]["OpenposeImageProcessorInvocation"] | components["schemas"]["MidasDepthImageProcessorInvocation"] | components["schemas"]["NormalbaeImageProcessorInvocation"] | components["schemas"]["MlsdImageProcessorInvocation"] | components["schemas"]["PidiImageProcessorInvocation"] | components["schemas"]["ContentShuffleImageProcessorInvocation"] | components["schemas"]["ZoeDepthImageProcessorInvocation"] | components["schemas"]["MediapipeFaceProcessorInvocation"] | components["schemas"]["LeresImageProcessorInvocation"] | components["schemas"]["TileResamplerProcessorInvocation"] | components["schemas"]["SegmentAnythingProcessorInvocation"];
       };
     };
     responses: {
@@ -6025,7 +6153,7 @@ export type operations = {
     };
     requestBody: {
       content: {
-        "application/json": components["schemas"]["ControlNetInvocation"] | components["schemas"]["ImageProcessorInvocation"] | components["schemas"]["MainModelLoaderInvocation"] | components["schemas"]["LoraLoaderInvocation"] | components["schemas"]["SDXLLoraLoaderInvocation"] | components["schemas"]["VaeLoaderInvocation"] | components["schemas"]["MetadataAccumulatorInvocation"] | components["schemas"]["SDXLModelLoaderInvocation"] | components["schemas"]["SDXLRefinerModelLoaderInvocation"] | components["schemas"]["CompelInvocation"] | components["schemas"]["SDXLCompelPromptInvocation"] | components["schemas"]["SDXLRefinerCompelPromptInvocation"] | components["schemas"]["ClipSkipInvocation"] | components["schemas"]["LoadImageInvocation"] | components["schemas"]["ShowImageInvocation"] | components["schemas"]["ImageCropInvocation"] | components["schemas"]["ImagePasteInvocation"] | components["schemas"]["MaskFromAlphaInvocation"] | components["schemas"]["ImageMultiplyInvocation"] | components["schemas"]["ImageChannelInvocation"] | components["schemas"]["ImageConvertInvocation"] | components["schemas"]["ImageBlurInvocation"] | components["schemas"]["ImageResizeInvocation"] | components["schemas"]["ImageScaleInvocation"] | components["schemas"]["ImageLerpInvocation"] | components["schemas"]["ImageInverseLerpInvocation"] | components["schemas"]["ImageNSFWBlurInvocation"] | components["schemas"]["ImageWatermarkInvocation"] | components["schemas"]["MaskEdgeInvocation"] | components["schemas"]["MaskCombineInvocation"] | components["schemas"]["ColorCorrectInvocation"] | components["schemas"]["ImageHueAdjustmentInvocation"] | components["schemas"]["ImageLuminosityAdjustmentInvocation"] | components["schemas"]["ImageSaturationAdjustmentInvocation"] | components["schemas"]["DenoiseLatentsInvocation"] | components["schemas"]["LatentsToImageInvocation"] | components["schemas"]["ResizeLatentsInvocation"] | components["schemas"]["ScaleLatentsInvocation"] | components["schemas"]["ImageToLatentsInvocation"] | components["schemas"]["ONNXPromptInvocation"] | components["schemas"]["ONNXTextToLatentsInvocation"] | components["schemas"]["ONNXLatentsToImageInvocation"] | components["schemas"]["OnnxModelLoaderInvocation"] | components["schemas"]["DynamicPromptInvocation"] | components["schemas"]["PromptsFromFileInvocation"] | components["schemas"]["AddInvocation"] | components["schemas"]["SubtractInvocation"] | components["schemas"]["MultiplyInvocation"] | components["schemas"]["DivideInvocation"] | components["schemas"]["RandomIntInvocation"] | components["schemas"]["ParamIntInvocation"] | components["schemas"]["ParamFloatInvocation"] | components["schemas"]["ParamStringInvocation"] | components["schemas"]["ParamPromptInvocation"] | components["schemas"]["CvInpaintInvocation"] | components["schemas"]["RangeInvocation"] | components["schemas"]["RangeOfSizeInvocation"] | components["schemas"]["RandomRangeInvocation"] | components["schemas"]["ImageCollectionInvocation"] | components["schemas"]["FloatLinearRangeInvocation"] | components["schemas"]["StepParamEasingInvocation"] | components["schemas"]["NoiseInvocation"] | components["schemas"]["ESRGANInvocation"] | components["schemas"]["InfillColorInvocation"] | components["schemas"]["InfillTileInvocation"] | components["schemas"]["InfillPatchMatchInvocation"] | components["schemas"]["GraphInvocation"] | components["schemas"]["IterateInvocation"] | components["schemas"]["CollectInvocation"] | components["schemas"]["CannyImageProcessorInvocation"] | components["schemas"]["HedImageProcessorInvocation"] | components["schemas"]["LineartImageProcessorInvocation"] | components["schemas"]["LineartAnimeImageProcessorInvocation"] | components["schemas"]["OpenposeImageProcessorInvocation"] | components["schemas"]["MidasDepthImageProcessorInvocation"] | components["schemas"]["NormalbaeImageProcessorInvocation"] | components["schemas"]["MlsdImageProcessorInvocation"] | components["schemas"]["PidiImageProcessorInvocation"] | components["schemas"]["ContentShuffleImageProcessorInvocation"] | components["schemas"]["ZoeDepthImageProcessorInvocation"] | components["schemas"]["MediapipeFaceProcessorInvocation"] | components["schemas"]["LeresImageProcessorInvocation"] | components["schemas"]["TileResamplerProcessorInvocation"] | components["schemas"]["SegmentAnythingProcessorInvocation"];
