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from functools import partial
from typing import List, Optional, Union
from einops import rearrange
import torch
from ...modules.diffusionmodules.openaimodel import *
from ...modules.video_attention import SpatialVideoTransformer
from ...util import default
from .util import AlphaBlender
class VideoResBlock(ResBlock):
def __init__(
self,
channels: int,
emb_channels: int,
dropout: float,
video_kernel_size: Union[int, List[int]] = 3,
merge_strategy: str = "fixed",
merge_factor: float = 0.5,
out_channels: Optional[int] = None,
use_conv: bool = False,
use_scale_shift_norm: bool = False,
dims: int = 2,
use_checkpoint: bool = False,
up: bool = False,
down: bool = False,
):
super().__init__(
channels,
emb_channels,
dropout,
out_channels=out_channels,
use_conv=use_conv,
use_scale_shift_norm=use_scale_shift_norm,
dims=dims,
use_checkpoint=use_checkpoint,
up=up,
down=down,
)
self.time_stack = ResBlock(
default(out_channels, channels),
emb_channels,
dropout=dropout,
dims=3,
out_channels=default(out_channels, channels),
use_scale_shift_norm=False,
use_conv=False,
up=False,
down=False,
kernel_size=video_kernel_size,
use_checkpoint=use_checkpoint,
exchange_temb_dims=True,
)
self.time_mixer = AlphaBlender(
alpha=merge_factor,
merge_strategy=merge_strategy,
rearrange_pattern="b t -> b 1 t 1 1",
)
def forward(
self,
x: th.Tensor,
emb: th.Tensor,
num_video_frames: int,
image_only_indicator: Optional[th.Tensor] = None,
) -> th.Tensor:
x = super().forward(x, emb)
x_mix = rearrange(x, "(b t) c h w -> b c t h w", t=num_video_frames)
x = rearrange(x, "(b t) c h w -> b c t h w", t=num_video_frames)
x = self.time_stack(
x, rearrange(emb, "(b t) ... -> b t ...", t=num_video_frames)
)
x = self.time_mixer(
x_spatial=x_mix, x_temporal=x, image_only_indicator=image_only_indicator
)
x = rearrange(x, "b c t h w -> (b t) c h w")
return x
class VideoUNet(nn.Module):
def __init__(
self,
in_channels: int,
model_channels: int,
out_channels: int,
num_frames: int,
num_res_blocks: int,
attention_resolutions: int,
dropout: float = 0.0,
channel_mult: List[int] = (1, 2, 4, 8),
conv_resample: bool = True,
dims: int = 2,
num_classes: Optional[int] = None,
use_checkpoint: bool = False,
num_heads: int = -1,
num_head_channels: int = -1,
num_heads_upsample: int = -1,
use_scale_shift_norm: bool = False,
resblock_updown: bool = False,
transformer_depth: Union[List[int], int] = 1,
transformer_depth_middle: Optional[int] = None,
context_dim: Optional[int] = None,
time_downup: bool = False,
time_context_dim: Optional[int] = None,
extra_ff_mix_layer: bool = False,
use_spatial_context: bool = False,
merge_strategy: str = "fixed",
merge_factor: float = 0.5,
spatial_transformer_attn_type: str = "softmax",
video_kernel_size: Union[int, List[int]] = 3,
use_linear_in_transformer: bool = False,
adm_in_channels: Optional[int] = None,
disable_temporal_crossattention: bool = False,
max_ddpm_temb_period: int = 10000,
):
super(VideoUNet, self).__init__()
assert context_dim is not None
if num_heads_upsample == -1:
num_heads_upsample = num_heads
if num_heads == -1:
assert num_head_channels != -1
if num_head_channels == -1:
assert num_heads != -1
self.in_channels = in_channels
self.model_channels = model_channels
self.out_channels = out_channels
self.num_frames = num_frames
if isinstance(transformer_depth, int):
transformer_depth = len(channel_mult) * [transformer_depth]
transformer_depth_middle = default(
transformer_depth_middle, transformer_depth[-1]
)
