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| # pytorch_diffusion + derived encoder decoder | |
| import math | |
| import torch | |
| import torch.nn as nn | |
| import numpy as np | |
| from einops import rearrange | |
| from audioldm.utils import instantiate_from_config | |
| from audioldm.latent_diffusion.attention import LinearAttention | |
| def get_timestep_embedding(timesteps, embedding_dim): | |
| """ | |
| This matches the implementation in Denoising Diffusion Probabilistic Models: | |
| From Fairseq. | |
| Build sinusoidal embeddings. | |
| This matches the implementation in tensor2tensor, but differs slightly | |
| from the description in Section 3.5 of "Attention Is All You Need". | |
| """ | |
| assert len(timesteps.shape) == 1 | |
| half_dim = embedding_dim // 2 | |
| emb = math.log(10000) / (half_dim - 1) | |
| emb = torch.exp(torch.arange(half_dim, dtype=torch.float32) * -emb) | |
| emb = emb.to(device=timesteps.device) | |
| emb = timesteps.float()[:, None] * emb[None, :] | |
| emb = torch.cat([torch.sin(emb), torch.cos(emb)], dim=1) | |
| if embedding_dim % 2 == 1: # zero pad | |
| emb = torch.nn.functional.pad(emb, (0, 1, 0, 0)) | |
| return emb | |
| def nonlinearity(x): | |
| # swish | |
| return x * torch.sigmoid(x) | |
| def Normalize(in_channels, num_groups=32): | |
| return torch.nn.GroupNorm( | |
| num_groups=num_groups, num_channels=in_channels, eps=1e-6, 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 UpsampleTimeStride4(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=5, stride=1, padding=2 | |
| ) | |
| def forward(self, x): | |
| x = torch.nn.functional.interpolate(x, scale_factor=(4.0, 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: | |
| # Do time downsampling here | |
| # no asymmetric padding in torch conv, must do it ourselves | |
| 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 DownsampleTimeStride4(nn.Module): | |
| def __init__(self, in_channels, with_conv): | |
| super().__init__() | |
| self.with_conv = with_conv | |
| if self.with_conv: | |
| # Do time downsampling here | |
| # no asymmetric padding in torch conv, must do it ourselves | |
| self.conv = torch.nn.Conv2d( | |
| in_channels, in_channels, kernel_size=5, stride=(4, 2), padding=1 | |
| ) | |
| 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=(4, 2), stride=(4, 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 LinAttnBlock(LinearAttention): | |
| """to match AttnBlock usage""" | |
| def __init__(self, in_channels): | |
| super().__init__(dim=in_channels, heads=1, dim_head=in_channels) | |
| 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_) | |
| # compute attention | |
| b, c, h, w = q.shape | |
| q = q.reshape(b, c, h * w).contiguous() | |
| q = q.permute(0, 2, 1).contiguous() # b,hw,c | |
| k = k.reshape(b, c, h * w).contiguous() # b,c,hw | |
| w_ = torch.bmm(q, k).contiguous() # b,hw,hw w[b,i,j]=sum_c q[b,i,c]k[b,c,j] | |
| w_ = w_ * (int(c) ** (-0.5)) | |
| w_ = torch.nn.functional.softmax(w_, dim=2) | |
| # attend to values | |
| v = v.reshape(b, c, h * w).contiguous() | |
| w_ = w_.permute(0, 2, 1).contiguous() # b,hw,hw (first hw of k, second of q) | |
| h_ = torch.bmm( | |
| v, w_ | |
| ).contiguous() # 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, w).contiguous() | |
| h_ = self.proj_out(h_) | |
| return x + h_ | |
| def make_attn(in_channels, attn_type="vanilla"): | |
| assert attn_type in ["vanilla", "linear", "none"], f"attn_type {attn_type} unknown" | |
| # print(f"making attention of type '{attn_type}' with {in_channels} in_channels") | |
