Move IdealSizeInvocation to its own file. No functional changes.

invokeai/app/invocations/ideal_size.py

@@ -0,0 +1,65 @@
+import math
+from typing import Tuple
+
+from invokeai.app.invocations.baseinvocation import BaseInvocation, BaseInvocationOutput, invocation, invocation_output
+from invokeai.app.invocations.constants import LATENT_SCALE_FACTOR
+from invokeai.app.invocations.fields import FieldDescriptions, InputField, OutputField
+from invokeai.app.invocations.model import UNetField
+from invokeai.app.services.shared.invocation_context import InvocationContext
+from invokeai.backend.model_manager.config import BaseModelType
+
+
+@invocation_output("ideal_size_output")
+class IdealSizeOutput(BaseInvocationOutput):
+    """Base class for invocations that output an image"""
+
+    width: int = OutputField(description="The ideal width of the image (in pixels)")
+    height: int = OutputField(description="The ideal height of the image (in pixels)")
+
+
+@invocation(
+    "ideal_size",
+    title="Ideal Size",
+    tags=["latents", "math", "ideal_size"],
+    version="1.0.3",
+)
+class IdealSizeInvocation(BaseInvocation):
+    """Calculates the ideal size for generation to avoid duplication"""
+
+    width: int = InputField(default=1024, description="Final image width")
+    height: int = InputField(default=576, description="Final image height")
+    unet: UNetField = InputField(default=None, description=FieldDescriptions.unet)
+    multiplier: float = InputField(
+        default=1.0,
+        description="Amount to multiply the model's dimensions by when calculating the ideal size (may result in "
+        "initial generation artifacts if too large)",
+    )
+
+    def trim_to_multiple_of(self, *args: int, multiple_of: int = LATENT_SCALE_FACTOR) -> Tuple[int, ...]:
+        return tuple((x - x % multiple_of) for x in args)
+
+    def invoke(self, context: InvocationContext) -> IdealSizeOutput:
+        unet_config = context.models.get_config(self.unet.unet.key)
+        aspect = self.width / self.height
+        dimension: float = 512
+        if unet_config.base == BaseModelType.StableDiffusion2:
+            dimension = 768
+        elif unet_config.base == BaseModelType.StableDiffusionXL:
+            dimension = 1024
+        dimension = dimension * self.multiplier
+        min_dimension = math.floor(dimension * 0.5)
+        model_area = dimension * dimension  # hardcoded for now since all models are trained on square images
+
+        if aspect > 1.0:
+            init_height = max(min_dimension, math.sqrt(model_area / aspect))
+            init_width = init_height * aspect
+        else:
+            init_width = max(min_dimension, math.sqrt(model_area * aspect))
+            init_height = init_width / aspect
+
+        scaled_width, scaled_height = self.trim_to_multiple_of(
+            math.floor(init_width),
+            math.floor(init_height),
+        )
+
+        return IdealSizeOutput(width=scaled_width, height=scaled_height)

invokeai/app/invocations/latent.py

@@ -1,6 +1,5 @@
 # Copyright (c) 2023 Kyle Schouviller (https://github.com/kyle0654)
 import inspect
-import math
 from contextlib import ExitStack
 from functools import singledispatchmethod
 from typing import Any, Dict, Iterator, List, Literal, Optional, Tuple, Union
@@ -1448,58 +1447,3 @@ class CropLatentsCoreInvocation(BaseInvocation):
         name = context.tensors.save(tensor=cropped_latents)
 
         return LatentsOutput.build(latents_name=name, latents=cropped_latents)
-
-
-@invocation_output("ideal_size_output")
-class IdealSizeOutput(BaseInvocationOutput):
-    """Base class for invocations that output an image"""
-
-    width: int = OutputField(description="The ideal width of the image (in pixels)")
-    height: int = OutputField(description="The ideal height of the image (in pixels)")
-
-
-@invocation(
-    "ideal_size",
-    title="Ideal Size",
-    tags=["latents", "math", "ideal_size"],
-    version="1.0.3",
-)
-class IdealSizeInvocation(BaseInvocation):
-    """Calculates the ideal size for generation to avoid duplication"""
-
-    width: int = InputField(default=1024, description="Final image width")
-    height: int = InputField(default=576, description="Final image height")
-    unet: UNetField = InputField(default=None, description=FieldDescriptions.unet)
-    multiplier: float = InputField(
-        default=1.0,
-        description="Amount to multiply the model's dimensions by when calculating the ideal size (may result in initial generation artifacts if too large)",
-    )
-
-    def trim_to_multiple_of(self, *args: int, multiple_of: int = LATENT_SCALE_FACTOR) -> Tuple[int, ...]:
-        return tuple((x - x % multiple_of) for x in args)
-
-    def invoke(self, context: InvocationContext) -> IdealSizeOutput:
-        unet_config = context.models.get_config(self.unet.unet.key)
-        aspect = self.width / self.height
-        dimension: float = 512
-        if unet_config.base == BaseModelType.StableDiffusion2:
-            dimension = 768
-        elif unet_config.base == BaseModelType.StableDiffusionXL:
-            dimension = 1024
-        dimension = dimension * self.multiplier
-        min_dimension = math.floor(dimension * 0.5)
-        model_area = dimension * dimension  # hardcoded for now since all models are trained on square images
-
-        if aspect > 1.0:
-            init_height = max(min_dimension, math.sqrt(model_area / aspect))
-            init_width = init_height * aspect
-        else:
-            init_width = max(min_dimension, math.sqrt(model_area * aspect))
-            init_height = init_width / aspect
-
-        scaled_width, scaled_height = self.trim_to_multiple_of(
-            math.floor(init_width),
-            math.floor(init_height),
-        )
-
-        return IdealSizeOutput(width=scaled_width, height=scaled_height)