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from functools import partial
from abc import abstractmethod
import torch
import torch.nn as nn
from einops import rearrange
import torch.nn.functional as F
from lvdm.models.utils_diffusion import timestep_embedding
from lvdm.common import checkpoint
from lvdm.basics import (
zero_module,
conv_nd,
linear,
avg_pool_nd,
normalization
)
from lvdm.modules.attention_freenoise import SpatialTransformer, TemporalTransformer
class TimestepBlock(nn.Module):
"""
Any module where forward() takes timestep embeddings as a second argument.
"""
@abstractmethod
def forward(self, x, emb):
"""
Apply the module to `x` given `emb` timestep embeddings.
"""
class TimestepEmbedSequential(nn.Sequential, TimestepBlock):
"""
A sequential module that passes timestep embeddings to the children that
support it as an extra input.
"""
def forward(self, x, emb, context=None, batch_size=None, use_injection=False, **kwargs):
for layer in self:
if isinstance(layer, TimestepBlock):
x = layer(x, emb, batch_size)
elif isinstance(layer, SpatialTransformer):
x = layer(x, context, use_injection=use_injection)
elif isinstance(layer, TemporalTransformer):
x = rearrange(x, '(b f) c h w -> b c f h w', b=batch_size)
x = layer(x, context, **kwargs)
x = rearrange(x, 'b c f h w -> (b f) c h w')
else:
x = layer(x,)
return x
class Downsample(nn.Module):
"""
A downsampling layer with an optional convolution.
:param channels: channels in the inputs and outputs.
:param use_conv: a bool determining if a convolution is applied.
:param dims: determines if the signal is 1D, 2D, or 3D. If 3D, then
downsampling occurs in the inner-two dimensions.
"""
def __init__(self, channels, use_conv, dims=2, out_channels=None, padding=1):
super().__init__()
self.channels = channels
self.out_channels = out_channels or channels
self.use_conv = use_conv
self.dims = dims
stride = 2 if dims != 3 else (1, 2, 2)
if use_conv:
self.op = conv_nd(
dims, self.channels, self.out_channels, 3, stride=stride, padding=padding
)
else:
assert self.channels == self.out_channels
self.op = avg_pool_nd(dims, kernel_size=stride, stride=stride)
def forward(self, x):
assert x.shape[1] == self.channels
return self.op(x)
class Upsample(nn.Module):
"""
An upsampling layer with an optional convolution.
:param channels: channels in the inputs and outputs.
:param use_conv: a bool determining if a convolution is applied.
:param dims: determines if the signal is 1D, 2D, or 3D. If 3D, then
upsampling occurs in the inner-two dimensions.
"""
def __init__(self, channels, use_conv, dims=2, out_channels=None, padding=1):
super().__init__()
self.channels = channels
self.out_channels = out_channels or channels
self.use_conv = use_conv
self.dims = dims
if use_conv:
self.conv = conv_nd(dims, self.channels, self.out_channels, 3, padding=padding)
def forward(self, x):
assert x.shape[1] == self.channels
if self.dims == 3:
x = F.interpolate(x, (x.shape[2], x.shape[3] * 2, x.shape[4] * 2), mode='nearest')
else:
x = F.interpolate(x, scale_factor=2, mode='nearest')
if self.use_conv:
x = self.conv(x)
return x
class ResBlock(TimestepBlock):
"""
A residual block that can optionally change the number of channels.
:param channels: the number of input channels.
:param emb_channels: the number of timestep embedding channels.
:param dropout: the rate of dropout.
:param out_channels: if specified, the number of out channels.
:param use_conv: if True and out_channels is specified, use a spatial
convolution instead of a smaller 1x1 convolution to change the
channels in the skip connection.
:param dims: determines if the signal is 1D, 2D, or 3D.
:param up: if True, use this block for upsampling.
:param down: if True, use this block for downsampling.
"""
def __init__(
self,
channels,
emb_channels,
dropout,
out_channels=None,
use_scale_shift_norm=False,
dims=2,
use_checkpoint=False,
use_conv=False,
up=False,
down=False,
use_temporal_conv=False,
tempspatial_aware=False
):
super().__init__()
self.channels = channels
self.emb_channels = emb_channels
self.dropout = dropout
self.out_channels = out_channels or channels
self.use_conv = use_conv
self.use_checkpoint = use_checkpoint
self.use_scale_shift_norm = use_scale_shift_norm
self.use_temporal_conv = use_temporal_conv
self.in_layers = nn.Sequential(
normalization(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()
self.emb_layers = nn.Sequential(
nn.SiLU(),
nn.Linear(
emb_channels,
2 * self.out_channels if use_scale_shift_norm else self.out_channels,
),
)
self.out_layers = nn.Sequential(
normalization(self.out_channels),
nn.SiLU(),
nn.Dropout(p=dropout),
zero_module(nn.Conv2d(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)
if self.use_temporal_conv:
self.temopral_conv = TemporalConvBlock(
self.out_channels,
self.out_channels,
dropout=0.1,
spatial_aware=tempspatial_aware
)
def forward(self, x, emb, batch_size=None):
"""
Apply the block to a Tensor, conditioned on a timestep embedding.
