331 lines
12 KiB
Python
331 lines
12 KiB
Python
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, tokens, 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 tokens: 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]))
|
|
tokens = F.pad(tokens, (0,int(pc*tokens.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(tokens.shape, device=tokens.device) < self.nil_guidance_fwd_proportion
|
|
tokens = torch.where(token_mask, self.mask_token_id, tokens)
|
|
code_emb = self.code_embedding(tokens).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)
|
|
|