| if attn_type == "vanilla": | |
| return AttnBlock(in_channels) | |
| elif attn_type == "none": | |
| return nn.Identity(in_channels) | |
| else: | |
| return LinAttnBlock(in_channels) | |
| class Model(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, | |
| use_timestep=True, | |
| use_linear_attn=False, | |
| attn_type="vanilla", | |
| ): | |
| super().__init__() | |
| if use_linear_attn: | |
| attn_type = "linear" | |
| self.ch = ch | |
| self.temb_ch = self.ch * 4 | |
| self.num_resolutions = len(ch_mult) | |
| self.num_res_blocks = num_res_blocks | |
| self.resolution = resolution | |
| self.in_channels = in_channels | |
| self.use_timestep = use_timestep | |
| if self.use_timestep: | |
| # timestep embedding | |
| self.temb = nn.Module() | |
| self.temb.dense = nn.ModuleList( | |
| [ | |
| torch.nn.Linear(self.ch, self.temb_ch), | |
| torch.nn.Linear(self.temb_ch, self.temb_ch), | |
| ] | |
| ) | |
| # downsampling | |
| 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(make_attn(block_in, attn_type=attn_type)) | |
| 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) | |
| # middle | |
| 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 = make_attn(block_in, attn_type=attn_type) | |
| self.mid.block_2 = ResnetBlock( | |
| in_channels=block_in, | |
| out_channels=block_in, | |
| temb_channels=self.temb_ch, | |
| dropout=dropout, | |
| ) | |
| # 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] | |
| skip_in = ch * ch_mult[i_level] | |
| for i_block in range(self.num_res_blocks + 1): | |
| if i_block == self.num_res_blocks: | |
| skip_in = ch * in_ch_mult[i_level] | |
| block.append( | |
| ResnetBlock( | |
| in_channels=block_in + skip_in, | |
| out_channels=block_out, | |
| temb_channels=self.temb_ch, | |
| dropout=dropout, | |
| ) | |
| ) | |
| block_in = block_out | |
| if curr_res in attn_resolutions: | |
| attn.append(make_attn(block_in, attn_type=attn_type)) | |
| 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) # prepend to get consistent order | |
| # end | |
| 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, x, t=None, context=None): | |
| # assert x.shape[2] == x.shape[3] == self.resolution | |
| if context is not None: | |
| # assume aligned context, cat along channel axis | |
| x = torch.cat((x, context), dim=1) | |
| if self.use_timestep: | |
| # timestep embedding | |
| assert t is not None | |
| temb = get_timestep_embedding(t, self.ch) | |
| temb = self.temb.dense[0](temb) | |
| temb = nonlinearity(temb) | |
| temb = self.temb.dense[1](temb) | |
| else: | |
| temb = None | |
| # downsampling | |
| 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])) | |
| # middle | |
| h = hs[-1] | |
| h = self.mid.block_1(h, temb) | |
| h = self.mid.attn_1(h) | |
| h = self.mid.block_2(h, temb) | |
| # upsampling | |
| 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]( | |
| torch.cat([h, hs.pop()], dim=1), 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) | |
| # end | |
| h = self.norm_out(h) | |
| h = nonlinearity(h) | |
| h = self.conv_out(h) | |
| return h | |
| def get_last_layer(self): | |
| return self.conv_out.weight | |
| 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, | |
| use_linear_attn=False, | |
| attn_type="vanilla", | |
| downsample_time_stride4_levels=[], | |
| **ignore_kwargs, | |
| ): | |
| super().__init__() | |
| if use_linear_attn: | |
| attn_type = "linear" | |
| 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.downsample_time_stride4_levels = downsample_time_stride4_levels | |
