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333 lines
10 KiB
Python
333 lines
10 KiB
Python
"""Classes for video sequence transformation"""
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import logging
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from typing import Optional
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import torch
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from einops import rearrange, repeat
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from torch import nn
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from imaginairy.modules.attention import XFORMERS_IS_AVAILABLE
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from imaginairy.modules.sgm.attention import (
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CrossAttention,
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FeedForward,
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MemoryEfficientCrossAttention,
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SpatialTransformer,
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)
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from imaginairy.modules.sgm.diffusionmodules.util import (
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AlphaBlender,
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checkpoint,
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linear,
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timestep_embedding,
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)
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from imaginairy.utils import exists
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logger = logging.getLogger(__name__)
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class TimeMixSequential(nn.Sequential):
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def forward(self, x, context=None, timesteps=None):
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for layer in self:
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x = layer(x, context, timesteps)
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return x
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class VideoTransformerBlock(nn.Module):
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ATTENTION_MODES = {
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"softmax": CrossAttention,
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"softmax-xformers": MemoryEfficientCrossAttention,
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}
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def __init__(
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self,
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dim,
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n_heads,
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d_head,
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dropout=0.0,
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context_dim=None,
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gated_ff=True,
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checkpoint=False,
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timesteps=None,
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ff_in=False,
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inner_dim=None,
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attn_mode="softmax",
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disable_self_attn=False,
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disable_temporal_crossattention=False,
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switch_temporal_ca_to_sa=False,
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):
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super().__init__()
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if not XFORMERS_IS_AVAILABLE and attn_mode == "softmax-xformers":
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logger.debug(
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f"Attention mode '{attn_mode}' is not available. Falling back to vanilla attention. "
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f"This is not a problem in Pytorch >= 2.0. FYI, you are running with PyTorch version {torch.__version__}"
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)
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attn_mode = "softmax"
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attn_cls = self.ATTENTION_MODES[attn_mode]
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self.ff_in = ff_in or inner_dim is not None
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if inner_dim is None:
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inner_dim = dim
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assert int(n_heads * d_head) == inner_dim
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self.is_res = inner_dim == dim
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if self.ff_in:
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self.norm_in = nn.LayerNorm(dim)
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self.ff_in = FeedForward(
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dim, dim_out=inner_dim, dropout=dropout, glu=gated_ff
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)
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self.timesteps = timesteps
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self.disable_self_attn = disable_self_attn
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if self.disable_self_attn:
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self.attn1 = attn_cls(
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query_dim=inner_dim,
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heads=n_heads,
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dim_head=d_head,
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context_dim=context_dim,
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dropout=dropout,
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) # is a cross-attention
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else:
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self.attn1 = attn_cls(
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query_dim=inner_dim, heads=n_heads, dim_head=d_head, dropout=dropout
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) # is a self-attention
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self.ff = FeedForward(inner_dim, dim_out=dim, dropout=dropout, glu=gated_ff)
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if disable_temporal_crossattention:
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if switch_temporal_ca_to_sa:
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raise ValueError
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else:
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self.attn2 = None
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else:
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self.norm2 = nn.LayerNorm(inner_dim)
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if switch_temporal_ca_to_sa:
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self.attn2 = attn_cls(
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query_dim=inner_dim, heads=n_heads, dim_head=d_head, dropout=dropout
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) # is a self-attention
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else:
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self.attn2 = attn_cls(
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query_dim=inner_dim,
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context_dim=context_dim,
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heads=n_heads,
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dim_head=d_head,
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dropout=dropout,
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) # is self-attn if context is none
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self.norm1 = nn.LayerNorm(inner_dim)
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self.norm3 = nn.LayerNorm(inner_dim)
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self.switch_temporal_ca_to_sa = switch_temporal_ca_to_sa
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self.checkpoint = checkpoint
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if self.checkpoint:
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print(f"{self.__class__.__name__} is using checkpointing")
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def forward(
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self,
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x: torch.Tensor,
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context: torch.Tensor = None,
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timesteps: Optional[int] = None,
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) -> torch.Tensor:
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if self.checkpoint:
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return checkpoint(self._forward, x, context, timesteps)
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else:
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return self._forward(x, context, timesteps=timesteps)
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def _forward(self, x, context=None, timesteps=None):
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assert self.timesteps or timesteps
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assert not (self.timesteps and timesteps) or self.timesteps == timesteps
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timesteps = self.timesteps or timesteps
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B, S, C = x.shape
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x = rearrange(x, "(b t) s c -> (b s) t c", t=timesteps)
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if self.ff_in:
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x_skip = x
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x = self.ff_in(self.norm_in(x))
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if self.is_res:
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x += x_skip
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if self.disable_self_attn:
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x = self.attn1(self.norm1(x), context=context) + x
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else:
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x = self.attn1(self.norm1(x)) + x
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if self.attn2 is not None:
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if self.switch_temporal_ca_to_sa:
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x = self.attn2(self.norm2(x)) + x
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else:
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x = self.attn2(self.norm2(x), context=context) + x
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x_skip = x
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x = self.ff(self.norm3(x))
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if self.is_res:
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x += x_skip
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x = rearrange(
