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Remove unused waveform_gens

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James Betker 2022-05-04 21:06:54 -06:00
parent 47662b9ec5
commit d66ab2d28c
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460
codes/models/audio/music/unet_diffusion_waveform_gen2.py
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import random
import torch
import torch.nn as nn
import torch.nn.functional as F
from torch import autocast
from x_transformers import Encoder
from models.diffusion.nn import timestep_embedding, normalization, zero_module, conv_nd, linear
from models.diffusion.unet_diffusion import AttentionBlock, TimestepEmbedSequential, \
Downsample, Upsample, TimestepBlock
from models.audio.tts.mini_encoder import AudioMiniEncoder
from models.audio.tts.unet_diffusion_tts7 import CheckpointedXTransformerEncoder
from scripts.audio.gen.use_diffuse_tts import ceil_multiple
from trainer.networks import register_model
from utils.util import checkpoint
def is_sequence(t):
return t.dtype == torch.long
class ResBlock(TimestepBlock):
def __init__(
self,
channels,
emb_channels,
dropout,
out_channels=None,
dims=2,
kernel_size=3,
efficient_config=True,
use_scale_shift_norm=False,
):
super().__init__()
self.channels = channels
self.emb_channels = emb_channels
self.dropout = dropout
self.out_channels = out_channels or channels
self.use_scale_shift_norm = use_scale_shift_norm
padding = {1: 0, 3: 1, 5: 2}[kernel_size]
eff_kernel = 1 if efficient_config else 3
eff_padding = 0 if efficient_config else 1
self.in_layers = nn.Sequential(
normalization(channels),
nn.SiLU(),
conv_nd(dims, channels, self.out_channels, eff_kernel, padding=eff_padding),
)
self.emb_layers = nn.Sequential(
nn.SiLU(),
linear(
emb_channels,
2 * self.out_channels if use_scale_shift_norm else self.out_channels,
),
)
self.out_layers = nn.Sequential(
normalization(self.out_channels),
nn.SiLU(),
nn.Dropout(p=dropout),
zero_module(
conv_nd(dims, self.out_channels, self.out_channels, kernel_size, padding=padding)
),
)
if self.out_channels == channels:
self.skip_connection = nn.Identity()
else:
self.skip_connection = conv_nd(dims, channels, self.out_channels, eff_kernel, padding=eff_padding)
def forward(self, x, emb):
"""
Apply the block to a Tensor, conditioned on a timestep embedding.
:param x: an [N x C x ...] Tensor of features.
:param emb: an [N x emb_channels] Tensor of timestep embeddings.
:return: an [N x C x ...] Tensor of outputs.
"""
return checkpoint(
self._forward, x, emb
)
def _forward(self, x, emb):
h = self.in_layers(x)
emb_out = self.emb_layers(emb).type(h.dtype)
while len(emb_out.shape) < len(h.shape):
emb_out = emb_out[..., None]
if self.use_scale_shift_norm:
out_norm, out_rest = self.out_layers[0], self.out_layers[1:]
scale, shift = torch.chunk(emb_out, 2, dim=1)
h = out_norm(h) * (1 + scale) + shift
h = out_rest(h)
else:
h = h + emb_out
h = self.out_layers(h)
return self.skip_connection(x) + h
class ResBlockSimple(nn.Module):
def __init__(
self,
channels,
dropout,
out_channels=None,
dims=1,
kernel_size=3,
efficient_config=True,
):
super().__init__()
self.channels = channels
self.dropout = dropout
self.out_channels = out_channels or channels
padding = {1: 0, 3: 1, 5: 2}[kernel_size]
eff_kernel = 1 if efficient_config else 3
eff_padding = 0 if efficient_config else 1
self.in_layers = nn.Sequential(
normalization(channels),
nn.SiLU(),
conv_nd(dims, channels, self.out_channels, eff_kernel, padding=eff_padding),
