diff --git a/src/torchmetrics/functional/image/arniqa.py b/src/torchmetrics/functional/image/arniqa.py
index b5fbf556a1c..c4e084a0555 100644
--- a/src/torchmetrics/functional/image/arniqa.py
+++ b/src/torchmetrics/functional/image/arniqa.py
@@ -27,10 +27,6 @@
 
 from torchmetrics.utilities.imports import _TORCH_GREATER_EQUAL_2_2, _TORCHVISION_AVAILABLE
 
-if _TORCHVISION_AVAILABLE:
-    from torchvision import transforms
-    from torchvision.models import resnet50
-
 _AVAILABLE_REGRESSOR_DATASETS = {
     "kadid10k": (1, 5),
     "koniq10k": (1, 100),
@@ -65,6 +61,8 @@ def __init__(self, regressor_dataset: _TYPE_REGRESSOR_DATASET = "koniq10k") -> N
                 " Either install as `pip install torchmetrics[image]` or `pip install torchvision`."
             )
 
+        from torchvision.models import resnet50
+
         valid_regressor_datasets = _AVAILABLE_REGRESSOR_DATASETS.keys()
         if regressor_dataset not in valid_regressor_datasets:
             raise ValueError(
@@ -116,6 +114,8 @@ def _preprocess_input(self, img: Tensor, normalize: bool = False) -> tuple[Tenso
         Obtains the half-scale version of the input image and applies normalization if needed.
 
         """
+        from torchvision import transforms
+
         h, w = img.shape[-2:]
         img_ds = transforms.Resize((h // 2, w // 2))(img)  # get the half-scale version of the image
         if normalize:
diff --git a/src/torchmetrics/functional/image/dists.py b/src/torchmetrics/functional/image/dists.py
index f3a81e022ef..abd803a3c7d 100644
--- a/src/torchmetrics/functional/image/dists.py
+++ b/src/torchmetrics/functional/image/dists.py
@@ -47,8 +47,6 @@
 
 if not _TORCHVISION_AVAILABLE:
     __doctest_skip__ = ["deep_image_structure_and_texture_similarity"]
-else:
-    from torchvision.models import VGG16_Weights, vgg16
 
 _PATH_WEIGHT_DISTS = Path(__file__).resolve().parent / "dists_models" / "weights.pt"
 
@@ -91,6 +89,8 @@ def __init__(self, load_weights: bool = True) -> None:
                 "DISTS requires torchvision to be installed. Please install it with `pip install torchvision`."
             )
 
+        from torchvision.models import VGG16_Weights, vgg16
+
         vgg_pretrained_features = vgg16(weights=VGG16_Weights.DEFAULT).features
         self.stage1 = torch.nn.Sequential()
         self.stage2 = torch.nn.Sequential()