diff --git a/invokeai/app/invocations/mask.py b/invokeai/app/invocations/mask.py
index b3588f204d..2d414ac2bd 100644
--- a/invokeai/app/invocations/mask.py
+++ b/invokeai/app/invocations/mask.py
@@ -1,15 +1,7 @@
-import numpy as np
 import torch
-from pydantic import BaseModel
 
-from invokeai.app.invocations.baseinvocation import (
-    BaseInvocation,
-    BaseInvocationOutput,
-    InvocationContext,
-    invocation,
-    invocation_output,
-)
-from invokeai.app.invocations.fields import ColorField, ImageField, InputField, OutputField, TensorField, WithMetadata
+from invokeai.app.invocations.baseinvocation import BaseInvocation, InvocationContext, invocation
+from invokeai.app.invocations.fields import InputField, TensorField, WithMetadata
 from invokeai.app.invocations.primitives import MaskOutput
 
 
@@ -42,76 +34,3 @@ class RectangleMaskInvocation(BaseInvocation, WithMetadata):
             width=self.width,
             height=self.height,
         )
-
-
-class PromptColorPair(BaseModel):
-    prompt: str
-    color: ColorField
-
-
-class PromptMaskPair(BaseModel):
-    prompt: str
-    mask: TensorField
-
-
-default_prompt_color_pairs = [
-    PromptColorPair(prompt="Strawberries", color=ColorField(r=200, g=0, b=0, a=255)),
-    PromptColorPair(prompt="Frog", color=ColorField(r=0, g=200, b=0, a=255)),
-    PromptColorPair(prompt="Banana", color=ColorField(r=0, g=0, b=200, a=255)),
-    PromptColorPair(prompt="A gnome", color=ColorField(r=215, g=0, b=255, a=255)),
-]
-
-
-@invocation_output("extract_masks_and_prompts_output")
-class ExtractMasksAndPromptsOutput(BaseInvocationOutput):
-    prompt_mask_pairs: list[PromptMaskPair] = OutputField(description="List of prompts and their corresponding masks.")
-
-
-@invocation(
-    "extract_masks_and_prompts",
-    title="Extract Masks and Prompts",
-    tags=["conditioning"],
-    category="conditioning",
-    version="1.0.0",
-)
-class ExtractMasksAndPromptsInvocation(BaseInvocation):
-    """Extract masks and prompts from a segmented mask image and prompt-to-color map."""
-
-    prompt_color_pairs: list[PromptColorPair] = InputField(
-        default=default_prompt_color_pairs, description="List of prompts and their corresponding colors."
-    )
-    image: ImageField = InputField(description="Mask to apply to the prompts.")
-
-    def invoke(self, context: InvocationContext) -> ExtractMasksAndPromptsOutput:
-        prompt_mask_pairs: list[PromptMaskPair] = []
-        image = context.images.get_pil(self.image.image_name)
-        image_as_tensor = torch.from_numpy(np.array(image, dtype=np.uint8))
-
-        for pair in self.prompt_color_pairs:
-            # TODO(ryand): Make this work for both RGB and RGBA images.
-            mask = torch.all(image_as_tensor == torch.tensor(pair.color.tuple()), dim=-1)
-            mask_tensor_name = context.tensors.save(mask)
-            prompt_mask_pairs.append(PromptMaskPair(prompt=pair.prompt, mask=TensorField(tensor_name=mask_tensor_name)))
-        return ExtractMasksAndPromptsOutput(prompt_mask_pairs=prompt_mask_pairs)
-
-
-@invocation_output("split_mask_prompt_pair_output")
-class SplitMaskPromptPairOutput(BaseInvocationOutput):
-    prompt: str = OutputField()
-    mask: TensorField = OutputField()
-
-
-@invocation(
-    "split_mask_prompt_pair",
-    title="Split Mask-Prompt pair",
-    tags=["conditioning"],
-    category="conditioning",
-    version="1.0.0",
-)
-class SplitMaskPromptPair(BaseInvocation):
-    """Extract masks and prompts from a segmented mask image and prompt-to-color map."""
-
-    prompt_mask_pair: PromptMaskPair = InputField()
-
-    def invoke(self, context: InvocationContext) -> SplitMaskPromptPairOutput:
-        return SplitMaskPromptPairOutput(mask=self.prompt_mask_pair.mask, prompt=self.prompt_mask_pair.prompt)