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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
@torch.no_grad()
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
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