diff --git a/codes/models/audio/music/tfdpc_v5.py b/codes/models/audio/music/tfdpc_v5.py
index 60a7674d..e1df8de0 100644
--- a/codes/models/audio/music/tfdpc_v5.py
+++ b/codes/models/audio/music/tfdpc_v5.py
@@ -225,7 +225,7 @@ class TransformerDiffusionWithPointConditioning(nn.Module):
             cond_right_enc = self.conditioning_encoder(cond_right_full, time_emb)
             ce = cond_right_enc[:,:,cond_right.shape[-1]-1]
         cond_enc = torch.cat([cs.unsqueeze(-1), ce.unsqueeze(-1)], dim=-1)
-        cond = F.interpolate(cond_enc, size=(N,), mode='linear').permute(0,2,1)
+        cond = F.interpolate(cond_enc, size=(N,), mode='linear', align_corners=True).permute(0,2,1)
         return cond
 
     def forward(self, x, timesteps, conditioning_input=None, conditioning_free=False, cond_start=0, custom_conditioning_fetcher=None):
@@ -304,6 +304,28 @@ def test_cheater_model():
         print(f'{k}: {prmsz(v)/1000000}')
 
 
+def test_conditioning_splitting_logic():
+    ts = torch.LongTensor([600])
+    class fake_conditioner(nn.Module):
+        def __init__(self):
+            super().__init__()
+        def forward(self, t, _):
+            print(t[:,0])
+            return t
+    model = TransformerDiffusionWithPointConditioning(in_channels=256, out_channels=512, model_channels=1024,
+                                                        contraction_dim=512, num_heads=8, num_layers=15, dropout=0,
+                                                        unconditioned_percentage=.4)
+    model.conditioning_encoder = fake_conditioner()
+    BASEDIM=30
+    for x in range(BASEDIM+1, BASEDIM+20):
+        start = random.randint(0,x-BASEDIM)
+        cl = torch.arange(1, x+1, 1).view(1,1,-1).float().repeat(1,256,1)
+        print("Effective input: " + str(cl[0, 0, start:BASEDIM+start]))
+        res = model.process_conditioning(cl, ts, BASEDIM, start, None)
+        print("Result: " + str(res[0,:,0]))
+        print()
+
+
 def inference_tfdpc5_with_cheater():
     with torch.no_grad():
         os.makedirs('results/tfdpc_v3', exist_ok=True)
@@ -384,5 +406,6 @@ def inference_tfdpc5_with_cheater():
             torchaudio.save(f'results/tfdpc_v3/{k}_ref.wav', sample.unsqueeze(0).cpu(), 22050)
 
 if __name__ == '__main__':
-    test_cheater_model()
+    #test_cheater_model()
+    test_conditioning_splitting_logic()
     #inference_tfdpc5_with_cheater()
diff --git a/codes/models/audio/music/transformer_diffusion12.py b/codes/models/audio/music/transformer_diffusion12.py
index 1d8d70da..10ede292 100644
--- a/codes/models/audio/music/transformer_diffusion12.py
+++ b/codes/models/audio/music/transformer_diffusion12.py
@@ -97,6 +97,7 @@ class TransformerDiffusion(nn.Module):
             out_channels=512,  # mean and variance
             num_heads=4,
             dropout=0,
+            use_corner_alignment=False,  # This is an interpolation parameter only provided for backwards compatibility. ALL NEW TRAINS SHOULD SET THIS TO TRUE.
             use_fp16=False,
             new_code_expansion=False,
             permute_codes=False,
@@ -117,6 +118,7 @@ class TransformerDiffusion(nn.Module):
         self.enable_fp16 = use_fp16
         self.new_code_expansion = new_code_expansion
         self.permute_codes = permute_codes
+        self.use_corner_alignment = use_corner_alignment
 
         self.inp_block = conv_nd(1, in_channels, prenet_channels, 3, 1, 1)
 
@@ -189,7 +191,7 @@ class TransformerDiffusion(nn.Module):
 
     def timestep_independent(self, prior, expected_seq_len):
         if self.new_code_expansion:
-            prior = F.interpolate(prior.permute(0,2,1), size=expected_seq_len, mode='linear').permute(0,2,1)
+            prior = F.interpolate(prior.permute(0,2,1), size=expected_seq_len, mode='linear', align_corners=self.use_corner_alignment).permute(0,2,1)
         code_emb = self.input_converter(prior)
         code_emb = self.code_converter(code_emb)