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/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 (
-    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
-    )
+from .baseinvocation import BaseInvocation, InputField, InvocationContext, UITypeHint, tags, title
 
 
 @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:
-        return IntCollectionOutput(collection=list(range(self.start, self.stop, self.step)))
+    def invoke(self, context: InvocationContext) -> IntegerCollectionOutput:
+        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:
-        return IntCollectionOutput(collection=list(range(self.start, self.start + self.size, self.step)))
+    def invoke(self, context: InvocationContext) -> IntegerCollectionOutput:
+        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")