import math
from collections import deque
from functools import partial
from inspect import isfunction
import numpy as np
import torch
import torch.nn.functional as F
from torch import nn
from torch.nn import Conv1d, Mish
from tqdm import tqdm
def exists(x):
return x is not None
def default(val, d):
if exists(val):
return val
return d() if isfunction(d) else d
def extract(a, t):
return a[t].reshape((1, 1, 1, 1))
def noise_like(shape, device, repeat=False):
def repeat_noise():
return torch.randn((1, *shape[1:]), device=device).repeat(shape[0], *((1,) * (len(shape) - 1)))
def noise():
return torch.randn(shape, device=device)
return repeat_noise() if repeat else noise()
def linear_beta_schedule(timesteps, max_beta=0.02):
"""
linear schedule
"""
betas = np.linspace(1e-4, max_beta, timesteps)
return betas
def cosine_beta_schedule(timesteps, s=0.008):
"""
cosine schedule
as proposed in https://openreview.net/forum?id=-NEXDKk8gZ
"""
steps = timesteps + 1
x = np.linspace(0, steps, steps)
alphas_cumprod = np.cos(((x / steps) + s) / (1 + s) * np.pi * 0.5) ** 2
alphas_cumprod = alphas_cumprod / alphas_cumprod[0]
betas = 1 - (alphas_cumprod[1:] / alphas_cumprod[:-1])
return np.clip(betas, a_min=0, a_max=0.999)
beta_schedule = {
"cosine": cosine_beta_schedule,
"linear": linear_beta_schedule,
}
def extract_1(a, t):
return a[t].reshape((1, 1, 1, 1))
def predict_stage0(noise_pred, noise_pred_prev):
return (noise_pred + noise_pred_prev) / 2
def predict_stage1(noise_pred, noise_list):
return (noise_pred * 3
- noise_list[-1]) / 2
def predict_stage2(noise_pred, noise_list):
return (noise_pred * 23
- noise_list[-1] * 16
+ noise_list[-2] * 5) / 12
def predict_stage3(noise_pred, noise_list):
return (noise_pred * 55
- noise_list[-1] * 59
+ noise_list[-2] * 37
- noise_list[-3] * 9) / 24
class SinusoidalPosEmb(nn.Module):
def __init__(self, dim):
super().__init__()
self.dim = dim
self.half_dim = dim // 2
self.emb = 9.21034037 / (self.half_dim - 1)
self.emb = torch.exp(torch.arange(self.half_dim) * torch.tensor(-self.emb)).unsqueeze(0)
self.emb = self.emb.cpu()
def forward(self, x):
emb = self.emb * x
emb = torch.cat((emb.sin(), emb.cos()), dim=-1)
return emb
class ResidualBlock(nn.Module):
def __init__(self, encoder_hidden, residual_channels, dilation):
super().__init__()
self.residual_channels = residual_channels
self.dilated_conv = Conv1d(residual_channels, 2 * residual_channels, 3, padding=dilation, dilation=dilation)
self.diffusion_projection = nn.Linear(residual_channels, residual_channels)
self.conditioner_projection = Conv1d(encoder_hidden, 2 * residual_channels, 1)
self.output_projection = Conv1d(residual_channels, 2 * residual_channels, 1)
def forward(self, x, conditioner, diffusion_step):
diffusion_step = self.diffusion_projection(diffusion_step).unsqueeze(-1)
conditioner = self.conditioner_projection(conditioner)
y = x + diffusion_step
y = self.dilated_conv(y) + conditioner
gate, filter_1 = torch.split(y, [self.residual_channels, self.residual_channels], dim=1)
y = torch.sigmoid(gate) * torch.tanh(filter_1)
y = self.output_projection(y)
residual, skip = torch.split(y, [self.residual_channels, self.residual_channels], dim=1)
return (x + residual) / 1.41421356, skip
class DiffNet(nn.Module):
def __init__(self, in_dims, n_layers, n_chans, n_hidden):
