diff --git a/test/test_functional_tensor.py b/test/test_functional_tensor.py
index 40a51775a09..8f923475664 100644
--- a/test/test_functional_tensor.py
+++ b/test/test_functional_tensor.py
@@ -850,6 +850,18 @@ def test_autocontrast(device, dtype, channels):
     )
 
 
+@pytest.mark.parametrize("device", cpu_and_gpu())
+@pytest.mark.parametrize("dtype", (None, torch.float32, torch.float64))
+@pytest.mark.parametrize("channels", [1, 3])
+def test_autocontrast_equal_minmax(device, dtype, channels):
+    a = _create_data_batch(32, 32, num_samples=1, channels=channels, device=device)
+    a = a / 2.0 + 0.3
+    assert (F.autocontrast(a)[0] == F.autocontrast(a[0])).all()
+
+    a[0, 0] = 0.7
+    assert (F.autocontrast(a)[0] == F.autocontrast(a[0])).all()
+
+
 @pytest.mark.parametrize("device", cpu_and_gpu())
 @pytest.mark.parametrize("channels", [1, 3])
 def test_equalize(device, channels):
diff --git a/test/test_transforms.py b/test/test_transforms.py
index 876452abab4..512a343ee59 100644
--- a/test/test_transforms.py
+++ b/test/test_transforms.py
@@ -573,6 +573,15 @@ def test_randomness(fn, trans, kwargs, seed, p):
     trans(**kwargs).__repr__()
 
 
+def test_autocontrast_equal_minmax():
+    img_tensor = torch.tensor([[[10]], [[128]], [[245]]], dtype=torch.uint8).expand(3, 32, 32)
+    img_pil = F.to_pil_image(img_tensor)
+
+    img_tensor = F.autocontrast(img_tensor)
+    img_pil = F.autocontrast(img_pil)
+    torch.testing.assert_close(img_tensor, F.pil_to_tensor(img_pil))
+
+
 class TestToPil:
     def _get_1_channel_tensor_various_types():
         img_data_float = torch.Tensor(1, 4, 4).uniform_()
diff --git a/torchvision/transforms/functional_tensor.py b/torchvision/transforms/functional_tensor.py
index dfb32a41adf..4e20c19e45f 100644
--- a/torchvision/transforms/functional_tensor.py
+++ b/torchvision/transforms/functional_tensor.py
@@ -945,10 +945,10 @@ def autocontrast(img: Tensor) -> Tensor:
 
     minimum = img.amin(dim=(-2, -1), keepdim=True).to(dtype)
     maximum = img.amax(dim=(-2, -1), keepdim=True).to(dtype)
-    eq_idxs = torch.where(minimum == maximum)[0]
-    minimum[eq_idxs] = 0
-    maximum[eq_idxs] = bound
     scale = bound / (maximum - minimum)
+    eq_idxs = torch.isfinite(scale).logical_not()
+    minimum[eq_idxs] = 0
+    scale[eq_idxs] = 1
 
     return ((img - minimum) * scale).clamp(0, bound).to(img.dtype)