| if len(self.downsample_time_stride4_levels) > 0: | |
| assert max(self.downsample_time_stride4_levels) < self.num_resolutions, ( | |
| "The level to perform downsample 4 operation need to be smaller than the total resolution number %s" | |
| % str(self.num_resolutions) | |
| ) | |
| # downsampling | |
| 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.in_ch_mult = in_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(make_attn(block_in, attn_type=attn_type)) | |
| down = nn.Module() | |
| down.block = block | |
| down.attn = attn | |
| if i_level != self.num_resolutions - 1: | |
| if i_level in self.downsample_time_stride4_levels: | |
| down.downsample = DownsampleTimeStride4(block_in, resamp_with_conv) | |
| else: | |
| down.downsample = Downsample(block_in, resamp_with_conv) | |
| curr_res = curr_res // 2 | |
| 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, | |
| dropout=dropout, | |
| ) | |
| self.mid.attn_1 = make_attn(block_in, attn_type=attn_type) | |
| self.mid.block_2 = ResnetBlock( | |
| in_channels=block_in, | |
| out_channels=block_in, | |
| temb_channels=self.temb_ch, | |
| dropout=dropout, | |
| ) | |
| # end | |
| 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): | |
| # timestep embedding | |
| temb = None | |
| # downsampling | |
| 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])) | |
| # middle | |
| h = hs[-1] | |
| h = self.mid.block_1(h, temb) | |
| h = self.mid.attn_1(h) | |
| h = self.mid.block_2(h, temb) | |
| # end | |
| 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, | |
| tanh_out=False, | |
| use_linear_attn=False, | |
| downsample_time_stride4_levels=[], | |
| attn_type="vanilla", | |
| **ignorekwargs, | |
| ): | |
| super().__init__() | |
| if use_linear_attn: | |
| attn_type = "linear" | |
| 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 | |
| self.tanh_out = tanh_out | |
| self.downsample_time_stride4_levels = downsample_time_stride4_levels | |
| if len(self.downsample_time_stride4_levels) > 0: | |
| assert max(self.downsample_time_stride4_levels) < self.num_resolutions, ( | |
| "The level to perform downsample 4 operation need to be smaller than the total resolution number %s" | |
| % str(self.num_resolutions) | |
| ) | |
| # compute in_ch_mult, block_in and curr_res at lowest res | |
| 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) | |
| # print("Working with z of shape {} = {} dimensions.".format( | |
| # self.z_shape, np.prod(self.z_shape))) | |
| # z to block_in | |
| self.conv_in = torch.nn.Conv2d( | |
| z_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, | |
| dropout=dropout, | |
| ) | |
| self.mid.attn_1 = make_attn(block_in, attn_type=attn_type) | |
| self.mid.block_2 = ResnetBlock( | |
| in_channels=block_in, | |
| out_channels=block_in, | |
| temb_channels=self.temb_ch, | |
| dropout=dropout, | |
| ) | |
| # 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, | |
| dropout=dropout, | |
| ) | |
| ) | |
| block_in = block_out | |
| if curr_res in attn_resolutions: | |
| attn.append(make_attn(block_in, attn_type=attn_type)) | |
| up = nn.Module() | |
| up.block = block | |
| up.attn = attn | |
| if i_level != 0: | |
| if i_level - 1 in self.downsample_time_stride4_levels: | |
| up.upsample = UpsampleTimeStride4(block_in, resamp_with_conv) | |
| else: | |
| up.upsample = Upsample(block_in, resamp_with_conv) | |
| curr_res = curr_res * 2 | |
| self.up.insert(0, up) # prepend to get consistent order | |
| # end | |
| 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): | |
| # assert z.shape[1:] == self.z_shape[1:] | |
