mirror of
https://github.com/Wan-Video/Wan2.1.git
synced 2025-11-04 14:16:57 +00:00
256 lines
9.3 KiB
Python
256 lines
9.3 KiB
Python
# Copyright (c) 2022 NVIDIA CORPORATION.
|
|
# Licensed under the MIT license.
|
|
|
|
# Adapted from https://github.com/jik876/hifi-gan under the MIT license.
|
|
# LICENSE is in incl_licenses directory.
|
|
|
|
import torch
|
|
import torch.nn as nn
|
|
from torch.nn import Conv1d, ConvTranspose1d
|
|
from torch.nn.utils.parametrizations import weight_norm
|
|
from torch.nn.utils.parametrize import remove_parametrizations
|
|
|
|
from ...ext.bigvgan import activations
|
|
from ...ext.bigvgan.alias_free_torch import *
|
|
from ...ext.bigvgan.utils import get_padding, init_weights
|
|
|
|
LRELU_SLOPE = 0.1
|
|
|
|
|
|
class AMPBlock1(torch.nn.Module):
|
|
|
|
def __init__(self, h, channels, kernel_size=3, dilation=(1, 3, 5), activation=None):
|
|
super(AMPBlock1, self).__init__()
|
|
self.h = h
|
|
|
|
self.convs1 = nn.ModuleList([
|
|
weight_norm(
|
|
Conv1d(channels,
|
|
channels,
|
|
kernel_size,
|
|
1,
|
|
dilation=dilation[0],
|
|
padding=get_padding(kernel_size, dilation[0]))),
|
|
weight_norm(
|
|
Conv1d(channels,
|
|
channels,
|
|
kernel_size,
|
|
1,
|
|
dilation=dilation[1],
|
|
padding=get_padding(kernel_size, dilation[1]))),
|
|
weight_norm(
|
|
Conv1d(channels,
|
|
channels,
|
|
kernel_size,
|
|
1,
|
|
dilation=dilation[2],
|
|
padding=get_padding(kernel_size, dilation[2])))
|
|
])
|
|
self.convs1.apply(init_weights)
|
|
|
|
self.convs2 = nn.ModuleList([
|
|
weight_norm(
|
|
Conv1d(channels,
|
|
channels,
|
|
kernel_size,
|
|
1,
|
|
dilation=1,
|
|
padding=get_padding(kernel_size, 1))),
|
|
weight_norm(
|
|
Conv1d(channels,
|
|
channels,
|
|
kernel_size,
|
|
1,
|
|
dilation=1,
|
|
padding=get_padding(kernel_size, 1))),
|
|
weight_norm(
|
|
Conv1d(channels,
|
|
channels,
|
|
kernel_size,
|
|
1,
|
|
dilation=1,
|
|
padding=get_padding(kernel_size, 1)))
|
|
])
|
|
self.convs2.apply(init_weights)
|
|
|
|
self.num_layers = len(self.convs1) + len(self.convs2) # total number of conv layers
|
|
|
|
if activation == 'snake': # periodic nonlinearity with snake function and anti-aliasing
|
|
self.activations = nn.ModuleList([
|
|
Activation1d(
|
|
activation=activations.Snake(channels, alpha_logscale=h.snake_logscale))
|
|
for _ in range(self.num_layers)
|
|
])
|
|
elif activation == 'snakebeta': # periodic nonlinearity with snakebeta function and anti-aliasing
|
|
self.activations = nn.ModuleList([
|
|
Activation1d(
|
|
activation=activations.SnakeBeta(channels, alpha_logscale=h.snake_logscale))
|
|
for _ in range(self.num_layers)
|
|
])
|
|
else:
|
|
raise NotImplementedError(
|
|
"activation incorrectly specified. check the config file and look for 'activation'."
|
|
)
|
|
|
|
def forward(self, x):
|
|
acts1, acts2 = self.activations[::2], self.activations[1::2]
|
|
for c1, c2, a1, a2 in zip(self.convs1, self.convs2, acts1, acts2):
|
|
xt = a1(x)
|
|
xt = c1(xt)
|
|
xt = a2(xt)
|
|
xt = c2(xt)
|
|
x = xt + x
|
|
|
|
return x
|
|
|
|
def remove_weight_norm(self):
|
|
for l in self.convs1:
|
|
remove_parametrizations(l, 'weight')
|
|
for l in self.convs2:
|
|
remove_parametrizations(l, 'weight')
|
|
|
|
|
|
class AMPBlock2(torch.nn.Module):
|
|
|
|
def __init__(self, h, channels, kernel_size=3, dilation=(1, 3), activation=None):
|
|
super(AMPBlock2, self).__init__()
|
|
self.h = h
|
|
|
|
self.convs = nn.ModuleList([
|
|
weight_norm(
|
|
Conv1d(channels,
|
|
channels,
|
|
kernel_size,
|
|
1,
|
|
dilation=dilation[0],
|
|
padding=get_padding(kernel_size, dilation[0]))),
|
|
weight_norm(
|
|
Conv1d(channels,
|
|
channels,
|
|
kernel_size,
|
|
1,
|
|
dilation=dilation[1],
|
|
padding=get_padding(kernel_size, dilation[1])))
|
|
])
|
|
self.convs.apply(init_weights)
|
|
|
|
self.num_layers = len(self.convs) # total number of conv layers
|
|
|
|
if activation == 'snake': # periodic nonlinearity with snake function and anti-aliasing
|
|
self.activations = nn.ModuleList([
|
|
Activation1d(
|
|
activation=activations.Snake(channels, alpha_logscale=h.snake_logscale))
|
|
for _ in range(self.num_layers)
|
|
])
|
|
elif activation == 'snakebeta': # periodic nonlinearity with snakebeta function and anti-aliasing
|
|
self.activations = nn.ModuleList([
|
|
Activation1d(
|
|
activation=activations.SnakeBeta(channels, alpha_logscale=h.snake_logscale))
|
|
for _ in range(self.num_layers)
|
|
])
|
|
else:
|
|
raise NotImplementedError(
|
|
"activation incorrectly specified. check the config file and look for 'activation'."
