974 lines
30 KiB
Python
974 lines
30 KiB
Python
# pytorch_diffusion + derived encoder decoder
|
|
import math
|
|
|
|
import numpy as np
|
|
import torch
|
|
import torch.nn as nn
|
|
|
|
|
|
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 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.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_)
|
|
|
|
# compute attention
|
|
b, c, h, w = q.shape
|
|
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_ = 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, w)
|
|
|
|
h_ = self.proj_out(h_)
|
|
|
|
return x + h_
|
|
|
|
|
|
class AttnBlock2_0(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_)
|
|
# output: [1, 512, 64, 64]
|
|
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).transpose()
|
|
# q = q.permute(0, 2, 1) # b,hw,c
|
|
# k = k.reshape(b, c, h * w) # b,c,hw
|
|
q = q.transpose(1, 2)
|
|
k = k.transpose(1, 2)
|
|
v = v.transpose(1, 2)
|
|
# (batch, num_heads, seq_len, head_dim)
|
|
hidden_states = torch.nn.functional.scaled_dot_product_attention(
|
|
q, k, v, attn_mask=None, dropout_p=0.0, is_causal=False
|
|
)
|
|
hidden_states = hidden_states.transpose(1, 2)
|
|
hidden_states = hidden_states.to(q.dtype)
|
|
|
|
h_ = self.proj_out(hidden_states)
|
|
|
|
return x + h_
|
|
|
|
|
|
def make_attn(in_channels, attn_type="vanilla", attn_kwargs=None):
|
|
assert attn_type in [
|
|
"vanilla",
|
|
"vanilla-xformers",
|
|
"memory-efficient-cross-attn",
|
|
"linear",
|
|
"none",
|
|
], f"attn_type {attn_type} unknown"
|
|
assert attn_kwargs is None
|
|
if hasattr(torch.nn.functional, "scaled_dot_product_attention"):
|
|
# print(f"Using torch.nn.functional.scaled_dot_product_attention")
|
|
return AttnBlock2_0(in_channels)
|
|
return AttnBlock(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",
|
|
**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
|
|
|
|
# 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:
|
|
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,
|
|
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
|
|
|
|
# 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:
|
|
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)
|
|
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
|