feat: remove unsucessful convmean, provide context only during downsampling

Author: flavioschneider

audio_diffusion_pytorch/model.py

@@ -23,7 +23,6 @@ def __init__(
         channels: int,
         patch_size: int,
         kernel_sizes_init: Sequence[int],
-        out_means: int,
         multipliers: Sequence[int],
         factors: Sequence[int],
         num_blocks: Sequence[int],
@@ -51,7 +50,6 @@ def __init__(
             resnet_groups=resnet_groups,
             kernel_multiplier_downsample=kernel_multiplier_downsample,
             kernel_sizes_init=kernel_sizes_init,
-            out_means=out_means,
             multipliers=multipliers,
             factors=factors,
             num_blocks=num_blocks,
@@ -100,7 +98,6 @@ def __init__(self, *args, **kwargs):
             channels=128,
             patch_size=16,
             kernel_sizes_init=[1, 3, 7],
-            out_means=4,
             multipliers=[1, 2, 4, 4, 4, 4, 4],
             factors=[4, 4, 4, 2, 2, 2],
             num_blocks=[2, 2, 2, 2, 2, 2],
@@ -136,7 +133,6 @@ def __init__(self, factor: int, in_channels: int = 1, *args, **kwargs):
             in_channels=in_channels,
             channels=128,
             patch_size=16,
-            out_means=4,
             kernel_sizes_init=[1, 3, 7],
             multipliers=[1, 2, 4, 4, 4, 4, 4],
             factors=[4, 4, 4, 2, 2, 2],

audio_diffusion_pytorch/modules.py

@@ -724,7 +724,6 @@ def __init__(
         use_skip_scale: bool,
         use_attention_bottleneck: bool,
         out_channels: Optional[int] = None,
-        out_means: int = 1,
         context_channels: Optional[Sequence[int]] = None,
     ):
         super().__init__()
@@ -802,11 +801,10 @@ def __init__(
             attention_features=attention_features,
         )
 
-        context_channels = context_channels + [0]  # Upsample skips first context
         self.upsamples = nn.ModuleList(
             [
                 UpsampleBlock1d(
-                    in_channels=channels * multipliers[i + 1] + context_channels[i + 2],
+                    in_channels=channels * multipliers[i + 1],
                     out_channels=channels * multipliers[i],
                     time_context_features=time_context_features,
                     num_layers=num_blocks[i] + (1 if attentions[i] else 0),
@@ -833,8 +831,7 @@ def __init__(
                 num_groups=resnet_groups,
                 time_context_features=time_context_features,
             ),
-            ConvMean1d(
-                num_means=out_means,
+            Conv1d(
                 in_channels=channels,
                 out_channels=out_channels * patch_size,
                 kernel_size=1,
@@ -889,7 +886,6 @@ def forward(
         for i, upsample in enumerate(self.upsamples):
             skips = skips_list.pop()
             x = upsample(x, skips, t)
-            x = self.add_context(x, context, layer=len(self.upsamples) - i)
 
         x = self.to_out(x)  # t?
 

setup.py

@@ -3,7 +3,7 @@
 setup(
     name="audio-diffusion-pytorch",
     packages=find_packages(exclude=[]),
-    version="0.0.21",
+    version="0.0.22",
     license="MIT",
     description="Audio Diffusion - PyTorch",
     long_description_content_type="text/markdown",