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from functools import partial
from typing import List, Optional, Union
from einops import rearrange, repeat
import copy
from ...modules.diffusionmodules.openaimodel import *
from ...modules.video_attention import SpatialVideoTransformer
from ...modules.diffusionmodules.model import FaceLocator
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,
skip_time: 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",
)
self.skip_time = skip_time
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)
if self.skip_time:
return x
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_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,
fine_tuning_method: str = None,
unfreeze_blocks: Optional[List[str]] = None,
adapter_kwargs: Optional[dict] = {},
audio_cond_method: str = None,
audio_dim: Optional[int] = 0,
additional_audio_frames: Optional[int] = 0,
skip_time: bool = False,
use_ada_aug: bool = False,
encode_landmarks: bool = False,
reference_to: str = None,
):
super().__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.additional_audio_frames = additional_audio_frames
audio_multiplier = additional_audio_frames * 2 + 1
audio_dim = audio_dim * audio_multiplier
self.audio_is_context = "both" in audio_cond_method
if "both" == audio_cond_method:
audio_cond_method = "to_time_emb_image"
elif "both_keyframes" == audio_cond_method:
audio_cond_method = "to_time_emb"
if "to_time_emb" in audio_cond_method:
adm_in_channels += audio_dim
print(adm_in_channels, audio_dim, audio_cond_method)
self.adapter = None
self.audio_cond_method = audio_cond_method
self.in_channels = in_channels
self.model_channels = model_channels
self.out_channels = out_channels
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),
)
self.use_ada_aug = use_ada_aug
if use_ada_aug:
self.map_aug = linear(9, 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":
if adm_in_channels > 0:
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:
# Disabling the label embedding
self.num_classes = None
else:
raise ValueError()
self.encode_landmarks = encode_landmarks
if encode_landmarks:
self.face_locator = FaceLocator(
320, conditioning_channels=3, block_out_channels=(16, 32, 96, 256)
)
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,
audio_context_dim=None,
):
return SpatialVideoTransformer(
ch,
num_heads,
dim_head,
depth=depth,
context_dim=context_dim,
audio_context_dim=audio_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,
skip_time=skip_time,
reference_to=reference_to,
)
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,
skip_time=skip_time,
)
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,
audio_context_dim=audio_dim
if "cross_attention" in audio_cond_method
else None,
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,
audio_context_dim=audio_dim
if "new_cross_attention" in audio_cond_method
else None,
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,
audio_context_dim=audio_dim
if "new_cross_attention" == audio_cond_method
else None,
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)),
)
if fine_tuning_method is not None:
# Freeze everything except the adapter
for param in self.parameters():
param.requires_grad = False
if self.adapter is not None:
for param in self.adapter.parameters():
param.requires_grad = True
if len(unfreeze_blocks):
if "input" in unfreeze_blocks:
for param in self.input_blocks[0].parameters():
param.requires_grad = True
# break # only unfreeze the first input block
if "label_emb" in unfreeze_blocks:
for param in self.label_emb.parameters():
param.requires_grad = True
def get_skip_attention_at(
self,
skip_attention_at: List[int],
curr_layer: int,
batch_size: int,
num_video_frames: int,
):
if skip_attention_at is None:
return None
skip_attention = th.zeros(len(skip_attention_at), 1, dtype=th.bool)
for i, layer in enumerate(skip_attention_at):
skip_attention[i] = layer == curr_layer
skip_attention = repeat(
skip_attention, "b ... -> (b t) ...", t=num_video_frames
)
assert skip_attention.shape[0] == batch_size, (
f"{skip_attention.shape[0]} != {batch_size}"
)
return skip_attention
def forward(
self,
x: th.Tensor,
timesteps: th.Tensor,
context: Optional[th.Tensor] = None,
reference_context: Optional[th.Tensor] = None,
y: Optional[th.Tensor] = None,
audio_emb: Optional[th.Tensor] = None,
landmarks: Optional[th.Tensor] = None,
aug_labels: Optional[th.Tensor] = None,
time_context: Optional[th.Tensor] = None,
num_video_frames: Optional[int] = 1,
image_only_indicator: Optional[th.Tensor] = None,
skip_spatial_attention_at: Optional[List[int]] = None,
skip_temporal_attention_at: Optional[List[int]] = None,
):
if self.audio_is_context:
assert audio_emb is None
audio_emb = context.clone()
curr_context_idx = 0
num_video_frames = (
num_video_frames
if isinstance(num_video_frames, int)
else num_video_frames[0]
)
if reference_context is not None:
copy_context = copy.deepcopy(reference_context)
mid = copy_context.pop(-1)
copy_context.insert((len(copy_context) // 2) - 1, mid)
reference_context = copy_context
