diff --git a/rfdiffusion/inference/model_runners.py b/rfdiffusion/inference/model_runners.py
index f47d0e96..8bcc3584 100644
--- a/rfdiffusion/inference/model_runners.py
+++ b/rfdiffusion/inference/model_runners.py
@@ -358,15 +358,15 @@ def sample_init(self, return_forward_trajectory=False):
                     chain_id = available_chains[0]
                     available_chains.remove(chain_id)
                 # Otherwise, use the chain of the fixed (motif) residues
+                # If fixed residues span multiple input chains (motif stitching), use the first one
                 else:
-                    assert len(chain_ids) == 1, f"Error: Multiple chain IDs in chain: {chain_ids}"
-                    chain_id = list(chain_ids)[0]
+                    chain_id = sorted(chain_ids)[0]
                 self.chain_idx += [chain_id] * (last_res - first_res)
             # If this is a fixed chain, maintain the chain and residue numbering
             else:
                 self.idx_pdb += [contig_ref[1] for contig_ref in self.contig_map.ref[first_res: last_res]]
-                assert len(chain_ids) == 1, f"Error: Multiple chain IDs in chain: {chain_ids}"
-                self.chain_idx += [list(chain_ids)[0]] * (last_res - first_res)
+                chain_id = sorted(chain_ids)[0]
+                self.chain_idx += [chain_id] * (last_res - first_res)
             first_res = last_res
 
         ####################################
@@ -939,7 +939,28 @@ def sample_init(self):
         ### Get hotspots ###
         ####################
         self.hotspot_0idx=iu.get_idx0_hotspots(self.mappings, self.ppi_conf, self.binderlen)
-        
+
+        #######################################
+        ### Resolve cyclic peptide indicies ###
+        #######################################
+        if self._conf.inference.cyclic:
+            if self._conf.inference.cyc_chains is None:
+                # default to all residues being cyclized
+                self.cyclic_reses = ~self.mask_str.to(self.device).squeeze()
+            else:
+                # use cyc_chains arg to determine cyclic_reses mask
+                assert type(self._conf.inference.cyc_chains) is str, 'cyc_chains arg must be string'
+                cyc_chains  = self._conf.inference.cyc_chains
+                cyc_chains  = [i.upper() for i in cyc_chains]
+                hal_idx     = self.contig_map.hal # the pdb indices of output, knowledge of different chains
+                is_cyclized = torch.zeros_like(self.mask_str).bool().to(self.device).squeeze() # initially empty
+                for ch in cyc_chains:
+                    ch_mask = torch.tensor([idx[0] == ch for idx in hal_idx]).bool()
+                    is_cyclized[ch_mask] = True # set this whole chain to be cyclic
+                self.cyclic_reses = is_cyclized
+        else:
+            self.cyclic_reses = torch.zeros_like(self.mask_str).bool().to(self.device).squeeze()
+
         #########################
         ### Set up potentials ###
         #########################