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from functools import partial
from typing import List, Optional, Union
import torch
from einops import rearrange, repeat
from sgm.modules.attention import checkpoint, exists
from sgm.modules.diffusionmodules.util import timestep_embedding
### VideoUnet #####
def forward_VideoUnet(
self,
x: torch.Tensor,
timesteps: torch.Tensor,
context: Optional[torch.Tensor] = None,
y: Optional[torch.Tensor] = None,
time_context: Optional[torch.Tensor] = None,
num_video_frames: Optional[int] = None,
image_only_indicator: Optional[torch.Tensor] = None,
RT: Optional[torch.Tensor] = None
):
if RT is not None:
context = {'RT': RT, 'context': context}
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"
hs = []
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)
h = x
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 = torch.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)
### VideoTransformerBlock #####
def forward_VideoTransformerBlock(self, x, context, timesteps):
if self.checkpoint:
return checkpoint(self._forward, x, context, timesteps)
else:
return self._forward(x, context, timesteps=timesteps)
def _forward_VideoTransformerBlock_attan2(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 isinstance(context, dict):
RT = context['RT'] # (b, t, 12)
context = context['context']
else:
RT = None
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 RT is not None:
# import pdb; pdb.set_trace()
RT = RT.repeat_interleave(repeats=S, dim=0) # (b*s, t, 12)
x = torch.cat([x, RT], dim=-1)
x = self.cc_projection(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
#### BasicTransformerBlock #####
def _forward_BasicTransformerBlock(
self, x, context=None, additional_tokens=None, n_times_crossframe_attn_in_self=0
):
if isinstance(context, dict):
context = context['context']
x = (
self.attn1(
self.norm1(x),
context=context if self.disable_self_attn else None,
additional_tokens=additional_tokens,
n_times_crossframe_attn_in_self=n_times_crossframe_attn_in_self
if not self.disable_self_attn
else 0,
)
+ x
)
x = (
self.attn2(
self.norm2(x), context=context, additional_tokens=additional_tokens
)
+ x
)
x = self.ff(self.norm3(x)) + x
return x
#### SpatialVideoTransformer #####
def forward_SpatialVideoTransformer(
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
if isinstance(context, dict):
RT = context['RT']
context = context['context']
else:
RT = None
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
if RT is not None:
x_mix = mix_block(x_mix, context={'context': time_context, 'RT': RT}, timesteps=timesteps)
else:
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 |