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"""Video Depth Anything, vendored verbatim from DepthAnything/Video-Depth-Anything @ 4f5ae23172ba60fd7bc11ef671cca678842c7072: the files below
concatenated, package-internal imports replaced by relative imports of the other vendored files (generated by scripts/model_ports/common/vendor_flat.py;
do not edit by hand). Code license: Apache-2.0."""
from .vda_motion_attention import CrossAttention, FeedForward, apply_rotary_emb, precompute_freqs_cis
# ==============================================================================================================
# Video-Depth-Anything/video_depth_anything/motion_module/motion_module.py
# ==============================================================================================================
# This file is originally from AnimateDiff/animatediff/models/motion_module.py at main · guoyww/AnimateDiff
# SPDX-License-Identifier: Apache-2.0 license
#
# This file may have been modified by ByteDance Ltd. and/or its affiliates on [date of modification]
# Original file was released under [ Apache-2.0 license], with the full license text available at [https://github.com/guoyww/AnimateDiff?tab=Apache-2.0-1-ov-file#readme].
import torch
import torch.nn.functional as F
from torch import nn
from einops import rearrange, repeat
import math
try:
import xformers
import xformers.ops
XFORMERS_AVAILABLE = True
except ImportError:
print("xFormers not available")
XFORMERS_AVAILABLE = False
def zero_module(module):
# Zero out the parameters of a module and return it.
for p in module.parameters():
p.detach().zero_()
return module
class TemporalModule(nn.Module):
def __init__(
self,
in_channels,
num_attention_heads = 8,
num_transformer_block = 2,
num_attention_blocks = 2,
norm_num_groups = 32,
temporal_max_len = 32,
zero_initialize = True,
pos_embedding_type = "ape",
):
super().__init__()
self.temporal_transformer = TemporalTransformer3DModel(
in_channels=in_channels,
num_attention_heads=num_attention_heads,
attention_head_dim=in_channels // num_attention_heads,
num_layers=num_transformer_block,
num_attention_blocks=num_attention_blocks,
norm_num_groups=norm_num_groups,
temporal_max_len=temporal_max_len,
pos_embedding_type=pos_embedding_type,
)
if zero_initialize:
self.temporal_transformer.proj_out = zero_module(self.temporal_transformer.proj_out)
def forward(self, input_tensor, encoder_hidden_states, attention_mask=None, cached_hidden_state_list=None):
hidden_states = input_tensor
hidden_states, output_hidden_state_list = self.temporal_transformer(hidden_states, encoder_hidden_states, attention_mask, cached_hidden_state_list)
output = hidden_states
return output, output_hidden_state_list # list of hidden states
class TemporalTransformer3DModel(nn.Module):
def __init__(
self,
in_channels,
num_attention_heads,
attention_head_dim,
num_layers,
num_attention_blocks = 2,
norm_num_groups = 32,
temporal_max_len = 32,
pos_embedding_type = "ape",
):
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,
num_attention_blocks=num_attention_blocks,
temporal_max_len=temporal_max_len,
pos_embedding_type=pos_embedding_type,
)
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, cached_hidden_state_list=None):
assert hidden_states.dim() == 5, f"Expected hidden_states to have ndim=5, but got ndim={hidden_states.dim()}."
output_hidden_state_list = []
video_length = hidden_states.shape[2]
hidden_states = rearrange(hidden_states, "b c f h w -> (b f) c h w")
batch, channel, height, width = 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 * width, inner_dim).contiguous()
hidden_states = self.proj_in(hidden_states)
# Transformer Blocks
if cached_hidden_state_list is not None:
n = len(cached_hidden_state_list) // len(self.transformer_blocks)
else:
n = 0
for i, block in enumerate(self.transformer_blocks):
hidden_states, hidden_state_list = block(hidden_states, encoder_hidden_states=encoder_hidden_states, video_length=video_length, attention_mask=attention_mask,
cached_hidden_state_list=cached_hidden_state_list[i*n:(i+1)*n] if n else None)
output_hidden_state_list.extend(hidden_state_list)
# output
hidden_states = self.proj_out(hidden_states)
hidden_states = hidden_states.reshape(batch, height, width, 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, output_hidden_state_list
class TemporalTransformerBlock(nn.Module):
def __init__(
self,
dim,
num_attention_heads,
attention_head_dim,
num_attention_blocks = 2,
temporal_max_len = 32,
pos_embedding_type = "ape",
):
super().__init__()
self.attention_blocks = nn.ModuleList(
[
TemporalAttention(
query_dim=dim,
heads=num_attention_heads,
dim_head=attention_head_dim,
temporal_max_len=temporal_max_len,
pos_embedding_type=pos_embedding_type,
)
