diff --git a/codes/models/gpt_voice/lucidrains_dvae.py b/codes/models/gpt_voice/lucidrains_dvae.py
index cba5c728..9b171619 100644
--- a/codes/models/gpt_voice/lucidrains_dvae.py
+++ b/codes/models/gpt_voice/lucidrains_dvae.py
@@ -1,3 +1,4 @@
+import functools
 import math
 from math import sqrt
 
@@ -41,6 +42,19 @@ class ResBlock(nn.Module):
         return self.net(x) + x
 
 
+class UpsampledConv(nn.Module):
+    def __init__(self, conv, *args, **kwargs):
+        super().__init__()
+        assert 'stride' in kwargs.keys()
+        self.stride = kwargs['stride']
+        del kwargs['stride']
+        self.conv = conv(*args, **kwargs)
+
+    def forward(self, x):
+        up = nn.functional.interpolate(x, scale_factor=self.stride, mode='nearest')
+        return self.conv(up)
+
+
 class DiscreteVAE(nn.Module):
     def __init__(
         self,
@@ -51,6 +65,10 @@ class DiscreteVAE(nn.Module):
         num_resnet_blocks = 0,
         hidden_dim = 64,
         channels = 3,
+        stride = 2,
+        kernel_size = 4,
+        use_transposed_convs = True,
+        encoder_norm = False,
         smooth_l1_loss = False,
         straight_through = False,
         normalization = None, # ((0.5,) * 3, (0.5,) * 3),
@@ -73,6 +91,8 @@ class DiscreteVAE(nn.Module):
         else:
             conv = nn.Conv1d
             conv_transpose = nn.ConvTranspose1d
+        if not use_transposed_convs:
+            conv_transpose = functools.partial(UpsampledConv, conv)
 
         enc_chans = [hidden_dim * 2 ** i for i in range(num_layers)]
         dec_chans = list(reversed(enc_chans))
@@ -87,9 +107,12 @@ class DiscreteVAE(nn.Module):
         enc_layers = []
         dec_layers = []
 
+        pad = (kernel_size - 1) // 2
         for (enc_in, enc_out), (dec_in, dec_out) in zip(enc_chans_io, dec_chans_io):
-            enc_layers.append(nn.Sequential(conv(enc_in, enc_out, 4, stride = 2, padding = 1), nn.ReLU()))
-            dec_layers.append(nn.Sequential(conv_transpose(dec_in, dec_out, 4, stride = 2, padding = 1), nn.ReLU()))
+            enc_layers.append(nn.Sequential(conv(enc_in, enc_out, kernel_size, stride = stride, padding = pad), nn.ReLU()))
+            if encoder_norm:
+                enc_layers.append(nn.GroupNorm(8, enc_out))
+            dec_layers.append(nn.Sequential(conv_transpose(dec_in, dec_out, kernel_size, stride = stride, padding = pad), nn.ReLU()))
 
         for _ in range(num_resnet_blocks):
             dec_layers.insert(0, ResBlock(dec_chans[1], conv))
@@ -171,7 +194,9 @@ class DiscreteVAE(nn.Module):
         sampled = sampled.permute((0,3,1,2) if len(img.shape) == 4 else (0,2,1))
 
         if self.training:
-            out = self.decoder(sampled)
+            out = sampled
+            for d in self.decoder:
+                out = d(out)
         else:
             # This is non-differentiable, but gives a better idea of how the network is actually performing.
             out = self.decode(codes)
@@ -202,7 +227,7 @@ if __name__ == '__main__':
     #v = DiscreteVAE()
     #o=v(torch.randn(1,3,256,256))
     #print(o.shape)
-    v = DiscreteVAE(channels=1, normalization=None, positional_dims=1, num_tokens=4096, codebook_dim=2048, hidden_dim=256)
-    v.eval()
-    o=v(torch.randn(1,1,256))
+    v = DiscreteVAE(channels=1, normalization=None, positional_dims=1, num_tokens=4096, codebook_dim=4096, hidden_dim=256, stride=4, num_resnet_blocks=1, kernel_size=5, num_layers=5, use_transposed_convs=False)
+    #v.eval()
+    o=v(torch.randn(1,1,4096))
     print(o[-1].shape)