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# Adapted from https://github.com/huggingface/diffusers/blob/main/src/diffusers/models/attention.py
from dataclasses import dataclass
from typing import Optional
import torch
import torch.nn.functional as F
from torch import nn
from diffusers.configuration_utils import ConfigMixin, register_to_config
from diffusers import ModelMixin
from diffusers.utils import BaseOutput
from diffusers.utils.import_utils import is_xformers_available
from diffusers.models.attention import Attention as CrossAttention, FeedForward, AdaLayerNorm
from typing import Any, Callable, Dict, List, Optional, Union
from einops import rearrange, repeat
import pdb
@dataclass
class Transformer3DModelOutput(BaseOutput):
sample: torch.FloatTensor
if is_xformers_available():
import xformers
import xformers.ops
else:
xformers = None
class Transformer3DModel(ModelMixin, ConfigMixin):
@register_to_config
def __init__(
self,
num_attention_heads: int = 16,
attention_head_dim: int = 88,
in_channels: Optional[int] = None,
num_layers: int = 1,
dropout: float = 0.0,
norm_num_groups: int = 32,
cross_attention_dim: Optional[int] = None,
attention_bias: bool = False,
activation_fn: str = "geglu",
num_embeds_ada_norm: Optional[int] = None,
use_linear_projection: bool = False,
only_cross_attention: bool = False,
upcast_attention: bool = False,
unet_use_cross_frame_attention=None,
unet_use_temporal_attention=None,
):
super().__init__()
self.use_linear_projection = use_linear_projection
self.num_attention_heads = num_attention_heads
self.attention_head_dim = attention_head_dim
inner_dim = num_attention_heads * attention_head_dim
# Define input layers
self.in_channels = in_channels
self.norm = torch.nn.GroupNorm(num_groups=norm_num_groups, num_channels=in_channels, eps=1e-6, affine=True)
if use_linear_projection:
self.proj_in = nn.Linear(in_channels, inner_dim)
else:
self.proj_in = nn.Conv2d(in_channels, inner_dim, kernel_size=1, stride=1, padding=0)
# Define transformers blocks
self.transformer_blocks = nn.ModuleList(
[
BasicTransformerBlock(
inner_dim,
num_attention_heads,
attention_head_dim,
dropout=dropout,
cross_attention_dim=cross_attention_dim,
activation_fn=activation_fn,
num_embeds_ada_norm=num_embeds_ada_norm,
attention_bias=attention_bias,
only_cross_attention=only_cross_attention,
upcast_attention=upcast_attention,
unet_use_cross_frame_attention=unet_use_cross_frame_attention,
unet_use_temporal_attention=unet_use_temporal_attention,
)
for d in range(num_layers)
]
)
# 4. Define output layers
if use_linear_projection:
self.proj_out = nn.Linear(in_channels, inner_dim)
else:
self.proj_out = nn.Conv2d(inner_dim, in_channels, kernel_size=1, stride=1, padding=0)
def forward(self, hidden_states, encoder_hidden_states=None, timestep=None, return_dict: bool = True):
# Input
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")
encoder_hidden_states = repeat(encoder_hidden_states, 'b n c -> (b f) n c', f=video_length)
batch, channel, height, weight = hidden_states.shape
residual = hidden_states
hidden_states = self.norm(hidden_states)
if not self.use_linear_projection:
hidden_states = self.proj_in(hidden_states)
inner_dim = hidden_states.shape[1]
hidden_states = hidden_states.permute(0, 2, 3, 1).reshape(batch, height * weight, inner_dim)
else:
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)
# Blocks
for block in self.transformer_blocks:
hidden_states = block(
hidden_states,
encoder_hidden_states=encoder_hidden_states,
timestep=timestep,
video_length=video_length
)
# Output
if not self.use_linear_projection:
hidden_states = (
hidden_states.reshape(batch, height, weight, inner_dim).permute(0, 3, 1, 2).contiguous()
)
hidden_states = self.proj_out(hidden_states)
else:
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)
if not return_dict:
return (output,)
return Transformer3DModelOutput(sample=output)
class BasicTransformerBlock(nn.Module):
def __init__(
self,
dim: int,
num_attention_heads: int,
attention_head_dim: int,
dropout=0.0,
cross_attention_dim: Optional[int] = None,
activation_fn: str = "geglu",
num_embeds_ada_norm: Optional[int] = None,
attention_bias: bool = False,
only_cross_attention: bool = False,
upcast_attention: bool = False,
unet_use_cross_frame_attention = None,