+        "application/json": components["schemas"]["BooleanInvocation"] | components["schemas"]["IntegerInvocation"] | components["schemas"]["FloatInvocation"] | components["schemas"]["StringInvocation"] | components["schemas"]["ImageInvocation"] | components["schemas"]["LatentsInvocation"] | components["schemas"]["ColorInvocation"] | components["schemas"]["ConditioningInvocation"] | components["schemas"]["ControlNetInvocation"] | components["schemas"]["ImageProcessorInvocation"] | components["schemas"]["MainModelLoaderInvocation"] | components["schemas"]["LoraLoaderInvocation"] | components["schemas"]["SDXLLoraLoaderInvocation"] | components["schemas"]["VaeLoaderInvocation"] | components["schemas"]["MetadataAccumulatorInvocation"] | components["schemas"]["SDXLModelLoaderInvocation"] | components["schemas"]["SDXLRefinerModelLoaderInvocation"] | components["schemas"]["CompelInvocation"] | components["schemas"]["SDXLCompelPromptInvocation"] | components["schemas"]["SDXLRefinerCompelPromptInvocation"] | components["schemas"]["ClipSkipInvocation"] | components["schemas"]["DenoiseLatentsInvocation"] | components["schemas"]["LatentsToImageInvocation"] | components["schemas"]["ResizeLatentsInvocation"] | components["schemas"]["ScaleLatentsInvocation"] | components["schemas"]["ImageToLatentsInvocation"] | components["schemas"]["ONNXPromptInvocation"] | components["schemas"]["ONNXTextToLatentsInvocation"] | components["schemas"]["ONNXLatentsToImageInvocation"] | components["schemas"]["OnnxModelLoaderInvocation"] | components["schemas"]["ShowImageInvocation"] | components["schemas"]["ImageCropInvocation"] | components["schemas"]["ImagePasteInvocation"] | components["schemas"]["MaskFromAlphaInvocation"] | components["schemas"]["ImageMultiplyInvocation"] | components["schemas"]["ImageChannelInvocation"] | components["schemas"]["ImageConvertInvocation"] | components["schemas"]["ImageBlurInvocation"] | components["schemas"]["ImageResizeInvocation"] | components["schemas"]["ImageScaleInvocation"] | components["schemas"]["ImageLerpInvocation"] | components["schemas"]["ImageInverseLerpInvocation"] | components["schemas"]["ImageNSFWBlurInvocation"] | components["schemas"]["ImageWatermarkInvocation"] | components["schemas"]["MaskEdgeInvocation"] | components["schemas"]["MaskCombineInvocation"] | components["schemas"]["ColorCorrectInvocation"] | components["schemas"]["ImageHueAdjustmentInvocation"] | components["schemas"]["ImageLuminosityAdjustmentInvocation"] | components["schemas"]["ImageSaturationAdjustmentInvocation"] | components["schemas"]["DynamicPromptInvocation"] | components["schemas"]["PromptsFromFileInvocation"] | components["schemas"]["CvInpaintInvocation"] | components["schemas"]["FloatLinearRangeInvocation"] | components["schemas"]["StepParamEasingInvocation"] | components["schemas"]["AddInvocation"] | components["schemas"]["SubtractInvocation"] | components["schemas"]["MultiplyInvocation"] | components["schemas"]["DivideInvocation"] | components["schemas"]["RandomIntInvocation"] | components["schemas"]["NoiseInvocation"] | components["schemas"]["RangeInvocation"] | components["schemas"]["RangeOfSizeInvocation"] | components["schemas"]["RandomRangeInvocation"] | components["schemas"]["ImageCollectionInvocation"] | components["schemas"]["ESRGANInvocation"] | components["schemas"]["InfillColorInvocation"] | components["schemas"]["InfillTileInvocation"] | components["schemas"]["InfillPatchMatchInvocation"] | components["schemas"]["GraphInvocation"] | components["schemas"]["IterateInvocation"] | components["schemas"]["CollectInvocation"] | components["schemas"]["CannyImageProcessorInvocation"] | components["schemas"]["HedImageProcessorInvocation"] | components["schemas"]["LineartImageProcessorInvocation"] | components["schemas"]["LineartAnimeImageProcessorInvocation"] | components["schemas"]["OpenposeImageProcessorInvocation"] | components["schemas"]["MidasDepthImageProcessorInvocation"] | components["schemas"]["NormalbaeImageProcessorInvocation"] | components["schemas"]["MlsdImageProcessorInvocation"] | components["schemas"]["PidiImageProcessorInvocation"] | components["schemas"]["ContentShuffleImageProcessorInvocation"] | components["schemas"]["ZoeDepthImageProcessorInvocation"] | components["schemas"]["MediapipeFaceProcessorInvocation"] | components["schemas"]["LeresImageProcessorInvocation"] | components["schemas"]["TileResamplerProcessorInvocation"] | components["schemas"]["SegmentAnythingProcessorInvocation"];
       };
     };
     responses: {

