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# Adapt from https://github.com/guoyww/AnimateDiff/blob/main/animatediff/models/motion_module.py
import math
from dataclasses import dataclass
from typing import Callable, Optional
import torch
from diffusers.models.attention import FeedForward
from diffusers.models.attention_processor import Attention, AttnProcessor
from diffusers.utils import BaseOutput
from diffusers.utils.import_utils import is_xformers_available
from einops import rearrange, repeat
from torch import nn
def zero_module(module):
# Zero out the parameters of a module and return it.
for p in module.parameters():
p.detach().zero_()
return module
@dataclass
class TemporalTransformer3DModelOutput(BaseOutput):
sample: torch.FloatTensor
if is_xformers_available():
import xformers
import xformers.ops
else:
xformers = None
def get_motion_module(in_channels, motion_module_type: str, motion_module_kwargs: dict):
if motion_module_type == "Vanilla":
return VanillaTemporalModule(
in_channels=in_channels,
**motion_module_kwargs,
)
else:
raise ValueError
class VanillaTemporalModule(nn.Module):
def __init__(
self,
in_channels,
num_attention_heads=8,
num_transformer_block=2,
attention_block_types=("Temporal_Self", "Temporal_Self"),
cross_frame_attention_mode=None,
temporal_position_encoding=False,
temporal_position_encoding_max_len=24,
temporal_attention_dim_div=1,
zero_initialize=True,
):
super().__init__()
self.temporal_transformer = TemporalTransformer3DModel(
in_channels=in_channels,
num_attention_heads=num_attention_heads,
attention_head_dim=in_channels
// num_attention_heads
// temporal_attention_dim_div,
num_layers=num_transformer_block,
attention_block_types=attention_block_types,
cross_frame_attention_mode=cross_frame_attention_mode,
temporal_position_encoding=temporal_position_encoding,
temporal_position_encoding_max_len=temporal_position_encoding_max_len,
)
if zero_initialize:
self.temporal_transformer.proj_out = zero_module(
self.temporal_transformer.proj_out
)
def forward(
self,
input_tensor,
temb,
encoder_hidden_states,
attention_mask=None,
anchor_frame_idx=None,
):
hidden_states = input_tensor
hidden_states = self.temporal_transformer(
hidden_states, encoder_hidden_states, attention_mask
)
output = hidden_states
return output
class TemporalTransformer3DModel(nn.Module):
def __init__(
self,
in_channels,
num_attention_heads,
attention_head_dim,
num_layers,
attention_block_types=(
"Temporal_Self",
"Temporal_Self",
),
dropout=0.0,
norm_num_groups=32,
cross_attention_dim=768,
activation_fn="geglu",
attention_bias=False,
upcast_attention=False,
cross_frame_attention_mode=None,
temporal_position_encoding=False,
temporal_position_encoding_max_len=24,
):
super().__init__()
inner_dim = num_attention_heads * attention_head_dim
self.norm = torch.nn.GroupNorm(
num_groups=norm_num_groups, num_channels=in_channels, eps=1e-6, affine=True
)
self.proj_in = nn.Linear(in_channels, inner_dim)
self.transformer_blocks = nn.ModuleList(
[
TemporalTransformerBlock(
dim=inner_dim,
num_attention_heads=num_attention_heads,
attention_head_dim=attention_head_dim,
attention_block_types=attention_block_types,
dropout=dropout,
norm_num_groups=norm_num_groups,
cross_attention_dim=cross_attention_dim,
activation_fn=activation_fn,
attention_bias=attention_bias,
upcast_attention=upcast_attention,
cross_frame_attention_mode=cross_frame_attention_mode,
temporal_position_encoding=temporal_position_encoding,
temporal_position_encoding_max_len=temporal_position_encoding_max_len,
)
for d in range(num_layers)
]
)
self.proj_out = nn.Linear(inner_dim, in_channels)
def forward(self, hidden_states, encoder_hidden_states=None, attention_mask=None):
assert (
hidden_states.dim() == 5
), f"Expected hidden_states to have ndim=5, but got ndim={hidden_states.dim()}."
