diff --git a/codes/models/gpt_voice/unet_diffusion_tts9.py b/codes/models/gpt_voice/unet_diffusion_tts9.py
index 3d03aef3..ceee0e4e 100644
--- a/codes/models/gpt_voice/unet_diffusion_tts9.py
+++ b/codes/models/gpt_voice/unet_diffusion_tts9.py
@@ -34,6 +34,7 @@ class ResBlock(TimestepBlock):
         out_channels=None,
         dims=2,
         kernel_size=3,
+        efficient_config=True,
     ):
         super().__init__()
         self.channels = channels
@@ -41,11 +42,13 @@ class ResBlock(TimestepBlock):
         self.dropout = dropout
         self.out_channels = out_channels or channels
         padding = {1: 0, 3: 1, 5: 2}[kernel_size]
+        eff_kernel = 1 if efficient_config else 3
+        eff_padding = 0 if efficient_config else 1
 
         self.in_layers = nn.Sequential(
             normalization(channels),
             nn.SiLU(),
-            conv_nd(dims, channels, self.out_channels, 1, padding=0),
+            conv_nd(dims, channels, self.out_channels, eff_kernel, padding=eff_padding),
         )
 
         self.emb_layers = nn.Sequential(
@@ -67,7 +70,7 @@ class ResBlock(TimestepBlock):
         if self.out_channels == channels:
             self.skip_connection = nn.Identity()
         else:
-            self.skip_connection = conv_nd(dims, channels, self.out_channels, 1)
+            self.skip_connection = conv_nd(dims, channels, self.out_channels, eff_kernel, padding=eff_padding)
 
     def forward(self, x, emb):
         """
@@ -146,6 +149,7 @@ class DiffusionTts(nn.Module):
             kernel_size=3,
             scale_factor=2,
             time_embed_dim_multiplier=4,
+            efficient_convs=True,  # Uses kernels with width of 1 in several places rather than 3.
             # Parameters for regularization.
             unconditioned_percentage=.1,  # This implements a mechanism similar to what is used in classifier-free training.
             # Parameters for super-sampling.
@@ -178,6 +182,7 @@ class DiffusionTts(nn.Module):
         self.jit_enabled = jit_enabled
         self.jit_forward = None
         padding = 1 if kernel_size == 3 else 2
+        down_kernel = 1 if efficient_convs else 3
 
         time_embed_dim = model_channels * time_embed_dim_multiplier
         self.time_embed = nn.Sequential(
@@ -251,6 +256,7 @@ class DiffusionTts(nn.Module):
                         out_channels=int(mult * model_channels),
                         dims=dims,
                         kernel_size=kernel_size,
+                        efficient_config=efficient_convs
                     )
                 ]
                 ch = int(mult * model_channels)
@@ -270,7 +276,7 @@ class DiffusionTts(nn.Module):
                 self.input_blocks.append(
                     TimestepEmbedSequential(
                         Downsample(
-                            ch, conv_resample, dims=dims, out_channels=out_ch, factor=scale_factor, ksize=1, pad=0
+                            ch, conv_resample, dims=dims, out_channels=out_ch, factor=scale_factor, ksize=down_kernel, pad=0 if down_kernel == 1 else 1
                         )
                     )
                 )
@@ -286,6 +292,7 @@ class DiffusionTts(nn.Module):
                 dropout,
                 dims=dims,
                 kernel_size=kernel_size,
+                efficient_config=efficient_convs,
             ),
             AttentionBlock(
                 ch,
@@ -298,6 +305,7 @@ class DiffusionTts(nn.Module):
                 dropout,
                 dims=dims,
                 kernel_size=kernel_size,
+                efficient_config=efficient_convs,
             ),
         )
         self._feature_size += ch
@@ -314,6 +322,7 @@ class DiffusionTts(nn.Module):
                         out_channels=int(model_channels * mult),
                         dims=dims,
                         kernel_size=kernel_size,
+                        efficient_config=efficient_convs,
                     )
                 ]
                 ch = int(model_channels * mult)
@@ -466,7 +475,8 @@ if __name__ == '__main__':
                          kernel_size=3,
                          scale_factor=2,
                          time_embed_dim_multiplier=4,
-                         super_sampling=False)
+                         super_sampling=False,
+                         efficient_convs=False)
     # Test with latent aligned conditioning
     o = model(clip, ts, aligned_latent, cond)
     # Test with sequence aligned conditioning