invokeai/frontend/web/src/services/api/types.ts

@@ -155,8 +155,6 @@ export type ZoeDepthImageProcessorInvocation =
 
 // Node Outputs
 export type ImageOutput = s['ImageOutput'];
-export type MaskOutput = s['MaskOutput'];
-export type PromptOutput = s['PromptOutput'];
 export type IterateInvocationOutput = s['IterateInvocationOutput'];
 export type CollectInvocationOutput = s['CollectInvocationOutput'];
 export type LatentsOutput = s['LatentsOutput'];

tests/nodes/test_node_graph.py

@@ -19,7 +19,7 @@ from invokeai.app.services.graph import (
 from invokeai.app.invocations.upscale import ESRGANInvocation
 from invokeai.app.invocations.image import *
 from invokeai.app.invocations.math import AddInvocation, SubtractInvocation
-from invokeai.app.invocations.params import ParamIntInvocation
+from invokeai.app.invocations.primitives import IntegerInvocation
 from invokeai.app.services.default_graphs import create_text_to_image
 import pytest
 
@@ -499,8 +499,8 @@ def test_graph_subgraph_t2i():
 
     g.add_node(n1)
 
-    n2 = ParamIntInvocation(id="2", a=512)
+    n2 = IntegerInvocation(id="2", a=512)
-    n3 = ParamIntInvocation(id="3", a=256)
+    n3 = IntegerInvocation(id="3", a=256)
 
     g.add_node(n2)
     g.add_node(n3)