diff --git a/rfdiffusion/inference/model_runners.py b/rfdiffusion/inference/model_runners.py
index c176925..469bfda 100644
--- a/rfdiffusion/inference/model_runners.py
+++ b/rfdiffusion/inference/model_runners.py
@@ -809,7 +809,7 @@ def __init__(self, conf: DictConfig):
         else:
             # initialize BlockAdjacency sampling class
             assert all(x is None for x in (conf.contigmap.inpaint_str_helix, conf.contigmap.inpaint_str_strand, conf.contigmap.inpaint_str_loop)), "can't provide scaffold_dir if you're also specifying per-residue ss"
-            self.blockadjacency = iu.BlockAdjacency(conf.scaffoldguided, conf.inference.num_designs)
+            self.blockadjacency = iu.BlockAdjacency(conf, conf.inference.num_designs)
 
 
         #################################################
@@ -983,6 +983,29 @@ def sample_init(self):
 
 
         xT = torch.clone(fa_stack[-1].squeeze()[:,:14,:])
+
+        #######################################
+        ### 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()
+
         return xT, seq_T
     
     def _preprocess(self, seq, xyz_t, t):