diff --git a/torchvision/transforms/functional_tensor.py b/torchvision/transforms/functional_tensor.py
index 20b76fbf079..9ef16c13cbe 100644
--- a/torchvision/transforms/functional_tensor.py
+++ b/torchvision/transforms/functional_tensor.py
@@ -262,9 +262,27 @@ def adjust_gamma(img: Tensor, gamma: float, gain: float = 1) -> Tensor:
 
 
 def _blend(img1: Tensor, img2: Tensor, ratio: float) -> Tensor:
+    if ratio == 1.0:
+        return img1
     ratio = float(ratio)
     bound = 1.0 if img1.is_floating_point() else 255.0
-    return (ratio * img1 + (1.0 - ratio) * img2).clamp(0, bound).to(img1.dtype)
+
+    if img2.is_floating_point():
+        # Since img2 is float, we can do in-place ops on it. It's a throw-away tensor.
+        # Our strategy is to convert img1 to float and copy it to avoid in-place modifications,
+        # update img2 in-place and add it on the result with an in-place op.
+        result = img1 * ratio
+        img2.mul_(1.0 - ratio)
+        result.add_(img2)
+    else:
+        # Since img2 is not float, we can't do in-place ops on it.
+        # To minimize copies/adds/muls we first convert img1 to float by multiplying it with ratio/(1-ratio).
+        # This permits us to add img2 in-place to it, without further copies.
+        # To ensure we have the correct result at the end, we multiply in-place with (1-ratio).
+        result = img1 * (ratio / (1.0 - ratio))
+        result.add_(img2).mul_(1.0 - ratio)
+
+    return result.clamp_(0, bound).to(img1.dtype)
 
 
 def _rgb2hsv(img: Tensor) -> Tensor: