diff --git a/torchvision/prototype/transforms/functional/_color.py b/torchvision/prototype/transforms/functional/_color.py
index e7977156d2c..67a55cfb1d7 100644
--- a/torchvision/prototype/transforms/functional/_color.py
+++ b/torchvision/prototype/transforms/functional/_color.py
@@ -385,18 +385,14 @@ def equalize_image_tensor(image: torch.Tensor) -> torch.Tensor:
     if image.numel() == 0:
         return image
 
-    # 1. The algorithm below can easily be extended to support arbitrary integer dtypes. However, the histogram that
-    #    would be needed to computed will have at least `torch.iinfo(dtype).max + 1` values. That is perfectly fine for
-    #    `torch.int8`, `torch.uint8`, and `torch.int16`, at least questionable for `torch.int32` and completely
-    #    unfeasible for `torch.int64`.
-    # 2. Floating point inputs need to be binned for this algorithm. Apart from converting them to an integer dtype, we
-    #    could also use PyTorch's builtin histogram functionality. However, that has its own set of issues: in addition
-    #    to being slow in general, PyTorch's implementation also doesn't support batches. In total, that makes it slower
-    #    and more complicated to implement than a simple conversion and a fast histogram implementation for integers.
-    # Since we need to convert in most cases anyway and out of the acceptable dtypes mentioned in 1. `torch.uint8` is
-    # by far the most common, we choose it as base.
     output_dtype = image.dtype
-    image = convert_dtype_image_tensor(image, torch.uint8)
+    if image.is_floating_point():
+        # Floating point inputs need to be binned for this algorithm. Apart from converting them to an integer dtype, we
+        # could also use PyTorch's builtin histogram functionality. However, that has its own set of issues: in addition
+        # to being slow in general, PyTorch's implementation also doesn't support batches. In total, that makes it
+        # slower and more complicated to implement than a simple conversion and a fast histogram implementation for
+        # integers.
+        image = convert_dtype_image_tensor(image, torch.uint8)
 
     # The histogram is computed by using the flattened image as index. For example, a pixel value of 127 in the image
     # corresponds to adding 1 to index 127 in the histogram.