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import torch
from ..modules.attention import *
from ..modules.diffusionmodules.util import AlphaBlender, linear, timestep_embedding
class TimeMixSequential(nn.Sequential):
def forward(self, x, context=None, timesteps=None):
for layer in self:
x = layer(x, context, timesteps)
return x
class VideoTransformerBlock(nn.Module):
ATTENTION_MODES = {
"softmax": CrossAttention,
"softmax-xformers": MemoryEfficientCrossAttention,
}
def __init__(
self,
dim,
n_heads,
d_head,
dropout=0.0,
context_dim=None,
gated_ff=True,
checkpoint=True,
timesteps=None,
ff_in=False,
inner_dim=None,
attn_mode="softmax",
disable_self_attn=False,
disable_temporal_crossattention=False,
switch_temporal_ca_to_sa=False,
):
super().__init__()
attn_cls = self.ATTENTION_MODES[attn_mode]
self.ff_in = ff_in or inner_dim is not None
if inner_dim is None:
inner_dim = dim
assert int(n_heads * d_head) == inner_dim
self.is_res = inner_dim == dim
if self.ff_in:
self.norm_in = nn.LayerNorm(dim)
self.ff_in = FeedForward(
dim, dim_out=inner_dim, dropout=dropout, glu=gated_ff
)
self.timesteps = timesteps
self.disable_self_attn = disable_self_attn
if self.disable_self_attn:
self.attn1 = attn_cls(
query_dim=inner_dim,
heads=n_heads,
dim_head=d_head,
context_dim=context_dim,
dropout=dropout,
) # is a cross-attention
else:
self.attn1 = attn_cls(
query_dim=inner_dim, heads=n_heads, dim_head=d_head, dropout=dropout
) # is a self-attention
self.ff = FeedForward(inner_dim, dim_out=dim, dropout=dropout, glu=gated_ff)
if disable_temporal_crossattention:
if switch_temporal_ca_to_sa:
raise ValueError
else:
self.attn2 = None
else:
self.norm2 = nn.LayerNorm(inner_dim)
if switch_temporal_ca_to_sa:
self.attn2 = attn_cls(
query_dim=inner_dim, heads=n_heads, dim_head=d_head, dropout=dropout
) # is a self-attention
else:
self.attn2 = attn_cls(
query_dim=inner_dim,
context_dim=context_dim,
heads=n_heads,
dim_head=d_head,
dropout=dropout,
) # is self-attn if context is none
self.norm1 = nn.LayerNorm(inner_dim)
self.norm3 = nn.LayerNorm(inner_dim)
self.switch_temporal_ca_to_sa = switch_temporal_ca_to_sa
self.checkpoint = checkpoint
'''
if self.checkpoint:
print(f"{self.__class__.__name__} is using checkpointing")
'''
def forward(
self, x: torch.Tensor, context: torch.Tensor = None, timesteps: int = None
) -> torch.Tensor:
if self.checkpoint:
return checkpoint(self._forward, x, context, timesteps)
else:
return self._forward(x, context, timesteps=timesteps)
def _forward(self, x, context=None, timesteps=None):
assert self.timesteps or timesteps
assert not (self.timesteps and timesteps) or self.timesteps == timesteps
timesteps = self.timesteps or timesteps
B, S, C = x.shape
x = rearrange(x, "(b t) s c -> (b s) t c", t=timesteps)
if self.ff_in:
x_skip = x
x = self.ff_in(self.norm_in(x))
if self.is_res:
x += x_skip
if self.disable_self_attn:
x = self.attn1(self.norm1(x), context=context) + x
else:
x = self.attn1(self.norm1(x)) + x
if self.attn2 is not None:
if self.switch_temporal_ca_to_sa:
x = self.attn2(self.norm2(x)) + x
else:
x = self.attn2(self.norm2(x), context=context) + x
x_skip = x
x = self.ff(self.norm3(x))
if self.is_res:
x += x_skip
x = rearrange(
x, "(b s) t c -> (b t) s c", s=S, b=B // timesteps, c=C, t=timesteps
)
return x
def get_last_layer(self):
return self.ff.net[-1].weight
class SpatialVideoTransformer(SpatialTransformer):
def __init__(
self,
in_channels,
n_heads,
d_head,
depth=1,
dropout=0.0,
use_linear=False,