)
self.out_layers = nn.Sequential(
normalization(self.out_channels),
nn.SiLU(),
nn.Dropout(p=dropout),
zero_module(
conv_nd(dims, self.out_channels, self.out_channels, kernel_size, padding=padding)
),
)
if self.out_channels == channels:
self.skip_connection = nn.Identity()
else:
self.skip_connection = conv_nd(dims, channels, self.out_channels, eff_kernel, padding=eff_padding)
def forward(self, x):
return checkpoint(
self._forward, x
)
def _forward(self, x):
h = self.in_layers(x)
h = self.out_layers(h)
return self.skip_connection(x) + h
class AudioVAE(nn.Module):
def __init__(self, channels, dropout):
super().__init__()
# 1, 4, 16, 64, 256
level_resblocks = [1, 1, 2, 2, 2]
level_ch_mult = [1, 2, 4, 6, 8]
levels = []
for i, (resblks, chdiv) in enumerate(zip(level_resblocks, level_ch_mult)):
blocks = [ResBlockSimple(channels*chdiv, dropout=dropout, kernel_size=5) for _ in range(resblks)]
if i != len(level_ch_mult)-1:
blocks.append(nn.Conv1d(channels*chdiv, channels*level_ch_mult[i+1], kernel_size=5, padding=2, stride=4))
levels.append(nn.Sequential(*blocks))
self.down_levels = nn.ModuleList(levels)
levels = []
lastdiv = None
for resblks, chdiv in reversed(list(zip(level_resblocks, level_ch_mult))):
if lastdiv is not None:
blocks = [nn.Conv1d(channels*lastdiv, channels*chdiv, kernel_size=5, padding=2)]
else:
blocks = []
blocks.extend([ResBlockSimple(channels*chdiv, dropout=dropout, kernel_size=5) for _ in range(resblks)])
levels.append(nn.Sequential(*blocks))
lastdiv = chdiv
self.up_levels = nn.ModuleList(levels)
def forward(self, x):
h = x
for level in self.down_levels:
h = level(h)
for k, level in enumerate(self.up_levels):
h = level(h)
if k != len(self.up_levels)-1:
h = F.interpolate(h, scale_factor=4, mode='linear')
return h
class Diffusion(nn.Module):
"""
The full UNet model with attention and timestep embedding.
Customized to be conditioned on an aligned prior derived from a autoregressive
GPT-style model.
:param in_channels: channels in the input Tensor.
:param in_latent_channels: channels from the input latent.
:param model_channels: base channel count for the model.
:param out_channels: channels in the output Tensor.
:param num_res_blocks: number of residual blocks per downsample.
:param dropout: the dropout probability.
:param channel_mult: channel multiplier for each level of the UNet.
:param conv_resample: if True, use learned convolutions for upsampling and
downsampling.
:param use_scale_shift_norm: use a FiLM-like conditioning mechanism.
:param resblock_updown: use residual blocks for up/downsampling.
:param use_new_attention_order: use a different attention pattern for potentially
increased efficiency.
"""
def __init__(
self,
model_channels,
in_channels=1,
out_channels=2, # mean and variance
dropout=0,
# res 1, 2, 4, 8,16,32,64,128,256,512, 1K, 2K
channel_mult= (1,1.5,2, 3, 4, 6, 8, 12, 16, 24, 32, 48),
num_res_blocks=(1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 2, 2),
# spec_cond: 1, 0, 0, 1, 0, 0, 1, 0, 0, 1, 0, 0)
# attn: 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1
conv_resample=True,
dims=1,
use_fp16=False,
kernel_size=3,
scale_factor=2,
time_embed_dim_multiplier=4,
efficient_convs=True, # Uses kernels with width of 1 in several places rather than 3.
use_scale_shift_norm=True,
freeze_main=False,
# Parameters for regularization.
unconditioned_percentage=.1, # This implements a mechanism similar to what is used in classifier-free training.