super().__init__()
self.encoder_hidden = n_hidden
self.residual_layers = n_layers
self.residual_channels = n_chans
self.input_projection = Conv1d(in_dims, self.residual_channels, 1)
self.diffusion_embedding = SinusoidalPosEmb(self.residual_channels)
dim = self.residual_channels
self.mlp = nn.Sequential(
nn.Linear(dim, dim * 4),
Mish(),
nn.Linear(dim * 4, dim)
)
self.residual_layers = nn.ModuleList([
ResidualBlock(self.encoder_hidden, self.residual_channels, 1)
for i in range(self.residual_layers)
])
self.skip_projection = Conv1d(self.residual_channels, self.residual_channels, 1)
self.output_projection = Conv1d(self.residual_channels, in_dims, 1)
nn.init.zeros_(self.output_projection.weight)
def forward(self, spec, diffusion_step, cond):
x = spec.squeeze(0)
x = self.input_projection(x) # x [B, residual_channel, T]
x = F.relu(x)
# skip = torch.randn_like(x)
diffusion_step = diffusion_step.float()
diffusion_step = self.diffusion_embedding(diffusion_step)
diffusion_step = self.mlp(diffusion_step)
x, skip = self.residual_layers[0](x, cond, diffusion_step)
# noinspection PyTypeChecker
for layer in self.residual_layers[1:]:
x, skip_connection = layer.forward(x, cond, diffusion_step)
skip = skip + skip_connection
x = skip / math.sqrt(len(self.residual_layers))
x = self.skip_projection(x)
x = F.relu(x)
x = self.output_projection(x) # [B, 80, T]
return x.unsqueeze(1)
class AfterDiffusion(nn.Module):
def __init__(self, spec_max, spec_min, v_type='a'):
super().__init__()
self.spec_max = spec_max
self.spec_min = spec_min
self.type = v_type
def forward(self, x):
x = x.squeeze(1).permute(0, 2, 1)
mel_out = (x + 1) / 2 * (self.spec_max - self.spec_min) + self.spec_min
if self.type == 'nsf-hifigan-log10':
mel_out = mel_out * 0.434294
return mel_out.transpose(2, 1)
class Pred(nn.Module):
def __init__(self, alphas_cumprod):
super().__init__()
self.alphas_cumprod = alphas_cumprod
def forward(self, x_1, noise_t, t_1, t_prev):
a_t = extract(self.alphas_cumprod, t_1).cpu()
a_prev = extract(self.alphas_cumprod, t_prev).cpu()
a_t_sq, a_prev_sq = a_t.sqrt().cpu(), a_prev.sqrt().cpu()
x_delta = (a_prev - a_t) * ((1 / (a_t_sq * (a_t_sq + a_prev_sq))) * x_1 - 1 / (
a_t_sq * (((1 - a_prev) * a_t).sqrt() + ((1 - a_t) * a_prev).sqrt())) * noise_t)
x_pred = x_1 + x_delta.cpu()
return x_pred
class GaussianDiffusion(nn.Module):
def __init__(self,
out_dims=128,
n_layers=20,
n_chans=384,
n_hidden=256,
timesteps=1000,
k_step=1000,
max_beta=0.02,
spec_min=-12,
spec_max=2):
super().__init__()
self.denoise_fn = DiffNet(out_dims, n_layers, n_chans, n_hidden)
self.out_dims = out_dims
self.mel_bins = out_dims
self.n_hidden = n_hidden
betas = beta_schedule['linear'](timesteps, max_beta=max_beta)
alphas = 1. - betas
alphas_cumprod = np.cumprod(alphas, axis=0)
alphas_cumprod_prev = np.append(1., alphas_cumprod[:-1])
timesteps, = betas.shape
self.num_timesteps = int(timesteps)
self.k_step = k_step
self.noise_list = deque(maxlen=4)
to_torch = partial(torch.tensor, dtype=torch.float32)
self.register_buffer('betas', to_torch(betas))
self.register_buffer('alphas_cumprod', to_torch(alphas_cumprod))
self.register_buffer('alphas_cumprod_prev', to_torch(alphas_cumprod_prev))
# calculations for diffusion q(x_t | x_{t-1}) and others
self.register_buffer('sqrt_alphas_cumprod', to_torch(np.sqrt(alphas_cumprod)))