| self.last_z_shape = z.shape | |
| # timestep embedding | |
| temb = None | |
| # z to block_in | |
| h = self.conv_in(z) | |
| # middle | |
| h = self.mid.block_1(h, temb) | |
| h = self.mid.attn_1(h) | |
| h = self.mid.block_2(h, temb) | |
| # upsampling | |
| 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) | |
| # end | |
| if self.give_pre_end: | |
| return h | |
| h = self.norm_out(h) | |
| h = nonlinearity(h) | |
| h = self.conv_out(h) | |
| if self.tanh_out: | |
| h = torch.tanh(h) | |
| return h | |
| class SimpleDecoder(nn.Module): | |
| def __init__(self, in_channels, out_channels, *args, **kwargs): | |
| super().__init__() | |
| self.model = nn.ModuleList( | |
| [ | |
| nn.Conv2d(in_channels, in_channels, 1), | |
| ResnetBlock( | |
| in_channels=in_channels, | |
| out_channels=2 * in_channels, | |
| temb_channels=0, | |
| dropout=0.0, | |
| ), | |
| ResnetBlock( | |
| in_channels=2 * in_channels, | |
| out_channels=4 * in_channels, | |
| temb_channels=0, | |
| dropout=0.0, | |
| ), | |
| ResnetBlock( | |
| in_channels=4 * in_channels, | |
| out_channels=2 * in_channels, | |
| temb_channels=0, | |
| dropout=0.0, | |
| ), | |
| nn.Conv2d(2 * in_channels, in_channels, 1), | |
| Upsample(in_channels, with_conv=True), | |
| ] | |
| ) | |
| # end | |
| self.norm_out = Normalize(in_channels) | |
| self.conv_out = torch.nn.Conv2d( | |
| in_channels, out_channels, kernel_size=3, stride=1, padding=1 | |
| ) | |
| def forward(self, x): | |
| for i, layer in enumerate(self.model): | |
| if i in [1, 2, 3]: | |
| x = layer(x, None) | |
| else: | |
| x = layer(x) | |
| h = self.norm_out(x) | |
| h = nonlinearity(h) | |
| x = self.conv_out(h) | |
| return x | |
| class UpsampleDecoder(nn.Module): | |
| def __init__( | |
| self, | |
| in_channels, | |
| out_channels, | |
| ch, | |
| num_res_blocks, | |
| resolution, | |
| ch_mult=(2, 2), | |
| dropout=0.0, | |
| ): | |
| super().__init__() | |
| # upsampling | |
| self.temb_ch = 0 | |
| self.num_resolutions = len(ch_mult) | |
| self.num_res_blocks = num_res_blocks | |
| block_in = in_channels | |
| curr_res = resolution // 2 ** (self.num_resolutions - 1) | |
| self.res_blocks = nn.ModuleList() | |
| self.upsample_blocks = nn.ModuleList() | |
| for i_level in range(self.num_resolutions): | |
| res_block = [] | |
| block_out = ch * ch_mult[i_level] | |
| for i_block in range(self.num_res_blocks + 1): | |
| res_block.append( | |
| ResnetBlock( | |
| in_channels=block_in, | |
| out_channels=block_out, | |
| temb_channels=self.temb_ch, | |
| dropout=dropout, | |
| ) | |
| ) | |
| block_in = block_out | |
| self.res_blocks.append(nn.ModuleList(res_block)) | |
| if i_level != self.num_resolutions - 1: | |
| self.upsample_blocks.append(Upsample(block_in, True)) | |
| curr_res = curr_res * 2 | |
| # end | |
| self.norm_out = Normalize(block_in) | |
| self.conv_out = torch.nn.Conv2d( | |
| block_in, out_channels, kernel_size=3, stride=1, padding=1 | |
| ) | |
| def forward(self, x): | |
| # upsampling | |
| h = x | |
| for k, i_level in enumerate(range(self.num_resolutions)): | |
| for i_block in range(self.num_res_blocks + 1): | |
| h = self.res_blocks[i_level][i_block](h, None) | |
| if i_level != self.num_resolutions - 1: | |
| h = self.upsample_blocks[k](h) | |
| h = self.norm_out(h) | |
| h = nonlinearity(h) | |
| h = self.conv_out(h) | |
| return h | |
| class LatentRescaler(nn.Module): | |
| def __init__(self, factor, in_channels, mid_channels, out_channels, depth=2): | |
| super().__init__() | |
| # residual block, interpolate, residual block | |