):
super().__init__()
self.in_channels = in_channels
self.model_channels = model_channels
self.out_channels = out_channels
self.dropout = dropout
self.channel_mult = channel_mult
self.conv_resample = conv_resample
self.dims = dims
self.unconditioned_percentage = unconditioned_percentage
self.enable_fp16 = use_fp16
self.alignment_size = max(2 ** (len(channel_mult)+1), 256)
padding = 1 if kernel_size == 3 else 2
down_kernel = 1 if efficient_convs else 3
time_embed_dim = model_channels * time_embed_dim_multiplier
self.time_embed = nn.Sequential(
linear(model_channels, time_embed_dim),
nn.SiLU(),
linear(time_embed_dim, time_embed_dim),
)
self.structural_cond_input = nn.Conv1d(in_channels, model_channels, kernel_size=5, padding=2)
self.aligned_latent_padding_embedding = nn.Parameter(torch.zeros(1,in_channels,1))
self.unconditioned_embedding = nn.Parameter(torch.randn(1,model_channels,1))
self.structural_processor = AudioVAE(model_channels, dropout)
self.surrogate_head = nn.Conv1d(model_channels, in_channels, 1)
self.input_block = conv_nd(dims, in_channels, model_channels, kernel_size, padding=padding)
self.input_blocks = nn.ModuleList(
[
TimestepEmbedSequential(
conv_nd(dims, model_channels*2, model_channels, 1)
)
]
)
self._feature_size = model_channels
input_block_chans = [model_channels]
ch = model_channels
ds = 1
for level, (mult, num_blocks) in enumerate(zip(channel_mult, num_res_blocks)):
for _ in range(num_blocks):
layers = [
ResBlock(
ch,
time_embed_dim,
dropout,
out_channels=int(mult * model_channels),
dims=dims,
kernel_size=kernel_size,
efficient_config=efficient_convs,
use_scale_shift_norm=use_scale_shift_norm,
)
]
ch = int(mult * model_channels)
self.input_blocks.append(TimestepEmbedSequential(*layers))
self._feature_size += ch
input_block_chans.append(ch)
if level != len(channel_mult) - 1:
out_ch = ch
self.input_blocks.append(
TimestepEmbedSequential(
Downsample(
ch, conv_resample, dims=dims, out_channels=out_ch, factor=scale_factor, ksize=down_kernel, pad=0 if down_kernel == 1 else 1
)
)
)
ch = out_ch
input_block_chans.append(ch)
ds *= 2
self._feature_size += ch
self.middle_block = TimestepEmbedSequential(
ResBlock(
ch,
time_embed_dim,
dropout,
dims=dims,
kernel_size=kernel_size,
efficient_config=efficient_convs,
use_scale_shift_norm=use_scale_shift_norm,
),
)
self._feature_size += ch
self.output_blocks = nn.ModuleList([])
for level, (mult, num_blocks) in list(enumerate(zip(channel_mult, num_res_blocks)))[::-1]:
for i in range(num_blocks + 1):
ich = input_block_chans.pop()
layers = [
ResBlock(
ch + ich,
time_embed_dim,
dropout,
out_channels=int(model_channels * mult),
dims=dims,
kernel_size=kernel_size,
efficient_config=efficient_convs,
use_scale_shift_norm=use_scale_shift_norm,
)
]
ch = int(model_channels * mult)
if level and i == num_blocks:
out_ch = ch
layers.append(
Upsample(ch, conv_resample, dims=dims, out_channels=out_ch, factor=scale_factor)
)
ds //= 2
self.output_blocks.append(TimestepEmbedSequential(*layers))
self._feature_size += ch
self.out = nn.Sequential(
normalization(ch),
nn.SiLU(),
zero_module(conv_nd(dims, model_channels, out_channels, kernel_size, padding=padding)),
)
if freeze_main:
for p in self.parameters():
p.DO_NOT_TRAIN = True
p.requires_grad = False
for m in [self.structural_processor, self.structural_cond_input, self.surrogate_head]:
for p in m.parameters():
del p.DO_NOT_TRAIN
p.requires_grad = True
def get_grad_norm_parameter_groups(self):
groups = {
'input_blocks': list(self.input_blocks.parameters()),
'output_blocks': list(self.output_blocks.parameters()),
'middle_transformer': list(self.middle_block.parameters()),
'structural_processor': list(self.structural_processor.parameters()),
}
return groups
def fix_alignment(self, x, aligned_conditioning):
"""
The UNet requires that the input <x> is a certain multiple of 2, defined by the UNet depth. Enforce this by
padding both <x> and <aligned_conditioning> before forward propagation and removing the padding before returning.
