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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.models import ModelMixin
from diffusers.utils import BaseOutput
from diffusers.models.attention import FeedForward, AdaLayerNorm
from einops import rearrange, repeat
@dataclass
class Transformer3DModelOutput(BaseOutput):
sample: torch.FloatTensor
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,
add_audio_layer=False,
):
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,
upcast_attention=upcast_attention,
add_audio_layer=add_audio_layer,
)
for d in range(num_layers)
]
)
# 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")
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,
upcast_attention: bool = False,
add_audio_layer=False,
):
super().__init__()
self.use_ada_layer_norm = num_embeds_ada_norm is not None
self.add_audio_layer = add_audio_layer
self.norm1 = AdaLayerNorm(dim, num_embeds_ada_norm) if self.use_ada_layer_norm else nn.LayerNorm(dim)
self.attn1 = Attention(
query_dim=dim,
heads=num_attention_heads,
dim_head=attention_head_dim,
dropout=dropout,
bias=attention_bias,
upcast_attention=upcast_attention,
)
# Cross-attn
if add_audio_layer:
self.norm2 = AdaLayerNorm(dim, num_embeds_ada_norm) if self.use_ada_layer_norm else nn.LayerNorm(dim)
self.attn2 = Attention(
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
# Feed-forward
self.ff = FeedForward(dim, dropout=dropout, activation_fn=activation_fn)
self.norm3 = nn.LayerNorm(dim)
def forward(
self, hidden_states, encoder_hidden_states=None, timestep=None, attention_mask=None, video_length=None
):
norm_hidden_states = (
self.norm1(hidden_states, timestep) if self.use_ada_layer_norm else self.norm1(hidden_states)
)
hidden_states = self.attn1(norm_hidden_states, attention_mask=attention_mask) + hidden_states
if self.attn2 is not None and encoder_hidden_states is not None:
if encoder_hidden_states.dim() == 4:
encoder_hidden_states = rearrange(encoder_hidden_states, "b f s d -> (b f) s d")
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
return hidden_states
class Attention(nn.Module):
def __init__(
self,
query_dim: int,
cross_attention_dim: Optional[int] = None,
heads: int = 8,
dim_head: int = 64,
dropout: float = 0.0,
bias=False,
upcast_attention: bool = False,
upcast_softmax: bool = False,
norm_num_groups: Optional[int] = None,
):
super().__init__()
inner_dim = dim_head * heads
cross_attention_dim = cross_attention_dim if cross_attention_dim is not None else query_dim
self.upcast_attention = upcast_attention
self.upcast_softmax = upcast_softmax
self.scale = dim_head**-0.5
self.heads = heads
if norm_num_groups is not None:
self.group_norm = nn.GroupNorm(num_channels=inner_dim, num_groups=norm_num_groups, eps=1e-5, affine=True)
else:
self.group_norm = None
self.to_q = nn.Linear(query_dim, inner_dim, bias=bias)
self.to_k = nn.Linear(cross_attention_dim, inner_dim, bias=bias)
self.to_v = nn.Linear(cross_attention_dim, inner_dim, bias=bias)
self.to_out = nn.ModuleList([])
self.to_out.append(nn.Linear(inner_dim, query_dim))
self.to_out.append(nn.Dropout(dropout))
def split_heads(self, tensor):
batch_size, seq_len, dim = tensor.shape
tensor = tensor.reshape(batch_size, seq_len, self.heads, dim // self.heads)
tensor = tensor.permute(0, 2, 1, 3)
return tensor
def concat_heads(self, tensor):
batch_size, heads, seq_len, head_dim = tensor.shape
tensor = tensor.permute(0, 2, 1, 3)
tensor = tensor.reshape(batch_size, seq_len, heads * head_dim)
return tensor
def forward(self, hidden_states, encoder_hidden_states=None, attention_mask=None):
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)
query = self.split_heads(query)
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)
key = self.split_heads(key)
value = self.split_heads(value)
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 PyTorch native implementation of FlashAttention-2
hidden_states = F.scaled_dot_product_attention(query, key, value, attn_mask=attention_mask)
hidden_states = self.concat_heads(hidden_states)
# linear proj
hidden_states = self.to_out[0](hidden_states)
# dropout
hidden_states = self.to_out[1](hidden_states)
return hidden_states
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