diff --git a/invokeai/app/util/controlnet_utils.py b/invokeai/app/util/controlnet_utils.py
index 920bca081b..67fd7bb43e 100644
--- a/invokeai/app/util/controlnet_utils.py
+++ b/invokeai/app/util/controlnet_utils.py
@@ -107,7 +107,6 @@ def np_img_resize(
     w: int,
     device: torch.device = torch.device('cpu')
 ):
-    print("in np_img_resize")
     # if 'inpaint' in module:
     #     np_img = np_img.astype(np.float32)
     # else:
@@ -192,7 +191,6 @@ def np_img_resize(
 
     # if resize_mode == external_code.ResizeMode.RESIZE:
     if resize_mode == "just_resize": # RESIZE
-        print("just resizing")
         np_img = high_quality_resize(np_img, (w, h))
         np_img = safe_numpy(np_img)
         return get_pytorch_control(np_img), np_img
@@ -207,7 +205,6 @@ def np_img_resize(
 
     # if resize_mode == external_code.ResizeMode.OUTER_FIT:
     if resize_mode == "fill_resize":  # OUTER_FIT
-        print("fill + resizing")
         k = min(k0, k1)
         borders = np.concatenate([np_img[0, :, :], np_img[-1, :, :], np_img[:, 0, :], np_img[:, -1, :]], axis=0)
         high_quality_border_color = np.median(borders, axis=0).astype(np_img.dtype)
@@ -224,7 +221,6 @@ def np_img_resize(
         np_img = safe_numpy(np_img)
         return get_pytorch_control(np_img), np_img
     else: # resize_mode == "crop_resize"  (INNER_FIT)
-        print("crop + resizing")
         k = max(k0, k1)
         np_img = high_quality_resize(np_img, (safeint(old_w * k), safeint(old_h * k)))
         new_h, new_w, _ = np_img.shape
@@ -233,3 +229,60 @@ def np_img_resize(
         np_img = np_img[pad_h:pad_h + h, pad_w:pad_w + w]
         np_img = safe_numpy(np_img)
         return get_pytorch_control(np_img), np_img
+
+def prepare_control_image(
+    # image used to be Union[PIL.Image.Image, List[PIL.Image.Image], torch.Tensor, List[torch.Tensor]]
+    # but now should be able to assume that image is a single PIL.Image, which simplifies things
+    image: Image,
+    # FIXME: need to fix hardwiring of width and height, change to basing on latents dimensions?
+    # latents_to_match_resolution, # TorchTensor of shape (batch_size, 3, height, width)
+    width=512,  # should be 8 * latent.shape[3]
+    height=512,  # should be 8 * latent height[2]
+    # batch_size=1, # currently no batching
+    # num_images_per_prompt=1, # currently only single image
+    device="cuda",
+    dtype=torch.float16,
+    do_classifier_free_guidance=True,
+    control_mode="balanced",
+    resize_mode="just_resize_simple",
+):
+    # FIXME: implement "crop_resize_simple" and "fill_resize_simple", or pull them out
+    if (resize_mode == "just_resize_simple" or
+        resize_mode == "crop_resize_simple" or
+        resize_mode == "fill_resize_simple"):
+        image = image.convert("RGB")
+        if (resize_mode == "just_resize_simple"):
+            image = image.resize((width, height), resample=PIL_INTERPOLATION["lanczos"])
+        elif (resize_mode == "crop_resize_simple"): # not yet implemented
+            pass
+        elif (resize_mode == "fill_resize_simple"): # not yet implemented
+            pass
+        nimage = np.array(image)
+        nimage = nimage[None, :]
+        nimage = np.concatenate([nimage], axis=0)
+        # normalizing RGB values to [0,1] range (in PIL.Image they are [0-255])
+        nimage = np.array(nimage).astype(np.float32) / 255.0
+        nimage = nimage.transpose(0, 3, 1, 2)
+        timage = torch.from_numpy(nimage)
+
+    # use fancy lvmin controlnet resizing
+    elif (resize_mode == "just_resize" or resize_mode == "crop_resize" or resize_mode == "fill_resize"):
+        nimage = np.array(image)
+        timage, nimage = np_img_resize(
+            np_img=nimage,
+            resize_mode=resize_mode,
+            h=height,
+            w=width,
+            # device=torch.device('cpu')
+            device=device,
+        )
+    else:
+        pass
+        print("ERROR: invalid resize_mode ==> ", resize_mode)
+        exit(1)
+
+    timage = timage.to(device=device, dtype=dtype)
+    cfg_injection = (control_mode == "more_control" or control_mode == "unbalanced")
+    if do_classifier_free_guidance and not cfg_injection:
+        timage = torch.cat([timage] * 2)
+    return timage