diff --git a/invokeai/app/invocations/controlnet_image_processors.py b/invokeai/app/invocations/controlnet_image_processors.py
index ed56344847..547b335a70 100644
--- a/invokeai/app/invocations/controlnet_image_processors.py
+++ b/invokeai/app/invocations/controlnet_image_processors.py
@@ -7,7 +7,6 @@ from typing import Dict, List, Literal, Union
 import cv2
 import numpy as np
 from controlnet_aux import (
-    CannyDetector,
     ContentShuffleDetector,
     HEDdetector,
     LeresDetector,
@@ -39,6 +38,7 @@ from invokeai.app.invocations.model import ModelIdentifierField
 from invokeai.app.invocations.primitives import ImageOutput
 from invokeai.app.invocations.util import validate_begin_end_step, validate_weights
 from invokeai.app.services.shared.invocation_context import InvocationContext
+from invokeai.backend.image_util.canny import get_canny_edges
 from invokeai.backend.image_util.depth_anything import DepthAnythingDetector
 from invokeai.backend.image_util.dw_openpose import DWOpenposeDetector
 
@@ -189,14 +189,13 @@ class CannyImageProcessorInvocation(ImageProcessorInvocation):
         # Keep alpha channel for Canny processing to detect edges of transparent areas
         return context.images.get_pil(self.image.image_name, "RGBA")
 
-    def run_processor(self, image):
-        canny_processor = CannyDetector()
-        processed_image = canny_processor(
+    def run_processor(self, image: Image.Image) -> Image.Image:
+        processed_image = get_canny_edges(
             image,
             self.low_threshold,
             self.high_threshold,
-            image_resolution=self.image_resolution,
             detect_resolution=self.detect_resolution,
+            image_resolution=self.image_resolution,
         )
         return processed_image
 
diff --git a/invokeai/backend/image_util/canny.py b/invokeai/backend/image_util/canny.py
new file mode 100644
index 0000000000..e0067bd7fd
--- /dev/null
+++ b/invokeai/backend/image_util/canny.py
@@ -0,0 +1,41 @@
+import cv2
+from PIL import Image
+
+from invokeai.backend.image_util.util import (
+    cv2_to_pil,
+    fit_image_to_resolution,
+    normalize_image_channel_count,
+    pil_to_cv2,
+)
+
+
+def get_canny_edges(
+    image: Image.Image, low_threshold: int, high_threshold: int, detect_resolution: int, image_resolution: int
+) -> Image.Image:
+    """Returns the edges of an image using the Canny edge detection algorithm.
+
+    This function is adapted from https://github.com/lllyasviel/ControlNet.
+
+    Args:
+        image: The input image.
+        low_threshold: The lower threshold for the hysteresis procedure.
+        high_threshold: The upper threshold for the hysteresis procedure.
+        input_resolution: The resolution of the input image. The image will be resized to this resolution before edge detection.
+        output_resolution: The resolution of the output image. The edges will be resized to this resolution before returning.
+
+    Returns:
+        The Canny edges of the input image.
+    """
+
+    if image.mode != "RGB":
+        image = image.convert("RGB")
+
+    np_image = pil_to_cv2(image)
+    np_image = normalize_image_channel_count(np_image)
+    np_image = fit_image_to_resolution(np_image, detect_resolution)
+
+    edge_map = cv2.Canny(np_image, low_threshold, high_threshold)
+    edge_map = normalize_image_channel_count(edge_map)
+    edge_map = fit_image_to_resolution(edge_map, image_resolution)
+
+    return cv2_to_pil(edge_map)