feat: add v-diffusion class/distribution, refactor diffusion

README.md

@@ -139,6 +139,7 @@ unet = UNet1d(
     use_nearest_upsample=False,
     use_skip_scale=True,
     use_context_time=True,
+    use_magnitude_channels=False
 )
 
 x = torch.randn(3, 1, 2 ** 16)
@@ -151,13 +152,20 @@ y = unet(x, t) # [3, 1, 32768], compute 3 samples of ~1.5 seconds at 22050Hz wit
 
 #### Training
 ```python
-from audio_diffusion_pytorch import Diffusion, LogNormalDistribution
+from audio_diffusion_pytorch import KDiffusion, VDiffusion, LogNormalDistribution, VDistribution
 
-diffusion = Diffusion(
+# Either use KDiffusion
+diffusion = KDiffusion(
     net=unet,
     sigma_distribution=LogNormalDistribution(mean = -3.0, std = 1.0),
     sigma_data=0.1,
-    dynamic_threshold=0.95
+    dynamic_threshold=0.0
+)
+
+# Or use VDiffusion
+diffusion = VDiffusion(
+    net=unet,
+    sigma_distribution=VDistribution()
 )
 
 x = torch.randn(3, 1, 2 ** 18) # Batch of training audio samples
@@ -239,6 +247,7 @@ y_long = composer(y, keep_start=True) # [1, 1, 98304]
 - [x] Add conditional model with classifier-free guidance.
 - [x] Add option to provide context features mapping.
 - [x] Add option to change number of (cross) attention blocks.
+- [x] Add `VDiffusionn` option.
 - [ ] Add flash attention.
 
 

audio_diffusion_pytorch/__init__.py

@@ -7,10 +7,13 @@
     Distribution,
     KarrasSampler,
     KarrasSchedule,
+    KDiffusion,
     LogNormalDistribution,
     Sampler,
     Schedule,
     SpanBySpanComposer,
+    VDiffusion,
+    VDistribution,
 )
 from .model import (
     AudioDiffusionAutoencoder,