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#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import math
from typing import Optional
import torch
import torch.nn as nn
from torch.nn.utils import weight_norm, remove_weight_norm
class Snake1d(nn.Module):
"""Snake activation: x + 1/(beta+eps) * sin(alpha*x)^2."""
def __init__(self, hidden_dim: int, logscale: bool = True):
super().__init__()
self.alpha = nn.Parameter(torch.zeros(1, hidden_dim, 1))
self.beta = nn.Parameter(torch.zeros(1, hidden_dim, 1))
self.logscale = logscale
def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
shape = hidden_states.shape
alpha = torch.exp(self.alpha) if self.logscale else self.alpha
beta = torch.exp(self.beta) if self.logscale else self.beta
hidden_states = hidden_states.reshape(shape[0], shape[1], -1)
hidden_states = hidden_states + (beta + 1e-9).reciprocal() * torch.sin(alpha * hidden_states).pow(2)
return hidden_states.reshape(shape)
class OobleckResidualUnit(nn.Module):
"""Residual unit: Snake1d → Conv1d(dilated) → Snake1d → Conv1d(1×1) + skip."""
def __init__(self, dimension: int = 16, dilation: int = 1):
super().__init__()
pad = ((7 - 1) * dilation) // 2
self.snake1 = Snake1d(dimension)
self.conv1 = weight_norm(nn.Conv1d(dimension, dimension, kernel_size=7, dilation=dilation, padding=pad))
self.snake2 = Snake1d(dimension)
self.conv2 = weight_norm(nn.Conv1d(dimension, dimension, kernel_size=1))
def forward(self, hidden_state: torch.Tensor) -> torch.Tensor:
output = self.conv1(self.snake1(hidden_state))
output = self.conv2(self.snake2(output))
padding = (hidden_state.shape[-1] - output.shape[-1]) // 2
if padding > 0:
hidden_state = hidden_state[..., padding:-padding]
return hidden_state + output
class OobleckEncoderBlock(nn.Module):
"""Encoder block: 3 residual units + downsampling conv."""
def __init__(self, input_dim: int, output_dim: int, stride: int = 1):
super().__init__()
self.res_unit1 = OobleckResidualUnit(input_dim, dilation=1)
self.res_unit2 = OobleckResidualUnit(input_dim, dilation=3)
self.res_unit3 = OobleckResidualUnit(input_dim, dilation=9)
self.snake1 = Snake1d(input_dim)
self.conv1 = weight_norm(
nn.Conv1d(input_dim, output_dim, kernel_size=2 * stride, stride=stride, padding=math.ceil(stride / 2))
)
def forward(self, hidden_state: torch.Tensor) -> torch.Tensor:
hidden_state = self.res_unit1(hidden_state)
hidden_state = self.res_unit2(hidden_state)
hidden_state = self.snake1(self.res_unit3(hidden_state))
return self.conv1(hidden_state)
class OobleckDecoderBlock(nn.Module):
"""Decoder block: upsampling conv + 3 residual units."""
def __init__(self, input_dim: int, output_dim: int, stride: int = 1):
super().__init__()
self.snake1 = Snake1d(input_dim)
self.conv_t1 = weight_norm(
nn.ConvTranspose1d(
input_dim, output_dim, kernel_size=2 * stride, stride=stride, padding=math.ceil(stride / 2),
)
)
self.res_unit1 = OobleckResidualUnit(output_dim, dilation=1)
self.res_unit2 = OobleckResidualUnit(output_dim, dilation=3)
self.res_unit3 = OobleckResidualUnit(output_dim, dilation=9)
def forward(self, hidden_state: torch.Tensor) -> torch.Tensor:
hidden_state = self.snake1(hidden_state)
hidden_state = self.conv_t1(hidden_state)
hidden_state = self.res_unit1(hidden_state)
hidden_state = self.res_unit2(hidden_state)
return self.res_unit3(hidden_state)
class OobleckEncoder(nn.Module):
"""Full encoder: audio → latent representation [B, encoder_hidden_size, T'].
conv1 → [blocks] → snake1 → conv2
"""
def __init__(
self,
encoder_hidden_size: int = 128,
audio_channels: int = 2,
downsampling_ratios: list = None,
channel_multiples: list = None,
):
super().__init__()
downsampling_ratios = downsampling_ratios or [2, 4, 4, 6, 10]
channel_multiples = channel_multiples or [1, 2, 4, 8, 16]
channel_multiples = [1] + channel_multiples
self.conv1 = weight_norm(nn.Conv1d(audio_channels, encoder_hidden_size, kernel_size=7, padding=3))
self.block = nn.ModuleList()
for stride_index, stride in enumerate(downsampling_ratios):
self.block.append(
OobleckEncoderBlock(
input_dim=encoder_hidden_size * channel_multiples[stride_index],
output_dim=encoder_hidden_size * channel_multiples[stride_index + 1],
stride=stride,
)
)
d_model = encoder_hidden_size * channel_multiples[-1]
self.snake1 = Snake1d(d_model)
self.conv2 = weight_norm(nn.Conv1d(d_model, encoder_hidden_size, kernel_size=3, padding=1))
def forward(self, hidden_state: torch.Tensor) -> torch.Tensor:
hidden_state = self.conv1(hidden_state)
for block in self.block:
hidden_state = block(hidden_state)
hidden_state = self.snake1(hidden_state)
return self.conv2(hidden_state)
class OobleckDecoder(nn.Module):
"""Full decoder: latent → audio waveform [B, audio_channels, T].
