diff --git a/protdiff/sampling.py b/protdiff/sampling.py
index 202e67c..76f8a9e 100644
--- a/protdiff/sampling.py
+++ b/protdiff/sampling.py
@@ -74,7 +74,6 @@ def p_sample_loop(
     noise: torch.Tensor,
     timesteps: int,
     betas: torch.Tensor,
-    noise_modulo: Optional[Union[float, torch.Tensor]] = None,
     is_angle: Union[bool, List[bool]] = [False, True, True, True],
 ) -> torch.Tensor:
     """
@@ -86,7 +85,7 @@ def p_sample_loop(
     # Report metrics on starting noise
     # amin and amax support reducing on multiple dimensions
     logging.info(
-        f"Starting from noise {noise.shape} with modulo {noise_modulo} and range {torch.amin(img, dim=(0, 1))} - {torch.amax(img, dim=(0, 1))} using {device}"
+        f"Starting from noise {noise.shape} with angularity {is_angle} and range {torch.amin(img, dim=(0, 1))} - {torch.amax(img, dim=(0, 1))} using {device}"
     )
 
     imgs = []
@@ -144,9 +143,28 @@ def sample(
             noise=noise,
             timesteps=train_dset.timesteps,
             betas=train_dset.alpha_beta_terms["betas"],
-            is_angle=True,
+            is_angle=train_dset.feature_is_angular["angles"],
         )
         # Gets to size (timesteps, seq_len, n_ft)
         trimmed_sampled = [sampled[:, i, :l, :] for i, l in enumerate(lengths)]
         retval.extend(trimmed_sampled)
+    # Note that we don't use means variable here directly because we may need a subset
+    # of it based on which features are active in the dataset. The function
+    # get_masked_means handles this gracefully
+    if (
+        hasattr(train_dset, "dset")
+        and hasattr(train_dset.dset, "get_masked_means")
+        and train_dset.dset.get_masked_means() is not None
+    ):
+        logging.info(
+            f"Shifting predicted values by original offset: {train_dset.dset.means}"
+        )
+        retval = [s + train_dset.dset.get_masked_means() for s in retval]
+        # Because shifting may have caused us to go across the circle boundary, re-wrap
+        angular_idx = np.where(train_dset.feature_is_angular["angles"])[0]
+        for s in retval:
+            s[..., angular_idx] = utils.modulo_with_wrapped_range(
+                s[..., angular_idx], range_min=-np.pi, range_max=np.pi
+            )
+
     return retval