self.register_buffer('sqrt_one_minus_alphas_cumprod', to_torch(np.sqrt(1. - alphas_cumprod)))
self.register_buffer('log_one_minus_alphas_cumprod', to_torch(np.log(1. - alphas_cumprod)))
self.register_buffer('sqrt_recip_alphas_cumprod', to_torch(np.sqrt(1. / alphas_cumprod)))
self.register_buffer('sqrt_recipm1_alphas_cumprod', to_torch(np.sqrt(1. / alphas_cumprod - 1)))
# calculations for posterior q(x_{t-1} | x_t, x_0)
posterior_variance = betas * (1. - alphas_cumprod_prev) / (1. - alphas_cumprod)
# above: equal to 1. / (1. / (1. - alpha_cumprod_tm1) + alpha_t / beta_t)
self.register_buffer('posterior_variance', to_torch(posterior_variance))
# below: log calculation clipped because the posterior variance is 0 at the beginning of the diffusion chain
self.register_buffer('posterior_log_variance_clipped', to_torch(np.log(np.maximum(posterior_variance, 1e-20))))
self.register_buffer('posterior_mean_coef1', to_torch(
betas * np.sqrt(alphas_cumprod_prev) / (1. - alphas_cumprod)))
self.register_buffer('posterior_mean_coef2', to_torch(
(1. - alphas_cumprod_prev) * np.sqrt(alphas) / (1. - alphas_cumprod)))
self.register_buffer('spec_min', torch.FloatTensor([spec_min])[None, None, :out_dims])
self.register_buffer('spec_max', torch.FloatTensor([spec_max])[None, None, :out_dims])
self.ad = AfterDiffusion(self.spec_max, self.spec_min)
self.xp = Pred(self.alphas_cumprod)
def q_mean_variance(self, x_start, t):
mean = extract(self.sqrt_alphas_cumprod, t, x_start.shape) * x_start
variance = extract(1. - self.alphas_cumprod, t, x_start.shape)
log_variance = extract(self.log_one_minus_alphas_cumprod, t, x_start.shape)
return mean, variance, log_variance
def predict_start_from_noise(self, x_t, t, noise):
return (
extract(self.sqrt_recip_alphas_cumprod, t, x_t.shape) * x_t -
extract(self.sqrt_recipm1_alphas_cumprod, t, x_t.shape) * noise
)
def q_posterior(self, x_start, x_t, t):
posterior_mean = (
extract(self.posterior_mean_coef1, t, x_t.shape) * x_start +
extract(self.posterior_mean_coef2, t, x_t.shape) * x_t
)
posterior_variance = extract(self.posterior_variance, t, x_t.shape)
posterior_log_variance_clipped = extract(self.posterior_log_variance_clipped, t, x_t.shape)
return posterior_mean, posterior_variance, posterior_log_variance_clipped
def p_mean_variance(self, x, t, cond):
noise_pred = self.denoise_fn(x, t, cond=cond)
x_recon = self.predict_start_from_noise(x, t=t, noise=noise_pred)
x_recon.clamp_(-1., 1.)
model_mean, posterior_variance, posterior_log_variance = self.q_posterior(x_start=x_recon, x_t=x, t=t)
return model_mean, posterior_variance, posterior_log_variance
@torch.no_grad()
def p_sample(self, x, t, cond, clip_denoised=True, repeat_noise=False):
b, *_, device = *x.shape, x.device
model_mean, _, model_log_variance = self.p_mean_variance(x=x, t=t, cond=cond)
noise = noise_like(x.shape, device, repeat_noise)
# no noise when t == 0
nonzero_mask = (1 - (t == 0).float()).reshape(b, *((1,) * (len(x.shape) - 1)))
return model_mean + nonzero_mask * (0.5 * model_log_variance).exp() * noise
@torch.no_grad()
def p_sample_plms(self, x, t, interval, cond, clip_denoised=True, repeat_noise=False):
"""
Use the PLMS method from
[Pseudo Numerical Methods for Diffusion Models on Manifolds](https://arxiv.org/abs/2202.09778).