"""
cm = ceil_multiple(x.shape[-1], self.alignment_size)
if cm != 0:
pc = (cm-x.shape[-1])/x.shape[-1]
x = F.pad(x, (0,cm-x.shape[-1]))
aligned_conditioning = F.pad(aligned_conditioning, (0,int(pc*aligned_conditioning.shape[-1])))
return x, aligned_conditioning
def forward(self, x, timesteps, conditioning, return_surrogate=True, conditioning_free=False):
"""
Apply the model to an input batch.
:param x: an [N x C x ...] Tensor of inputs.
:param timesteps: a 1-D batch of timesteps.
:param conditioning: should just be the truth value. produces a latent through an autoencoder, then uses diffusion to decode that latent.
at inference, only the latent is passed in.
:param conditioning_free: When set, all conditioning inputs (including tokens and conditioning_input) will not be considered.
:return: an [N x C x ...] Tensor of outputs.
"""
# Fix input size to the proper multiple of 2 so we don't get alignment errors going down and back up the U-net.
orig_x_shape = x.shape[-1]
x, aligned_conditioning = self.fix_alignment(x, conditioning)
with autocast(x.device.type, enabled=self.enable_fp16):
# Note: this block does not need to repeated on inference, since it is not timestep-dependent.
if conditioning_free:
code_emb = self.unconditioned_embedding.repeat(x.shape[0], 1, 1)
surrogate = torch.zeros_like(x)
else:
code_emb = self.structural_cond_input(aligned_conditioning)
code_emb = self.structural_processor(code_emb)
code_emb = F.interpolate(code_emb, size=(x.shape[-1],), mode='linear')
surrogate = self.surrogate_head(code_emb)
x = self.input_block(x)
x = torch.cat([x, code_emb], dim=1)
# Everything after this comment is timestep dependent.
hs = []
time_emb = self.time_embed(timestep_embedding(timesteps, self.model_channels))
time_emb = time_emb.float()
h = x
for k, module in enumerate(self.input_blocks):
with autocast(x.device.type, enabled=self.enable_fp16 and not first):
# First block has autocast disabled to allow a high precision signal to be properly vectorized.
h = module(h, time_emb)
hs.append(h)
h = self.middle_block(h, time_emb)
for module in self.output_blocks:
h = torch.cat([h, hs.pop()], dim=1)
h = module(h, time_emb)
# Last block also has autocast disabled for high-precision outputs.
h = h.float()
out = self.out(h)
# Involve probabilistic or possibly unused parameters in loss so we don't get DDP errors.
extraneous_addition = 0
params = [self.aligned_latent_padding_embedding, self.unconditioned_embedding]
for p in params:
extraneous_addition = extraneous_addition + p.mean()
out = out + extraneous_addition * 0
if return_surrogate:
return out[:, :, :orig_x_shape], surrogate[:, :, :orig_x_shape]
else:
return out[:, :, :orig_x_shape]
@register_model
def register_unet_diffusion_waveform_gen2(opt_net, opt):
return Diffusion(**opt_net['kwargs'])
if __name__ == '__main__':
clip = torch.randn(2, 1, 32868)
aligned_sequence = torch.randn(2,1,32868)
ts = torch.LongTensor([600, 600])
model = Diffusion(128,
channel_mult=[1,1.5,2, 3, 4, 6, 8],
num_res_blocks=[2, 2, 2, 2, 2, 2, 1],
kernel_size=3,
scale_factor=2,
time_embed_dim_multiplier=4,
efficient_convs=False)
# Test with sequence aligned conditioning
o = model(clip, ts, aligned_sequence)

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codes/models/audio/music/unet_diffusion_waveform_gen3.py
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import random
import torch
import torch.nn as nn
import torch.nn.functional as F
from torch import autocast
from x_transformers import Encoder
from models.diffusion.nn import timestep_embedding, normalization, zero_module, conv_nd, linear
