diff --git a/test/test_transforms.py b/test/test_transforms.py
index 8423bf99ee3..f8101a1d862 100644
--- a/test/test_transforms.py
+++ b/test/test_transforms.py
@@ -532,7 +532,13 @@ def test_convert_image_dtype_float_to_float(self):
             for output_dtype in output_dtypes:
                 with self.subTest(input_dtype=input_dtype, output_dtype=output_dtype):
                     transform = transforms.ConvertImageDtype(output_dtype)
+                    transform_script = torch.jit.script(F.convert_image_dtype)
+
                     output_image = transform(input_image)
+                    output_image_script = transform_script(input_image, output_dtype)
+
+                    script_diff = output_image_script - output_image
+                    self.assertTrue(script_diff.abs().max() < 1e-6)
 
                     actual_min, actual_max = output_image.tolist()
                     desired_min, desired_max = 0.0, 1.0
@@ -546,6 +552,7 @@ def test_convert_image_dtype_float_to_int(self):
             for output_dtype in int_dtypes():
                 with self.subTest(input_dtype=input_dtype, output_dtype=output_dtype):
                     transform = transforms.ConvertImageDtype(output_dtype)
+                    transform_script = torch.jit.script(F.convert_image_dtype)
 
                     if (input_dtype == torch.float32 and output_dtype in (torch.int32, torch.int64)) or (
                             input_dtype == torch.float64 and output_dtype == torch.int64
@@ -554,6 +561,10 @@ def test_convert_image_dtype_float_to_int(self):
                             transform(input_image)
                     else:
                         output_image = transform(input_image)
+                        output_image_script = transform_script(input_image, output_dtype)
+
+                        script_diff = output_image_script - output_image
+                        self.assertTrue(script_diff.abs().max() < 1e-6)
 
                         actual_min, actual_max = output_image.tolist()
                         desired_min, desired_max = 0, torch.iinfo(output_dtype).max
@@ -567,7 +578,13 @@ def test_convert_image_dtype_int_to_float(self):
             for output_dtype in float_dtypes():
                 with self.subTest(input_dtype=input_dtype, output_dtype=output_dtype):
                     transform = transforms.ConvertImageDtype(output_dtype)
+                    transform_script = torch.jit.script(F.convert_image_dtype)
+
                     output_image = transform(input_image)
+                    output_image_script = transform_script(input_image, output_dtype)
+
+                    script_diff = output_image_script - output_image
+                    self.assertTrue(script_diff.abs().max() < 1e-6)
 
                     actual_min, actual_max = output_image.tolist()
                     desired_min, desired_max = 0.0, 1.0
@@ -586,7 +603,13 @@ def test_convert_image_dtype_int_to_int(self):
 
                 with self.subTest(input_dtype=input_dtype, output_dtype=output_dtype):
                     transform = transforms.ConvertImageDtype(output_dtype)
+                    transform_script = torch.jit.script(F.convert_image_dtype)
+
                     output_image = transform(input_image)
+                    output_image_script = transform_script(input_image, output_dtype)
+
+                    script_diff = output_image_script - output_image
+                    self.assertTrue(script_diff.abs().max() < 1e-6)
 
                     actual_min, actual_max = output_image.tolist()
                     desired_min, desired_max = 0, output_max
diff --git a/torchvision/transforms/functional.py b/torchvision/transforms/functional.py
index e49ff063dc8..c9609774c24 100644
--- a/torchvision/transforms/functional.py
+++ b/torchvision/transforms/functional.py
@@ -113,6 +113,11 @@ def pil_to_tensor(pic):
     return img
 
 
+def _is_floating_point(dtype: torch.dtype) -> bool:
+    # helper function since torch.dtype.is_floating_point is not scriptable
+    return isinstance(dtype, (torch.float32, torch.float, torch.float64, torch.double))
+
+
 def convert_image_dtype(image: torch.Tensor, dtype: torch.dtype = torch.float) -> torch.Tensor:
     """Convert a tensor image to the given ``dtype`` and scale the values accordingly
 
@@ -137,9 +142,12 @@ def convert_image_dtype(image: torch.Tensor, dtype: torch.dtype = torch.float) -
     if image.dtype == dtype:
         return image
 
-    if image.dtype.is_floating_point:
+    input_is_float = _is_floating_point(image.dtype)
+    output_is_float = _is_floating_point(dtype)
+
+    if input_is_float:
         # float to float
-        if dtype.is_floating_point:
+        if output_is_float:
             return image.to(dtype)
 
         # float to int
@@ -153,7 +161,7 @@ def convert_image_dtype(image: torch.Tensor, dtype: torch.dtype = torch.float) -
         return image.mul(torch.iinfo(dtype).max + 1 - eps).to(dtype)
     else:
         # int to float
-        if dtype.is_floating_point:
+        if output_is_float:
             max = torch.iinfo(image.dtype).max
             image = image.to(dtype)
             return image / max