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.num_classes = num_classes
self.use_checkpoint = use_checkpoint
self.num_heads = num_heads
self.num_head_channels = num_head_channels
self.num_heads_upsample = num_heads_upsample
time_embed_dim = model_channels * 4
self.time_embed = nn.Sequential(
linear(model_channels, time_embed_dim),
nn.SiLU(),
linear(time_embed_dim, time_embed_dim),
)
if self.num_classes is not None:
if isinstance(self.num_classes, int):
self.label_emb = nn.Embedding(num_classes, time_embed_dim)
elif self.num_classes == "continuous":
print("setting up linear c_adm embedding layer")
self.label_emb = nn.Linear(1, time_embed_dim)
elif self.num_classes == "timestep":
self.label_emb = nn.Sequential(
Timestep(model_channels),
nn.Sequential(
linear(model_channels, time_embed_dim),
nn.SiLU(),
linear(time_embed_dim, time_embed_dim),
),
)
elif self.num_classes == "sequential":
assert adm_in_channels is not None
self.label_emb = nn.Sequential(
nn.Sequential(
linear(adm_in_channels, time_embed_dim),
nn.SiLU(),
linear(time_embed_dim, time_embed_dim),
)
)
else:
raise ValueError()
self.input_blocks = nn.ModuleList(
[
TimestepEmbedSequential(
conv_nd(dims, in_channels, model_channels, 3, padding=1)
)
]
)
self._feature_size = model_channels
input_block_chans = [model_channels]
ch = model_channels
ds = 1
def get_attention_layer(
ch,
num_heads,
dim_head,
depth=1,
context_dim=None,
use_checkpoint=False,
disabled_sa=False,
):
return SpatialVideoTransformer(
ch,
num_heads,
dim_head,
depth=depth,
context_dim=context_dim,
time_context_dim=time_context_dim,
dropout=dropout,
ff_in=extra_ff_mix_layer,
use_spatial_context=use_spatial_context,
merge_strategy=merge_strategy,
merge_factor=merge_factor,
checkpoint=use_checkpoint,
use_linear=use_linear_in_transformer,
attn_mode=spatial_transformer_attn_type,
disable_self_attn=disabled_sa,
disable_temporal_crossattention=disable_temporal_crossattention,
max_time_embed_period=max_ddpm_temb_period,
)
def get_resblock(
merge_factor,
merge_strategy,
video_kernel_size,
ch,
time_embed_dim,
dropout,
out_ch,
dims,
use_checkpoint,
use_scale_shift_norm,
down=False,
up=False,
):
return VideoResBlock(
merge_factor=merge_factor,
merge_strategy=merge_strategy,
video_kernel_size=video_kernel_size,
channels=ch,
emb_channels=time_embed_dim,
dropout=dropout,
out_channels=out_ch,
dims=dims,
use_checkpoint=use_checkpoint,
use_scale_shift_norm=use_scale_shift_norm,
down=down,
up=up,
)
for level, mult in enumerate(channel_mult):
for _ in range(num_res_blocks):
layers = [
get_resblock(
merge_factor=merge_factor,
merge_strategy=merge_strategy,
video_kernel_size=video_kernel_size,
ch=ch,
time_embed_dim=time_embed_dim,
dropout=dropout,
out_ch=mult * model_channels,
dims=dims,
use_checkpoint=use_checkpoint,
use_scale_shift_norm=use_scale_shift_norm,
)
]
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(
get_attention_layer(
ch,
num_heads,
dim_head,
depth=transformer_depth[level],
context_dim=context_dim,
use_checkpoint=use_checkpoint,
disabled_sa=False,
)
)
self.input_blocks.append(TimestepEmbedSequential(*layers))
self._feature_size += ch
input_block_chans.append(ch)
if level != len(channel_mult) - 1:
ds *= 2
out_ch = ch
self.input_blocks.append(
TimestepEmbedSequential(
get_resblock(
merge_factor=merge_factor,
merge_strategy=merge_strategy,
video_kernel_size=video_kernel_size,
ch=ch,
time_embed_dim=time_embed_dim,
dropout=dropout,
out_ch=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,
third_down=time_downup,
)
)
)
ch = out_ch
input_block_chans.append(ch)