"""
def get_x_pred(x, noise_t, t):
a_t = extract(self.alphas_cumprod, t)
a_prev = extract(self.alphas_cumprod, torch.max(t - interval, torch.zeros_like(t)))
a_t_sq, a_prev_sq = a_t.sqrt(), a_prev.sqrt()
x_delta = (a_prev - a_t) * ((1 / (a_t_sq * (a_t_sq + a_prev_sq))) * x - 1 / (
a_t_sq * (((1 - a_prev) * a_t).sqrt() + ((1 - a_t) * a_prev).sqrt())) * noise_t)
x_pred = x + x_delta
return x_pred
noise_list = self.noise_list
noise_pred = self.denoise_fn(x, t, cond=cond)
if len(noise_list) == 0:
x_pred = get_x_pred(x, noise_pred, t)
noise_pred_prev = self.denoise_fn(x_pred, max(t - interval, 0), cond=cond)
noise_pred_prime = (noise_pred + noise_pred_prev) / 2
elif len(noise_list) == 1:
noise_pred_prime = (3 * noise_pred - noise_list[-1]) / 2
elif len(noise_list) == 2:
noise_pred_prime = (23 * noise_pred - 16 * noise_list[-1] + 5 * noise_list[-2]) / 12
else:
noise_pred_prime = (55 * noise_pred - 59 * noise_list[-1] + 37 * noise_list[-2] - 9 * noise_list[-3]) / 24
x_prev = get_x_pred(x, noise_pred_prime, t)
noise_list.append(noise_pred)
return x_prev
def q_sample(self, x_start, t, noise=None):
noise = default(noise, lambda: torch.randn_like(x_start))
return (
extract(self.sqrt_alphas_cumprod, t, x_start.shape) * x_start +
extract(self.sqrt_one_minus_alphas_cumprod, t, x_start.shape) * noise
)
def p_losses(self, x_start, t, cond, noise=None, loss_type='l2'):
noise = default(noise, lambda: torch.randn_like(x_start))
x_noisy = self.q_sample(x_start=x_start, t=t, noise=noise)
x_recon = self.denoise_fn(x_noisy, t, cond)
if loss_type == 'l1':
loss = (noise - x_recon).abs().mean()
elif loss_type == 'l2':
loss = F.mse_loss(noise, x_recon)
else:
raise NotImplementedError()
return loss
def org_forward(self,
condition,
init_noise=None,
gt_spec=None,
infer=True,
infer_speedup=100,
method='pndm',
k_step=1000,
use_tqdm=True):
"""
conditioning diffusion, use fastspeech2 encoder output as the condition
"""
cond = condition
b, device = condition.shape[0], condition.device
if not infer:
spec = self.norm_spec(gt_spec)
t = torch.randint(0, self.k_step, (b,), device=device).long()
norm_spec = spec.transpose(1, 2)[:, None, :, :] # [B, 1, M, T]
return self.p_losses(norm_spec, t, cond=cond)
else:
shape = (cond.shape[0], 1, self.out_dims, cond.shape[2])
if gt_spec is None:
t = self.k_step
if init_noise is None:
x = torch.randn(shape, device=device)
else:
x = init_noise
else:
t = k_step
norm_spec = self.norm_spec(gt_spec)
norm_spec = norm_spec.transpose(1, 2)[:, None, :, :]
x = self.q_sample(x_start=norm_spec, t=torch.tensor([t - 1], device=device).long())
if method is not None and infer_speedup > 1:
if method == 'dpm-solver':
from .dpm_solver_pytorch import (
DPM_Solver,
NoiseScheduleVP,
model_wrapper,
)
