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"""Audio diffusion model classes."""
import torch
from torch import Tensor, nn
from .utils import groupby
from .sampler import UniformDistribution
class LinearSchedule(nn.Module):
def forward(self, num_steps: int, device) -> Tensor:
sigmas = torch.linspace(1, 0, num_steps + 1)[:-1]
return sigmas
class VSampler(nn.Module):
pass
class Model1d(nn.Module):
def __init__(self, unet_type: str = "base", **kwargs):
super().__init__()
diffusion_kwargs, kwargs = groupby("diffusion_", kwargs)
self.unet = None
self.diffusion = None
def forward(self, x: Tensor, **kwargs) -> Tensor:
return self.diffusion(x, **kwargs)
def sample(self, *args, **kwargs) -> Tensor:
return self.diffusion.sample(*args, **kwargs)
def get_default_model_kwargs():
return dict(
channels=128,
patch_size=16,
multipliers=[1, 2, 4, 4, 4, 4, 4],
factors=[4, 4, 4, 2, 2, 2],
num_blocks=[2, 2, 2, 2, 2, 2],
attentions=[0, 0, 0, 1, 1, 1, 1],
attention_heads=8,
attention_features=64,
attention_multiplier=2,
attention_use_rel_pos=False,
diffusion_type="v",
diffusion_sigma_distribution=UniformDistribution(),
)
def get_default_sampling_kwargs():
return dict(sigma_schedule=LinearSchedule(), sampler=VSampler(), clamp=True)
class AudioDiffusionConditional(Model1d):
def __init__(self, embedding_features: int, embedding_max_length: int, embedding_mask_proba: float = 0.1, **kwargs):
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,
)
super().__init__(**{**default_kwargs, **kwargs})
def forward(self, *args, **kwargs):
default_kwargs = dict(embedding_mask_proba=self.embedding_mask_proba)
return super().forward(*args, **{**default_kwargs, **kwargs})
def sample(self, *args, **kwargs):
default_kwargs = dict(**get_default_sampling_kwargs(), embedding_scale=5.0)
return super().sample(*args, **{**default_kwargs, **kwargs})
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