from models.diffusion.unet_diffusion import AttentionBlock, TimestepEmbedSequential, \
Downsample, Upsample, TimestepBlock
from models.audio.tts.mini_encoder import AudioMiniEncoder
from models.audio.tts.unet_diffusion_tts7 import CheckpointedXTransformerEncoder
from scripts.audio.gen.use_diffuse_tts import ceil_multiple
from trainer.networks import register_model
from utils.util import checkpoint
def is_sequence(t):
return t.dtype == torch.long
class ResBlock(TimestepBlock):
def __init__(
self,
channels,
emb_channels,
dropout,
out_channels=None,
dims=2,
kernel_size=3,
efficient_config=True,
use_scale_shift_norm=False,
):
super().__init__()
self.channels = channels
self.emb_channels = emb_channels
self.dropout = dropout
self.out_channels = out_channels or channels
self.use_scale_shift_norm = use_scale_shift_norm
padding = {1: 0, 3: 1, 5: 2}[kernel_size]
eff_kernel = 1 if efficient_config else 3
eff_padding = 0 if efficient_config else 1
self.in_layers = nn.Sequential(
normalization(channels),
nn.SiLU(),
conv_nd(dims, channels, self.out_channels, eff_kernel, padding=eff_padding),
)
self.emb_layers = nn.Sequential(
nn.SiLU(),
linear(
emb_channels,
2 * self.out_channels if use_scale_shift_norm else self.out_channels,
),
)
self.out_layers = nn.Sequential(
normalization(self.out_channels),
nn.SiLU(),
nn.Dropout(p=dropout),
zero_module(
conv_nd(dims, self.out_channels, self.out_channels, kernel_size, padding=padding)
),
)
if self.out_channels == channels:
self.skip_connection = nn.Identity()
else:
self.skip_connection = conv_nd(dims, channels, self.out_channels, eff_kernel, padding=eff_padding)
def forward(self, x, emb):
"""
Apply the block to a Tensor, conditioned on a timestep embedding.
:param x: an [N x C x ...] Tensor of features.
:param emb: an [N x emb_channels] Tensor of timestep embeddings.
:return: an [N x C x ...] Tensor of outputs.
"""
return checkpoint(
self._forward, x, emb
)
def _forward(self, x, emb):
h = self.in_layers(x)
emb_out = self.emb_layers(emb).type(h.dtype)
while len(emb_out.shape) < len(h.shape):
emb_out = emb_out[..., None]
if self.use_scale_shift_norm:
out_norm, out_rest = self.out_layers[0], self.out_layers[1:]
scale, shift = torch.chunk(emb_out, 2, dim=1)
h = out_norm(h) * (1 + scale) + shift
h = out_rest(h)
else:
h = h + emb_out
h = self.out_layers(h)
return self.skip_connection(x) + h
class ResBlockSimple(nn.Module):
def __init__(
self,
channels,
dropout,
out_channels=None,
dims=1,
kernel_size=3,
efficient_config=True,
):
super().__init__()
self.channels = channels
self.dropout = dropout
self.out_channels = out_channels or channels
padding = {1: 0, 3: 1, 5: 2}[kernel_size]
eff_kernel = 1 if efficient_config else 3
eff_padding = 0 if efficient_config else 1
self.in_layers = nn.Sequential(
normalization(channels),
nn.SiLU(),
conv_nd(dims, channels, self.out_channels, eff_kernel, padding=eff_padding),
)
self.out_layers = nn.Sequential(
normalization(self.out_channels),
nn.SiLU(),
nn.Dropout(p=dropout),
zero_module(
conv_nd(dims, self.out_channels, self.out_channels, kernel_size, padding=padding)
),
)
if self.out_channels == channels:
self.skip_connection = nn.Identity()
else:
self.skip_connection = conv_nd(dims, channels, self.out_channels, eff_kernel, padding=eff_padding)
def forward(self, x):
return checkpoint(
self._forward, x
)
def _forward(self, x):
h = self.in_layers(x)
h = self.out_layers(h)
return self.skip_connection(x) + h
class DiffusionTts(nn.Module):
"""
The full UNet model with attention and timestep embedding.
Customized to be conditioned on an aligned prior derived from a autoregressive
GPT-style model.