context_dim=None,
use_spatial_context=False,
timesteps=None,
merge_strategy: str = "fixed",
merge_factor: float = 0.5,
time_context_dim=None,
ff_in=False,
checkpoint=False,
time_depth=1,
attn_mode="softmax",
disable_self_attn=False,
disable_temporal_crossattention=False,
max_time_embed_period: int = 10000,
):
super().__init__(
in_channels,
n_heads,
d_head,
depth=depth,
dropout=dropout,
attn_type=attn_mode,
use_checkpoint=checkpoint,
context_dim=context_dim,
use_linear=use_linear,
disable_self_attn=disable_self_attn,
)
self.time_depth = time_depth
self.depth = depth
self.max_time_embed_period = max_time_embed_period
time_mix_d_head = d_head
n_time_mix_heads = n_heads
time_mix_inner_dim = int(time_mix_d_head * n_time_mix_heads)
inner_dim = n_heads * d_head
if use_spatial_context:
time_context_dim = context_dim
self.time_stack = nn.ModuleList(
[
VideoTransformerBlock(
inner_dim,
n_time_mix_heads,
time_mix_d_head,
dropout=dropout,
context_dim=time_context_dim,
timesteps=timesteps,
checkpoint=checkpoint,
ff_in=ff_in,
inner_dim=time_mix_inner_dim,
attn_mode=attn_mode,
disable_self_attn=disable_self_attn,
disable_temporal_crossattention=disable_temporal_crossattention,
)
for _ in range(self.depth)
]
)
assert len(self.time_stack) == len(self.transformer_blocks)
self.use_spatial_context = use_spatial_context
self.in_channels = in_channels
time_embed_dim = self.in_channels * 4
self.time_pos_embed = nn.Sequential(
linear(self.in_channels, time_embed_dim),
nn.SiLU(),
linear(time_embed_dim, self.in_channels),
)
self.time_mixer = AlphaBlender(
alpha=merge_factor, merge_strategy=merge_strategy
)
def forward(
self,
x: torch.Tensor,
context: Optional[torch.Tensor] = None,
time_context: Optional[torch.Tensor] = None,
timesteps: Optional[int] = None,
image_only_indicator: Optional[torch.Tensor] = None,
) -> torch.Tensor:
_, _, h, w = x.shape
x_in = x
spatial_context = None
if exists(context):
spatial_context = context
if self.use_spatial_context:
assert (
context.ndim == 3
), f"n dims of spatial context should be 3 but are {context.ndim}"
time_context = context
time_context_first_timestep = time_context[::timesteps]
time_context = repeat(
time_context_first_timestep, "b ... -> (b n) ...", n=h * w
)
elif time_context is not None and not self.use_spatial_context:
time_context = repeat(time_context, "b ... -> (b n) ...", n=h * w)
if time_context.ndim == 2:
time_context = rearrange(time_context, "b c -> b 1 c")
x = self.norm(x)
if not self.use_linear:
x = self.proj_in(x)
x = rearrange(x, "b c h w -> b (h w) c")
if self.use_linear:
x = self.proj_in(x)
num_frames = torch.arange(timesteps, device=x.device)
num_frames = repeat(num_frames, "t -> b t", b=x.shape[0] // timesteps)
num_frames = rearrange(num_frames, "b t -> (b t)")
t_emb = timestep_embedding(
num_frames,
self.in_channels,
repeat_only=False,
max_period=self.max_time_embed_period,
)
emb = self.time_pos_embed(t_emb)
emb = emb[:, None, :]
for it_, (block, mix_block) in enumerate(
zip(self.transformer_blocks, self.time_stack)
):
x = block(
x,
context=spatial_context,
)
x_mix = x
x_mix = x_mix + emb
x_mix = mix_block(x_mix, context=time_context, timesteps=timesteps)
x = self.time_mixer(
x_spatial=x,
x_temporal=x_mix,
image_only_indicator=image_only_indicator,
)
if self.use_linear:
x = self.proj_out(x)
x = rearrange(x, "b (h w) c -> b c h w", h=h, w=w)
if not self.use_linear:
x = self.proj_out(x)
out = x + x_in
return out
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