| self.factor = factor | |
| self.conv_in = nn.Conv2d( | |
| in_channels, mid_channels, kernel_size=3, stride=1, padding=1 | |
| ) | |
| self.res_block1 = nn.ModuleList( | |
| [ | |
| ResnetBlock( | |
| in_channels=mid_channels, | |
| out_channels=mid_channels, | |
| temb_channels=0, | |
| dropout=0.0, | |
| ) | |
| for _ in range(depth) | |
| ] | |
| ) | |
| self.attn = AttnBlock(mid_channels) | |
| self.res_block2 = nn.ModuleList( | |
| [ | |
| ResnetBlock( | |
| in_channels=mid_channels, | |
| out_channels=mid_channels, | |
| temb_channels=0, | |
| dropout=0.0, | |
| ) | |
| for _ in range(depth) | |
| ] | |
| ) | |
| self.conv_out = nn.Conv2d( | |
| mid_channels, | |
| out_channels, | |
| kernel_size=1, | |
| ) | |
| def forward(self, x): | |
| x = self.conv_in(x) | |
| for block in self.res_block1: | |
| x = block(x, None) | |
| x = torch.nn.functional.interpolate( | |
| x, | |
| size=( | |
| int(round(x.shape[2] * self.factor)), | |
| int(round(x.shape[3] * self.factor)), | |
| ), | |
| ) | |
| x = self.attn(x).contiguous() | |
| for block in self.res_block2: | |
| x = block(x, None) | |
| x = self.conv_out(x) | |
| return x | |
| class MergedRescaleEncoder(nn.Module): | |
| def __init__( | |
| self, | |
| in_channels, | |
| ch, | |
| resolution, | |
| out_ch, | |
| num_res_blocks, | |
| attn_resolutions, | |
| dropout=0.0, | |
| resamp_with_conv=True, | |
| ch_mult=(1, 2, 4, 8), | |
| rescale_factor=1.0, | |
| rescale_module_depth=1, | |
| ): | |
| super().__init__() | |
| intermediate_chn = ch * ch_mult[-1] | |
| self.encoder = Encoder( | |
| in_channels=in_channels, | |
| num_res_blocks=num_res_blocks, | |
| ch=ch, | |
| ch_mult=ch_mult, | |
| z_channels=intermediate_chn, | |
| double_z=False, | |
| resolution=resolution, | |
| attn_resolutions=attn_resolutions, | |
| dropout=dropout, | |
| resamp_with_conv=resamp_with_conv, | |
| out_ch=None, | |
| ) | |
| self.rescaler = LatentRescaler( | |
| factor=rescale_factor, | |
| in_channels=intermediate_chn, | |
| mid_channels=intermediate_chn, | |
| out_channels=out_ch, | |
| depth=rescale_module_depth, | |
| ) | |
| def forward(self, x): | |
| x = self.encoder(x) | |
| x = self.rescaler(x) | |
| return x | |
| class MergedRescaleDecoder(nn.Module): | |
| def __init__( | |
| self, | |
| z_channels, | |
| out_ch, | |
| resolution, | |
| num_res_blocks, | |
| attn_resolutions, | |
| ch, | |
| ch_mult=(1, 2, 4, 8), | |
| dropout=0.0, | |
| resamp_with_conv=True, | |
| rescale_factor=1.0, | |
| rescale_module_depth=1, | |
| ): | |
| super().__init__() | |
| tmp_chn = z_channels * ch_mult[-1] | |
| self.decoder = Decoder( | |
| out_ch=out_ch, | |
| z_channels=tmp_chn, | |
| attn_resolutions=attn_resolutions, | |
| dropout=dropout, | |
| resamp_with_conv=resamp_with_conv, | |
| in_channels=None, | |
| num_res_blocks=num_res_blocks, | |
| ch_mult=ch_mult, | |
| resolution=resolution, | |
| ch=ch, | |
| ) | |
| self.rescaler = LatentRescaler( | |
| factor=rescale_factor, | |
| in_channels=z_channels, | |
| mid_channels=tmp_chn, | |
| out_channels=tmp_chn, | |
| depth=rescale_module_depth, | |
| ) | |
| def forward(self, x): | |
| x = self.rescaler(x) | |
| x = self.decoder(x) | |
| return x | |
| class Upsampler(nn.Module): | |
| def __init__(self, in_size, out_size, in_channels, out_channels, ch_mult=2): | |
| super().__init__() | |
| assert out_size >= in_size | |
| num_blocks = int(np.log2(out_size // in_size)) + 1 | |
| factor_up = 1.0 + (out_size % in_size) | |
| print( | |
| f"Building {self.__class__.__name__} with in_size: {in_size} --> out_size {out_size} and factor {factor_up}" | |
