diff --git a/invokeai/backend/image_util/grounded_sam/grounding_dino_pipeline.py b/invokeai/backend/image_util/grounded_sam/grounding_dino_pipeline.py
index c03a3d8cab..d998eb1d57 100644
--- a/invokeai/backend/image_util/grounded_sam/grounding_dino_pipeline.py
+++ b/invokeai/backend/image_util/grounded_sam/grounding_dino_pipeline.py
@@ -5,9 +5,10 @@ from PIL import Image
 from transformers.pipelines import ZeroShotObjectDetectionPipeline
 
 from invokeai.backend.image_util.grounded_sam.detection_result import DetectionResult
+from invokeai.backend.raw_model import RawModel
 
 
-class GroundingDinoPipeline:
+class GroundingDinoPipeline(RawModel):
     """A wrapper class for a ZeroShotObjectDetectionPipeline that makes it compatible with the model manager's memory
     management system.
     """
@@ -20,14 +21,13 @@ class GroundingDinoPipeline:
         results = [DetectionResult.model_validate(result) for result in results]
         return results
 
-    def to(self, device: Optional[torch.device] = None, dtype: Optional[torch.dtype] = None) -> "GroundingDinoPipeline":
+    def to(self, device: Optional[torch.device] = None, dtype: Optional[torch.dtype] = None):
         # HACK(ryand): The GroundingDinoPipeline does not work on MPS devices. We only allow it to be moved to CPU or
         # CUDA.
         if device is not None and device.type not in {"cpu", "cuda"}:
             device = None
         self._pipeline.model.to(device=device, dtype=dtype)
         self._pipeline.device = self._pipeline.model.device
-        return self
 
     def calc_size(self) -> int:
         # HACK(ryand): Fix the circular import issue.
diff --git a/invokeai/backend/image_util/grounded_sam/segment_anything_model.py b/invokeai/backend/image_util/grounded_sam/segment_anything_model.py
index c9959424f2..5147141303 100644
--- a/invokeai/backend/image_util/grounded_sam/segment_anything_model.py
+++ b/invokeai/backend/image_util/grounded_sam/segment_anything_model.py
@@ -6,21 +6,21 @@ from transformers.models.sam import SamModel
 from transformers.models.sam.processing_sam import SamProcessor
 
 from invokeai.backend.image_util.grounded_sam.detection_result import DetectionResult
+from invokeai.backend.raw_model import RawModel
 
 
-class SegmentAnythingModel:
+class SegmentAnythingModel(RawModel):
     """A wrapper class for the transformers SAM model and processor that makes it compatible with the model manager."""
 
     def __init__(self, sam_model: SamModel, sam_processor: SamProcessor):
         self._sam_model = sam_model
         self._sam_processor = sam_processor
 
-    def to(self, device: Optional[torch.device] = None, dtype: Optional[torch.dtype] = None) -> "SegmentAnythingModel":
+    def to(self, device: Optional[torch.device] = None, dtype: Optional[torch.dtype] = None):
         # HACK(ryand): The SAM pipeline does not work on MPS devices. We only allow it to be moved to CPU or CUDA.
         if device is not None and device.type not in {"cpu", "cuda"}:
             device = None
         self._sam_model.to(device=device, dtype=dtype)
-        return self
 
     def calc_size(self) -> int:
         # HACK(ryand): Fix the circular import issue.