diff --git a/extras/inpaint_mask.py b/extras/inpaint_mask.py
index 2fd776d8..a4f0e7c4 100644
--- a/extras/inpaint_mask.py
+++ b/extras/inpaint_mask.py
@@ -1,13 +1,12 @@
+import modules.config
 import numpy as np
 import torch
+from extras.GroundingDINO.util.inference import default_groundingdino
 from extras.sam.predictor import SamPredictor
 from rembg import remove, new_session
 from segment_anything import sam_model_registry
 from segment_anything.utils.amg import remove_small_regions
 
-from extras.GroundingDINO.util.inference import default_groundingdino
-import modules.config
-
 
 class SAMOptions:
     def __init__(self,
@@ -26,6 +25,7 @@ class SAMOptions:
         self.dino_box_threshold = dino_box_threshold
         self.dino_text_threshold = dino_text_threshold
         self.dino_erode_or_dilate = dino_erode_or_dilate
+        self.dino_debug = dino_debug
         self.max_num_boxes = max_num_boxes
         self.model_type = model_type
 
@@ -68,30 +68,6 @@ def generate_mask_from_image(image: np.ndarray, mask_model: str = 'sam', extras=
         box_threshold=sam_options.dino_box_threshold,
         text_threshold=sam_options.dino_text_threshold
     )
-    # detection_boxes = detections.xyxy
-    # # use full image if no box has been found
-    # detection_boxes = np.array([[0, 0, image.shape[1], image.shape[0]]]) if len(detection_boxes) == 0 else detection_boxes
-    #
-    #
-    # for idx, box in enumerate(detection_boxes):
-    #     box_list = box.tolist()
-    #     if dino_erode_or_dilate != 0:
-    #         box_list[0] -= dino_erode_or_dilate
-    #         box_list[1] -= dino_erode_or_dilate
-    #         box_list[2] += dino_erode_or_dilate
-    #         box_list[3] += dino_erode_or_dilate
-    #     extras['sam_prompt'] += [{"type": "rectangle", "data": box_list}]
-    #
-    # if debug_dino:
-    #     from PIL import ImageDraw, Image
-    #     debug_dino_image = Image.new("RGB", (image.shape[1], image.shape[0]), color="black")
-    #     draw = ImageDraw.Draw(debug_dino_image)
-    #     for box in extras['sam_prompt']:
-    #         draw.rectangle(box['data'], fill="white")
-    #     return np.array(debug_dino_image)
-
-    # TODO add support for dino_erode_or_dilate again
-    # TODO add dino_debug again
 
     H, W = image.shape[0], image.shape[1]
     boxes = boxes * torch.Tensor([W, H, W, H])
@@ -107,6 +83,21 @@ def generate_mask_from_image(image: np.ndarray, mask_model: str = 'sam', extras=
     if boxes.size(0) > 0:
         sam_predictor.set_image(image)
 
+        for index in range(boxes.size(0)):
+            assert boxes.size(1) == 4
+            boxes[index][0] -= sam_options.dino_erode_or_dilate
+            boxes[index][1] -= sam_options.dino_erode_or_dilate
+            boxes[index][2] += sam_options.dino_erode_or_dilate
+            boxes[index][3] += sam_options.dino_erode_or_dilate
+
+        if sam_options.dino_debug:
+            from PIL import ImageDraw, Image
+            debug_dino_image = Image.new("RGB", (image.shape[1], image.shape[0]), color="black")
+            draw = ImageDraw.Draw(debug_dino_image)
+            for box in boxes.numpy():
+                draw.rectangle(box.tolist(), fill="white")
+            return np.array(debug_dino_image)
+
         transformed_boxes = sam_predictor.transform.apply_boxes_torch(boxes, image.shape[:2])
         masks, _, _ = sam_predictor.predict_torch(
             point_coords=None,