| ) | |
| self.rescaler = LatentRescaler( | |
| factor=factor_up, | |
| in_channels=in_channels, | |
| mid_channels=2 * in_channels, | |
| out_channels=in_channels, | |
| ) | |
| self.decoder = Decoder( | |
| out_ch=out_channels, | |
| resolution=out_size, | |
| z_channels=in_channels, | |
| num_res_blocks=2, | |
| attn_resolutions=[], | |
| in_channels=None, | |
| ch=in_channels, | |
| ch_mult=[ch_mult for _ in range(num_blocks)], | |
| ) | |
| def forward(self, x): | |
| x = self.rescaler(x) | |
| x = self.decoder(x) | |
| return x | |
| class Resize(nn.Module): | |
| def __init__(self, in_channels=None, learned=False, mode="bilinear"): | |
| super().__init__() | |
| self.with_conv = learned | |
| self.mode = mode | |
| if self.with_conv: | |
| print( | |
| f"Note: {self.__class__.__name} uses learned downsampling and will ignore the fixed {mode} mode" | |
| ) | |
| raise NotImplementedError() | |
| assert in_channels is not None | |
| # no asymmetric padding in torch conv, must do it ourselves | |
| self.conv = torch.nn.Conv2d( | |
| in_channels, in_channels, kernel_size=4, stride=2, padding=1 | |
| ) | |
| def forward(self, x, scale_factor=1.0): | |
| if scale_factor == 1.0: | |
| return x | |
| else: | |
| x = torch.nn.functional.interpolate( | |
| x, mode=self.mode, align_corners=False, scale_factor=scale_factor | |
| ) | |
| return x | |
| class FirstStagePostProcessor(nn.Module): | |
| def __init__( | |
| self, | |
| ch_mult: list, | |
| in_channels, | |
| pretrained_model: nn.Module = None, | |
| reshape=False, | |
| n_channels=None, | |
| dropout=0.0, | |
| pretrained_config=None, | |
| ): | |
| super().__init__() | |
| if pretrained_config is None: | |
| assert ( | |
| pretrained_model is not None | |
| ), 'Either "pretrained_model" or "pretrained_config" must not be None' | |
| self.pretrained_model = pretrained_model | |
| else: | |
| assert ( | |
| pretrained_config is not None | |
| ), 'Either "pretrained_model" or "pretrained_config" must not be None' | |
| self.instantiate_pretrained(pretrained_config) | |
| self.do_reshape = reshape | |
| if n_channels is None: | |
| n_channels = self.pretrained_model.encoder.ch | |
| self.proj_norm = Normalize(in_channels, num_groups=in_channels // 2) | |
| self.proj = nn.Conv2d( | |
| in_channels, n_channels, kernel_size=3, stride=1, padding=1 | |
| ) | |
| blocks = [] | |
| downs = [] | |
| ch_in = n_channels | |
| for m in ch_mult: | |
| blocks.append( | |
| ResnetBlock( | |
| in_channels=ch_in, out_channels=m * n_channels, dropout=dropout | |
| ) | |
| ) | |
| ch_in = m * n_channels | |
| downs.append(Downsample(ch_in, with_conv=False)) | |
| self.model = nn.ModuleList(blocks) | |
| self.downsampler = nn.ModuleList(downs) | |
| def instantiate_pretrained(self, config): | |
| model = instantiate_from_config(config) | |
| self.pretrained_model = model.eval() | |
| # self.pretrained_model.train = False | |
| for param in self.pretrained_model.parameters(): | |
| param.requires_grad = False | |
| def encode_with_pretrained(self, x): | |
| c = self.pretrained_model.encode(x) | |
| if isinstance(c, DiagonalGaussianDistribution): | |
| c = c.mode() | |
| return c | |
| def forward(self, x): | |
| z_fs = self.encode_with_pretrained(x) | |
| z = self.proj_norm(z_fs) | |
| z = self.proj(z) | |
| z = nonlinearity(z) | |
| for submodel, downmodel in zip(self.model, self.downsampler): | |
| z = submodel(z, temb=None) | |
| z = downmodel(z) | |
| if self.do_reshape: | |
| z = rearrange(z, "b c h w -> b (h w) c") | |
| return z | |