diff --git a/codes/models/audio/music/transformer_diffusion8.py b/codes/models/audio/music/transformer_diffusion8.py
index 73a8b3e3..145a7260 100644
--- a/codes/models/audio/music/transformer_diffusion8.py
+++ b/codes/models/audio/music/transformer_diffusion8.py
@@ -196,13 +196,14 @@ class TransformerDiffusion(nn.Module):
 
 
 class TransformerDiffusionWithQuantizer(nn.Module):
-    def __init__(self, train_quantizer_reconstruction_until=-1, freeze_quantizer_until=10000, **kwargs):
+    def __init__(self, quantizer_dims=[1024], train_quantizer_reconstruction_until=-1, freeze_quantizer_until=10000, **kwargs):
         super().__init__()
         self.internal_step = 0
         self.freeze_quantizer_until = freeze_quantizer_until
         self.train_quantizer_reconstruction_until = train_quantizer_reconstruction_until
         self.diff = TransformerDiffusion(**kwargs)
-        self.quantizer = MusicQuantizer2(inp_channels=256, inner_dim=[1024], codevector_dim=1024, codebook_size=256,
+        self.quantizer = MusicQuantizer2(inp_channels=kwargs['in_channels'], inner_dim=quantizer_dims,
+                                         codevector_dim=quantizer_dims[0], codebook_size=256,
                                         codebook_groups=2, max_gumbel_temperature=4, min_gumbel_temperature=.5)
         self.quantizer.quantizer.temperature = self.quantizer.min_gumbel_temperature
         if train_quantizer_reconstruction_until == -1:
@@ -327,11 +328,11 @@ def register_transformer_diffusion8_with_ar_prior(opt_net, opt):
 
 
 def test_quant_model():
-    clip = torch.randn(2, 256, 400)
+    clip = torch.randn(2, 100, 401)
     ts = torch.LongTensor([600, 600])
-    model = TransformerDiffusionWithQuantizer(model_channels=2048, block_channels=1024, prenet_channels=1024,
-                                              input_vec_dim=1024, num_layers=16, prenet_layers=6,
-                                              train_quantizer_reconstruction_until=1000)
+    model = TransformerDiffusionWithQuantizer(in_channels=100, model_channels=2048, block_channels=1024, prenet_channels=1024,
+                                              input_vec_dim=1024, num_layers=16, prenet_layers=6, quantizer_dims=[1024,896,768,512],
+                                              train_quantizer_reconstruction_until=-1)
     model.get_grad_norm_parameter_groups()
 
     print_network(model)