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# pytorch_diffusion + derived encoder decoder | |
# Removed Pytorch-lightning by Zigang Geng (zigang@mail.ustc.edu.cn) | |
import math | |
import torch | |
import torch.nn as nn | |
import numpy as np | |
from einops import rearrange | |
from ldm.util import instantiate_from_config | |
from ldm.modules.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, default_eps, num_groups=32): | |
if default_eps: | |
return torch.nn.GroupNorm(num_groups=num_groups, num_channels=in_channels, affine=True) | |
else: | |
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, default_eps, force_type_convert, 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.force_type_convert = force_type_convert | |
self.norm1 = Normalize(in_channels, default_eps) | |
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, default_eps) | |
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 | |
if self.force_type_convert: | |
h = self.norm1.float()(h.float()) | |
h = h.half() | |
else: | |
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] | |
if self.force_type_convert: | |
h = self.norm2.float()(h.float()) | |
h = h.half() | |
else: | |
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, default_eps, force_type_convert): | |
super().__init__() | |
self.in_channels = in_channels | |
self.force_type_convert = force_type_convert | |
self.norm = Normalize(in_channels, default_eps) | |
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 | |
if self.force_type_convert: | |
h_ = self.norm.float()(h_.float()) | |
h_ = h_.half() | |
else: | |
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_ | |
def make_attn(in_channels, default_eps, force_type_convert, 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, default_eps, force_type_convert) | |
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.type(self.conv_in.weight.dtype))] | |
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, default_eps=False, force_type_convert=False, 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, | |
default_eps=default_eps, | |
force_type_convert=force_type_convert, | |
temb_channels=self.temb_ch, | |
dropout=dropout)) | |
block_in = block_out | |
if curr_res in attn_resolutions: | |
attn.append(make_attn(block_in, default_eps, force_type_convert, 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, | |
default_eps=default_eps, | |
force_type_convert=force_type_convert, | |
temb_channels=self.temb_ch, | |
dropout=dropout) | |
self.mid.attn_1 = make_attn(block_in, default_eps, force_type_convert=force_type_convert, attn_type=attn_type) | |
self.mid.block_2 = ResnetBlock(in_channels=block_in, | |
out_channels=block_in, | |
default_eps=default_eps, | |
force_type_convert=force_type_convert, | |
temb_channels=self.temb_ch, | |
dropout=dropout) | |
# end | |
self.force_type_convert = force_type_convert | |
self.norm_out = Normalize(block_in, default_eps) | |
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.type(self.conv_in.weight.dtype).to(self.conv_in.weight.device))] | |
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 | |
if self.force_type_convert: | |
h = self.norm_out.float()(h.float()) | |
h = h.half() | |
else: | |
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, default_eps=False, force_type_convert=False, 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, | |
default_eps=default_eps, | |
force_type_convert=force_type_convert, | |
out_channels=block_in, | |
temb_channels=self.temb_ch, | |
dropout=dropout) | |
self.mid.attn_1 = make_attn(block_in, default_eps, force_type_convert, attn_type=attn_type) | |
self.mid.block_2 = ResnetBlock(in_channels=block_in, | |
default_eps=default_eps, | |
force_type_convert=force_type_convert, | |
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, | |
default_eps=default_eps, | |
force_type_convert=force_type_convert, | |
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, default_eps, force_type_convert, 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.force_type_convert = force_type_convert | |
self.norm_out = Normalize(block_in, default_eps) | |
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 | |
if self.force_type_convert: | |
h = self.norm_out.float()(h.float()) | |
h = h.half() | |
else: | |
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.+ (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., | |
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 | |