diff --git a/test/test_transforms_v2.py b/test/test_transforms_v2.py
index 4f8d0027bd6..23f75accb52 100644
--- a/test/test_transforms_v2.py
+++ b/test/test_transforms_v2.py
@@ -1393,6 +1393,28 @@ def test_transform_unknown_fill_error(self):
         with pytest.raises(TypeError, match="Got inappropriate fill arg"):
             transforms.RandomAffine(degrees=0, fill="fill")
 
+    @pytest.mark.parametrize("dtype", [torch.uint8, torch.float32])
+    def test_bilinear_no_blend_artifacts(self, dtype):
+        # Regression test for https://github.com/pytorch/vision/issues/8083
+        value = 0.5
+        if not dtype.is_floating_point:
+            value = int(value * get_max_value(dtype))
+
+        input = torch.full((1, 200, 200), value, dtype=dtype)
+
+        output = F.affine(
+            input,
+            angle=30.0,
+            translate=(0, 0),
+            scale=1.0,
+            shear=(0, 0),
+            interpolation=F.InterpolationMode.BILINEAR,
+            fill=value,
+        )
+
+        # Since the fill color is the same as the input, affine should be a no-op
+        assert_equal(output, input)
+
 
 class TestVerticalFlip:
     @pytest.mark.parametrize("dtype", [torch.float32, torch.uint8])
diff --git a/torchvision/transforms/v2/functional/_geometry.py b/torchvision/transforms/v2/functional/_geometry.py
index d6d42344fcb..dd71049289a 100644
--- a/torchvision/transforms/v2/functional/_geometry.py
+++ b/torchvision/transforms/v2/functional/_geometry.py
@@ -585,13 +585,8 @@ def _apply_grid_transform(img: torch.Tensor, grid: torch.Tensor, mode: str, fill
         mask = mask.expand_as(float_img)
         fill_list = fill if isinstance(fill, (tuple, list)) else [float(fill)]  # type: ignore[arg-type]
         fill_img = torch.tensor(fill_list, dtype=float_img.dtype, device=float_img.device).view(1, -1, 1, 1)
-        if mode == "nearest":
-            bool_mask = mask < 0.5
-            float_img[bool_mask] = fill_img.expand_as(float_img)[bool_mask]
-        else:  # 'bilinear'
-            # The following is mathematically equivalent to:
-            # img * mask + (1.0 - mask) * fill = img * mask - fill * mask + fill = mask * (img - fill) + fill
-            float_img = float_img.sub_(fill_img).mul_(mask).add_(fill_img)
+        bool_mask = mask < 1
+        float_img[bool_mask] = fill_img.expand_as(float_img)[bool_mask]
 
     img = float_img.round_().to(img.dtype) if not fp else float_img