| |
| from __future__ import annotations |
| import torch |
| import torch.nn as nn |
|
|
| def nonlinearity(x): |
| return x * torch.sigmoid(x) |
|
|
| def Normalize(in_channels): |
| return torch.nn.GroupNorm(num_groups=32, num_channels=in_channels, eps=1e-06, affine=True) |
|
|
| 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.Conv2d(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: |
| self.conv = torch.nn.Conv2d(in_channels, in_channels, kernel_size=3, stride=2, padding=0) |
|
|
| def forward(self, x): |
| if self.with_conv: |
| pad = (0, 1, 0, 1) |
| x = torch.nn.functional.pad(x, pad, mode='constant', value=0) |
| x = self.conv(x) |
| else: |
| x = torch.nn.functional.avg_pool2d(x, kernel_size=2, stride=2) |
| return x |
|
|
| class ResnetBlock(nn.Module): |
|
|
| def __init__(self, *, in_channels, out_channels=None, conv_shortcut=False, dropout, 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.Conv2d(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.dropout = torch.nn.Dropout(dropout) |
| self.conv2 = torch.nn.Conv2d(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.Conv2d(in_channels, out_channels, kernel_size=3, stride=1, padding=1) |
| else: |
| self.nin_shortcut = torch.nn.Conv2d(in_channels, out_channels, kernel_size=1, stride=1, padding=0) |
|
|
| def forward(self, x, temb): |
| h = x |
| h = self.norm1(h) |
| h = nonlinearity(h) |
| h = self.conv1(h) |
| if temb is not None: |
| h = h + self.temb_proj(nonlinearity(temb))[:, :, None, None] |
| h = self.norm2(h) |
| h = nonlinearity(h) |
| h = self.dropout(h) |
| h = self.conv2(h) |
| if self.in_channels != self.out_channels: |
| if self.use_conv_shortcut: |
| x = self.conv_shortcut(x) |
| else: |
| x = self.nin_shortcut(x) |
| return x + h |
|
|
| 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.Conv2d(in_channels, in_channels, kernel_size=1, stride=1, padding=0) |
| self.k = torch.nn.Conv2d(in_channels, in_channels, kernel_size=1, stride=1, padding=0) |
| self.v = torch.nn.Conv2d(in_channels, in_channels, kernel_size=1, stride=1, padding=0) |
| self.proj_out = torch.nn.Conv2d(in_channels, in_channels, kernel_size=1, stride=1, padding=0) |
|
|
| def forward(self, x): |
| h_ = x |
| h_ = self.norm(h_) |
| q = self.q(h_) |
| k = self.k(h_) |
| v = self.v(h_) |
| (b, c, h, w) = q.shape |
| q = q.reshape(b, c, h * w) |
| q = q.permute(0, 2, 1) |
| k = k.reshape(b, c, h * w) |
| w_ = torch.bmm(q, k) |
| w_ = w_ * int(c) ** -0.5 |
| w_ = torch.nn.functional.softmax(w_, dim=2) |
| v = v.reshape(b, c, h * w) |
| w_ = w_.permute(0, 2, 1) |
| h_ = torch.bmm(v, w_) |
| h_ = h_.reshape(b, c, h, w) |
| h_ = self.proj_out(h_) |
| return x + h_ |
|
|
| class Encoder(nn.Module): |
|
|
| def __init__(self, *, ch, out_ch, ch_mult=(1, 2, 4, 8), num_res_blocks, attn_resolutions, dropout=0.0, resamp_with_conv=True, in_channels, resolution, z_channels, double_z=True, **ignore_kwargs): |
| super().__init__() |
| self.ch = ch |
| self.temb_ch = 0 |
| self.num_resolutions = len(ch_mult) |
| self.num_res_blocks = num_res_blocks |
| self.resolution = resolution |
| self.in_channels = in_channels |
| self.conv_in = torch.nn.Conv2d(in_channels, self.ch, kernel_size=3, stride=1, padding=1) |
| curr_res = resolution |
| 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, dropout=dropout)) |
| block_in = block_out |