:param x: an [N x C x ...] Tensor of features.
:param emb: an [N x emb_channels] Tensor of timestep embeddings.
:return: an [N x C x ...] Tensor of outputs.
"""
input_tuple = (x, emb,)
if batch_size:
forward_batchsize = partial(self._forward, batch_size=batch_size)
return checkpoint(forward_batchsize, input_tuple, self.parameters(), self.use_checkpoint)
return checkpoint(self._forward, input_tuple, self.parameters(), self.use_checkpoint)
def _forward(self, x, emb, batch_size=None,):
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)
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)
h = self.skip_connection(x) + h
if self.use_temporal_conv and batch_size:
h = rearrange(h, '(b t) c h w -> b c t h w', b=batch_size)
h = self.temopral_conv(h)
h = rearrange(h, 'b c t h w -> (b t) c h w')
return h
class TemporalConvBlock(nn.Module):
"""
Adapted from modelscope: https://github.com/modelscope/modelscope/blob/master/modelscope/models/multi_modal/video_synthesis/unet_sd.py
"""
def __init__(self, in_channels, out_channels=None, dropout=0.0, spatial_aware=False):
super(TemporalConvBlock, self).__init__()
if out_channels is None:
out_channels = in_channels
self.in_channels = in_channels
self.out_channels = out_channels
kernel_shape = (3, 1, 1) if not spatial_aware else (3, 3, 3)
padding_shape = (1, 0, 0) if not spatial_aware else (1, 1, 1)
# conv layers
self.conv1 = nn.Sequential(
nn.GroupNorm(32, in_channels), nn.SiLU(),
nn.Conv3d(in_channels, out_channels, kernel_shape, padding=padding_shape))
self.conv2 = nn.Sequential(
nn.GroupNorm(32, out_channels), nn.SiLU(), nn.Dropout(dropout),
nn.Conv3d(out_channels, in_channels, kernel_shape, padding=padding_shape))
self.conv3 = nn.Sequential(
nn.GroupNorm(32, out_channels), nn.SiLU(), nn.Dropout(dropout),
nn.Conv3d(out_channels, in_channels, (3, 1, 1), padding=(1, 0, 0)))
self.conv4 = nn.Sequential(
nn.GroupNorm(32, out_channels), nn.SiLU(), nn.Dropout(dropout),
nn.Conv3d(out_channels, in_channels, (3, 1, 1), padding=(1, 0, 0)))
# zero out the last layer params,so the conv block is identity
nn.init.zeros_(self.conv4[-1].weight)
nn.init.zeros_(self.conv4[-1].bias)
def forward(self, x):
identity = x
x = self.conv1(x)
x = self.conv2(x)
x = self.conv3(x)
x = self.conv4(x)
return x + identity
class UNetModel(nn.Module):
"""
The full UNet model with attention and timestep embedding.
:param in_channels: in_channels in the input Tensor.
:param model_channels: base channel count for the model.
:param out_channels: channels in the output Tensor.
:param num_res_blocks: number of residual blocks per downsample.
:param attention_resolutions: a collection of downsample rates at which
attention will take place. May be a set, list, or tuple.
For example, if this contains 4, then at 4x downsampling, attention
will be used.
:param dropout: the dropout probability.
:param channel_mult: channel multiplier for each level of the UNet.
:param conv_resample: if True, use learned convolutions for upsampling and
downsampling.
:param dims: determines if the signal is 1D, 2D, or 3D.
:param num_classes: if specified (as an int), then this model will be
class-conditional with `num_classes` classes.
:param use_checkpoint: use gradient checkpointing to reduce memory usage.
:param num_heads: the number of attention heads in each attention layer.
:param num_heads_channels: if specified, ignore num_heads and instead use
a fixed channel width per attention head.
:param num_heads_upsample: works with num_heads to set a different number
of heads for upsampling. Deprecated.
:param use_scale_shift_norm: use a FiLM-like conditioning mechanism.
:param resblock_updown: use residual blocks for up/downsampling.