self._feature_size += ch
if num_head_channels == -1:
dim_head = ch // num_heads
else:
num_heads = ch // num_head_channels
dim_head = num_head_channels
self.middle_block = TimestepEmbedSequential(
get_resblock(
merge_factor=merge_factor,
merge_strategy=merge_strategy,
video_kernel_size=video_kernel_size,
ch=ch,
time_embed_dim=time_embed_dim,
out_ch=None,
dropout=dropout,
dims=dims,
use_checkpoint=use_checkpoint,
use_scale_shift_norm=use_scale_shift_norm,
),
get_attention_layer(
ch,
num_heads,
dim_head,
depth=transformer_depth_middle,
context_dim=context_dim,
use_checkpoint=use_checkpoint,
),
get_resblock(
merge_factor=merge_factor,
merge_strategy=merge_strategy,
video_kernel_size=video_kernel_size,
ch=ch,
out_ch=None,
time_embed_dim=time_embed_dim,
dropout=dropout,
dims=dims,
use_checkpoint=use_checkpoint,
use_scale_shift_norm=use_scale_shift_norm,
),
)
self._feature_size += ch
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 = [
get_resblock(
merge_factor=merge_factor,
merge_strategy=merge_strategy,
video_kernel_size=video_kernel_size,
ch=ch + ich,
time_embed_dim=time_embed_dim,
dropout=dropout,
out_ch=model_channels * mult,
dims=dims,
use_checkpoint=use_checkpoint,
use_scale_shift_norm=use_scale_shift_norm,
)
]
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(
get_attention_layer(
ch,
num_heads,
dim_head,
depth=transformer_depth[level],
context_dim=context_dim,
use_checkpoint=use_checkpoint,
disabled_sa=False,
)
)
if level and i == num_res_blocks:
out_ch = ch
ds //= 2
layers.append(
get_resblock(
merge_factor=merge_factor,
merge_strategy=merge_strategy,
video_kernel_size=video_kernel_size,
ch=ch,
time_embed_dim=time_embed_dim,
dropout=dropout,
out_ch=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,
third_up=time_downup,
)
)
self.output_blocks.append(TimestepEmbedSequential(*layers))
self._feature_size += ch
self.out = nn.Sequential(
normalization(ch),
nn.SiLU(),
zero_module(conv_nd(dims, model_channels, out_channels, 3, padding=1)),
)
def forward(
self,
x: th.Tensor,
timesteps: th.Tensor,
context: Optional[th.Tensor] = None,
y: Optional[th.Tensor] = None,
time_context: Optional[th.Tensor] = None,
num_video_frames: Optional[int] = None,
image_only_indicator: Optional[th.Tensor] = None,
):
assert (y is not None) == (
self.num_classes is not None
), "must specify y if and only if the model is class-conditional -> no, relax this TODO"
t_emb = timestep_embedding(timesteps, self.model_channels, repeat_only=False)
emb = self.time_embed(t_emb)
## tbd: check the role of "image_only_indicator"
num_video_frames = self.num_frames
image_only_indicator = torch.zeros(
x.shape[0]//num_video_frames, num_video_frames
).to(x.device) if image_only_indicator is None else image_only_indicator
if self.num_classes is not None:
assert y.shape[0] == x.shape[0]
emb = emb + self.label_emb(y)
## x shape: [bt,c,h,w]
h = x
hs = []
for module in self.input_blocks:
h = module(
h,
emb,
context=context,
image_only_indicator=image_only_indicator,
time_context=time_context,
num_video_frames=num_video_frames,
)
hs.append(h)
h = self.middle_block(
h,
emb,
context=context,
image_only_indicator=image_only_indicator,
time_context=time_context,
num_video_frames=num_video_frames,
)
for module in self.output_blocks:
h = th.cat([h, hs.pop()], dim=1)
h = module(
h,
emb,
context=context,
image_only_indicator=image_only_indicator,
time_context=time_context,
num_video_frames=num_video_frames,
)
h = h.type(x.dtype)
return self.out(h)
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