video_length = hidden_states.shape[2]
hidden_states = rearrange(hidden_states, "b c f h w -> (b f) c h w")
batch, channel, height, weight = hidden_states.shape
residual = hidden_states
hidden_states = self.norm(hidden_states)
inner_dim = hidden_states.shape[1]
hidden_states = hidden_states.permute(0, 2, 3, 1).reshape(
batch, height * weight, inner_dim
)
hidden_states = self.proj_in(hidden_states)
# Transformer Blocks
for block in self.transformer_blocks:
hidden_states = block(
hidden_states,
encoder_hidden_states=encoder_hidden_states,
video_length=video_length,
)
# output
hidden_states = self.proj_out(hidden_states)
hidden_states = (
hidden_states.reshape(batch, height, weight, inner_dim)
.permute(0, 3, 1, 2)
.contiguous()
)
output = hidden_states + residual
output = rearrange(output, "(b f) c h w -> b c f h w", f=video_length)
return output
class TemporalTransformerBlock(nn.Module):
def __init__(
self,
dim,
num_attention_heads,
attention_head_dim,
attention_block_types=(
"Temporal_Self",
"Temporal_Self",
),
dropout=0.0,
norm_num_groups=32,
cross_attention_dim=768,
activation_fn="geglu",
attention_bias=False,
upcast_attention=False,
cross_frame_attention_mode=None,
temporal_position_encoding=False,
temporal_position_encoding_max_len=24,
):
super().__init__()
attention_blocks = []
norms = []
for block_name in attention_block_types:
attention_blocks.append(
VersatileAttention(
attention_mode=block_name.split("_")[0],
cross_attention_dim=cross_attention_dim
if block_name.endswith("_Cross")
else None,
query_dim=dim,
heads=num_attention_heads,
dim_head=attention_head_dim,
dropout=dropout,
bias=attention_bias,
upcast_attention=upcast_attention,
cross_frame_attention_mode=cross_frame_attention_mode,
temporal_position_encoding=temporal_position_encoding,
temporal_position_encoding_max_len=temporal_position_encoding_max_len,
)
)
norms.append(nn.LayerNorm(dim))
self.attention_blocks = nn.ModuleList(attention_blocks)
self.norms = nn.ModuleList(norms)
self.ff = FeedForward(dim, dropout=dropout, activation_fn=activation_fn)
self.ff_norm = nn.LayerNorm(dim)
def forward(
self,
hidden_states,
encoder_hidden_states=None,
attention_mask=None,
video_length=None,
):
for attention_block, norm in zip(self.attention_blocks, self.norms):
norm_hidden_states = norm(hidden_states)
hidden_states = (
attention_block(
norm_hidden_states,
encoder_hidden_states=encoder_hidden_states
if attention_block.is_cross_attention
else None,
video_length=video_length,
)
+ hidden_states
)
hidden_states = self.ff(self.ff_norm(hidden_states)) + hidden_states
output = hidden_states
return output
class PositionalEncoding(nn.Module):
def __init__(self, d_model, dropout=0.0, max_len=24):
super().__init__()
self.dropout = nn.Dropout(p=dropout)
position = torch.arange(max_len).unsqueeze(1)
div_term = torch.exp(
torch.arange(0, d_model, 2) * (-math.log(10000.0) / d_model)
)
pe = torch.zeros(1, max_len, d_model)
pe[0, :, 0::2] = torch.sin(position * div_term)
pe[0, :, 1::2] = torch.cos(position * div_term)
self.register_buffer("pe", pe)
def forward(self, x):
x = x + self.pe[:, : x.size(1)]
return self.dropout(x)
class VersatileAttention(Attention):
def __init__(
self,
attention_mode=None,
cross_frame_attention_mode=None,
temporal_position_encoding=False,
temporal_position_encoding_max_len=24,
*args,
**kwargs,
):
super().__init__(*args, **kwargs)
assert attention_mode == "Temporal"
self.attention_mode = attention_mode
self.is_cross_attention = kwargs["cross_attention_dim"] is not None
self.pos_encoder = (
PositionalEncoding(
kwargs["query_dim"],
dropout=0.0,
max_len=temporal_position_encoding_max_len,
)
if (temporal_position_encoding and attention_mode == "Temporal")
else None
)
def extra_repr(self):
return f"(Module Info) Attention_Mode: {self.attention_mode}, Is_Cross_Attention: {self.is_cross_attention}"
def set_use_memory_efficient_attention_xformers(
self,
use_memory_efficient_attention_xformers: bool,
attention_op: Optional[Callable] = None,
):
if use_memory_efficient_attention_xformers:
if not is_xformers_available():
raise ModuleNotFoundError(
(
"Refer to https://github.com/facebookresearch/xformers for more information on how to install"
" xformers"
),
name="xformers",
)
elif not torch.cuda.is_available():
raise ValueError(
"torch.cuda.is_available() should be True but is False. xformers' memory efficient attention is"
" only available for GPU "
)
else:
try:
# Make sure we can run the memory efficient attention
_ = xformers.ops.memory_efficient_attention(
torch.randn((1, 2, 40), device="cuda"),
torch.randn((1, 2, 40), device="cuda"),
torch.randn((1, 2, 40), device="cuda"),
)
except Exception as e:
raise e
# XFormersAttnProcessor corrupts video generation and work with Pytorch 1.13.
# Pytorch 2.0.1 AttnProcessor works the same as XFormersAttnProcessor in Pytorch 1.13.
# You don't need XFormersAttnProcessor here.
# processor = XFormersAttnProcessor(
# attention_op=attention_op,
# )
processor = AttnProcessor()
else:
processor = AttnProcessor()
self.set_processor(processor)
def forward(
self,
hidden_states,
encoder_hidden_states=None,
attention_mask=None,
video_length=None,
**cross_attention_kwargs,
):
if self.attention_mode == "Temporal":
d = hidden_states.shape[1] # d means HxW
hidden_states = rearrange(
hidden_states, "(b f) d c -> (b d) f c", f=video_length
)
if self.pos_encoder is not None:
hidden_states = self.pos_encoder(hidden_states)
encoder_hidden_states = (
repeat(encoder_hidden_states, "b n c -> (b d) n c", d=d)
if encoder_hidden_states is not None
else encoder_hidden_states
)
else:
raise NotImplementedError
hidden_states = self.processor(
self,
hidden_states,
encoder_hidden_states=encoder_hidden_states,
attention_mask=attention_mask,
**cross_attention_kwargs,
)
if self.attention_mode == "Temporal":
hidden_states = rearrange(hidden_states, "(b d) f c -> (b f) d c", d=d)
return hidden_states
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