# 1. Define the noise schedule.
noise_schedule = NoiseScheduleVP(schedule='discrete', betas=self.betas[:t])
# 2. Convert your discrete-time `model` to the continuous-time
# noise prediction model. Here is an example for a diffusion model
# `model` with the noise prediction type ("noise") .
def my_wrapper(fn):
def wrapped(x, t, **kwargs):
ret = fn(x, t, **kwargs)
if use_tqdm:
self.bar.update(1)
return ret
return wrapped
model_fn = model_wrapper(
my_wrapper(self.denoise_fn),
noise_schedule,
model_type="noise", # or "x_start" or "v" or "score"
model_kwargs={"cond": cond}
)
# 3. Define dpm-solver and sample by singlestep DPM-Solver.
# (We recommend singlestep DPM-Solver for unconditional sampling)
# You can adjust the `steps` to balance the computation
# costs and the sample quality.
dpm_solver = DPM_Solver(model_fn, noise_schedule)
steps = t // infer_speedup
if use_tqdm:
self.bar = tqdm(desc="sample time step", total=steps)
x = dpm_solver.sample(
x,
steps=steps,
order=3,
skip_type="time_uniform",
method="singlestep",
)
if use_tqdm:
self.bar.close()
elif method == 'pndm':
self.noise_list = deque(maxlen=4)
if use_tqdm:
for i in tqdm(
reversed(range(0, t, infer_speedup)), desc='sample time step',
total=t // infer_speedup,
):
x = self.p_sample_plms(
x, torch.full((b,), i, device=device, dtype=torch.long),
infer_speedup, cond=cond
)
else:
for i in reversed(range(0, t, infer_speedup)):
x = self.p_sample_plms(
x, torch.full((b,), i, device=device, dtype=torch.long),
infer_speedup, cond=cond
)
else:
raise NotImplementedError(method)
else:
if use_tqdm:
for i in tqdm(reversed(range(0, t)), desc='sample time step', total=t):
x = self.p_sample(x, torch.full((b,), i, device=device, dtype=torch.long), cond)
else:
for i in reversed(range(0, t)):
x = self.p_sample(x, torch.full((b,), i, device=device, dtype=torch.long), cond)
x = x.squeeze(1).transpose(1, 2) # [B, T, M]
return self.denorm_spec(x).transpose(2, 1)
def norm_spec(self, x):
return (x - self.spec_min) / (self.spec_max - self.spec_min) * 2 - 1
def denorm_spec(self, x):
return (x + 1) / 2 * (self.spec_max - self.spec_min) + self.spec_min
def get_x_pred(self, x_1, noise_t, t_1, t_prev):
a_t = extract(self.alphas_cumprod, t_1)
a_prev = extract(self.alphas_cumprod, t_prev)
a_t_sq, a_prev_sq = a_t.sqrt(), a_prev.sqrt()
x_delta = (a_prev - a_t) * ((1 / (a_t_sq * (a_t_sq + a_prev_sq))) * x_1 - 1 / (
a_t_sq * (((1 - a_prev) * a_t).sqrt() + ((1 - a_t) * a_prev).sqrt())) * noise_t)
x_pred = x_1 + x_delta
return x_pred
def OnnxExport(self, project_name=None, init_noise=None, hidden_channels=256, export_denoise=True, export_pred=True, export_after=True):
cond = torch.randn([1, self.n_hidden, 10]).cpu()
if init_noise is None:
x = torch.randn((1, 1, self.mel_bins, cond.shape[2]), dtype=torch.float32).cpu()
else:
x = init_noise
pndms = 100
org_y_x = self.org_forward(cond, init_noise=x)
device = cond.device
n_frames = cond.shape[2]
step_range = torch.arange(0, self.k_step, pndms, dtype=torch.long, device=device).flip(0)
plms_noise_stage = torch.tensor(0, dtype=torch.long, device=device)
