diff --git a/src/algs/adv/evaluator.py b/src/algs/adv/evaluator.py
index c6ba9814..6527a52a 100644
--- a/src/algs/adv/evaluator.py
+++ b/src/algs/adv/evaluator.py
@@ -83,6 +83,7 @@ def encode_dataset(
     encoder: SplitLatentAe,
     device: Union[str, torch.device],
     invariant_to: Literal["y"] = ...,
+    use_amp: bool = False,
 ) -> InvariantDatasets[Dataset[Tensor], None]:
     ...
 
@@ -94,6 +95,7 @@ def encode_dataset(
     encoder: SplitLatentAe,
     device: Union[str, torch.device],
     invariant_to: Literal["s"] = ...,
+    use_amp: bool = False,
 ) -> InvariantDatasets[None, Dataset[Tensor]]:
     ...
 
@@ -105,6 +107,7 @@ def encode_dataset(
     encoder: SplitLatentAe,
     device: Union[str, torch.device],
     invariant_to: Literal["both"],
+    use_amp: bool = False,
 ) -> InvariantDatasets[Dataset[Tensor], Dataset[Tensor]]:
     ...
 
@@ -115,23 +118,25 @@ def encode_dataset(
     encoder: SplitLatentAe,
     device: Union[str, torch.device],
     invariant_to: InvariantAttr = "s",
+    use_amp: bool = False,
 ) -> InvariantDatasets:
     device = resolve_device(device)
     zy_ls, zs_ls, s_ls, y_ls = [], [], [], []
-    with torch.no_grad():
-        for batch in tqdm(dl, desc="Encoding dataset", colour=_PBAR_COL):
-            x = batch.x.to(device, non_blocking=True)
-            s_ls.append(batch.s)
-            y_ls.append(batch.y)
+    with torch.cuda.amp.autocast(enabled=use_amp): # type: ignore
+        with torch.no_grad():
+            for batch in tqdm(dl, desc="Encoding dataset", colour=_PBAR_COL):
+                x = batch.x.to(device, non_blocking=True)
+                s_ls.append(batch.s)
+                y_ls.append(batch.y)
 
-            # don't do the zs transform here because we might want to look at the raw distribution
-            encodings = encoder.encode(x, transform_zs=False)
+                # don't do the zs transform here because we might want to look at the raw distribution
+                encodings = encoder.encode(x, transform_zs=False)
 
-            if invariant_to in ("s", "both"):
-                zy_ls.append(encodings.zy.detach().cpu())
+                if invariant_to in ("s", "both"):
+                    zy_ls.append(encodings.zy.detach().cpu())
 
-            if invariant_to in ("y", "both"):
-                zs_ls.append(encodings.zs.detach().cpu())
+                if invariant_to in ("y", "both"):
+                    zs_ls.append(encodings.zs.detach().cpu())
 
     s_ls = torch.cat(s_ls, dim=0)
     y_ls = torch.cat(y_ls, dim=0)