curr_context_idx = 0
if num_video_frames > 1:
reference_context = [
repeat(ref_context, "b h w -> (b t) h w", t=num_video_frames)
for ref_context in reference_context
]
or_batch_size = x.shape[0] // num_video_frames
if (
image_only_indicator is not None
and image_only_indicator.shape[0] != or_batch_size
):
# TODO: fix this
image_only_indicator = repeat(
image_only_indicator, "b ... -> (b t) ...", t=2
)
if context is not None and x.shape[0] != context.shape[0]:
context = repeat(context, "b ... -> b t ...", t=num_video_frames)
context = rearrange(context, "b t ... -> (b t) ...", t=num_video_frames)
if "cross_attention" in self.audio_cond_method:
assert audio_emb is not None
if audio_emb.ndim == 4:
audio_emb = rearrange(audio_emb, "b t d c -> b (t d) c")
# context = th.cat([context, audio_emb], dim=1)
if self.audio_cond_method == "cross_time":
assert audio_emb is not None
time_context = audio_emb
if y is not None and y.shape[0] != x.shape[0]:
y = repeat(y, "b ... -> b t ...", t=num_video_frames)
y = rearrange(y, "b t ... -> (b t) ...", t=num_video_frames)
hs = []
t_emb = timestep_embedding(timesteps, self.model_channels, repeat_only=False)
emb = self.time_embed(t_emb)
if self.num_classes is not None:
assert y is not None or "to_time_emb" in self.audio_cond_method
if self.audio_cond_method == "to_time_emb":
assert audio_emb is not None
audio_emb = rearrange(audio_emb, "b t c -> (b t) c")
if y is not None:
y = th.cat([y, audio_emb], dim=1)
else:
y = audio_emb
elif self.audio_cond_method == "to_time_emb_image":
assert audio_emb is not None
audio_emb = rearrange(audio_emb, "b t c -> b (t c)")
if y is not None:
y = th.cat([y, audio_emb], dim=1)
else:
y = audio_emb
assert y.shape[0] == x.shape[0], (
f"{y.shape} != {x.shape} and audio_emb.shape: {audio_emb.shape}"
)
emb = emb + self.label_emb(y)
if self.use_ada_aug:
assert aug_labels is not None, (
"must provide aug_labels if use_ada_aug is True"
)
emb = emb + self.map_aug(aug_labels)
h = x
if self.encode_landmarks:
landmarks_emb = self.face_locator(landmarks)
landmarks_emb = rearrange(landmarks_emb, "b c t h w -> (b t) c h w")
# print("landmarks_emb:", landmarks_emb.shape)
for i, module in enumerate(self.input_blocks):
# print(image_only_indicator.shape, num_video_frames, h.shape)
if i == 1 and self.encode_landmarks:
h = h + landmarks_emb
# print("h.shape:", h.shape, i)
skip_spatial_attention = self.get_skip_attention_at(
skip_spatial_attention_at,
curr_context_idx,
x.shape[0],
num_video_frames,
)
skip_temporal_attention = self.get_skip_attention_at(
skip_temporal_attention_at,
curr_context_idx,
x.shape[0],
num_video_frames,
)
h, is_attention = module(
h,
emb,
context=context,
reference_context=reference_context[curr_context_idx]
if reference_context is not None
else None,
audio_context=audio_emb
if "cross_attention" in self.audio_cond_method
else None,
image_only_indicator=image_only_indicator,
time_context=time_context,
num_video_frames=num_video_frames,
skip_spatial_attention=skip_spatial_attention,
skip_temporal_attention=skip_temporal_attention,
)
if is_attention:
curr_context_idx = (
None if curr_context_idx is None else curr_context_idx + 1
)
hs.append(h)
skip_spatial_attention = self.get_skip_attention_at(
skip_spatial_attention_at, curr_context_idx, x.shape[0], num_video_frames
)
skip_temporal_attention = self.get_skip_attention_at(
skip_temporal_attention_at, curr_context_idx, x.shape[0], num_video_frames
)
h, is_attention = self.middle_block(
h,
emb,
context=context,
reference_context=reference_context[curr_context_idx]
if reference_context is not None
else None,
audio_context=audio_emb
if "cross_attention" in self.audio_cond_method
else None,
image_only_indicator=image_only_indicator,
time_context=time_context,
num_video_frames=num_video_frames,
skip_spatial_attention=skip_spatial_attention,
skip_temporal_attention=skip_temporal_attention,
)
curr_context_idx = None if curr_context_idx is None else curr_context_idx + 1
for i, module in enumerate(self.output_blocks):
skip_x = hs.pop()
if self.adapter is not None:
skip_x = self.adapter[i](
skip_x, n_frames=num_video_frames, condition=audio_emb
)
h = th.cat([h, skip_x], dim=1)
skip_spatial_attention = self.get_skip_attention_at(
skip_spatial_attention_at,
curr_context_idx,
x.shape[0],
num_video_frames,
)
skip_temporal_attention = self.get_skip_attention_at(
skip_temporal_attention_at,
curr_context_idx,
x.shape[0],
num_video_frames,
)
h, is_attention = module(
h,
emb,
context=context,
reference_context=reference_context[curr_context_idx]
if reference_context is not None
else None,
audio_context=audio_emb
if "cross_attention" in self.audio_cond_method
else None,
image_only_indicator=image_only_indicator,
time_context=time_context,
num_video_frames=num_video_frames,
skip_spatial_attention=skip_spatial_attention,
skip_temporal_attention=skip_temporal_attention,
)
if is_attention:
curr_context_idx = (
None if curr_context_idx is None else curr_context_idx + 1
)
# h = h.type(x.dtype)
return self.out(h)
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