diff --git a/invokeai/app/invocations/primitives.py b/invokeai/app/invocations/primitives.py
index 8c30417eff..9254befd62 100644
--- a/invokeai/app/invocations/primitives.py
+++ b/invokeai/app/invocations/primitives.py
@@ -423,6 +423,7 @@ class ColorInvocation(BaseInvocation):
 
 # region Conditioning
 
+
 class DenoisingArea(BaseModel):
     top_y: int = Field(description="TODO")
     left_x: int = Field(description="TODO")
@@ -439,7 +440,10 @@ class ConditioningField(BaseModel):
     #     description="The mask associated with this conditioning tensor. Excluded regions should be set to 0, included "
     #     "regions should be set to 1.",
     # )
-    denoising_area: DenoisingArea = Field(description="The area to apply this conditioning to. Coordinates are in latent space.")
+    denoising_area: Optional[DenoisingArea] = Field(
+        description="The area to apply this conditioning to. Coordinates are in latent space.",
+        default=None,
+    )
     mask_strength: float = Field(
         default=1.0,
         description="The strength of the mask. Only has an effect if mask_name is set. The strength is relative to "
diff --git a/invokeai/backend/stable_diffusion/diffusion/conditioning_data.py b/invokeai/backend/stable_diffusion/diffusion/conditioning_data.py
index 892de0ad50..00cb5375d3 100644
--- a/invokeai/backend/stable_diffusion/diffusion/conditioning_data.py
+++ b/invokeai/backend/stable_diffusion/diffusion/conditioning_data.py
@@ -45,7 +45,7 @@ class TextConditioningInfoWithArea:
     def __init__(
         self,
         text_conditioning_info: Union[BasicConditioningInfo, SDXLConditioningInfo],
-        area: DenoisingArea,
+        area: Optional[DenoisingArea],
         mask_strength: float,
     ):
         self.text_conditioning_info = text_conditioning_info
diff --git a/invokeai/backend/stable_diffusion/diffusion/shared_invokeai_diffusion.py b/invokeai/backend/stable_diffusion/diffusion/shared_invokeai_diffusion.py
index e62bae75be..4bc9db29fe 100644
--- a/invokeai/backend/stable_diffusion/diffusion/shared_invokeai_diffusion.py
+++ b/invokeai/backend/stable_diffusion/diffusion/shared_invokeai_diffusion.py
@@ -5,11 +5,10 @@ from contextlib import contextmanager
 from typing import Any, Callable, Optional, Union
 
 import torch
-import torchvision
 from diffusers import UNet2DConditionModel
 from typing_extensions import TypeAlias
-from invokeai.app.invocations.primitives import DenoisingArea
 
+from invokeai.app.invocations.primitives import DenoisingArea
 from invokeai.app.services.config import InvokeAIAppConfig
 from invokeai.backend.stable_diffusion.diffusion.conditioning_data import (
     BasicConditioningInfo,
@@ -256,13 +255,21 @@ class InvokeAIDiffuserComponent:
                 # )
             else:
                 area = text_conditioning.area
-                # TODO(ryand): Use LATENT_SCALE_FACTOR instead of hard-coding to 8.
-                latent_area = DenoisingArea(
-                    height=max(1, area.height // 8),
-                    width=max(1, area.width // 8),
-                    top_y=area.top_y // 8,
-                    left_x=area.left_x // 8,
-                )
+                if area is None:
+                    latent_area = DenoisingArea(
+                        height=latent_shape[2],
+                        width=latent_shape[3],
+                        top_y=0,
+                        left_x=0,
+                    )
+                else:
+                    # TODO(ryand): Use LATENT_SCALE_FACTOR instead of hard-coding to 8.
+                    latent_area = DenoisingArea(
+                        height=max(1, area.height // 8),
+                        width=max(1, area.width // 8),
+                        top_y=area.top_y // 8,
+                        left_x=area.left_x // 8,
+                    )
                 area_sample = sample[
                     :,
                     :,
@@ -288,16 +295,19 @@ class InvokeAIDiffuserComponent:
             cond_next_xs[
                 :,
                 i,
+                :,
                 latent_area.top_y : latent_area.top_y + latent_area.height,
                 latent_area.left_x : latent_area.left_x + latent_area.width,
             ] = conditioned_next_x * text_conditioning.mask_strength
             uncond_next_xs[
                 :,
                 i,
+                :,
                 latent_area.top_y : latent_area.top_y + latent_area.height,
                 latent_area.left_x : latent_area.left_x + latent_area.width,
             ] = unconditioned_next_x * text_conditioning.mask_strength
             cond_count[
+                :,
                 :,
                 latent_area.top_y : latent_area.top_y + latent_area.height,
                 latent_area.left_x : latent_area.left_x + latent_area.width,