from models.diffusion.fp16_util import convert_module_to_f32, convert_module_to_f16
from models.diffusion.nn import timestep_embedding, normalization, zero_module, conv_nd, linear
from models.diffusion.unet_diffusion import AttentionPool2d, AttentionBlock, ResBlock, TimestepEmbedSequential, \
Downsample, Upsample
import torch
import torch.nn as nn
from models.gpt_voice.mini_encoder import AudioMiniEncoder, EmbeddingCombiner
from trainer.networks import register_model
from utils.util import get_mask_from_lengths
class DiscreteSpectrogramConditioningBlock(nn.Module):
def __init__(self, dvae_channels, channels, level):
super().__init__()
self.intg = nn.Sequential(nn.Conv1d(dvae_channels, channels, kernel_size=1),
normalization(channels),
nn.SiLU(),
nn.Conv1d(channels, channels, kernel_size=3))
self.level = level
"""
Embeds the given codes and concatenates them onto x. Return shape is the same as x.shape.
:param x: bxcxS waveform latent
:param codes: bxN discrete codes, N <= S
"""
def forward(self, x, dvae_in):
b, c, S = x.shape
_, q, N = dvae_in.shape
emb = self.intg(dvae_in)
emb = nn.functional.interpolate(emb, size=(S,), mode='nearest')
return torch.cat([x, emb], dim=1)
class DiffusionVocoderWithRef(nn.Module):
"""
The full UNet model with attention and timestep embedding.
Customized to be conditioned on a spectrogram prior.
:param in_channels: channels in the input Tensor.
:param spectrogram_channels: channels in the conditioning spectrogram.
: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,
out_channels=2, # mean and variance
discrete_codes=512,
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
spectrogram_conditioning_resolutions=(512,),
attention_resolutions=(512,1024,2048),
conv_resample=True,
dims=1,
use_fp16=False,
num_heads=1,
num_head_channels=-1,
num_heads_upsample=-1,
use_scale_shift_norm=False,
resblock_updown=False,
use_new_attention_order=False,
kernel_size=3,
scale_factor=2,
conditioning_inputs_provided=True,
conditioning_input_dim=80,
time_embed_dim_multiplier=4,
freeze_layers_below=None, # powers of 2; ex: 1,2,4,8,16,32,etc..
):
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.attention_resolutions = attention_resolutions
self.dropout = dropout
self.channel_mult = channel_mult
self.conv_resample = conv_resample
self.dtype = torch.float16 if use_fp16 else torch.float32
self.num_heads = num_heads
self.num_head_channels = num_head_channels
self.num_heads_upsample = num_heads_upsample
self.dims = dims
padding = 1 if kernel_size == 3 else 2
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.conditioning_enabled = conditioning_inputs_provided
if conditioning_inputs_provided:
self.contextual_embedder = AudioMiniEncoder(in_channels, time_embed_dim, base_channels=32, depth=6, resnet_blocks=1,
attn_blocks=2, num_attn_heads=2, dropout=dropout, downsample_factor=4, kernel_size=5)
seqlyr = TimestepEmbedSequential(
conv_nd(dims, in_channels, model_channels, kernel_size, padding=padding)
)
seqlyr.level = 0
self.input_blocks = nn.ModuleList([seqlyr])
spectrogram_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 spectrogram_conditioning_resolutions:
spec_cond_block = DiscreteSpectrogramConditioningBlock(discrete_codes, ch, 2 ** level)
self.input_blocks.append(spec_cond_block)
spectrogram_blocks.append(spec_cond_block)
ch *= 2
for _ in range(num_blocks):
layers = [
ResBlock(
ch,
time_embed_dim,
dropout,
out_channels=int(mult * model_channels),
dims=dims,
use_scale_shift_norm=use_scale_shift_norm,
kernel_size=kernel_size,
)
]
ch = int(mult * model_channels)
if ds in attention_resolutions:
layers.append(
AttentionBlock(
ch,
num_heads=num_heads,
num_head_channels=num_head_channels,
use_new_attention_order=use_new_attention_order,
)
)
layer = TimestepEmbedSequential(*layers)
layer.level = 2 ** level
self.input_blocks.append(layer)
self._feature_size += ch
input_block_chans.append(ch)
if level != len(channel_mult) - 1:
out_ch = ch
upblk = TimestepEmbedSequential(
ResBlock(
ch,
time_embed_dim,
dropout,
out_channels=out_ch,
dims=dims,
use_scale_shift_norm=use_scale_shift_norm,
down=True,
kernel_size=kernel_size,
)
if resblock_updown
else Downsample(
ch, conv_resample, dims=dims, out_channels=out_ch, factor=scale_factor
)
)
upblk.level = 2 ** level
self.input_blocks.append(upblk)
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,
use_scale_shift_norm=use_scale_shift_norm,
kernel_size=kernel_size,
),
AttentionBlock(
ch,
num_heads=num_heads,
num_head_channels=num_head_channels,
use_new_attention_order=use_new_attention_order,
),
ResBlock(
ch,
time_embed_dim,
dropout,
dims=dims,
use_scale_shift_norm=use_scale_shift_norm,
kernel_size=kernel_size,
),
)
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,
use_scale_shift_norm=use_scale_shift_norm,
kernel_size=kernel_size,
)
]
ch = int(model_channels * mult)
if ds in attention_resolutions:
layers.append(
AttentionBlock(
ch,
num_heads=num_heads_upsample,
num_head_channels=num_head_channels,
use_new_attention_order=use_new_attention_order,
)
)
if level and i == num_blocks:
out_ch = ch
layers.append(
ResBlock(
ch,
time_embed_dim,
dropout,
out_channels=out_ch,
dims=dims,
use_scale_shift_norm=use_scale_shift_norm,
up=True,
kernel_size=kernel_size,
)
if resblock_updown
else Upsample(ch, conv_resample, dims=dims, out_channels=out_ch, factor=scale_factor)
)
ds //= 2
layer = TimestepEmbedSequential(*layers)
layer.level = 2 ** level
self.output_blocks.append(layer)
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_layers_below is not None:
# Freeze all parameters first.
for p in self.parameters():
p.DO_NOT_TRAIN = True
p.requires_grad = False
# Now un-freeze the modules we actually want to train.
unfrozen_modules = [self.out]
for blk in self.input_blocks:
if blk.level <= freeze_layers_below:
unfrozen_modules.append(blk)
last_frozen_output_block = None
for blk in self.output_blocks:
if blk.level <= freeze_layers_below:
unfrozen_modules.append(blk)
else:
last_frozen_output_block = blk
# And finally, the last upsample block in output blocks.
unfrozen_modules.append(last_frozen_output_block[1])
unfrozen_params = 0
for m in unfrozen_modules:
for p in m.parameters():
del p.DO_NOT_TRAIN
p.requires_grad = True
unfrozen_params += 1
print(f"freeze_layers_below specified. Training a total of {unfrozen_params} parameters.")
def forward(self, x, timesteps, spectrogram, conditioning_input=None):
"""
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 y: an [N] Tensor of labels, if class-conditional.
:return: an [N x C x ...] Tensor of outputs.
"""
assert x.shape[-1] % 2048 == 0 # This model operates at base//2048 at it's bottom levels, thus this requirement.
if self.conditioning_enabled:
assert conditioning_input is not None
hs = []
emb1 = self.time_embed(timestep_embedding(timesteps, self.model_channels))
if self.conditioning_enabled:
emb2 = self.contextual_embedder(conditioning_input)
emb = emb1 + emb2
else:
emb = emb1
h = x.type(self.dtype)
for k, module in enumerate(self.input_blocks):
if isinstance(module, DiscreteSpectrogramConditioningBlock):
h = module(h, spectrogram)
else:
h = module(h, emb)
hs.append(h)
h = self.middle_block(h, emb)
for module in self.output_blocks:
h = torch.cat([h, hs.pop()], dim=1)
h = module(h, emb)
h = h.type(x.dtype)
return self.out(h)
def move_all_layers_down(pretrained_path, output_path, layers_to_be_added=3):
# layers_to_be_added should be=num_res_blocks+1+[1if spectrogram_conditioning_resolutions;else0]
sd = torch.load(pretrained_path)
out = sd.copy()
replaced = []
for n, p in sd.items():
if n.startswith('input_blocks.') and not n.startswith('input_blocks.0.'):
if n not in replaced:
del out[n]
components = n.split('.')
components[1] = str(int(components[1]) + layers_to_be_added)
new_name = '.'.join(components)
out[new_name] = p
replaced.append(new_name)
torch.save(out, output_path)
@register_model
def register_unet_diffusion_vocoder_with_ref(opt_net, opt):
return DiffusionVocoderWithRef(**opt_net['kwargs'])
# Test for ~4 second audio clip at 22050Hz
if __name__ == '__main__':
path = 'X:\\dlas\\experiments\\train_diffusion_vocoder_with_cond_new_dvae_full\\models\\6100_generator_ema.pth'
move_all_layers_down(path, 'diffuse_new_lyr.pth', layers_to_be_added=2)
clip = torch.randn(2, 1, 40960)
spec = torch.randn(2,80,160)
cond = torch.randn(2, 1, 40960)
ts = torch.LongTensor([555, 556])
model = DiffusionVocoderWithRef(model_channels=128, channel_mult=[1,1,1.5,2, 3, 4, 6, 8, 8, 8, 8 ],
num_res_blocks=[1,2, 2, 2, 2, 2, 2, 2, 2, 1, 1 ], spectrogram_conditioning_resolutions=[2,512],
dropout=.05, attention_resolutions=[512,1024], num_heads=4, kernel_size=3, scale_factor=2,
conditioning_inputs_provided=True, conditioning_input_dim=80, time_embed_dim_multiplier=4,
discrete_codes=80, freeze_layers_below=1)
loading_errors = model.load_state_dict(torch.load('diffuse_new_lyr.pth'), strict=False)
new_params = loading_errors.missing_keys
new_params_trained = []
existing_params_trained = []
for n,p in model.named_parameters():
if not hasattr(p, 'DO_NOT_TRAIN'):
if n in new_params:
new_params_trained.append(n)
else:
existing_params_trained.append(n)
for n in new_params:
if n not in new_params_trained:
print(f"{n} is a new parameter, but it is not marked as trainable.")
print(model(clip, ts, spec, cond).shape)