"""
def __init__(self,
in_channels,
model_channels,
out_channels,
num_res_blocks,
attention_resolutions,
dropout=0.0,
channel_mult=(1, 2, 4, 8),
conv_resample=True,
dims=2,
context_dim=None,
use_scale_shift_norm=False,
resblock_updown=False,
num_heads=-1,
num_head_channels=-1,
transformer_depth=1,
use_linear=False,
use_checkpoint=False,
temporal_conv=False,
tempspatial_aware=False,
temporal_attention=True,
temporal_selfatt_only=True,
use_relative_position=True,
use_causal_attention=False,
temporal_length=None,
use_fp16=False,
addition_attention=False,
use_image_attention=False,
temporal_transformer_depth=1,
fps_cond=False,
):
super(UNetModel, self).__init__()
if num_heads == -1:
assert num_head_channels != -1, 'Either num_heads or num_head_channels has to be set'
if num_head_channels == -1:
assert num_heads != -1, 'Either num_heads or num_head_channels has to be set'
self.in_channels = in_channels
self.model_channels = model_channels
self.out_channels = out_channels
self.num_res_blocks = num_res_blocks
self.attention_resolutions = attention_resolutions
self.dropout = dropout
self.channel_mult = channel_mult
self.conv_resample = conv_resample
self.temporal_attention = temporal_attention
time_embed_dim = model_channels * 4
self.use_checkpoint = use_checkpoint
self.dtype = torch.float16 if use_fp16 else torch.float32
self.addition_attention=addition_attention
self.use_image_attention = use_image_attention
self.fps_cond=fps_cond
self.time_embed = nn.Sequential(
linear(model_channels, time_embed_dim),
nn.SiLU(),
linear(time_embed_dim, time_embed_dim),
)
if self.fps_cond:
self.fps_embedding = nn.Sequential(
linear(model_channels, time_embed_dim),
nn.SiLU(),
linear(time_embed_dim, time_embed_dim),
)
self.input_blocks = nn.ModuleList(
[
TimestepEmbedSequential(conv_nd(dims, in_channels, model_channels, 3, padding=1))
]
)
if self.addition_attention:
self.init_attn=TimestepEmbedSequential(
TemporalTransformer(
model_channels,
n_heads=8,
d_head=num_head_channels,
depth=transformer_depth,
context_dim=context_dim,
use_checkpoint=use_checkpoint, only_self_att=temporal_selfatt_only,
causal_attention=use_causal_attention, relative_position=use_relative_position,
temporal_length=temporal_length))
input_block_chans = [model_channels]
ch = model_channels
ds = 1
for level, mult in enumerate(channel_mult):
for _ in range(num_res_blocks):
layers = [
ResBlock(ch, time_embed_dim, dropout,
out_channels=mult * model_channels, dims=dims, use_checkpoint=use_checkpoint,
use_scale_shift_norm=use_scale_shift_norm, tempspatial_aware=tempspatial_aware,
use_temporal_conv=temporal_conv
)
]
ch = mult * model_channels
if ds in attention_resolutions:
if num_head_channels == -1:
dim_head = ch // num_heads
else:
num_heads = ch // num_head_channels
dim_head = num_head_channels
layers.append(
SpatialTransformer(ch, num_heads, dim_head,
depth=transformer_depth, context_dim=context_dim, use_linear=use_linear,
use_checkpoint=use_checkpoint, disable_self_attn=False,
img_cross_attention=self.use_image_attention, injection=True
)
)
if self.temporal_attention:
layers.append(
TemporalTransformer(ch, num_heads, dim_head,
depth=temporal_transformer_depth, context_dim=context_dim, use_linear=use_linear,
use_checkpoint=use_checkpoint, only_self_att=temporal_selfatt_only,
causal_attention=use_causal_attention, relative_position=use_relative_position,
temporal_length=temporal_length
)
)
self.input_blocks.append(TimestepEmbedSequential(*layers))
input_block_chans.append(ch)
if level != len(channel_mult) - 1:
out_ch = ch
self.input_blocks.append(
TimestepEmbedSequential(
ResBlock(ch, time_embed_dim, dropout,
out_channels=out_ch, dims=dims, use_checkpoint=use_checkpoint,
use_scale_shift_norm=use_scale_shift_norm,
down=True
)
if resblock_updown
else Downsample(ch, conv_resample, dims=dims, out_channels=out_ch)
)
)
ch = out_ch
input_block_chans.append(ch)
ds *= 2
if num_head_channels == -1:
dim_head = ch // num_heads
else:
num_heads = ch // num_head_channels
dim_head = num_head_channels
layers = [
ResBlock(ch, time_embed_dim, dropout,
dims=dims, use_checkpoint=use_checkpoint,
use_scale_shift_norm=use_scale_shift_norm, tempspatial_aware=tempspatial_aware,
use_temporal_conv=temporal_conv
),
SpatialTransformer(ch, num_heads, dim_head,
