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import torch
from torch import nn
from torch.utils import checkpoint
from .utils import conv_nd, apply_initialization
from .openaimodel import Upsample, Downsample
class TimeEmbedLayer(nn.Module):
def __init__(
self,
base_channels,
time_embed_channels,
linear_init_mode="0"
):
super(TimeEmbedLayer, self).__init__()
self.layer = nn.Sequential(
nn.Linear(base_channels, time_embed_channels),
nn.SiLU(),
nn.Linear(time_embed_channels, time_embed_channels),
)
self.linear_init_mode = linear_init_mode
def forward(self, x):
return self.layer(x)
def reset_parameters(self):
apply_initialization(self.layer[0], linear_mode=self.linear_init_mode)
apply_initialization(self.layer[2], linear_mode=self.linear_init_mode)
class TimeEmbedResBlock(nn.Module):
r"""
Code is adapted from https://github.com/CompVis/stable-diffusion/blob/21f890f9da3cfbeaba8e2ac3c425ee9e998d5229/ldm/modules/diffusionmodules/openaimodel.py
Modifications:
1. Change GroupNorm32 to use arbitrary `num_groups`.
2. Add method `self.reset_parameters()`.
3. Use gradient checkpoint from PyTorch instead of the stable diffusion implementation https://github.com/CompVis/stable-diffusion/blob/21f890f9da3cfbeaba8e2ac3c425ee9e998d5229/ldm/modules/diffusionmodules/util.py#L102.
4. If no input time embed, it degrades to res block.
"""
def __init__(
self,
channels,
dropout,
emb_channels=None,
out_channels=None,
use_conv=False,
use_embed=True,
use_scale_shift_norm=False,
dims=2,
use_checkpoint=False,
up=False,
down=False,
norm_groups=32,
):
r"""
Parameters
----------
channels
dropout
emb_channels
out_channels
use_conv
use_embed: bool
include `emb` as input in `self.forward()`
use_scale_shift_norm: bool
take effect only when `use_embed == True`
dims
use_checkpoint
up
down
norm_groups
"""
super().__init__()
self.channels = channels
self.dropout = dropout
self.use_embed = use_embed
if use_embed:
assert isinstance(emb_channels, int)
self.emb_channels = emb_channels
self.out_channels = out_channels or channels
self.use_conv = use_conv
if use_checkpoint:
warnings.warn("use_checkpoint is not supported yet.")
use_checkpoint = False
self.use_checkpoint = use_checkpoint
self.use_scale_shift_norm = use_scale_shift_norm
self.in_layers = nn.Sequential(
nn.GroupNorm(num_groups=norm_groups if channels % norm_groups == 0 else channels,
num_channels=channels),
nn.SiLU(),
conv_nd(dims, channels, self.out_channels, 3, padding=1),
)
self.updown = up or down
if up:
self.h_upd = Upsample(channels, False, dims)
self.x_upd = Upsample(channels, False, dims)
elif down:
self.h_upd = Downsample(channels, False, dims)
self.x_upd = Downsample(channels, False, dims)
else:
self.h_upd = self.x_upd = nn.Identity()
if use_embed:
self.emb_layers = nn.Sequential(
nn.SiLU(),
nn.Linear(
in_features=emb_channels,
out_features=2 * self.out_channels if use_scale_shift_norm else self.out_channels,
),
)
self.out_layers = nn.Sequential(
nn.GroupNorm(num_groups=norm_groups if self.out_channels % norm_groups == 0 else self.out_channels,
num_channels=self.out_channels),
nn.SiLU(),
nn.Dropout(p=dropout),
conv_nd(dims, self.out_channels, self.out_channels, 3, padding=1),
)
if self.out_channels == channels:
self.skip_connection = nn.Identity()
elif use_conv:
self.skip_connection = conv_nd(
dims, channels, self.out_channels, 3, padding=1
)
else:
self.skip_connection = conv_nd(dims, channels, self.out_channels, 1)
self.reset_parameters()
def forward(self, x, emb=None):
"""
Apply the block to a Tensor, conditioned on a timestep embedding.
Parameters
----------
x: an [N x C x ...] Tensor of features.
emb: an [N x emb_channels] Tensor of timestep embeddings.
Returns
-------
out: an [N x C x ...] Tensor of outputs.
"""
if self.updown:
in_rest, in_conv = self.in_layers[:-1], self.in_layers[-1]
h = in_rest(x)
h = self.h_upd(h)
x = self.x_upd(x)
h = in_conv(h)
else:
h = self.in_layers(x)
if self.use_embed:
emb_out = self.emb_layers(emb).type(h.dtype)
while len(emb_out.shape) < len(h.shape):
emb_out = emb_out[..., None]
if self.use_scale_shift_norm:
out_norm, out_rest = self.out_layers[0], self.out_layers[1:]
scale, shift = torch.chunk(emb_out, 2, dim=1)
h = out_norm(h) * (1 + scale) + shift
h = out_rest(h)
else:
h = h + emb_out
h = self.out_layers(h)
else:
h = self.out_layers(h)
return self.skip_connection(x) + h
def reset_parameters(self):
for m in self.modules():
apply_initialization(m)
for p in self.out_layers[-1].parameters():
nn.init.zeros_(p)
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