modules/commons/taming_tfm_modules.py

import torch
import torch.nn as nn
import torch.nn.functional as F


def nonlinearity(x):
    # swish
    return x * torch.sigmoid(x)


class Normalize(nn.Module):
    def __init__(self, channels, eps=1e-5):
        super().__init__()
        self.channels = channels
        self.eps = eps

        self.gamma = nn.Parameter(torch.ones(channels))
        self.beta = nn.Parameter(torch.zeros(channels))
        self.proj = nn.Linear(channels, channels)

    def forward(self, x):
        x = x.transpose(1, 2)
        x = F.layer_norm(x, (self.channels,), self.gamma, self.beta, self.eps)
        x = self.proj(x)
        return x.transpose(1, 2)


class Upsample(nn.Module):
    def __init__(self, in_channels, with_conv):
        super().__init__()
        self.with_conv = with_conv
        if self.with_conv:
            self.conv = torch.nn.Conv1d(in_channels,
                                        in_channels,
                                        kernel_size=3,
                                        stride=1,
                                        padding=1)

    def forward(self, x):
        x = torch.nn.functional.interpolate(x, scale_factor=2.0, mode="nearest")
        if self.with_conv:
            x = self.conv(x)
        return x


class Downsample(nn.Module):
    def __init__(self, in_channels, with_conv):
        super().__init__()
        self.with_conv = with_conv
        if self.with_conv:
            # no asymmetric padding in torch conv, must do it ourselves
            self.conv = torch.nn.Conv1d(in_channels,
                                        in_channels,
                                        kernel_size=4,
                                        stride=2,
                                        padding=1)

    def forward(self, x):
        if self.with_conv:
            x = self.conv(x)
        else:
            x = torch.nn.functional.avg_pool1d(x, kernel_size=2, stride=2)
        return x


class ResnetBlock(nn.Module):
    def __init__(self, *, in_channels, out_channels=None, conv_shortcut=False,
                 temb_channels=512):
        super().__init__()
        self.in_channels = in_channels
        out_channels = in_channels if out_channels is None else out_channels
        self.out_channels = out_channels
        self.use_conv_shortcut = conv_shortcut

        self.norm1 = Normalize(in_channels)
        self.conv1 = torch.nn.Conv1d(in_channels,
                                     out_channels,
                                     kernel_size=3,
                                     stride=1,
                                     padding=1)
        if temb_channels > 0:
            self.temb_proj = torch.nn.Linear(temb_channels,
                                             out_channels)
        self.norm2 = Normalize(out_channels)
        self.conv2 = torch.nn.Conv1d(out_channels,
                                     out_channels,
                                     kernel_size=3,
                                     stride=1,
                                     padding=1)
        if self.in_channels != self.out_channels:
            if self.use_conv_shortcut:
                self.conv_shortcut = torch.nn.Conv1d(in_channels,
                                                     out_channels,
                                                     kernel_size=3,
                                                     stride=1,
                                                     padding=1)
            else:
                self.nin_shortcut = torch.nn.Conv1d(in_channels,
                                                    out_channels,
                                                    kernel_size=1,
                                                    stride=1,
                                                    padding=0)

    def forward(self, x, _, x_mask):
        x = x * x_mask
        h = x
        h = self.norm1(h) * x_mask
        h = nonlinearity(h) * x_mask
        h = self.conv1(h) * x_mask

        h = self.norm2(h) * x_mask
        h = nonlinearity(h) * x_mask
        h = self.conv2(h) * x_mask

        if self.in_channels != self.out_channels:
            if self.use_conv_shortcut:
                x = self.conv_shortcut(x) * x_mask
            else:
                x = self.nin_shortcut(x) * x_mask

        return (x + h) * x_mask


class AttnBlock(nn.Module):
    def __init__(self, in_channels):
        super().__init__()
        self.in_channels = in_channels

        self.norm = Normalize(in_channels)
        self.q = torch.nn.Conv1d(in_channels,
                                 in_channels,
                                 kernel_size=1,
                                 stride=1,
                                 padding=0)
        self.k = torch.nn.Conv1d(in_channels,
                                 in_channels,
                                 kernel_size=1,
                                 stride=1,
                                 padding=0)
        self.v = torch.nn.Conv1d(in_channels,
                                 in_channels,
                                 kernel_size=1,
                                 stride=1,
                                 padding=0)
        self.proj_out = torch.nn.Conv1d(in_channels,
                                        in_channels,
                                        kernel_size=1,
                                        stride=1,
                                        padding=0)

    def forward(self, x, x_mask):
        h_ = x * x_mask
        h_ = self.norm(h_) * x_mask
        q = self.q(h_) * x_mask
        k = self.k(h_) * x_mask
        v = self.v(h_) * x_mask

