diff --git a/invokeai/backend/image_util/lineart.py b/invokeai/backend/image_util/lineart.py
index 524ebf3adf..8fcca24b0e 100644
--- a/invokeai/backend/image_util/lineart.py
+++ b/invokeai/backend/image_util/lineart.py
@@ -214,8 +214,14 @@ class LineartEdgeDetector:
             line = line.cpu().numpy()
             line = (line * 255.0).clip(0, 255).astype(np.uint8)
 
-        detected_map = line
+        detected_map = 255 - line
 
-        detected_map = 255 - detected_map
+        # The lineart model often outputs a lot of almost-black noise. SD1.5 ControlNets seem to be OK with this, but
+        # SDXL ControlNets are not - they need a cleaner map. 12 was experimentally determined to be a good threshold,
+        # eliminating all the noise while keeping the actual edges. Other approaches to thresholding may be better,
+        # for example stretching the contrast or removing noise.
+        detected_map[detected_map < 12] = 0
 
-        return np_to_pil(detected_map)
+        output = np_to_pil(detected_map)
+
+        return output
diff --git a/invokeai/backend/image_util/lineart_anime.py b/invokeai/backend/image_util/lineart_anime.py
index 8e7e0e5807..09dcb6655e 100644
--- a/invokeai/backend/image_util/lineart_anime.py
+++ b/invokeai/backend/image_util/lineart_anime.py
@@ -260,8 +260,14 @@ class LineartAnimeEdgeDetector:
             line = cv2.resize(line, (width, height), interpolation=cv2.INTER_CUBIC)
             line = line.clip(0, 255).astype(np.uint8)
 
-        detected_map = line
-        detected_map = 255 - detected_map
+        detected_map = 255 - line
+
+        # The lineart model often outputs a lot of almost-black noise. SD1.5 ControlNets seem to be OK with this, but
+        # SDXL ControlNets are not - they need a cleaner map. 12 was experimentally determined to be a good threshold,
+        # eliminating all the noise while keeping the actual edges. Other approaches to thresholding may be better,
+        # for example stretching the contrast or removing noise.
+        detected_map[detected_map < 12] = 0
+
         output = np_to_pil(detected_map)
 
         return output