:param in_channels: channels in the input Tensor.
:param in_latent_channels: channels from the input latent.
:param model_channels: base channel count for the model.
:param out_channels: channels in the output Tensor.
:param num_res_blocks: number of residual blocks per downsample.
:param attention_resolutions: a collection of downsample rates at which
attention will take place. May be a set, list, or tuple.
For example, if this contains 4, then at 4x downsampling, attention
will be used.
:param dropout: the dropout probability.
:param channel_mult: channel multiplier for each level of the UNet.
:param conv_resample: if True, use learned convolutions for upsampling and
downsampling.
:param dims: determines if the signal is 1D, 2D, or 3D.
:param num_heads: the number of attention heads in each attention layer.
:param num_heads_channels: if specified, ignore num_heads and instead use
a fixed channel width per attention head.
:param num_heads_upsample: works with num_heads to set a different number
of heads for upsampling. Deprecated.
:param use_scale_shift_norm: use a FiLM-like conditioning mechanism.
:param resblock_updown: use residual blocks for up/downsampling.
:param use_new_attention_order: use a different attention pattern for potentially
increased efficiency.
"""
def __init__(
self,
model_channels,
in_channels=1,
in_latent_channels=1024,
in_mel_channels=120,
conditioning_dim_factor=8,
conditioning_expansion=4,
out_channels=2, # mean and variance
dropout=0,
# res 1, 2, 4, 8,16,32,64,128,256,512, 1K, 2K
channel_mult= (1,1.5,2, 3, 4, 6, 8, 12, 16, 24, 32, 48),
num_res_blocks=(1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 2, 2),
# spec_cond: 1, 0, 0, 1, 0, 0, 1, 0, 0, 1, 0, 0)
# attn: 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1
token_conditioning_resolutions=(1,16,),
conv_resample=True,
dims=1,
use_fp16=False,
num_heads=1,
num_head_channels=-1,
num_heads_upsample=-1,
kernel_size=3,
scale_factor=2,
time_embed_dim_multiplier=4,
efficient_convs=True, # Uses kernels with width of 1 in several places rather than 3.
use_scale_shift_norm=True,
# Parameters for regularization.
unconditioned_percentage=.1, # This implements a mechanism similar to what is used in classifier-free training.
):
super().__init__()
if num_heads_upsample == -1:
num_heads_upsample = num_heads
self.in_channels = in_channels
self.model_channels = model_channels
self.out_channels = out_channels
self.dropout = dropout
self.channel_mult = channel_mult
self.conv_resample = conv_resample
self.num_heads = num_heads
self.num_head_channels = num_head_channels
self.num_heads_upsample = num_heads_upsample
self.dims = dims
self.unconditioned_percentage = unconditioned_percentage
self.enable_fp16 = use_fp16
self.alignment_size = 2 ** (len(channel_mult)+1)
self.in_mel_channels = in_mel_channels
padding = 1 if kernel_size == 3 else 2
down_kernel = 1 if efficient_convs else 3
time_embed_dim = model_channels * time_embed_dim_multiplier
self.time_embed = nn.Sequential(
linear(model_channels, time_embed_dim),
nn.SiLU(),
linear(time_embed_dim, time_embed_dim),
)
