diff --git a/invokeai/app/invocations/controlnet_image_processors.py b/invokeai/app/invocations/controlnet_image_processors.py
index 1392f43767..ba7d1c8265 100644
--- a/invokeai/app/invocations/controlnet_image_processors.py
+++ b/invokeai/app/invocations/controlnet_image_processors.py
@@ -230,15 +230,18 @@ class HedImageProcessorInvocation(ImageProcessorInvocation):
     scribble: bool = InputField(default=False, description=FieldDescriptions.scribble_mode)
 
     def run_processor(self, image: Image.Image) -> Image.Image:
-        hed_processor = HEDProcessor()
-        processed_image = hed_processor.run(
-            image,
-            detect_resolution=self.detect_resolution,
-            image_resolution=self.image_resolution,
-            # safe not supported in controlnet_aux v0.0.3
-            # safe=self.safe,
-            scribble=self.scribble,
-        )
+        hed_weights = self._context.models.load_remote_model("lllyasviel/Annotators::/ControlNetHED.pth")
+        with hed_weights as weights:
+            assert isinstance(weights, dict)
+            hed_processor = HEDProcessor(weights)
+            processed_image = hed_processor.run(
+                image,
+                detect_resolution=self.detect_resolution,
+                image_resolution=self.image_resolution,
+                # safe not supported in controlnet_aux v0.0.3
+                # safe=self.safe,
+                scribble=self.scribble,
+            )
         return processed_image
 
 
diff --git a/invokeai/backend/image_util/hed.py b/invokeai/backend/image_util/hed.py
index 97706df8b9..8a7aea8403 100644
--- a/invokeai/backend/image_util/hed.py
+++ b/invokeai/backend/image_util/hed.py
@@ -4,8 +4,9 @@ import cv2
 import numpy as np
 import torch
 from einops import rearrange
-from huggingface_hub import hf_hub_download
+from invokeai.backend.model_manager.config import AnyModel
 from PIL import Image
+from typing import Dict
 
 from invokeai.backend.image_util.util import (
     nms,
@@ -76,16 +77,11 @@ class HEDProcessor:
     On instantiation, loads the HED model from the HuggingFace Hub.
     """
 
-    def __init__(self):
-        model_path = hf_hub_download("lllyasviel/Annotators", "ControlNetHED.pth")
+    def __init__(self, state_dict: Dict[str, torch.Tensor]):
         self.network = ControlNetHED_Apache2()
-        self.network.load_state_dict(torch.load(model_path, map_location="cpu"))
+        self.network.load_state_dict(state_dict)
         self.network.float().eval()
 
-    def to(self, device: torch.device):
-        self.network.to(device)
-        return self
-
     def run(
         self,
         input_image: Image.Image,