fix: inpainting noise, composer initial span, readme

README.md

@@ -45,7 +45,7 @@ t = torch.tensor([40, 10, 20])
 y = unet(x, t) # [3, 1, 32768], 3 audio tracks of ~1.6s sampled at 20050 Hz
 ```
 
-### Elucidated Diffusion
+### Diffusion
 
 ```python
 from audio_diffusion_pytorch.diffusion.elucidated import Diffusion, DiffusionSampler, LogNormalSampler, KerrasSchedule
@@ -72,42 +72,51 @@ sampler = DiffusionSampler(
     s_churn=40,
     s_noise=1.003
 )
+# Generate a sample starting from the provided noise
 y = sampler(x = torch.randn(1,1,2 ** 15))
 
 ```
 
 
-### Gaussian Diffusion (Old)
-Note that this requires `use_learned_time_embedding=False` on the `UNet1d`.
+### Diffusion Inpainting and Infinite Generation
+
 ```py
-from audio_diffusion_pytorch.diffusion.ddpm import Diffusion, DiffusionSampler
-# Build diffusion to train denoise function
-diffusion = Diffusion(
-    denoise_fn=unet,
-    num_timesteps=50,
-    loss_fn='l1',
-    loss_weight_gamma=0.5,
-    loss_weight_k=1
+from audio_diffusion_pytorch.diffusion.elucidated import DiffusionInpainter, KerrasSchedule, SpanBySpanComposer
+
+inpainter = DiffusionInpainter(
+    diffusion,
+    num_steps=2,
+    num_resamples=5,
+    sigma_schedule=KerrasSchedule(
+        sigma_min=0.002,
+        sigma_max=1
+    ),
+    s_tmin=0,
+    s_tmax=10,
+    s_churn=40,
+    s_noise=1.003
 )
 
-x = torch.randn(3, 1, 2 ** 15)
-loss = diffusion(x)
-loss.backwards() # Do this many times
+inpaint = torch.randn(1,1,2 ** 15) # This should not be random but your start track, e.g. one sampled with DiffusionSampler
+inpaint_mask = torch.randint(0,2, (1,1,2 ** 15), dtype=torch.bool) # Set to `True` the parts you want to keep
+y = inpainter(inpaint = inpaint, inpaint_mask = inpaint_mask) # [1, 1, 32768]
 
 
-# Sample from diffusion model by converting normal tensor to audio
-sampler = DiffusionSampler(diffusion)
-y = sampler(x = torch.randn(1, 1, 2 ** 15)) # [1, 1, 32768]
+# Infinite generation using SpanBySpanComposer
+composer = SpanBySpanComposer(inpainter, num_spans=4) # Generates 4 additional spans
+y_long = composer(y, keep_start=True) # [1, 1, 98304]
+
 ```
 
+
 ## Experiments
 
 
 | Report | Snapshot | Description |
 | --- | --- | --- |
 | [Alpha](https://wandb.ai/schneider/audio/reports/Audio-Diffusion-UNet-Alpha---VmlldzoyMjk3MzIz?accessToken=y0l3igdvnm4ogn4d3ph3b0i8twwcf7meufbviwt15f0qtasyn1i14hg340bkk1te) | [6bd9279f19](https://github.com/archinetai/audio-diffusion-pytorch/tree/6bd9279f192fc0c11eb8a21cd919d9c41181bf35) | Initial tests on LJSpeech dataset with new architecture and basic DDPM diffusion model. |
 | [Bravo](https://wandb.ai/schneider/audio/reports/Audio-Diffusion-Bravo---VmlldzoyMzE4NjIx?accessToken=qt2w1jeqch9l5v3ffjns99p69jsmexk849dszyiennfbivgg396378u6ken2fm2d) | [a05f30aa94](https://github.com/archinetai/audio-diffusion-pytorch/tree/a05f30aa94e07600038d36cfb96f8492ef735a99) | Elucidated diffusion, improved architecture with patching, longer duration, initial good (unsupervised) results on LJSpeech.
-| Charlie | (current) | . |
+| [Charlie](https://wandb.ai/schneider/audio/reports/Audio-Diffusion-Charlie---VmlldzoyMzYyNDA1?accessToken=71gmurcwndv5e2abqrjnlh3n74j5555j3tycpd7h40tnv8fvb17k5pjkb57j9xxa) | (current) | Train on music with YoutubeDataset, larger patch tests for longer tracks, inpainting tests, initial test with infinite generation using StepByStepComposer. |
 
 
 ## Appreciation

audio_diffusion_pytorch/diffusion/elucidated.py

@@ -252,7 +252,7 @@ def step(
         epsilon = self.s_noise * torch.randn_like(x)
         noise = sqrt(sigma_hat ** 2 - sigma ** 2) * epsilon
         # Add increased noise to mixed value
-        x_hat = (x * ~inpaint_mask + inpaint * inpaint_mask) * noise
+        x_hat = x * ~inpaint_mask + inpaint * inpaint_mask + noise
         # Evaluate ∂x/∂sigma at sigma_hat
         d = (x_hat - self.denoise_fn(x_hat, sigma=sigma_hat, clamp=clamp)) / sigma_hat
         # Take euler step from sigma_hat to sigma_next
@@ -321,8 +321,10 @@ def __init__(
     def forward(self, start: Tensor, keep_start: bool = False) -> Tensor:
         half_length = start.shape[2] // 2
 
-        spans = [start[:, :, :half_length]] if keep_start else []
-        inpaint = start
+        spans = list(start.chunk(chunks=2, dim=-1)) if keep_start else []
+        # Inpaint second half from first half
+        inpaint = torch.zeros_like(start)
+        inpaint[:, :, :half_length] = start[:, :, half_length:]
         inpaint_mask = sequential_mask(like=start, start=half_length)
 
         for i in range(self.num_spans):

setup.py

@@ -3,7 +3,7 @@
 setup(
     name="audio-diffusion-pytorch",
     packages=find_packages(exclude=[]),
-    version="0.0.6",
+    version="0.0.7",
     license="MIT",
     description="Audio Diffusion - PyTorch",
     long_description_content_type="text/markdown",