diff --git a/invokeai/backend/stable_diffusion/diffusers_pipeline.py b/invokeai/backend/stable_diffusion/diffusers_pipeline.py
index 228fbd0585..8acfb100a6 100644
--- a/invokeai/backend/stable_diffusion/diffusers_pipeline.py
+++ b/invokeai/backend/stable_diffusion/diffusers_pipeline.py
@@ -219,6 +219,7 @@ class ControlNetData:
     begin_step_percent: float = Field(default=0.0)
     end_step_percent: float = Field(default=1.0)
     control_mode: str = Field(default="balanced")
+    resize_mode: str = Field(default="just_resize")
 
 
 @dataclass
@@ -653,7 +654,7 @@ class StableDiffusionGeneratorPipeline(StableDiffusionPipeline):
                     if cfg_injection:
                         # Inferred ControlNet only for the conditional batch.
                         # To apply the output of ControlNet to both the unconditional and conditional batches,
-                        #   add 0 to the unconditional batch to keep it unchanged.
+                        #    prepend zeros for unconditional batch
                         down_samples = [torch.cat([torch.zeros_like(d), d]) for d in down_samples]
                         mid_sample = torch.cat([torch.zeros_like(mid_sample), mid_sample])
 
@@ -954,53 +955,3 @@ class StableDiffusionGeneratorPipeline(StableDiffusionPipeline):
             debug_image(
                 img, f"latents {msg} {i+1}/{len(decoded)}", debug_status=True
             )
-
-    # Copied from diffusers pipeline_stable_diffusion_controlnet.py
-    # Returns torch.Tensor of shape (batch_size, 3, height, width)
-    @staticmethod
-    def prepare_control_image(
-        image,
-        # FIXME: need to fix hardwiring of width and height, change to basing on latents dimensions?
-        # latents,
-        width=512,  # should be 8 * latent.shape[3]
-        height=512, # should be 8 * latent height[2]
-        batch_size=1,
-        num_images_per_prompt=1,
-        device="cuda",
-        dtype=torch.float16,
-        do_classifier_free_guidance=True,
-        control_mode="balanced"
-    ):
-
-        if not isinstance(image, torch.Tensor):
-            if isinstance(image, PIL.Image.Image):
-                image = [image]
-
-            if isinstance(image[0], PIL.Image.Image):
-                images = []
-                for image_ in image:
-                    image_ = image_.convert("RGB")
-                    image_ = image_.resize((width, height), resample=PIL_INTERPOLATION["lanczos"])
-                    image_ = np.array(image_)
-                    image_ = image_[None, :]
-                    images.append(image_)
-                image = images
-                image = np.concatenate(image, axis=0)
-                image = np.array(image).astype(np.float32) / 255.0
-                image = image.transpose(0, 3, 1, 2)
-                image = torch.from_numpy(image)
-            elif isinstance(image[0], torch.Tensor):
-                image = torch.cat(image, dim=0)
-
-        image_batch_size = image.shape[0]
-        if image_batch_size == 1:
-            repeat_by = batch_size
-        else:
-            # image batch size is the same as prompt batch size
-            repeat_by = num_images_per_prompt
-        image = image.repeat_interleave(repeat_by, dim=0)
-        image = image.to(device=device, dtype=dtype)
-        cfg_injection = (control_mode == "more_control" or control_mode == "unbalanced")
-        if do_classifier_free_guidance and not cfg_injection:
-            image = torch.cat([image] * 2)
-        return image