depth=transformer_depth, context_dim=context_dim, use_linear=use_linear,
use_checkpoint=use_checkpoint, disable_self_attn=False,
img_cross_attention=self.use_image_attention, injection=True
)
]
if self.temporal_attention:
layers.append(
TemporalTransformer(ch, num_heads, dim_head,
depth=temporal_transformer_depth, context_dim=context_dim, use_linear=use_linear,
use_checkpoint=use_checkpoint, only_self_att=temporal_selfatt_only,
causal_attention=use_causal_attention, relative_position=use_relative_position,
temporal_length=temporal_length
)
)
layers.append(
ResBlock(ch, time_embed_dim, dropout,
dims=dims, use_checkpoint=use_checkpoint,
use_scale_shift_norm=use_scale_shift_norm, tempspatial_aware=tempspatial_aware,
use_temporal_conv=temporal_conv
)
)
self.middle_block = TimestepEmbedSequential(*layers)
self.output_blocks = nn.ModuleList([])
for level, mult in list(enumerate(channel_mult))[::-1]:
for i in range(num_res_blocks + 1):
ich = input_block_chans.pop()
layers = [
ResBlock(ch + ich, time_embed_dim, dropout,
out_channels=mult * model_channels, dims=dims, use_checkpoint=use_checkpoint,
use_scale_shift_norm=use_scale_shift_norm, tempspatial_aware=tempspatial_aware,
use_temporal_conv=temporal_conv
)
]
ch = model_channels * mult
if ds in attention_resolutions:
if num_head_channels == -1:
dim_head = ch // num_heads
else:
num_heads = ch // num_head_channels
dim_head = num_head_channels
layers.append(
SpatialTransformer(ch, num_heads, dim_head,
depth=transformer_depth, context_dim=context_dim, use_linear=use_linear,
use_checkpoint=use_checkpoint, disable_self_attn=False,
img_cross_attention=self.use_image_attention, injection=False
)
)
if self.temporal_attention:
layers.append(
TemporalTransformer(ch, num_heads, dim_head,
depth=temporal_transformer_depth, context_dim=context_dim, use_linear=use_linear,
use_checkpoint=use_checkpoint, only_self_att=temporal_selfatt_only,
causal_attention=use_causal_attention, relative_position=use_relative_position,
temporal_length=temporal_length
)
)
if level and i == num_res_blocks:
out_ch = ch
layers.append(
ResBlock(ch, time_embed_dim, dropout,
out_channels=out_ch, dims=dims, use_checkpoint=use_checkpoint,
use_scale_shift_norm=use_scale_shift_norm,
up=True
)
if resblock_updown
else Upsample(ch, conv_resample, dims=dims, out_channels=out_ch)
)
ds //= 2
self.output_blocks.append(TimestepEmbedSequential(*layers))
self.out = nn.Sequential(
normalization(ch),
nn.SiLU(),
zero_module(conv_nd(dims, model_channels, out_channels, 3, padding=1)),
)
def forward(self, x, timesteps, context=None, features_adapter=None, fps=16, **kwargs):
t_emb = timestep_embedding(timesteps, self.model_channels, repeat_only=False)
emb = self.time_embed(t_emb)
if self.fps_cond:
if type(fps) == int:
fps = torch.full_like(timesteps, fps)
fps_emb = timestep_embedding(fps,self.model_channels, repeat_only=False)
emb += self.fps_embedding(fps_emb)
b,_,t,_,_ = x.shape
## repeat t times for context [(b t) 77 768] & time embedding
if len(context.shape) < 4:
context = context.repeat_interleave(repeats=t, dim=0)
else:
context = context.view(-1, context.shape[2], context.shape[3])
# context = context.repeat_interleave(repeats=t, dim=0)
emb = emb.repeat_interleave(repeats=t, dim=0)
## always in shape (b t) c h w, except for temporal layer
x = rearrange(x, 'b c t h w -> (b t) c h w')
h = x.type(self.dtype)
adapter_idx = 0
hs = []
for id, module in enumerate(self.input_blocks):
h = module(h, emb, context=context, batch_size=b, **kwargs)
if id ==0 and self.addition_attention:
h = self.init_attn(h, emb, context=context, batch_size=b, **kwargs)
## plug-in adapter features
if ((id+1)%3 == 0) and features_adapter is not None:
h = h + features_adapter[adapter_idx]
adapter_idx += 1
hs.append(h)
if features_adapter is not None:
assert len(features_adapter)==adapter_idx, 'Wrong features_adapter'
h = self.middle_block(h, emb, context=context, batch_size=b, **kwargs)
for module in self.output_blocks:
h = torch.cat([h, hs.pop()], dim=1)
h = module(h, emb, context=context, batch_size=b, **kwargs)
h = h.type(x.dtype)
y = self.out(h)
# reshape back to (b c t h w)
y = rearrange(y, '(b t) c h w -> b c t h w', b=b)
return y
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