diff --git a/codes/models/audio/music/transformer_diffusion12.py b/codes/models/audio/music/transformer_diffusion12.py
index 3faf3f8b..d9f0fc64 100644
--- a/codes/models/audio/music/transformer_diffusion12.py
+++ b/codes/models/audio/music/transformer_diffusion12.py
@@ -99,6 +99,7 @@ class TransformerDiffusion(nn.Module):
             dropout=0,
             use_fp16=False,
             ar_prior=False,
+            code_expansion_mode='nearest',
             # Parameters for regularization.
             unconditioned_percentage=.1,  # This implements a mechanism similar to what is used in classifier-free training.
             # Parameters for re-training head
@@ -114,6 +115,7 @@ class TransformerDiffusion(nn.Module):
         self.dropout = dropout
         self.unconditioned_percentage = unconditioned_percentage
         self.enable_fp16 = use_fp16
+        self.code_expansion_mode = code_expansion_mode
 
         self.inp_block = conv_nd(1, in_channels, prenet_channels, 3, 1, 1)
 
@@ -217,7 +219,7 @@ class TransformerDiffusion(nn.Module):
             code_emb = torch.where(unconditioned_batches, self.unconditioned_embedding.repeat(prior.shape[0], 1, 1),
                                    code_emb)
 
-        expanded_code_emb = F.interpolate(code_emb.permute(0,2,1), size=expected_seq_len, mode='nearest').permute(0,2,1)
+        expanded_code_emb = F.interpolate(code_emb.permute(0,2,1), size=expected_seq_len, mode=self.code_expansion_mode).permute(0,2,1)
         return expanded_code_emb
 
     def forward(self, x, timesteps, codes=None, conditioning_input=None, precomputed_code_embeddings=None, conditioning_free=False):