|
|
)
|
|
|
|
def forward(self, x):
|
|
for c, a in zip(self.convs, self.activations):
|
|
xt = a(x)
|
|
xt = c(xt)
|
|
x = xt + x
|
|
|
|
return x
|
|
|
|
def remove_weight_norm(self):
|
|
for l in self.convs:
|
|
remove_parametrizations(l, 'weight')
|
|
|
|
|
|
class BigVGANVocoder(torch.nn.Module):
|
|
# this is our main BigVGAN model. Applies anti-aliased periodic activation for resblocks.
|
|
def __init__(self, h):
|
|
super().__init__()
|
|
self.h = h
|
|
|
|
self.num_kernels = len(h.resblock_kernel_sizes)
|
|
self.num_upsamples = len(h.upsample_rates)
|
|
|
|
# pre conv
|
|
self.conv_pre = weight_norm(Conv1d(h.num_mels, h.upsample_initial_channel, 7, 1, padding=3))
|
|
|
|
# define which AMPBlock to use. BigVGAN uses AMPBlock1 as default
|
|
resblock = AMPBlock1 if h.resblock == '1' else AMPBlock2
|
|
|
|
# transposed conv-based upsamplers. does not apply anti-aliasing
|
|
self.ups = nn.ModuleList()
|
|
for i, (u, k) in enumerate(zip(h.upsample_rates, h.upsample_kernel_sizes)):
|
|
self.ups.append(
|
|
nn.ModuleList([
|
|
weight_norm(
|
|
ConvTranspose1d(h.upsample_initial_channel // (2**i),
|
|
h.upsample_initial_channel // (2**(i + 1)),
|
|
k,
|
|
u,
|
|
padding=(k - u) // 2))
|
|
]))
|
|
|
|
# residual blocks using anti-aliased multi-periodicity composition modules (AMP)
|
|
self.resblocks = nn.ModuleList()
|
|
for i in range(len(self.ups)):
|
|
ch = h.upsample_initial_channel // (2**(i + 1))
|
|
for j, (k, d) in enumerate(zip(h.resblock_kernel_sizes, h.resblock_dilation_sizes)):
|
|
self.resblocks.append(resblock(h, ch, k, d, activation=h.activation))
|
|
|
|
# post conv
|
|
if h.activation == "snake": # periodic nonlinearity with snake function and anti-aliasing
|
|
activation_post = activations.Snake(ch, alpha_logscale=h.snake_logscale)
|
|
self.activation_post = Activation1d(activation=activation_post)
|
|
elif h.activation == "snakebeta": # periodic nonlinearity with snakebeta function and anti-aliasing
|
|
activation_post = activations.SnakeBeta(ch, alpha_logscale=h.snake_logscale)
|
|
self.activation_post = Activation1d(activation=activation_post)
|
|
else:
|
|
raise NotImplementedError(
|
|
"activation incorrectly specified. check the config file and look for 'activation'."
|
|
)
|
|
|
|
self.conv_post = weight_norm(Conv1d(ch, 1, 7, 1, padding=3))
|
|
|
|
# weight initialization
|
|
for i in range(len(self.ups)):
|
|
self.ups[i].apply(init_weights)
|
|
self.conv_post.apply(init_weights)
|
|
|
|
def forward(self, x):
|
|
# pre conv
|
|
x = self.conv_pre(x)
|
|
|
|
for i in range(self.num_upsamples):
|
|
# upsampling
|
|
for i_up in range(len(self.ups[i])):
|
|
x = self.ups[i][i_up](x)
|
|
# AMP blocks
|
|
xs = None
|
|
for j in range(self.num_kernels):
|
|
if xs is None:
|
|
xs = self.resblocks[i * self.num_kernels + j](x)
|
|
else:
|
|
xs += self.resblocks[i * self.num_kernels + j](x)
|
|
x = xs / self.num_kernels
|
|
|
|
# post conv
|
|
x = self.activation_post(x)
|
|
x = self.conv_post(x)
|
|
x = torch.tanh(x)
|
|
|
|
return x
|
|
|
|
def remove_weight_norm(self):
|
|
print('Removing weight norm...')
|
|
for l in self.ups:
|
|
for l_i in l:
|
|
remove_parametrizations(l_i, 'weight')
|
|
for l in self.resblocks:
|
|
l.remove_weight_norm()
|
|
remove_parametrizations(self.conv_pre, 'weight')
|
|
remove_parametrizations(self.conv_post, 'weight')
|