fix ruff?

invokeai/app/invocations/image.py

@@ -1074,8 +1074,6 @@ def invoke(self, context: InvocationContext) -> CanvasV2MaskAndCropOutput:
         )
 
 
-
-
 @invocation_output("crop_to_object_output")
 class CropToObjectOutput(ImageOutput):
     offset_top: int = OutputField(description="The number of pixels cropped from the top")
@@ -1096,9 +1094,8 @@ class CropToObjectInvocation(BaseInvocation, WithMetadata, WithBoard):
 
     image: ImageField = InputField(description="An input mask image with black and white content")
     margin: int = InputField(default=0, ge=0, description="The desired margin around the object, as measured in pixels")
-    object_color: Literal['white', 'black'] = InputField(
-        default='white',
-        description="The color of the object to crop around (either 'white' or 'black')"
+    object_color: Literal["white", "black"] = InputField(
+        default="white", description="The color of the object to crop around (either 'white' or 'black')"
     )
 
     def invoke(self, context: InvocationContext) -> CropToObjectOutput:
@@ -1112,7 +1109,7 @@ def invoke(self, context: InvocationContext) -> CropToObjectOutput:
         np_image = numpy.array(grayscale_image)
 
         # Depending on the object color, find the object pixels
-        if self.object_color == 'white':
+        if self.object_color == "white":
             # Find white pixels (value > 0)
             object_pixels = numpy.argwhere(np_image > 0)
         else:
@@ -1162,4 +1159,4 @@ def invoke(self, context: InvocationContext) -> CropToObjectOutput:
             offset_left=offset_left,
             offset_right=offset_right,
             offset_bottom=offset_bottom,
-        )
+        )

invokeai/app/invocations/mask.py

@@ -108,15 +108,14 @@ class ImageMaskToTensorInvocation(BaseInvocation, WithMetadata):
     def invoke(self, context: InvocationContext) -> MaskOutput:
         image = context.images.get_pil(self.image.image_name)
         np_image = np.array(image)
-
         # Handle different image modes
-        if image.mode == 'RGBA':
+        if image.mode == "RGBA":
             alpha_channel = np_image[:, :, 3]  # Extract alpha channel
-        elif image.mode == 'RGB':
+        elif image.mode == "RGB":
             # For RGB images, treat all non-black pixels as opaque.
             non_black_mask = np.any(np_image > 0, axis=2)  # True for any non-black pixels
             alpha_channel = non_black_mask.astype(np.uint8) * 255  # Convert to a mask of 0 or 255
-        elif image.mode == 'L':  # Grayscale images
+        elif image.mode == "L":  # Grayscale images
             alpha_channel = np_image  # Grayscale image, so we directly use it
         else:
             raise ValueError(f"Unsupported image mode: {image.mode}")
@@ -135,8 +134,6 @@ def invoke(self, context: InvocationContext) -> MaskOutput:
         )
 
 
-
-
 @invocation(
     "tensor_mask_to_image",
     title="Tensor Mask to Image",