Spaces:
Running on Zero
Running on Zero
File size: 9,031 Bytes
b8c861f | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 | # SPDX-FileCopyrightText: Copyright (c) 2025 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
# SPDX-License-Identifier: Apache-2.0
#
# 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.
"""Cosmos3 AVAE Audio Tokenizer — decoder-only implementation.
The decoder reuses the Oobleck architecture (Snake1d activations + weight-norm convs + residual units), inlined here
instead of imported so the audio module is self-contained. The corresponding encoder is intentionally not inlined:
upstream Cosmos3 uses a spec-convnext encoder whose tensor layout doesn't map onto Oobleck's encoder.
"""
import math
import torch
import torch.nn as nn
from torch.nn.utils import weight_norm
from ...configuration_utils import ConfigMixin, register_to_config
from ...utils.accelerate_utils import apply_forward_hook
from ..modeling_utils import ModelMixin
# Copied from diffusers.models.autoencoders.autoencoder_oobleck.Snake1d
class Snake1d(nn.Module):
"""
A 1-dimensional Snake activation function module.
"""
def __init__(self, hidden_dim, logscale=True):
super().__init__()
self.alpha = nn.Parameter(torch.zeros(1, hidden_dim, 1))
self.beta = nn.Parameter(torch.zeros(1, hidden_dim, 1))
self.alpha.requires_grad = True
self.beta.requires_grad = True
self.logscale = logscale
def forward(self, hidden_states):
shape = hidden_states.shape
alpha = self.alpha if not self.logscale else torch.exp(self.alpha)
beta = self.beta if not self.logscale else torch.exp(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)
hidden_states = hidden_states.reshape(shape)
return hidden_states
# Copied from diffusers.models.autoencoders.autoencoder_oobleck.OobleckResidualUnit with Oobleck->Cosmos3Audio
class Cosmos3AudioResidualUnit(nn.Module):
"""
A residual unit composed of Snake1d and weight-normalized Conv1d layers with dilations.
"""
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):
"""
Forward pass through the residual unit.
Args:
hidden_state (`torch.Tensor` of shape `(batch_size, channels, time_steps)`):
Input tensor .
Returns:
output_tensor (`torch.Tensor` of shape `(batch_size, channels, time_steps)`)
Input tensor after passing through the residual unit.
"""
output_tensor = hidden_state
output_tensor = self.conv1(self.snake1(output_tensor))
output_tensor = self.conv2(self.snake2(output_tensor))
padding = (hidden_state.shape[-1] - output_tensor.shape[-1]) // 2
if padding > 0:
hidden_state = hidden_state[..., padding:-padding]
output_tensor = hidden_state + output_tensor
return output_tensor
"""
Copied from diffusers.models.autoencoders.autoencoder_oobleck.OobleckDecoderBlock with Oobleck->Cosmos3Audio with
output_padding enabled.
"""
class Cosmos3AudioDecoderBlock(nn.Module):
"""Decoder block used in Cosmos3Audio decoder."""
def __init__(self, input_dim, output_dim, stride: int = 1, output_padding: int = 0):
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),
output_padding=output_padding,
)
)
self.res_unit1 = Cosmos3AudioResidualUnit(output_dim, dilation=1)
self.res_unit2 = Cosmos3AudioResidualUnit(output_dim, dilation=3)
self.res_unit3 = Cosmos3AudioResidualUnit(output_dim, dilation=9)
def forward(self, hidden_state):
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)
hidden_state = self.res_unit3(hidden_state)
return hidden_state
"""
Copied from diffusers.models.autoencoders.autoencoder_oobleck.OobleckDecoder with Oobleck->Cosmos3Audio and one change
of adding "output_padding=stride % 2,"
"""
class Cosmos3AudioDecoder(nn.Module):
"""Cosmos3Audio Decoder"""
def __init__(self, channels, input_channels, audio_channels, upsampling_ratios, channel_multiples):
super().__init__()
strides = upsampling_ratios
channel_multiples = [1] + channel_multiples
# Add first conv layer
self.conv1 = weight_norm(nn.Conv1d(input_channels, channels * channel_multiples[-1], kernel_size=7, padding=3))
# Add upsampling + MRF blocks
block = []
for stride_index, stride in enumerate(strides):
block += [
Cosmos3AudioDecoderBlock(
input_dim=channels * channel_multiples[len(strides) - stride_index],
output_dim=channels * channel_multiples[len(strides) - stride_index - 1],
stride=stride,
output_padding=stride % 2,
)
]
self.block = nn.ModuleList(block)
output_dim = channels
self.snake1 = Snake1d(output_dim)
self.conv2 = weight_norm(nn.Conv1d(channels, audio_channels, kernel_size=7, padding=3, bias=False))
def forward(self, hidden_state):
hidden_state = self.conv1(hidden_state)
for layer in self.block:
hidden_state = layer(hidden_state)
hidden_state = self.snake1(hidden_state)
hidden_state = self.conv2(hidden_state)
return hidden_state
class Cosmos3AVAEAudioTokenizer(ModelMixin, ConfigMixin):
"""Decoder-only audio tokenizer for Cosmos3 sound generation.
Wraps the Cosmos3Audio decoder (an inlined copy of Oobleck) used in the AVAE (Audio VAE) component of the Cosmos3
omni model. Provides the interface expected by ``Cosmos3OmniPipeline`` when ``enable_sound=True``.
For now encoder part of the Tokenizer is not supported. The encoder support will be added in the future.
Parameters:
sampling_rate (`int`, defaults to `48000`): Audio sample rate in Hz.
vocoder_input_dim (`int`, defaults to `64`): Latent channel count fed into the decoder
(``== transformer sound_dim``).
dec_dim (`int`, defaults to `320`): Base decoder channel count.
dec_c_mults (`tuple[int, ...]`, defaults to `(1, 2, 4, 8, 16)`): Channel multipliers.
dec_strides (`tuple[int, ...]`, defaults to `(2, 4, 5, 6, 8)`): Upsampling strides.
dec_out_channels (`int`, defaults to `2`): Output audio channels (2 = stereo).
"""
_supports_gradient_checkpointing = False
_supports_group_offloading = False
@register_to_config
def __init__(
self,
sampling_rate: int = 48000,
vocoder_input_dim: int = 64,
dec_dim: int = 320,
dec_c_mults: tuple = (1, 2, 4, 8, 16),
dec_strides: tuple = (2, 4, 5, 6, 8),
dec_out_channels: int = 2,
):
super().__init__()
self.decoder = Cosmos3AudioDecoder(
channels=dec_dim,
input_channels=vocoder_input_dim,
audio_channels=dec_out_channels,
upsampling_ratios=list(reversed(dec_strides)),
channel_multiples=list(dec_c_mults),
)
self._hop_size: int = math.prod(dec_strides)
@apply_forward_hook
def decode(self, latents: torch.Tensor) -> torch.Tensor:
"""Decode sound latents into an audio waveform.
Args:
latents: ``[B, C, T]`` or ``[C, T]`` tensor of diffusion-model latents.
Returns:
Waveform tensor ``[B, audio_channels, N]`` or ``[audio_channels, N]``.
"""
squeeze = latents.ndim == 2
if squeeze:
latents = latents.unsqueeze(0)
audio = self.decoder(latents).clamp(-1.0, 1.0)
return audio.squeeze(0) if squeeze else audio
|