diff --git a/extras/GroundingDINO/util/inference.py b/extras/GroundingDINO/util/inference.py
index 259094f2..bc8b6429 100644
--- a/extras/GroundingDINO/util/inference.py
+++ b/extras/GroundingDINO/util/inference.py
@@ -25,7 +25,7 @@ class GroundingDinoModel(Model):
             caption: str,
             box_threshold: float = 0.35,
             text_threshold: float = 0.25
-    ) -> Tuple[sv.Detections, List[str]]:
+    ) -> Tuple[sv.Detections, torch.Tensor, torch.Tensor, List[str]]:
         if self.model is None:
             filename = load_file_from_url(
                 url="https://github.com/IDEA-Research/GroundingDINO/releases/download/v0.1.0-alpha/groundingdino_swint_ogc.pth",
@@ -56,7 +56,7 @@ class GroundingDinoModel(Model):
             source_w=source_w,
             boxes=boxes,
             logits=logits)
-        return detections, phrases
+        return detections, boxes, logits, phrases
 
 
 def predict(
diff --git a/extras/inpaint_mask.py b/extras/inpaint_mask.py
index 4999f258..ea6e8819 100644
--- a/extras/inpaint_mask.py
+++ b/extras/inpaint_mask.py
@@ -10,17 +10,17 @@ device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
 def run_grounded_sam(input_image, text_prompt, box_threshold, text_threshold):
 
     # run grounding dino model
-    boxes, _ = default_groundingdino(
+    detections, _, _, _ = default_groundingdino(
         image=np.array(input_image),
         caption=text_prompt,
         box_threshold=box_threshold,
         text_threshold=text_threshold
     )
 
-    return boxes.xyxy
+    return detections.xyxy
 
 
-def generate_mask_from_image(image, mask_model, extras):
+def generate_mask_from_image(image, mask_model, extras, box_erode_or_dilate: int=0):
     if image is None:
         return
 
@@ -28,15 +28,29 @@ def generate_mask_from_image(image, mask_model, extras):
         image = image['image']
 
     if mask_model == 'sam':
-        boxes = run_grounded_sam(Image.fromarray(image), extras['sam_prompt_text'], box_threshold=extras['box_threshold'], text_threshold=extras['text_threshold'])
+        img = Image.fromarray(image)
+        boxes = run_grounded_sam(img, extras['sam_prompt_text'], box_threshold=extras['box_threshold'], text_threshold=extras['text_threshold'])
+        # use full image if no box has been found
         boxes = np.array([[0, 0, image.shape[1], image.shape[0]]]) if len(boxes) == 0 else boxes
+
         extras['sam_prompt'] = []
+        # from PIL import ImageDraw
+        # draw = ImageDraw.Draw(img)
         for idx, box in enumerate(boxes):
-            extras['sam_prompt'] += [{"type": "rectangle", "data": box.tolist()}]
+            box_list = box.tolist()
+            if box_erode_or_dilate != 0:
+                box_list[0] -= box_erode_or_dilate
+                box_list[1] -= box_erode_or_dilate
+                box_list[2] += box_erode_or_dilate
+                box_list[3] += box_erode_or_dilate
+        #     draw.rectangle(box_list, fill=128, outline ="red")
+            extras['sam_prompt'] += [{"type": "rectangle", "data": box_list}]
+        # img.show()
 
     return remove(
         image,
         session=new_session(mask_model, **extras),
         only_mask=True,
+        # post_process_mask=True,
         **extras
     )