import torch
import torch.nn as nn
import torch.nn.functional as F
from torch import autocast
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.lucidrains.x_transformers import Encoder
from scripts.audio.gen.use_diffuse_tts import ceil_multiple
from trainer.networks import register_model
from utils.util import checkpoint
class ResBlock(TimestepBlock):
def __init__(
self,
channels,
emb_channels,
dropout,
out_channels=None,
dims=2,
kernel_size=3,
):
super().__init__()
self.channels = channels
self.emb_channels = emb_channels
self.dropout = dropout
self.out_channels = out_channels or channels
padding = 1 if kernel_size == 3 else 2
self.in_layers = nn.Sequential(
normalization(channels),
nn.SiLU(),
conv_nd(dims, channels, self.out_channels, 1, padding=0),
)
self.emb_layers = nn.Sequential(
nn.SiLU(),
linear(
emb_channels,
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, 1)
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]
h = h + emb_out
h = self.out_layers(h)
return self.skip_connection(x) + h
class DiffusionTts(nn.Module):
def __init__(
self,
model_channels,
in_channels=100,
num_tokens=256,
out_channels=200, # mean and variance
dropout=0,
# m 1, 2, 4, 8
block_channels= (512,640, 768,1024),
num_res_blocks= (3, 3, 3, 3),
token_conditioning_resolutions=(2,4,8),
attention_resolutions=(2,4,8),
conv_resample=True,
dims=1,
use_fp16=False,
kernel_size=3,
scale_factor=2,
num_heads=None,
time_embed_dim_multiplier=4,
nil_guidance_fwd_proportion=.15,
):
super().__init__()
self.in_channels = in_channels
self.model_channels = model_channels
self.out_channels = out_channels
self.attention_resolutions = attention_resolutions
self.dropout = dropout
self.conv_resample = conv_resample
self.dtype = torch.float16 if use_fp16 else torch.float32
self.dims = dims
self.nil_guidance_fwd_proportion = nil_guidance_fwd_proportion
self.mask_token_id = num_tokens
num_heads = model_channels // 64 if num_heads is None else num_heads
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.code_embedding = nn.Embedding(num_tokens+1, model_channels)
self.conditioning_embedder = nn.Sequential(nn.Conv1d(in_channels, model_channels // 2, 3, padding=1, stride=2),
nn.Conv1d(model_channels//2, model_channels,3,padding=1,stride=2))
self.conditioning_encoder = Encoder(
dim=model_channels,
depth=4,
heads=num_heads,
ff_dropout=dropout,
attn_dropout=dropout,
use_rmsnorm=True,
ff_glu=True,
rotary_pos_emb=True,
)
self.codes_encoder = Encoder(
dim=model_channels,
depth=8,
heads=num_heads,
ff_dropout=dropout,
attn_dropout=dropout,
use_rms_scaleshift_norm=True,
ff_glu=True,
rotary_pos_emb=True,
zero_init_branch_output=True,
)
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, (blk_chan, num_blocks) in enumerate(zip(block_channels, num_res_blocks)):
if ds in token_conditioning_resolutions:
token_conditioning_block = nn.Conv1d(model_channels, 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=blk_chan,
dims=dims,
kernel_size=kernel_size,
)
]
ch = blk_chan
if ds in attention_resolutions:
layers.append(
AttentionBlock(
ch,
num_heads=num_heads,
)
)
self.input_blocks.append(TimestepEmbedSequential(*layers))
self._feature_size += ch
input_block_chans.append(ch)
if level != len(block_channels) - 1:
out_ch = ch
self.input_blocks.append(
TimestepEmbedSequential(
Downsample(
ch, conv_resample, dims=dims, out_channels=out_ch, factor=scale_factor, ksize=1, pad=0
)
)
)
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,
),
AttentionBlock(
ch,
num_heads=num_heads,
),
ResBlock(
ch,
time_embed_dim,
dropout,
dims=dims,
),
)
self._feature_size += ch
self.output_blocks = nn.ModuleList([])
for level, (blk_chan, num_blocks) in list(enumerate(zip(block_channels, 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=blk_chan,
dims=dims,
kernel_size=kernel_size,
)
]
ch = blk_chan
if ds in attention_resolutions:
layers.append(
AttentionBlock(
ch,
)
)
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 forward(self, x, timesteps, codes, 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 codes: an aligned text input.
:return: an [N x C x ...] Tensor of outputs.
"""
with autocast(x.device.type):
orig_x_shape = x.shape[-1]
cm = ceil_multiple(x.shape[-1], 16)
if cm != 0:
pc = (cm-x.shape[-1])/x.shape[-1]
x = F.pad(x, (0,cm-x.shape[-1]))
codes = F.pad(codes, (0, int(pc * codes.shape[-1])))
hs = []
time_emb = self.time_embed(timestep_embedding(timesteps, self.model_channels))
# Mask out guidance tokens for un-guided diffusion.
if self.training and self.nil_guidance_fwd_proportion > 0:
token_mask = torch.rand(codes.shape, device=codes.device) < self.nil_guidance_fwd_proportion
codes = torch.where(token_mask, self.mask_token_id, codes)
code_emb = self.code_embedding(codes).permute(0, 2, 1)
cond_emb = self.conditioning_embedder(conditioning_input).permute(0,2,1)
cond_emb = self.conditioning_encoder(cond_emb)[:, 0]
code_emb = self.codes_encoder(code_emb.permute(0,2,1), norm_scale_shift_inp=cond_emb).permute(0,2,1)
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=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)
return out[:, :, :orig_x_shape]
@register_model
def register_diffusion_tts10(opt_net, opt):
return DiffusionTts(**opt_net['kwargs'])
if __name__ == '__main__':
clip = torch.randn(2, 100, 500).cuda()
tok = torch.randint(0,256, (2,230)).cuda()
cond = torch.randn(2, 100, 300).cuda()
ts = torch.LongTensor([600, 600]).cuda()
model = DiffusionTts(512).cuda()
print(sum(p.numel() for p in model.parameters()) / 1000000)
model(clip, ts, tok, cond)