diff --git a/inference/model_runners.py b/inference/model_runners.py
index a2e0f38..2b55323 100644
--- a/inference/model_runners.py
+++ b/inference/model_runners.py
@@ -324,7 +324,7 @@ def sample_init(self, return_forward_trajectory=False):
             xyz_motif_prealign = xyz_mapped.clone()
             motif_prealign_com = xyz_motif_prealign[0,0,:,1].mean(dim=0)
             self.motif_com = xyz_27[contig_map.ref_idx0,1].mean(dim=0)
-            xyz_mapped = get_init_xyz(xyz_mapped).squeeze()
+            xyz_mapped = get_init_xyz(xyz_mapped, center=self.symmetry is None).squeeze()
             # adjust the size of the input atom map
             atom_mask_mapped = torch.full((L_mapped, 27), False)
             atom_mask_mapped[contig_map.hal_idx0] = mask_27[contig_map.ref_idx0]
@@ -352,6 +352,11 @@ def sample_init(self, return_forward_trajectory=False):
         seq_t    = torch.nn.functional.one_hot(seq_t, num_classes=22).float() # [L,22]
         seq_orig = torch.nn.functional.one_hot(seq_orig, num_classes=22).float() # [L,22]
 
+        ############################################################################
+        if self.symmetry is not None:
+            xyz_mapped, seq_t = self.symmetry.apply_symmetry(xyz_mapped, seq_t)
+        ############################################################################
+
         fa_stack, xyz_true = self.diffuser.diffuse_pose(
             xyz_mapped,
             torch.clone(seq_t),
diff --git a/kinematics.py b/kinematics.py
index 1fbc015..76a6781 100644
--- a/kinematics.py
+++ b/kinematics.py
@@ -282,7 +282,7 @@ def c6d_to_bins2(c6d, same_chain, negative=False, params=PARAMS):
     
     return torch.stack([db,ob,tb,pb],axis=-1).long()
 
-def get_init_xyz(xyz_t):
+def get_init_xyz(xyz_t, center=True):
     # input: xyz_t (B, T, L, 14, 3)
     # ouput: xyz (B, T, L, 14, 3)
     B, T, L = xyz_t.shape[:3]
@@ -292,8 +292,10 @@ def get_init_xyz(xyz_t):
 
     mask = torch.isnan(xyz_t[:,:,:,:3]).any(dim=-1).any(dim=-1) # (B, T, L)
     #
-    center_CA = ((~mask[:,:,:,None]) * torch.nan_to_num(xyz_t[:,:,:,1,:])).sum(dim=2) / ((~mask[:,:,:,None]).sum(dim=2)+1e-4) # (B, T, 3)
-    xyz_t = xyz_t - center_CA.view(B,T,1,1,3)
+
+    if center:
+        center_CA = ((~mask[:,:,:,None]) * torch.nan_to_num(xyz_t[:,:,:,1,:])).sum(dim=2) / ((~mask[:,:,:,None]).sum(dim=2)+1e-4) # (B, T, 3)
+        xyz_t = xyz_t - center_CA.view(B,T,1,1,3)
     #
     idx_s = list()
     for i_b in range(B):