for i in range(num_attention_blocks)
]
)
self.norms = nn.ModuleList(
[
nn.LayerNorm(dim)
for i in range(num_attention_blocks)
]
)
self.ff = FeedForward(dim, dropout=0.0, activation_fn="geglu")
self.ff_norm = nn.LayerNorm(dim)
def forward(self, hidden_states, encoder_hidden_states=None, attention_mask=None, video_length=None, cached_hidden_state_list=None):
output_hidden_state_list = []
for i, (attention_block, norm) in enumerate(zip(self.attention_blocks, self.norms)):
norm_hidden_states = norm(hidden_states)
residual_hidden_states, output_hidden_states = attention_block(
norm_hidden_states,
encoder_hidden_states=encoder_hidden_states,
video_length=video_length,
attention_mask=attention_mask,
cached_hidden_states=cached_hidden_state_list[i] if cached_hidden_state_list is not None else None,
)
hidden_states = residual_hidden_states + hidden_states
output_hidden_state_list.append(output_hidden_states)
hidden_states = self.ff(self.ff_norm(hidden_states)) + hidden_states
output = hidden_states
return output, output_hidden_state_list
class PositionalEncoding(nn.Module):
def __init__(
self,
d_model,
dropout = 0.,
max_len = 32
):
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)].to(x.dtype)
return self.dropout(x)
class TemporalAttention(CrossAttention):
def __init__(
self,
temporal_max_len = 32,
pos_embedding_type = "ape",
*args, **kwargs
):
super().__init__(*args, **kwargs)
self.pos_embedding_type = pos_embedding_type
self._use_memory_efficient_attention_xformers = True
self.pos_encoder = None
self.freqs_cis = None
if self.pos_embedding_type == "ape":
self.pos_encoder = PositionalEncoding(
kwargs["query_dim"],
dropout=0.,
max_len=temporal_max_len
)
elif self.pos_embedding_type == "rope":
self.freqs_cis = precompute_freqs_cis(
kwargs["query_dim"],
temporal_max_len
)
else:
raise NotImplementedError
def forward(self, hidden_states, encoder_hidden_states=None, attention_mask=None, video_length=None, cached_hidden_states=None):
# TODO: support cache for these
assert encoder_hidden_states is None
assert attention_mask is None
d = hidden_states.shape[1]
d_in = 0
if cached_hidden_states is None:
hidden_states = rearrange(hidden_states, "(b f) d c -> (b d) f c", f=video_length)
input_hidden_states = hidden_states # (bxd) f c
else:
hidden_states = rearrange(hidden_states, "(b f) d c -> (b d) f c", f=1)
input_hidden_states = hidden_states
d_in = cached_hidden_states.shape[1]
hidden_states = torch.cat([cached_hidden_states, hidden_states], dim=1)
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
if self.group_norm is not None:
hidden_states = self.group_norm(hidden_states.transpose(1, 2)).transpose(1, 2)
query = self.to_q(hidden_states[:, d_in:, ...])
dim = query.shape[-1]
if self.added_kv_proj_dim is not None:
raise NotImplementedError
encoder_hidden_states = encoder_hidden_states if encoder_hidden_states is not None else hidden_states
key = self.to_k(encoder_hidden_states)
value = self.to_v(encoder_hidden_states)
if self.freqs_cis is not None:
seq_len = query.shape[1]
freqs_cis = self.freqs_cis[:seq_len].to(query.device)
query, key = apply_rotary_emb(query, key, freqs_cis)
if attention_mask is not None:
if attention_mask.shape[-1] != query.shape[1]:
target_length = query.shape[1]
attention_mask = F.pad(attention_mask, (0, target_length), value=0.0)
attention_mask = attention_mask.repeat_interleave(self.heads, dim=0)
use_memory_efficient = XFORMERS_AVAILABLE and self._use_memory_efficient_attention_xformers
if use_memory_efficient and (dim // self.heads) % 8 != 0:
# print('Warning: the dim {} cannot be divided by 8. Fall into normal attention'.format(dim // self.heads))
use_memory_efficient = False
# attention, what we cannot get enough of
if use_memory_efficient:
query = self.reshape_heads_to_4d(query)
key = self.reshape_heads_to_4d(key)
value = self.reshape_heads_to_4d(value)
hidden_states = self._memory_efficient_attention_xformers(query, key, value, attention_mask)
# Some versions of xformers return output in fp32, cast it back to the dtype of the input
hidden_states = hidden_states.to(query.dtype)
else:
query = self.reshape_heads_to_batch_dim(query)
key = self.reshape_heads_to_batch_dim(key)
value = self.reshape_heads_to_batch_dim(value)
if self._slice_size is None or query.shape[0] // self._slice_size == 1:
hidden_states = self._attention(query, key, value, attention_mask)
else:
raise NotImplementedError
# hidden_states = self._sliced_attention(query, key, value, sequence_length, dim, attention_mask)
# linear proj
hidden_states = self.to_out[0](hidden_states)
# dropout
hidden_states = self.to_out[1](hidden_states)
hidden_states = rearrange(hidden_states, "(b d) f c -> (b f) d c", d=d)
return hidden_states, input_hidden_states
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