diff --git a/invokeai/app/invocations/controlnet_image_processors.py b/invokeai/app/invocations/controlnet_image_processors.py
index 5e8ad06bc2..9a841af7c8 100644
--- a/invokeai/app/invocations/controlnet_image_processors.py
+++ b/invokeai/app/invocations/controlnet_image_processors.py
@@ -9,12 +9,24 @@ from .baseinvocation import (
     InvocationConfig,
 )
 
-from controlnet_aux import CannyDetector
+from controlnet_aux import (
+    CannyDetector,
+    HEDdetector,
+    LineartDetector,
+    LineartAnimeDetector,
+    MidasDetector,
+    MLSDdetector,
+    NormalBaeDetector,
+    OpenposeDetector,
+    PidiNetDetector,
+    ContentShuffleDetector,
+    # StyleShuffleDetector,
+    ZoeDetector)
+
 from .image import ImageOutput, build_image_output, PILInvocationConfig
 
 
 class ControlField(BaseModel):
-
     image: ImageField = Field(default=None, description="processed image")
     # width: Optional[int] = Field(default=None, description="The width of the image in pixels")
     # height: Optional[int] = Field(default=None, description="The height of the image in pixels")
@@ -38,66 +50,69 @@ class ControlOutput(BaseInvocationOutput):
     # image: ImageField = Field(default=None, description="outputs just them image info (which is also included in control output)")
     # fmt: on
 
+
 class PreprocessedControlInvocation(BaseInvocation, PILInvocationConfig):
-     """Base class for invocations that preprocess images for ControlNet"""
+    """Base class for invocations that preprocess images for ControlNet"""
 
-     # fmt: off
-     type: Literal["preprocessed_control"] = "preprocessed_control"
+    # fmt: off
+    type: Literal["preprocessed_control"] = "preprocessed_control"
 
-     # Inputs
-     image: ImageField = Field(default=None, description="image to process")
-     control_model: str = Field(default=None, description="control model to use")
-     control_weight: float = Field(default=0.5, ge=0, le=1, description="control weight")
-     # begin_step_percent: float = Field(default=0, ge=0, le=1,
-     #                                    description="% of total steps at which controlnet is first applied")
-     # end_step_percent: float = Field(default=1, ge=0, le=1,
-     #                                  description="% of total steps at which controlnet is last applied")
-     # guess_mode: bool = Field(default=False, description="use guess mode (controlnet ignores prompt)")
-     # fmt: on
+    # Inputs
+    image: ImageField = Field(default=None, description="image to process")
+    control_model: str = Field(default=None, description="control model to use")
+    control_weight: float = Field(default=0.5, ge=0, le=1, description="control weight")
 
-     # This super class handles invoke() call, which in turn calls run_processor(image)
-     # subclasses override run_processor instead of implementing their own invoke()
-     def run_processor(self, image):
-         # super class pass through of image
-         return image
+    # begin_step_percent: float = Field(default=0, ge=0, le=1,
+    #                                    description="% of total steps at which controlnet is first applied")
+    # end_step_percent: float = Field(default=1, ge=0, le=1,
+    #                                  description="% of total steps at which controlnet is last applied")
+    # guess_mode: bool = Field(default=False, description="use guess mode (controlnet ignores prompt)")
+    # fmt: on
 
-     def invoke(self, context: InvocationContext) -> ControlOutput:
-         image = context.services.images.get(
-             self.image.image_type, self.image.image_name
-         )
-         # image type should be PIL.PngImagePlugin.PngImageFile ?
-         processed_image = self.run_processor(image)
-         image_type = ImageType.INTERMEDIATE
-         image_name = context.services.images.create_name(
-             context.graph_execution_state_id, self.id
-         )
-         metadata = context.services.metadata.build_metadata(
-             session_id=context.graph_execution_state_id, node=self
-         )
-         context.services.images.save(image_type, image_name, processed_image, metadata)
+    # This super class handles invoke() call, which in turn calls run_processor(image)
+    # subclasses override run_processor instead of implementing their own invoke()
+    def run_processor(self, image):
+        # superclass just passes through image without processing
+        return image
 
-         """Builds an ImageOutput and its ImageField"""
-         image_field = ImageField(
-             image_name=image_name,
-             image_type=image_type,
-         )
-         return ControlOutput(
-             control=ControlField(
-                 image=image_field,
-                 control_model=self.control_model,
-                 control_weight=self.control_weight,
-             )
-         )
+    def invoke(self, context: InvocationContext) -> ControlOutput:
+        image = context.services.images.get(
+            self.image.image_type, self.image.image_name
+        )
+        # image type should be PIL.PngImagePlugin.PngImageFile ?
+        processed_image = self.run_processor(image)
+        image_type = ImageType.INTERMEDIATE
+        image_name = context.services.images.create_name(
+            context.graph_execution_state_id, self.id
+        )
+        metadata = context.services.metadata.build_metadata(
+            session_id=context.graph_execution_state_id, node=self
+        )
+        context.services.images.save(image_type, image_name, processed_image, metadata)
+
+        """Builds an ImageOutput and its ImageField"""
+        image_field = ImageField(
+            image_name=image_name,
+            image_type=image_type,
+        )
+        return ControlOutput(
+            control=ControlField(
+                image=image_field,
+                control_model=self.control_model,
+                control_weight=self.control_weight,
+            )
+        )
 
 
 class CannyControlInvocation(PreprocessedControlInvocation, PILInvocationConfig):
     """Canny edge detection for ControlNet"""
 
     # fmt: off
-    type: Literal["cannycontrol"] = "cannycontrol"
+    type: Literal["canny_control"] = "canny_control"
     # Inputs
     low_threshold: float = Field(default=100, ge=0, description="low threshold of Canny pixel gradient")
     high_threshold: float = Field(default=200, ge=0, description="high threshold of Canny pixel gradient")
+
     # fmt: on
 
     def run_processor(self, image):