feat: update cross attention, option to change attention blocks, defa…

…ult to 16 patch factor

README.md

@@ -125,17 +125,16 @@ from audio_diffusion_pytorch import UNet1d
 unet = UNet1d(
     in_channels=1,
     channels=128,
-    patch_blocks=4,
-    patch_factor=2,
+    patch_blocks=16,
+    patch_factor=1,
     kernel_sizes_init=[1, 3, 7],
     multipliers=[1, 2, 4, 4, 4, 4, 4],
     factors=[4, 4, 4, 2, 2, 2],
-    attentions=[False, False, False, True, True, True],
+    attentions=[0, 0, 0, 1, 1, 1, 1],
     num_blocks=[2, 2, 2, 2, 2, 2],
     attention_heads=8,
     attention_features=64,
     attention_multiplier=2,
-    use_attention_bottleneck=True,
     resnet_groups=8,
     kernel_multiplier_downsample=2,
     use_nearest_upsample=False,
@@ -229,16 +228,20 @@ y_long = composer(y, keep_start=True) # [1, 1, 98304]
 - [x] Add elucidated diffusion.
 - [x] Add ancestral DPM2 sampler.
 - [x] Add dynamic thresholding.
-- [x] Add (variational) autoencoder option to compress audio before diffusion.
+- [x] Add (variational) autoencoder option to compress audio before diffusion (removed).
 - [x] Fix inpainting and make it work with ADPM2 sampler.
 - [x] Add trainer with experiments.
 - [x] Add diffusion upsampler.
 - [x] Add ancestral euler sampler `AEulerSampler`.
 - [x] Add diffusion autoencoder.
+- [x] Add diffusion upsampler.
 - [x] Add autoencoder bottleneck option for quantization.
-- [x] Add option to provide context tokens (resnet cross attention).
+- [x] Add option to provide context tokens (cross attention).
 - [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.
+- [ ] Add flash attention.
+
 
 ## Appreciation
 

audio_diffusion_pytorch/model.py

@@ -202,17 +202,16 @@ def decode(self, latent: Tensor, **kwargs) -> Tensor:
 def get_default_model_kwargs():
     return dict(
         channels=128,
-        patch_blocks=4,
-        patch_factor=2,
+        patch_blocks=1,
+        patch_factor=16,
         kernel_sizes_init=[1, 3, 7],
         multipliers=[1, 2, 4, 4, 4, 4, 4],
         factors=[4, 4, 4, 2, 2, 2],
         num_blocks=[2, 2, 2, 2, 2, 2],
-        attentions=[False, False, False, True, True, True],
+        attentions=[0, 0, 0, 1, 1, 1, 1],
         attention_heads=8,
         attention_features=64,
         attention_multiplier=2,
-        use_attention_bottleneck=True,
         resnet_groups=8,
         kernel_multiplier_downsample=2,
         use_nearest_upsample=False,