        # compute attention
        b, c, h = q.shape
        w = 1
        q = q.reshape(b, c, h * w)
        q = q.permute(0, 2, 1)  # b,hw,c
        k = k.reshape(b, c, h * w)  # b,c,hw
        w_ = torch.bmm(q, k)  # b,hw,hw    w[b,i,j]=sum_c q[b,i,c]k[b,c,j]
        w_ = w_ * (int(c) ** (-0.5))
        w_ = w_ + ((1 - x_mask) * -1e8) + ((1 - x_mask) * -1e8).transpose(1, 2)
        w_ = torch.nn.functional.softmax(w_, dim=2)

        # attend to values
        v = v.reshape(b, c, h * w)
        w_ = w_.permute(0, 2, 1)  # b,hw,hw (first hw of k, second of q)
        h_ = torch.bmm(v, w_)  # b, c,hw (hw of q) h_[b,c,j] = sum_i v[b,c,i] w_[b,i,j]
        h_ = h_.reshape(b, c, h)

        h_ = self.proj_out(h_) * x_mask

        return (x + h_) * x_mask


class Encoder(nn.Module):
    def __init__(self, *, ch, out_ch, ch_mult=(1, 2, 4, 8), num_res_blocks,
                 resamp_with_conv=False, in_channels):
        super().__init__()
        self.ch = ch
        self.temb_ch = 0
        self.num_resolutions = len(ch_mult)
        self.num_res_blocks = num_res_blocks
        self.in_channels = in_channels

        # downsampling
        self.conv_in = torch.nn.Conv1d(in_channels,
                                       self.ch,
                                       kernel_size=3,
                                       stride=1,
                                       padding=1)

        in_ch_mult = (1,) + tuple(ch_mult)
        self.down = nn.ModuleList()
        for i_level in range(self.num_resolutions):
            block = nn.ModuleList()
            attn = nn.ModuleList()
            block_in = ch * in_ch_mult[i_level]
            block_out = ch * ch_mult[i_level]
            for i_block in range(self.num_res_blocks):
                block.append(ResnetBlock(in_channels=block_in,
                                         out_channels=block_out,
                                         temb_channels=self.temb_ch))
                block_in = block_out
                if i_level == self.num_resolutions - 1:
                    attn.append(AttnBlock(block_in))
            down = nn.Module()
            down.block = block
            down.attn = attn
            if i_level != self.num_resolutions - 1:
                down.downsample = Downsample(block_in, resamp_with_conv)
            self.down.append(down)

        # middle
        self.mid = nn.Module()
        self.mid.block_1 = ResnetBlock(in_channels=block_in,
                                       out_channels=block_in,
                                       temb_channels=self.temb_ch)
        self.mid.attn_1 = AttnBlock(block_in)
        self.mid.block_2 = ResnetBlock(in_channels=block_in,
                                       out_channels=block_in,
                                       temb_channels=self.temb_ch)

        # end
        self.norm_out = Normalize(block_in)
        self.conv_out = torch.nn.Conv1d(block_in,
                                        out_ch,
                                        kernel_size=3,
                                        stride=1,
                                        padding=1)

    def forward(self, x, x_mask):
        if x_mask is None:
            x_mask = torch.ones_like(x_mask[:, :, :1])
        x = x.permute(0, 2, 1)
        x_mask = x_mask.permute(0, 2, 1)

        temb = None
        # downsampling
        hs = [self.conv_in(x) * x_mask]
        for i_level in range(self.num_resolutions):
            x_mask_ = x_mask[:, :, ::2 ** i_level]
            for i_block in range(self.num_res_blocks):
                h = self.down[i_level].block[i_block](hs[-1], temb, x_mask_) * x_mask_
                if len(self.down[i_level].attn) > 0:
                    h = self.down[i_level].attn[i_block](h, x_mask_) * x_mask_
                hs.append(h)
            if i_level != self.num_resolutions - 1:
                hs.append(self.down[i_level].downsample(hs[-1]) * x_mask_[:, :, ::2])

        x_mask_ = x_mask[:, :, ::2 ** (self.num_resolutions - 1)]
        # middle
        h = hs[-1] * x_mask_
        h = self.mid.block_1(h, temb, x_mask_) * x_mask_
        h = self.mid.attn_1(h, x_mask_) * x_mask_
        h = self.mid.block_2(h, temb, x_mask_) * x_mask_

