Use staticmethods rather than inner functions for the Grounding DINO and SAM model loaders.

invokeai/app/invocations/grounded_sam.py

@@ -84,19 +84,8 @@ class GroundedSAMInvocation(BaseInvocation):
         image_dto = context.images.save(image=mask_pil)
         return ImageOutput.build(image_dto)
 
-    def _detect(
+    @staticmethod
-        self,
+    def _load_grounding_dino(model_path: Path):
-        context: InvocationContext,
-        image: Image.Image,
-        labels: list[str],
-        threshold: float = 0.3,
-    ) -> list[DetectionResult]:
-        """Use Grounding DINO to detect bounding boxes for a set of labels in an image."""
-        # TODO(ryand): I copied this "."-handling logic from the transformers example code. Test it and see if it
-        # actually makes a difference.
-        labels = [label if label.endswith(".") else label + "." for label in labels]
-
-        def load_grounding_dino(model_path: Path):
         grounding_dino_pipeline = pipeline(
             model=str(model_path),
             task="zero-shot-object-detection",
@@ -108,19 +97,8 @@ class GroundedSAMInvocation(BaseInvocation):
         assert isinstance(grounding_dino_pipeline, ZeroShotObjectDetectionPipeline)
         return GroundingDinoPipeline(grounding_dino_pipeline)
 
-        with context.models.load_remote_model(source=GROUNDING_DINO_MODEL_ID, loader=load_grounding_dino) as detector:
+    @staticmethod
-            assert isinstance(detector, GroundingDinoPipeline)
+    def _load_sam_model(model_path: Path):
-            return detector.detect(image=image, candidate_labels=labels, threshold=threshold)
-
-    def _segment(
-        self,
-        context: InvocationContext,
-        image: Image.Image,
-        detection_results: list[DetectionResult],
-    ) -> list[DetectionResult]:
-        """Use Segment Anything (SAM) to generate masks given an image + a set of bounding boxes."""
-
-        def load_sam_model(model_path: Path):
         sam_model = AutoModelForMaskGeneration.from_pretrained(
             model_path,
             local_files_only=True,
@@ -134,11 +112,37 @@ class GroundedSAMInvocation(BaseInvocation):
         assert isinstance(sam_processor, SamProcessor)
         return SegmentAnythingModel(sam_model=sam_model, sam_processor=sam_processor)
 
+    def _detect(
+        self,
+        context: InvocationContext,
+        image: Image.Image,
+        labels: list[str],
+        threshold: float = 0.3,
+    ) -> list[DetectionResult]:
+        """Use Grounding DINO to detect bounding boxes for a set of labels in an image."""
+        # TODO(ryand): I copied this "."-handling logic from the transformers example code. Test it and see if it
+        # actually makes a difference.
+        labels = [label if label.endswith(".") else label + "." for label in labels]
+
+        with context.models.load_remote_model(
+            source=GROUNDING_DINO_MODEL_ID, loader=GroundedSAMInvocation._load_grounding_dino
+        ) as detector:
+            assert isinstance(detector, GroundingDinoPipeline)
+            return detector.detect(image=image, candidate_labels=labels, threshold=threshold)
+
+    def _segment(
+        self,
+        context: InvocationContext,
+        image: Image.Image,
+        detection_results: list[DetectionResult],
+    ) -> list[DetectionResult]:
+        """Use Segment Anything (SAM) to generate masks given an image + a set of bounding boxes."""
         with (
-            context.models.load_remote_model(source=SEGMENT_ANYTHING_MODEL_ID, loader=load_sam_model) as sam_pipeline,
+            context.models.load_remote_model(
+                source=SEGMENT_ANYTHING_MODEL_ID, loader=GroundedSAMInvocation._load_sam_model
+            ) as sam_pipeline,
         ):
             assert isinstance(sam_pipeline, SegmentAnythingModel)
-
             masks = sam_pipeline.segment(image=image, detection_results=detection_results)
 
         masks = self._to_numpy_masks(masks)