diff --git a/codes/models/audio/music/tfdpc_v5.py b/codes/models/audio/music/tfdpc_v5.py
index 38c0a09b..8d327baa 100644
--- a/codes/models/audio/music/tfdpc_v5.py
+++ b/codes/models/audio/music/tfdpc_v5.py
@@ -214,7 +214,7 @@ class TransformerDiffusionWithPointConditioning(nn.Module):
             cond_left = conditioning_input[:,:,:max(cond_start, 20)]
             left_pt = cond_start-1
             cond_right = conditioning_input[:,:,min(N+cond_start, conditioning_input.shape[-1]-20):]
-            right_pt = cond_right.shape[-1] - (conditioning_input.shape[-1] - (N+cond_start))
+            right_pt = min(cond_right.shape[-1]-1, cond_right.shape[-1] - (conditioning_input.shape[-1] - (N+cond_start)))
         elif cond_left is None:
             assert conditioning_input.shape[-1] - cond_start - N >= 0, f'Some sort of conditioning misalignment, {conditioning_input.shape[-1], cond_start, N}'
             cond_pre = conditioning_input[:,:,:cond_start]
@@ -313,12 +313,12 @@ def test_cheater_model():
 
     # For music:
     model = TransformerDiffusionWithPointConditioning(in_channels=256, out_channels=512, model_channels=1024,
-                                                      contraction_dim=512, num_heads=8, num_layers=15, dropout=0,
+                                                      contraction_dim=512, num_heads=8, num_layers=40, dropout=0,
                                                       unconditioned_percentage=.4, checkpoint_conditioning=False,
                                                       regularization=True, new_cond=True)
     print_network(model)
-    for cs in range(276,cl.shape[-1]-clip.shape[-1]):
-        o = model(clip, ts, cl, cond_start=cs)
+    #for cs in range(276,cl.shape[-1]-clip.shape[-1]):
+    #    o = model(clip, ts, cl, cond_start=cs)
     pg = model.get_grad_norm_parameter_groups()
     def prmsz(lp):
         sz = 0