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x, "(b s) t c -> (b t) s c", s=S, b=B // timesteps, c=C, t=timesteps
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)
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return x
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def get_last_layer(self):
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return self.ff.net[-1].weight
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class SpatialVideoTransformer(SpatialTransformer):
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def __init__(
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self,
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in_channels,
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n_heads,
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d_head,
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depth=1,
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dropout=0.0,
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use_linear=False,
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context_dim=None,
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use_spatial_context=False,
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timesteps=None,
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merge_strategy: str = "fixed",
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merge_factor: float = 0.5,
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time_context_dim=None,
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ff_in=False,
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checkpoint=False,
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time_depth=1,
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attn_mode="softmax",
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disable_self_attn=False,
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disable_temporal_crossattention=False,
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max_time_embed_period: int = 10000,
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):
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super().__init__(
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in_channels,
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n_heads,
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d_head,
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depth=depth,
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dropout=dropout,
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attn_type=attn_mode,
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use_checkpoint=checkpoint,
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context_dim=context_dim,
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use_linear=use_linear,
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disable_self_attn=disable_self_attn,
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)
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self.time_depth = time_depth
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self.depth = depth
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self.max_time_embed_period = max_time_embed_period
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time_mix_d_head = d_head
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n_time_mix_heads = n_heads
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time_mix_inner_dim = int(time_mix_d_head * n_time_mix_heads)
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inner_dim = n_heads * d_head
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if use_spatial_context:
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time_context_dim = context_dim
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self.time_stack = nn.ModuleList(
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[
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VideoTransformerBlock(
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inner_dim,
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n_time_mix_heads,
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time_mix_d_head,
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dropout=dropout,
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context_dim=time_context_dim,
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timesteps=timesteps,
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checkpoint=checkpoint,
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ff_in=ff_in,
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inner_dim=time_mix_inner_dim,
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attn_mode=attn_mode,
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disable_self_attn=disable_self_attn,
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disable_temporal_crossattention=disable_temporal_crossattention,
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)
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for _ in range(self.depth)
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]
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)
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assert len(self.time_stack) == len(self.transformer_blocks)
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self.use_spatial_context = use_spatial_context
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self.in_channels = in_channels
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time_embed_dim = self.in_channels * 4
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self.time_pos_embed = nn.Sequential(
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linear(self.in_channels, time_embed_dim),
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nn.SiLU(),
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linear(time_embed_dim, self.in_channels),
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)
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self.time_mixer = AlphaBlender(
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alpha=merge_factor, merge_strategy=merge_strategy
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)
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def forward(
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self,
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x: torch.Tensor,
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context: Optional[torch.Tensor] = None,
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time_context: Optional[torch.Tensor] = None,
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timesteps: Optional[int] = None,
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image_only_indicator: Optional[torch.Tensor] = None,
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) -> torch.Tensor:
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_, _, h, w = x.shape
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x_in = x
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spatial_context = None
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if exists(context):
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spatial_context = context
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if self.use_spatial_context:
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assert (
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context.ndim == 3
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), f"n dims of spatial context should be 3 but are {context.ndim}"
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time_context = context
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time_context_first_timestep = time_context[::timesteps]
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time_context = repeat(
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time_context_first_timestep, "b ... -> (b n) ...", n=h * w
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)
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elif time_context is not None and not self.use_spatial_context:
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time_context = repeat(time_context, "b ... -> (b n) ...", n=h * w)
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if time_context.ndim == 2:
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time_context = rearrange(time_context, "b c -> b 1 c")
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x = self.norm(x)
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if not self.use_linear:
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x = self.proj_in(x)
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x = rearrange(x, "b c h w -> b (h w) c")
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if self.use_linear:
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x = self.proj_in(x)
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num_frames = torch.arange(timesteps, device=x.device)
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num_frames = repeat(num_frames, "t -> b t", b=x.shape[0] // timesteps)
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num_frames = rearrange(num_frames, "b t -> (b t)")
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t_emb = timestep_embedding(
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num_frames,
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self.in_channels,
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repeat_only=False,
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max_period=self.max_time_embed_period,
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)
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emb = self.time_pos_embed(t_emb)
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emb = emb[:, None, :]
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for it_, (block, mix_block) in enumerate(
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zip(self.transformer_blocks, self.time_stack)
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):
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x = block(
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x,
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context=spatial_context,
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)
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x_mix = x
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x_mix = x_mix + emb
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x_mix = mix_block(x_mix, context=time_context, timesteps=timesteps)
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x = self.time_mixer(
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x_spatial=x,
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x_temporal=x_mix,
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image_only_indicator=image_only_indicator,
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)
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if self.use_linear:
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x = self.proj_out(x)
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x = rearrange(x, "b (h w) c -> b c h w", h=h, w=w)
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if not self.use_linear:
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x = self.proj_out(x)
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out = x + x_in
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return out
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