diff --git a/invokeai/app/invocations/controlnet_image_processors.py b/invokeai/app/invocations/controlnet_image_processors.py
index 2bd0a5cf04..870f14dc27 100644
--- a/invokeai/app/invocations/controlnet_image_processors.py
+++ b/invokeai/app/invocations/controlnet_image_processors.py
@@ -1,8 +1,9 @@
-# InvokeAI nodes for ControlNet image preprocessors
+# Invocations for ControlNet image preprocessors
 # initial implementation by Gregg Helt, 2023
 # heavily leverages controlnet_aux package: https://github.com/patrickvonplaten/controlnet_aux
 from builtins import float, bool
 
+import cv2
 import numpy as np
 from typing import Literal, Optional, Union, List, Dict
 from PIL import Image, ImageFilter, ImageOps
@@ -33,7 +34,7 @@ from controlnet_aux import (
     # LeresDetector,
 )
 
-from controlnet_aux.util import ade_palette
+from controlnet_aux.util import HWC3, ade_palette
 
 
 from .image import ImageOutput, PILInvocationConfig
@@ -483,6 +484,43 @@ class MediapipeFaceProcessorInvocation(ImageProcessorInvocation, PILInvocationCo
 #                                           image_resolution=self.image_resolution)
 #         return processed_image
 
+
+class TileResamplerProcessorInvocation(ImageProcessorInvocation, PILInvocationConfig):
+
+    # fmt: off
+    type: Literal["tile_image_processor"] = "tile_image_processor"
+    # Inputs
+    #res: int = Field(default=512, ge=0, le=1024, description="The pixel resolution for each tile")
+    down_sampling_rate: float = Field(default=1.0, ge=1.0, le=8.0, description="Down sampling rate")
+    # fmt: on
+
+    # tile_resample copied from sd-webui-controlnet/scripts/processor.py
+    def tile_resample(self,
+                      np_img: np.ndarray,
+                      res=512,   # never used?
+                      down_sampling_rate=1.0,
+                      ):
+        np_img = HWC3(np_img)
+        if down_sampling_rate < 1.1:
+            return np_img
+        H, W, C = np_img.shape
+        H = int(float(H) / float(down_sampling_rate))
+        W = int(float(W) / float(down_sampling_rate))
+        np_img = cv2.resize(np_img, (W, H), interpolation=cv2.INTER_AREA)
+        return np_img
+
+    def run_processor(self, img):
+        np_img = np.array(img, dtype=np.uint8)
+        processed_np_image = self.tile_resample(np_img,
+                                                #res=self.tile_size,
+                                                down_sampling_rate=self.down_sampling_rate
+                                                )
+        processed_image = Image.fromarray(processed_np_image)
+        return processed_image
+
+
+
+
 class SegmentAnythingProcessorInvocation(ImageProcessorInvocation, PILInvocationConfig):
     """Applies segment anything processing to image"""
     # fmt: off
@@ -492,7 +530,8 @@ class SegmentAnythingProcessorInvocation(ImageProcessorInvocation, PILInvocation
     def run_processor(self, image):
         # segment_anything_processor = SamDetector.from_pretrained("ybelkada/segment-anything", subfolder="checkpoints")
         segment_anything_processor = SamDetectorReproducibleColors.from_pretrained("ybelkada/segment-anything", subfolder="checkpoints")
-        processed_image = segment_anything_processor(image)
+        np_img = np.array(image, dtype=np.uint8)
+        processed_image = segment_anything_processor(np_img)
         return processed_image
 
 class SamDetectorReproducibleColors(SamDetector):