        # end
        h = self.norm_out(h) * x_mask_
        h = nonlinearity(h) * x_mask_
        h = self.conv_out(h) * x_mask_
        h = h.permute(0, 2, 1)
        return h


class Decoder(nn.Module):
    def __init__(self, *, ch, out_ch, ch_mult=(1, 2, 4, 8), num_res_blocks,
                 resamp_with_conv=True, in_channels, give_pre_end=False):
        super().__init__()
        self.ch = ch
        self.temb_ch = 0
        self.num_resolutions = len(ch_mult)
        self.num_res_blocks = num_res_blocks
        self.in_channels = in_channels
        self.give_pre_end = give_pre_end

        # compute in_ch_mult, block_in and curr_res at lowest res
        block_in = ch * ch_mult[self.num_resolutions - 1]

        # z to block_in
        self.conv_in = torch.nn.Conv1d(in_channels,
                                       block_in,
                                       kernel_size=3,
                                       stride=1,
                                       padding=1)

        # middle
        self.mid = nn.Module()
        self.mid.block_1 = ResnetBlock(in_channels=block_in,
                                       out_channels=block_in,
                                       temb_channels=self.temb_ch)
        self.mid.attn_1 = AttnBlock(block_in)
        self.mid.block_2 = ResnetBlock(in_channels=block_in,
                                       out_channels=block_in,
                                       temb_channels=self.temb_ch)

        # upsampling
        self.up = nn.ModuleList()
        for i_level in reversed(range(self.num_resolutions)):
            block = nn.ModuleList()
            attn = nn.ModuleList()
            block_out = ch * ch_mult[i_level]
            for i_block in range(self.num_res_blocks + 1):
                block.append(ResnetBlock(in_channels=block_in,
                                         out_channels=block_out,
                                         temb_channels=self.temb_ch))
                block_in = block_out
                if i_level == self.num_resolutions - 1:
                    attn.append(AttnBlock(block_in))
            up = nn.Module()
            up.block = block
            up.attn = attn
            if i_level != 0:
                up.upsample = Upsample(block_in, resamp_with_conv)
            self.up.insert(0, up)  # prepend to get consistent order

        # end
        self.norm_out = Normalize(block_in)
        self.conv_out = torch.nn.Conv1d(block_in,
                                        out_ch,
                                        kernel_size=3,
                                        stride=1,
                                        padding=1)

    def forward(self, z, x_mask):
        if x_mask is None:
            x_mask = torch.ones_like(z[:, :, :1]).repeat(1, 8, 1)
        z = z.permute(0, 2, 1)
        x_mask = x_mask.permute(0, 2, 1)

        # timestep embedding
        temb = None

        # z to block_in
        h = self.conv_in(z)

        # middle
        i_level = self.num_resolutions - 1
        x_mask_ = x_mask[:, :, ::2 ** i_level]
        h = self.mid.block_1(h, temb, x_mask_)
        h = self.mid.attn_1(h, x_mask_)
        h = self.mid.block_2(h, temb, x_mask_)

        # upsampling
        for i_level in reversed(range(self.num_resolutions)):
            x_mask_ = x_mask[:, :, ::2 ** i_level]
            for i_block in range(self.num_res_blocks + 1):
                h = self.up[i_level].block[i_block](h, temb, x_mask_)
                if len(self.up[i_level].attn) > 0:
                    h = self.up[i_level].attn[i_block](h, x_mask_)
            if i_level != 0:
                h = self.up[i_level].upsample(h)

        # end
        if self.give_pre_end:
            return h

        h = self.norm_out(h)
        h = nonlinearity(h)
        h = self.conv_out(h) * x_mask
        h = h.permute(0, 2, 1)
        return h