diff --git a/src/anomalib/models/image/dsr/lightning_model.py b/src/anomalib/models/image/dsr/lightning_model.py
index 29f3ad9454..e9eb4d2693 100644
--- a/src/anomalib/models/image/dsr/lightning_model.py
+++ b/src/anomalib/models/image/dsr/lightning_model.py
@@ -94,7 +94,7 @@ def configure_optimizers(
     def on_train_start(self) -> None:
         """Load pretrained weights of the discrete model when starting training."""
         ckpt: Path = self.prepare_pretrained_model()
-        self.model.load_pretrained_discrete_model_weights(ckpt)
+        self.model.load_pretrained_discrete_model_weights(ckpt, self.device)
 
     def on_train_epoch_start(self) -> None:
         """Display a message when starting to train the upsampling module."""
diff --git a/src/anomalib/models/image/dsr/torch_model.py b/src/anomalib/models/image/dsr/torch_model.py
index 416e1b7451..e5f11a6618 100644
--- a/src/anomalib/models/image/dsr/torch_model.py
+++ b/src/anomalib/models/image/dsr/torch_model.py
@@ -81,9 +81,9 @@ def __init__(
         for parameters in self.discrete_latent_model.parameters():
             parameters.requires_grad = False
 
-    def load_pretrained_discrete_model_weights(self, ckpt: Path) -> None:
+    def load_pretrained_discrete_model_weights(self, ckpt: Path, device: torch.device | str | None = None) -> None:
         """Load pre-trained model weights."""
-        self.discrete_latent_model.load_state_dict(torch.load(ckpt))
+        self.discrete_latent_model.load_state_dict(torch.load(ckpt, map_location=device))
 
     def forward(
         self,