diff --git a/invokeai/app/invocations/latent.py b/invokeai/app/invocations/latent.py
index b4ab48c4f9..0f579cd9e7 100644
--- a/invokeai/app/invocations/latent.py
+++ b/invokeai/app/invocations/latent.py
@@ -202,14 +202,14 @@ class CreateGradientMaskInvocation(BaseInvocation):
         default=None,
         description="OPTIONAL: Only connect for specialized Inpainting models, masked_latents will be generated from the image with the VAE",
         title="[OPTIONAL] Image",
-        ui_order=6
+        ui_order=6,
     )
     vae: Optional[VAEField] = InputField(
         default=None,
         description="OPTIONAL: Only connect for specialized Inpainting models, masked_latents will be generated from the image with the VAE",
         title="[OPTIONAL] VAE",
         input=Input.Connection,
-        ui_order=7
+        ui_order=7,
     )
     tiled: bool = InputField(default=False, description=FieldDescriptions.tiled, ui_order=8)
     fp32: bool = InputField(
@@ -218,7 +218,6 @@ class CreateGradientMaskInvocation(BaseInvocation):
         ui_order=9,
     )
 
-
     @torch.no_grad()
     def invoke(self, context: InvocationContext) -> GradientMaskOutput:
         mask_image = context.images.get_pil(self.mask.image_name, mode="L")
@@ -254,9 +253,8 @@ class CreateGradientMaskInvocation(BaseInvocation):
         expanded_image_dto = context.images.save(expanded_mask_image)
 
         masked_latents_name = None
-        # Check for Inpaint model and generate masked_latents
         if self.vae is not None and self.image is not None:
-            #both fields must be present at the same time
+            # both fields must be present at the same time
             mask = blur_tensor
             vae_info: LoadedModel = context.models.load(self.vae.vae)
             image = context.images.get_pil(self.image.image_name)
@@ -264,11 +262,10 @@ class CreateGradientMaskInvocation(BaseInvocation):
             if image_tensor.dim() == 3:
                 image_tensor = image_tensor.unsqueeze(0)
             img_mask = tv_resize(mask, image_tensor.shape[-2:], T.InterpolationMode.BILINEAR, antialias=False)
-            masked_image = image_tensor * torch.where(img_mask < 0.5, 0.0, 1.0) # <1 to include gradient area
+            masked_image = image_tensor * torch.where(img_mask < 0.5, 0.0, 1.0)
             masked_latents = ImageToLatentsInvocation.vae_encode(vae_info, self.fp32, self.tiled, masked_image.clone())
             masked_latents_name = context.tensors.save(tensor=masked_latents)
 
-
         return GradientMaskOutput(
             denoise_mask=DenoiseMaskField(mask_name=mask_name, masked_latents_name=masked_latents_name, gradient=True),
             expanded_mask_area=ImageField(image_name=expanded_image_dto.image_name),