| if curr_res in attn_resolutions: |
| 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) |
| curr_res = curr_res // 2 |
| self.down.append(down) |
| self.mid = nn.Module() |
| self.mid.block_1 = ResnetBlock(in_channels=block_in, out_channels=block_in, temb_channels=self.temb_ch, dropout=dropout) |
| 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, dropout=dropout) |
| self.norm_out = Normalize(block_in) |
| self.conv_out = torch.nn.Conv2d(block_in, 2 * z_channels if double_z else z_channels, kernel_size=3, stride=1, padding=1) |
|
|
| def forward(self, x): |
| temb = None |
| hs = [self.conv_in(x)] |
| for i_level in range(self.num_resolutions): |
| for i_block in range(self.num_res_blocks): |
| h = self.down[i_level].block[i_block](hs[-1], temb) |
| if len(self.down[i_level].attn) > 0: |
| h = self.down[i_level].attn[i_block](h) |
| hs.append(h) |
| if i_level != self.num_resolutions - 1: |
| hs.append(self.down[i_level].downsample(hs[-1])) |
| h = hs[-1] |
| h = self.mid.block_1(h, temb) |
| h = self.mid.attn_1(h) |
| h = self.mid.block_2(h, temb) |
| h = self.norm_out(h) |
| h = nonlinearity(h) |
| h = self.conv_out(h) |
| return h |
|
|
| class Decoder(nn.Module): |
|
|
| def __init__(self, *, ch, out_ch, ch_mult=(1, 2, 4, 8), num_res_blocks, attn_resolutions, dropout=0.0, resamp_with_conv=True, in_channels, resolution, z_channels, give_pre_end=False, **ignorekwargs): |
| super().__init__() |
| self.ch = ch |
| self.temb_ch = 0 |
| self.num_resolutions = len(ch_mult) |
| self.num_res_blocks = num_res_blocks |
| self.resolution = resolution |
| self.in_channels = in_channels |
| self.give_pre_end = give_pre_end |
| in_ch_mult = (1,) + tuple(ch_mult) |
| block_in = ch * ch_mult[self.num_resolutions - 1] |
| curr_res = resolution // 2 ** (self.num_resolutions - 1) |
| self.z_shape = (1, z_channels, curr_res, curr_res) |
| self.conv_in = torch.nn.Conv2d(z_channels, block_in, kernel_size=3, stride=1, padding=1) |
| self.mid = nn.Module() |
| self.mid.block_1 = ResnetBlock(in_channels=block_in, out_channels=block_in, temb_channels=self.temb_ch, dropout=dropout) |
| 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, dropout=dropout) |
| 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, dropout=dropout)) |
| block_in = block_out |
| if curr_res in attn_resolutions: |
| 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) |
| curr_res = curr_res * 2 |
| self.up.insert(0, up) |
| self.norm_out = Normalize(block_in) |
| self.conv_out = torch.nn.Conv2d(block_in, out_ch, kernel_size=3, stride=1, padding=1) |
|
|
| def forward(self, z, return_intermediate_feature=False): |
| self.last_z_shape = z.shape |
| temb = None |
| h = self.conv_in(z) |
| h = self.mid.block_1(h, temb) |
| h = self.mid.attn_1(h) |
| h = self.mid.block_2(h, temb) |
| im_feat_list = [] |
| for i_level in reversed(range(self.num_resolutions)): |
| for i_block in range(self.num_res_blocks + 1): |
| h = self.up[i_level].block[i_block](h, temb) |
| if len(self.up[i_level].attn) > 0: |
| h = self.up[i_level].attn[i_block](h) |
| if i_level != 0: |
| h = self.up[i_level].upsample(h) |
| im_feat_list.append(h) |
| if self.give_pre_end: |
| return h |
| h = self.norm_out(h) |
| h = nonlinearity(h) |
| h = self.conv_out(h) |
| if return_intermediate_feature: |
| return (h, im_feat_list) |
| return h |
|
|