unet_use_temporal_attention = None,
):
super().__init__()
self.only_cross_attention = only_cross_attention
self.use_ada_layer_norm = num_embeds_ada_norm is not None
self.unet_use_cross_frame_attention = unet_use_cross_frame_attention
self.unet_use_temporal_attention = unet_use_temporal_attention
# SC-Attn
assert unet_use_cross_frame_attention is not None
if unet_use_cross_frame_attention:
self.attn1 = SparseCausalAttention2D(
query_dim=dim,
heads=num_attention_heads,
dim_head=attention_head_dim,
dropout=dropout,
bias=attention_bias,
cross_attention_dim=cross_attention_dim if only_cross_attention else None,
upcast_attention=upcast_attention,
)
else:
self.attn1 = CrossAttention(
query_dim=dim,
heads=num_attention_heads,
dim_head=attention_head_dim,
dropout=dropout,
bias=attention_bias,
upcast_attention=upcast_attention,
)
self.norm1 = AdaLayerNorm(dim, num_embeds_ada_norm) if self.use_ada_layer_norm else nn.LayerNorm(dim)
# Cross-Attn
if cross_attention_dim is not None:
self.attn2 = CrossAttention(
query_dim=dim,
cross_attention_dim=cross_attention_dim,
heads=num_attention_heads,
dim_head=attention_head_dim,
dropout=dropout,
bias=attention_bias,
upcast_attention=upcast_attention,
)
else:
self.attn2 = None
if cross_attention_dim is not None:
self.norm2 = AdaLayerNorm(dim, num_embeds_ada_norm) if self.use_ada_layer_norm else nn.LayerNorm(dim)
else:
self.norm2 = None
# Feed-forward
self.ff = FeedForward(dim, dropout=dropout, activation_fn=activation_fn)
self.norm3 = nn.LayerNorm(dim)
# Temp-Attn
assert unet_use_temporal_attention is not None
if unet_use_temporal_attention:
self.attn_temp = CrossAttention(
query_dim=dim,
heads=num_attention_heads,
dim_head=attention_head_dim,
dropout=dropout,
bias=attention_bias,
upcast_attention=upcast_attention,
)
nn.init.zeros_(self.attn_temp.to_out[0].weight.data)
self.norm_temp = AdaLayerNorm(dim, num_embeds_ada_norm) if self.use_ada_layer_norm else nn.LayerNorm(dim)
def set_use_memory_efficient_attention_xformers(self, use_memory_efficient_attention_xformers: bool, attention_op: Optional[Callable] = None):
if not is_xformers_available():
print("Here is how to install it")
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
self.attn1._use_memory_efficient_attention_xformers = use_memory_efficient_attention_xformers
if self.attn2 is not None:
self.attn2._use_memory_efficient_attention_xformers = use_memory_efficient_attention_xformers
# self.attn_temp._use_memory_efficient_attention_xformers = use_memory_efficient_attention_xformers
def forward(self, hidden_states, encoder_hidden_states=None, timestep=None, attention_mask=None, video_length=None):
# SparseCausal-Attention
norm_hidden_states = (
self.norm1(hidden_states, timestep) if self.use_ada_layer_norm else self.norm1(hidden_states)
)
# if self.only_cross_attention:
# hidden_states = (
# self.attn1(norm_hidden_states, encoder_hidden_states, attention_mask=attention_mask) + hidden_states
# )
# else:
# hidden_states = self.attn1(norm_hidden_states, attention_mask=attention_mask, video_length=video_length) + hidden_states
# pdb.set_trace()
if self.unet_use_cross_frame_attention:
hidden_states = self.attn1(norm_hidden_states, attention_mask=attention_mask, video_length=video_length) + hidden_states
else:
hidden_states = self.attn1(norm_hidden_states, attention_mask=attention_mask) + hidden_states
if self.attn2 is not None:
# Cross-Attention
norm_hidden_states = (
self.norm2(hidden_states, timestep) if self.use_ada_layer_norm else self.norm2(hidden_states)
)
hidden_states = (
self.attn2(
norm_hidden_states, encoder_hidden_states=encoder_hidden_states, attention_mask=attention_mask
)
+ hidden_states
)
# Feed-forward
hidden_states = self.ff(self.norm3(hidden_states)) + hidden_states
# Temporal-Attention
if self.unet_use_temporal_attention:
d = hidden_states.shape[1]
hidden_states = rearrange(hidden_states, "(b f) d c -> (b d) f c", f=video_length)
norm_hidden_states = (
self.norm_temp(hidden_states, timestep) if self.use_ada_layer_norm else self.norm_temp(hidden_states)
)
hidden_states = self.attn_temp(norm_hidden_states) + hidden_states
hidden_states = rearrange(hidden_states, "(b d) f c -> (b f) d c", d=d)
return hidden_states
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