diff --git a/torchvision/ops/giou_loss.py b/torchvision/ops/giou_loss.py
index c43a788063e..455c9af34e9 100644
--- a/torchvision/ops/giou_loss.py
+++ b/torchvision/ops/giou_loss.py
@@ -2,6 +2,7 @@
 from torch import Tensor
 
 from ..utils import _log_api_usage_once
+from .boxes import generalized_box_iou
 
 
 def _upcast(t: Tensor) -> Tensor:
@@ -48,30 +49,14 @@ def generalized_box_iou_loss(
 
     boxes1 = _upcast(boxes1)
     boxes2 = _upcast(boxes2)
-    x1, y1, x2, y2 = boxes1.unbind(dim=-1)
-    x1g, y1g, x2g, y2g = boxes2.unbind(dim=-1)
-
-    # Intersection keypoints
-    xkis1 = torch.max(x1, x1g)
-    ykis1 = torch.max(y1, y1g)
-    xkis2 = torch.min(x2, x2g)
-    ykis2 = torch.min(y2, y2g)
-
-    intsctk = torch.zeros_like(x1)
-    mask = (ykis2 > ykis1) & (xkis2 > xkis1)
-    intsctk[mask] = (xkis2[mask] - xkis1[mask]) * (ykis2[mask] - ykis1[mask])
-    unionk = (x2 - x1) * (y2 - y1) + (x2g - x1g) * (y2g - y1g) - intsctk
-    iouk = intsctk / (unionk + eps)
-
-    # smallest enclosing box
-    xc1 = torch.min(x1, x1g)
-    yc1 = torch.min(y1, y1g)
-    xc2 = torch.max(x2, x2g)
-    yc2 = torch.max(y2, y2g)
-
-    area_c = (xc2 - xc1) * (yc2 - yc1)
-    miouk = iouk - ((area_c - unionk) / (area_c + eps))
 
+    # here we can use diagonal matrix with no problem because the resultant tensor of `generalized_box_iou` will be having shape = N X N
+    if boxes1.shape == torch.Size([4]):
+        boxes1 = boxes1[None, :]
+        boxes2 = boxes2[None, :]
+        miouk = generalized_box_iou(boxes1, boxes2)[0][0]
+    else:
+        miouk = generalized_box_iou(boxes1, boxes2).diagonal()
     loss = 1 - miouk
 
     if reduction == "mean":