feat: add xunet, refactor all models with xunet

README.md

@@ -150,8 +150,7 @@ from audio_diffusion_pytorch import UNet1d
 unet = UNet1d(
     in_channels=1,
     channels=128,
-    patch_factor=16,
-    patch_blocks=1,
+    patch_size=16,
     multipliers=[1, 2, 4, 4, 4, 4, 4],
     factors=[4, 4, 4, 2, 2, 2],
     attentions=[0, 0, 0, 1, 1, 1, 1],

audio_diffusion_pytorch/__init__.py

@@ -35,4 +35,4 @@
     DiffusionVocoder1d,
     Model1d,
 )
-from .modules import NumberEmbedder, T5Embedder, UNet1d
+from .modules import NumberEmbedder, T5Embedder, UNet1d, XUNet1d

audio_diffusion_pytorch/model.py

@@ -9,7 +9,7 @@
 from tqdm import tqdm
 
 from .diffusion import LinearSchedule, UniformDistribution, VSampler, XDiffusion
-from .modules import STFT, SinusoidalEmbedding, UNet1d, UNetCFG1d, rand_bool
+from .modules import STFT, SinusoidalEmbedding, XUNet1d, rand_bool
 from .utils import (
     closest_power_2,
     default,
@@ -28,18 +28,11 @@
 
 
 class Model1d(nn.Module):
-    def __init__(
-        self, diffusion_type: str, use_classifier_free_guidance: bool = False, **kwargs
-    ):
+    def __init__(self, unet_type: str = "base", **kwargs):
         super().__init__()
         diffusion_kwargs, kwargs = groupby("diffusion_", kwargs)
-
-        UNet = UNetCFG1d if use_classifier_free_guidance else UNet1d
-        self.unet = UNet(**kwargs)
-
-        self.diffusion = XDiffusion(
-            type=diffusion_type, net=self.unet, **diffusion_kwargs
-        )
+        self.unet = XUNet1d(type=unet_type, **kwargs)
+        self.diffusion = XDiffusion(net=self.unet, **diffusion_kwargs)
 
     def forward(self, x: Tensor, **kwargs) -> Tensor:
         return self.diffusion(x, **kwargs)
@@ -119,10 +112,10 @@ def __init__(
         encoder_channels: int,
         encoder_factors: Sequence[int],
         encoder_multipliers: Sequence[int],
-        diffusion_type: str,
         encoder_patch_size: int = 1,
         bottleneck: Union[Bottleneck, Sequence[Bottleneck]] = [],
         bottleneck_channels: Optional[int] = None,
+        unet_type: str = "base",
         **kwargs,
     ):
         super().__init__()
@@ -138,13 +131,14 @@ def __init__(
         else:
             context_channels += [encoder_channels * encoder_multipliers[-1]]
 
-        self.unet = UNet1d(
-            in_channels=in_channels, context_channels=context_channels, **kwargs
+        self.unet = XUNet1d(
+            type=unet_type,
+            in_channels=in_channels,
+            context_channels=context_channels,
+            **kwargs,
         )
 
-        self.diffusion = XDiffusion(
-            type=diffusion_type, net=self.unet, **diffusion_kwargs
-        )
+        self.diffusion = XDiffusion(net=self.unet, **diffusion_kwargs)
 
         self.encoder = Encoder1d(
             in_channels=in_channels,
@@ -207,6 +201,7 @@ def __init__(
         encoder_patch_size: int = 1,
         bottleneck: Union[Bottleneck, Sequence[Bottleneck]] = [],
         bottleneck_channels: Optional[int] = None,
+        unet_type: str = "base",
         **kwargs,
     ):
         super().__init__()
@@ -233,7 +228,8 @@ def __init__(
             use_complex=False,  # Magnitude encoding
         )
 
-        self.unet = UNet1d(
+        self.unet = XUNet1d(
+            type=unet_type,
             in_channels=in_channels,
             context_channels=context_channels,
             use_stft=True,
@@ -546,9 +542,9 @@ def __init__(
         self.embedding_mask_proba = embedding_mask_proba
         default_kwargs = dict(
             **get_default_model_kwargs(),
+            unet_type="cfg",
             context_embedding_features=embedding_features,
             context_embedding_max_length=embedding_max_length,
-            use_classifier_free_guidance=True,
         )
         super().__init__(**{**default_kwargs, **kwargs})
 

audio_diffusion_pytorch/modules.py

@@ -1247,6 +1247,19 @@ def forward(self, *args, **kwargs):  # type: ignore
         return UNetCFG1d.forward(self, *args, **kwargs)
 
 
+def XUNet1d(type: str = "base", **kwargs) -> UNet1d:
+    if type == "base":
+        return UNet1d(**kwargs)
+    elif type == "all":
+        return UNetAll1d(**kwargs)
+    elif type == "cfg":
+        return UNetCFG1d(**kwargs)
+    elif type == "ncca":
+        return UNetNCCA1d(**kwargs)
+    else:
+        raise ValueError(f"Unknown XUNet1d type: {type}")
+
+
 class T5Embedder(nn.Module):
     def __init__(self, model: str = "t5-base", max_length: int = 64):
         super().__init__()

setup.py

@@ -3,7 +3,7 @@
 setup(
     name="audio-diffusion-pytorch",
     packages=find_packages(exclude=[]),
-    version="0.0.94",
+    version="0.0.95",
     license="MIT",
     description="Audio Diffusion - PyTorch",
     long_description_content_type="text/markdown",