conditioning_dim = model_channels * conditioning_dim_factor
# Either code_converter or latent_converter is used, depending on what type of conditioning data is fed.
# This model is meant to be able to be trained on both for efficiency purposes - it is far less computationally
# complex to generate tokens, while generating latents will normally mean propagating through a deep autoregressive
# transformer network.
self.mel_converter = nn.Sequential(
nn.Conv1d(in_mel_channels, conditioning_dim, 3, padding=1),
ResBlockSimple(conditioning_dim, dropout, efficient_config=False),
ResBlockSimple(conditioning_dim, dropout, efficient_config=False),
ResBlockSimple(conditioning_dim, dropout, efficient_config=False),
ResBlockSimple(conditioning_dim, dropout, efficient_config=False),
ResBlockSimple(conditioning_dim, dropout, efficient_config=False)
)
self.latent_converter = nn.Conv1d(in_latent_channels, conditioning_dim, 1)
self.aligned_latent_padding_embedding = nn.Parameter(torch.randn(1,in_latent_channels,1))
self.unconditioned_embedding = nn.Parameter(torch.randn(1,conditioning_dim,1))
self.conditioning_expansion = conditioning_expansion
self.input_blocks = nn.ModuleList(
[
TimestepEmbedSequential(
conv_nd(dims, in_channels, model_channels, kernel_size, padding=padding)
)
]
)
token_conditioning_blocks = []
self._feature_size = model_channels
input_block_chans = [model_channels]
ch = model_channels
ds = 1
for level, (mult, num_blocks) in enumerate(zip(channel_mult, num_res_blocks)):
if ds in token_conditioning_resolutions:
token_conditioning_block = nn.Conv1d(conditioning_dim, ch, 1)
token_conditioning_block.weight.data *= .02
self.input_blocks.append(token_conditioning_block)
token_conditioning_blocks.append(token_conditioning_block)
for _ in range(num_blocks):
layers = [
ResBlock(
ch,
time_embed_dim,
dropout,
out_channels=int(mult * model_channels),
dims=dims,
kernel_size=kernel_size,
efficient_config=efficient_convs,
use_scale_shift_norm=use_scale_shift_norm,
)
]
ch = int(mult * model_channels)
self.input_blocks.append(TimestepEmbedSequential(*layers))
self._feature_size += ch
input_block_chans.append(ch)
if level != len(channel_mult) - 1:
out_ch = ch
self.input_blocks.append(
TimestepEmbedSequential(
Downsample(
ch, conv_resample, dims=dims, out_channels=out_ch, factor=scale_factor, ksize=down_kernel, pad=0 if down_kernel == 1 else 1
)
)
)
ch = out_ch
input_block_chans.append(ch)
ds *= 2
self._feature_size += ch
self.middle_block = TimestepEmbedSequential(
ResBlock(
ch,
time_embed_dim,
dropout,
dims=dims,
kernel_size=kernel_size,
efficient_config=efficient_convs,
use_scale_shift_norm=use_scale_shift_norm,
),
ResBlock(
ch,
time_embed_dim,
dropout,
dims=dims,
kernel_size=kernel_size,
efficient_config=efficient_convs,
use_scale_shift_norm=use_scale_shift_norm,
),
)
self._feature_size += ch
self.output_blocks = nn.ModuleList([])
for level, (mult, num_blocks) in list(enumerate(zip(channel_mult, num_res_blocks)))[::-1]:
for i in range(num_blocks + 1):
ich = input_block_chans.pop()
layers = [
ResBlock(
ch + ich,
time_embed_dim,
dropout,
out_channels=int(model_channels * mult),
dims=dims,
kernel_size=kernel_size,
efficient_config=efficient_convs,
use_scale_shift_norm=use_scale_shift_norm,
)
]
ch = int(model_channels * mult)
if level and i == num_blocks:
out_ch = ch
layers.append(
Upsample(ch, conv_resample, dims=dims, out_channels=out_ch, factor=scale_factor)
)
ds //= 2
self.output_blocks.append(TimestepEmbedSequential(*layers))
self._feature_size += ch
self.out = nn.Sequential(
normalization(ch),
nn.SiLU(),
zero_module(conv_nd(dims, model_channels, out_channels, kernel_size, padding=padding)),
)
def get_grad_norm_parameter_groups(self):
groups = {
'input_blocks': list(self.input_blocks.parameters()),
'output_blocks': list(self.output_blocks.parameters()),
'middle_transformer': list(self.middle_block.parameters()),
}
return groups
def is_latent(self, t):
return t.shape[1] != self.in_mel_channels
def fix_alignment(self, x, aligned_conditioning):
"""
The UNet requires that the input <x> is a certain multiple of 2, defined by the UNet depth. Enforce this by
padding both <x> and <aligned_conditioning> before forward propagation and removing the padding before returning.
