diff --git a/CHANGELOG.md b/CHANGELOG.md
index 1377394d14f..3c1c8cbedd9 100644
--- a/CHANGELOG.md
+++ b/CHANGELOG.md
@@ -27,6 +27,9 @@ and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0
 
 ### Fixed
 
+- Fixed how backprop is handled in `LPIPS` metric ([#2326](https://github.com/Lightning-AI/torchmetrics/pull/2326))
+
+
 - Fixed `MultitaskWrapper` not being able to be logged in lightning when using metric collections ([#2349](https://github.com/Lightning-AI/torchmetrics/pull/2349))
 
 
diff --git a/src/torchmetrics/functional/image/lpips.py b/src/torchmetrics/functional/image/lpips.py
index 6f8d4b4a450..2ce1de9886d 100644
--- a/src/torchmetrics/functional/image/lpips.py
+++ b/src/torchmetrics/functional/image/lpips.py
@@ -275,7 +275,7 @@ def __init__(
             net: Indicate backbone to use, choose between ['alex','vgg','squeeze']
             spatial: If input should be spatial averaged
             pnet_rand: If backbone should be random or use imagenet pre-trained weights
-            pnet_tune: If backprop should be enabled
+            pnet_tune: If backprop should be enabled for both backbone and linear layers
             use_dropout: If dropout layers should be added
             model_path: Model path to load pretained models from
             eval_mode: If network should be in evaluation mode
@@ -327,6 +327,10 @@ def __init__(
         if eval_mode:
             self.eval()
 
+        if not self.pnet_tune:
+            for param in self.parameters():
+                param.requires_grad = False
+
     def forward(
         self, in0: Tensor, in1: Tensor, retperlayer: bool = False, normalize: bool = False
     ) -> Union[Tensor, Tuple[Tensor, List[Tensor]]]:
@@ -423,7 +427,7 @@ def learned_perceptual_image_patch_similarity(
         >>> img1 = (torch.rand(10, 3, 100, 100) * 2) - 1
         >>> img2 = (torch.rand(10, 3, 100, 100) * 2) - 1
         >>> learned_perceptual_image_patch_similarity(img1, img2, net_type='squeeze')
-        tensor(0.1008, grad_fn=<DivBackward0>)
+        tensor(0.1008)
 
     """
     net = _NoTrainLpips(net=net_type).to(device=img1.device, dtype=img1.dtype)
diff --git a/src/torchmetrics/image/lpip.py b/src/torchmetrics/image/lpip.py
index 77b87f61344..c094003fa4f 100644
--- a/src/torchmetrics/image/lpip.py
+++ b/src/torchmetrics/image/lpip.py
@@ -86,7 +86,7 @@ class LearnedPerceptualImagePatchSimilarity(Metric):
         >>> img1 = (torch.rand(10, 3, 100, 100) * 2) - 1
         >>> img2 = (torch.rand(10, 3, 100, 100) * 2) - 1
         >>> lpips(img1, img2)
-        tensor(0.1046, grad_fn=<SqueezeBackward0>)
+        tensor(0.1046)
 
     """
 
diff --git a/tests/unittests/image/test_lpips.py b/tests/unittests/image/test_lpips.py
index 3db11ba5d78..75182338960 100644
--- a/tests/unittests/image/test_lpips.py
+++ b/tests/unittests/image/test_lpips.py
@@ -133,3 +133,15 @@ def test_error_on_wrong_update(inp1, inp2):
     metric = LearnedPerceptualImagePatchSimilarity()
     with pytest.raises(ValueError, match="Expected both input arguments to be normalized tensors .*"):
         metric(inp1, inp2)
+
+
+def test_check_for_backprop():
+    """Check that by default the metric supports propagation of gradients, but does not update its parameters."""
+    metric = LearnedPerceptualImagePatchSimilarity()
+    assert not metric.net.lin0.model[1].weight.requires_grad
+    preds, target = _inputs.img1[0], _inputs.img2[0]
+    preds.requires_grad = True
+    loss = metric(preds, target)
+    assert loss.requires_grad
+    loss.backward()
+    assert metric.net.lin0.model[1].weight.grad is None