conv1 → [blocks] → snake1 → conv2(no bias)
"""
def __init__(
self,
channels: int = 128,
input_channels: int = 64,
audio_channels: int = 2,
upsampling_ratios: list = None,
channel_multiples: list = None,
):
super().__init__()
upsampling_ratios = upsampling_ratios or [10, 6, 4, 4, 2]
channel_multiples = channel_multiples or [1, 2, 4, 8, 16]
channel_multiples = [1] + channel_multiples
self.conv1 = weight_norm(nn.Conv1d(input_channels, channels * channel_multiples[-1], kernel_size=7, padding=3))
self.block = nn.ModuleList()
for stride_index, stride in enumerate(upsampling_ratios):
self.block.append(
OobleckDecoderBlock(
input_dim=channels * channel_multiples[len(upsampling_ratios) - stride_index],
output_dim=channels * channel_multiples[len(upsampling_ratios) - stride_index - 1],
stride=stride,
)
)
self.snake1 = Snake1d(channels)
# conv2 has no bias (matches checkpoint: only weight_g/weight_v, no bias key)
self.conv2 = weight_norm(nn.Conv1d(channels, audio_channels, kernel_size=7, padding=3, bias=False))
def forward(self, hidden_state: torch.Tensor) -> torch.Tensor:
hidden_state = self.conv1(hidden_state)
for block in self.block:
hidden_state = block(hidden_state)
hidden_state = self.snake1(hidden_state)
return self.conv2(hidden_state)
class OobleckDiagonalGaussianDistribution(object):
def __init__(self, parameters: torch.Tensor, deterministic: bool = False):
self.parameters = parameters
self.mean, self.scale = parameters.chunk(2, dim=1)
self.std = nn.functional.softplus(self.scale) + 1e-4
self.var = self.std * self.std
self.logvar = torch.log(self.var)
self.deterministic = deterministic
def sample(self, generator: torch.Generator | None = None) -> torch.Tensor:
# make sure sample is on the same device as the parameters and has same dtype
sample = torch.randn(
self.mean.shape,
generator=generator,
device=self.parameters.device,
dtype=self.parameters.dtype,
)
x = self.mean + self.std * sample
return x
def kl(self, other: "OobleckDiagonalGaussianDistribution" = None) -> torch.Tensor:
if self.deterministic:
return torch.Tensor([0.0])
else:
if other is None:
return (self.mean * self.mean + self.var - self.logvar - 1.0).sum(1).mean()
else:
normalized_diff = torch.pow(self.mean - other.mean, 2) / other.var
var_ratio = self.var / other.var
logvar_diff = self.logvar - other.logvar
kl = normalized_diff + var_ratio + logvar_diff - 1
kl = kl.sum(1).mean()
return kl
class AceStepVAE(nn.Module):
"""Audio VAE for ACE-Step (AutoencoderOobleck architecture).
Encodes audio waveform → latent, decodes latent → audio waveform.
Uses Snake1d activations and weight-normalized convolutions.
"""
def __init__(
self,
encoder_hidden_size: int = 128,
downsampling_ratios: list = None,
channel_multiples: list = None,
decoder_channels: int = 128,
decoder_input_channels: int = 64,
audio_channels: int = 2,
sampling_rate: int = 48000,
):
super().__init__()
downsampling_ratios = downsampling_ratios or [2, 4, 4, 6, 10]
channel_multiples = channel_multiples or [1, 2, 4, 8, 16]
upsampling_ratios = downsampling_ratios[::-1]
self.encoder = OobleckEncoder(
encoder_hidden_size=encoder_hidden_size,
audio_channels=audio_channels,
downsampling_ratios=downsampling_ratios,
channel_multiples=channel_multiples,
)
self.decoder = OobleckDecoder(
channels=decoder_channels,
input_channels=decoder_input_channels,
audio_channels=audio_channels,
upsampling_ratios=upsampling_ratios,
channel_multiples=channel_multiples,
)
self.sampling_rate = sampling_rate
def encode(self, x: torch.Tensor) -> torch.Tensor:
"""Audio waveform [B, audio_channels, T] → latent [B, decoder_input_channels, T']."""
h = self.encoder(x)
output = OobleckDiagonalGaussianDistribution(h).sample()
return output
def decode(self, z: torch.Tensor) -> torch.Tensor:
"""Latent [B, decoder_input_channels, T] → audio waveform [B, audio_channels, T']."""
return self.decoder(z)
def forward(self, sample: torch.Tensor) -> torch.Tensor:
"""Full round-trip: encode → decode."""
z = self.encode(sample)
return self.decode(z)
def remove_weight_norm(self):
"""Remove weight normalization from all conv layers (for export/inference)."""
for module in self.modules():
if isinstance(module, nn.Conv1d) or isinstance(module, nn.ConvTranspose1d):
remove_weight_norm(module)
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