"""
cm = ceil_multiple(x.shape[-1], self.alignment_size)
if cm != 0:
pc = (cm-x.shape[-1])/x.shape[-1]
x = F.pad(x, (0,cm-x.shape[-1]))
# Also fix aligned_latent, which is aligned to x.
if self.is_latent(aligned_conditioning):
aligned_conditioning = torch.cat([aligned_conditioning,
self.aligned_latent_padding_embedding.repeat(x.shape[0], 1, int(pc * aligned_conditioning.shape[-1]))], dim=-1)
else:
aligned_conditioning = F.pad(aligned_conditioning, (0,int(pc*aligned_conditioning.shape[-1])))
return x, aligned_conditioning
def forward(self, x, timesteps, aligned_conditioning, conditioning_free=False):
"""
Apply the model to an input batch.
:param x: an [N x C x ...] Tensor of inputs.
:param timesteps: a 1-D batch of timesteps.
:param aligned_conditioning: an aligned latent or sequence of tokens providing useful data about the sample to be produced.
:param conditioning_free: When set, all conditioning inputs (including tokens and conditioning_input) will not be considered.
:return: an [N x C x ...] Tensor of outputs.
"""
# Shuffle aligned_latent to BxCxS format
if self.is_latent(aligned_conditioning):
aligned_conditioning = aligned_conditioning.permute(0, 2, 1)
# Fix input size to the proper multiple of 2 so we don't get alignment errors going down and back up the U-net.
orig_x_shape = x.shape[-1]
x, aligned_conditioning = self.fix_alignment(x, aligned_conditioning)
with autocast(x.device.type, enabled=self.enable_fp16):
hs = []
time_emb = self.time_embed(timestep_embedding(timesteps, self.model_channels))
# Note: this block does not need to repeated on inference, since it is not timestep-dependent.
if conditioning_free:
code_emb = self.unconditioned_embedding.repeat(x.shape[0], 1, 1)
else:
if self.is_latent(aligned_conditioning):
code_emb = self.latent_converter(aligned_conditioning)
else:
code_emb = self.mel_converter(aligned_conditioning)
# Everything after this comment is timestep dependent.
code_emb = torch.repeat_interleave(code_emb, self.conditioning_expansion, dim=-1)
first = True
time_emb = time_emb.float()
h = x
for k, module in enumerate(self.input_blocks):
if isinstance(module, nn.Conv1d):
h_tok = F.interpolate(module(code_emb), size=(h.shape[-1]), mode='nearest')
h = h + h_tok
else:
with autocast(x.device.type, enabled=self.enable_fp16 and not first):
# First block has autocast disabled to allow a high precision signal to be properly vectorized.
h = module(h, time_emb)
hs.append(h)
first = False
h = self.middle_block(h, time_emb)
for module in self.output_blocks:
h = torch.cat([h, hs.pop()], dim=1)
h = module(h, time_emb)
# Last block also has autocast disabled for high-precision outputs.
h = h.float()
out = self.out(h)
# Involve probabilistic or possibly unused parameters in loss so we don't get DDP errors.
extraneous_addition = 0
params = [self.aligned_latent_padding_embedding, self.unconditioned_embedding] + list(self.latent_converter.parameters())
for p in params:
extraneous_addition = extraneous_addition + p.mean()
out = out + extraneous_addition * 0
return out[:, :, :orig_x_shape]
@register_model
def register_unet_diffusion_waveform_gen3(opt_net, opt):
return DiffusionTts(**opt_net['kwargs'])
if __name__ == '__main__':
clip = torch.randn(2, 1, 32868)
aligned_latent = torch.randn(2,388,1024)
aligned_sequence = torch.randn(2,120,220)
ts = torch.LongTensor([600, 600])
model = DiffusionTts(128,
channel_mult=[1,1.5,2, 3, 4, 6, 8],
num_res_blocks=[2, 2, 2, 2, 2, 2, 1],
token_conditioning_resolutions=[1,4,16,64],
num_heads=8,
kernel_size=3,
scale_factor=2,
time_embed_dim_multiplier=4,
efficient_convs=False)
# Test with latent aligned conditioning
o = model(clip, ts, aligned_latent)
# Test with sequence aligned conditioning
o = model(clip, ts, aligned_sequence)