noise_list = torch.zeros((0, 1, 1, self.mel_bins, n_frames), device=device)
ot = step_range[0]
ot_1 = torch.full((1,), ot, device=device, dtype=torch.long)
if export_denoise:
torch.onnx.export(
self.denoise_fn,
(x.cpu(), ot_1.cpu(), cond.cpu()),
f"{project_name}_denoise.onnx",
input_names=["noise", "time", "condition"],
output_names=["noise_pred"],
dynamic_axes={
"noise": [3],
"condition": [2]
},
opset_version=16
)
for t in step_range:
t_1 = torch.full((1,), t, device=device, dtype=torch.long)
noise_pred = self.denoise_fn(x, t_1, cond)
t_prev = t_1 - pndms
t_prev = t_prev * (t_prev > 0)
if plms_noise_stage == 0:
if export_pred:
torch.onnx.export(
self.xp,
(x.cpu(), noise_pred.cpu(), t_1.cpu(), t_prev.cpu()),
f"{project_name}_pred.onnx",
input_names=["noise", "noise_pred", "time", "time_prev"],
output_names=["noise_pred_o"],
dynamic_axes={
"noise": [3],
"noise_pred": [3]
},
opset_version=16
)
x_pred = self.get_x_pred(x, noise_pred, t_1, t_prev)
noise_pred_prev = self.denoise_fn(x_pred, t_prev, cond=cond)
noise_pred_prime = predict_stage0(noise_pred, noise_pred_prev)
elif plms_noise_stage == 1:
noise_pred_prime = predict_stage1(noise_pred, noise_list)
elif plms_noise_stage == 2:
noise_pred_prime = predict_stage2(noise_pred, noise_list)
else:
noise_pred_prime = predict_stage3(noise_pred, noise_list)
noise_pred = noise_pred.unsqueeze(0)
if plms_noise_stage < 3:
noise_list = torch.cat((noise_list, noise_pred), dim=0)
plms_noise_stage = plms_noise_stage + 1
else:
noise_list = torch.cat((noise_list[-2:], noise_pred), dim=0)
x = self.get_x_pred(x, noise_pred_prime, t_1, t_prev)
if export_after:
torch.onnx.export(
self.ad,
x.cpu(),
f"{project_name}_after.onnx",
input_names=["x"],
output_names=["mel_out"],
dynamic_axes={
"x": [3]
},
opset_version=16
)
x = self.ad(x)
print((x == org_y_x).all())
return x
def forward(self, condition=None, init_noise=None, pndms=None, k_step=None):
cond = condition
x = init_noise
device = cond.device
n_frames = cond.shape[2]
step_range = torch.arange(0, k_step.item(), pndms.item(), dtype=torch.long, device=device).flip(0)
plms_noise_stage = torch.tensor(0, dtype=torch.long, device=device)
noise_list = torch.zeros((0, 1, 1, self.mel_bins, n_frames), device=device)
for t in step_range:
t_1 = torch.full((1,), t, device=device, dtype=torch.long)
noise_pred = self.denoise_fn(x, t_1, cond)
t_prev = t_1 - pndms
t_prev = t_prev * (t_prev > 0)
if plms_noise_stage == 0:
x_pred = self.get_x_pred(x, noise_pred, t_1, t_prev)
noise_pred_prev = self.denoise_fn(x_pred, t_prev, cond=cond)
noise_pred_prime = predict_stage0(noise_pred, noise_pred_prev)
elif plms_noise_stage == 1:
noise_pred_prime = predict_stage1(noise_pred, noise_list)
elif plms_noise_stage == 2:
noise_pred_prime = predict_stage2(noise_pred, noise_list)
else:
noise_pred_prime = predict_stage3(noise_pred, noise_list)
noise_pred = noise_pred.unsqueeze(0)
if plms_noise_stage < 3:
noise_list = torch.cat((noise_list, noise_pred), dim=0)
plms_noise_stage = plms_noise_stage + 1
else:
noise_list = torch.cat((noise_list[-2:], noise_pred), dim=0)
x = self.get_x_pred(x, noise_pred_prime, t_1, t_prev)
x = self.ad(x)
return x