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from dataclasses import dataclass
from typing import Optional
import torch
from diffusers.configuration_utils import ConfigMixin, register_to_config
from diffusers.models import ModelMixin
from diffusers.utils import BaseOutput
from einops import rearrange, repeat
from torch import nn
from memo.models.attention import JointAudioTemporalBasicTransformerBlock, TemporalBasicTransformerBlock
def create_custom_forward(module, return_dict=None):
def custom_forward(*inputs):
if return_dict is not None:
return module(*inputs, return_dict=return_dict)
return module(*inputs)
return custom_forward
@dataclass
class Transformer3DModelOutput(BaseOutput):
sample: torch.FloatTensor
class Transformer3DModel(ModelMixin, ConfigMixin):
_supports_gradient_checkpointing = True
@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",
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,
use_audio_module=False,
depth=0,
unet_block_name=None,
emo_drop_rate=0.3,
is_final_block=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
self.use_audio_module = use_audio_module
# Define input layers
self.in_channels = in_channels
self.is_final_block = is_final_block
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)
if use_audio_module:
self.transformer_blocks = nn.ModuleList(
[
JointAudioTemporalBasicTransformerBlock(
dim=inner_dim,
num_attention_heads=num_attention_heads,
attention_head_dim=attention_head_dim,
dropout=dropout,
cross_attention_dim=cross_attention_dim,
activation_fn=activation_fn,
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,
depth=depth,
unet_block_name=unet_block_name,
use_ada_layer_norm=True,
emo_drop_rate=emo_drop_rate,
is_final_block=(is_final_block and d == num_layers - 1),
)
for d in range(num_layers)
]
)
else:
self.transformer_blocks = nn.ModuleList(
[
TemporalBasicTransformerBlock(
inner_dim,
num_attention_heads,
attention_head_dim,
dropout=dropout,
cross_attention_dim=cross_attention_dim,
activation_fn=activation_fn,
attention_bias=attention_bias,
only_cross_attention=only_cross_attention,
upcast_attention=upcast_attention,
)
for _ 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)
self.gradient_checkpointing = False
def _set_gradient_checkpointing(self, module, value=False):
if hasattr(module, "gradient_checkpointing"):
module.gradient_checkpointing = value
def forward(
self,
hidden_states,
ref_img_feature=None,
encoder_hidden_states=None,
attention_mask=None,
timestep=None,
emotion=None,
uc_mask=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")
if self.use_audio_module:
if encoder_hidden_states.dim() == 4:
encoder_hidden_states = rearrange(
encoder_hidden_states,
"bs f margin dim -> (bs f) margin dim",
)
else:
if encoder_hidden_states.shape[0] != hidden_states.shape[0]:
encoder_hidden_states = repeat(encoder_hidden_states, "b n c -> (b f) n c", f=video_length)
batch, _, height, weight = hidden_states.shape
residual = hidden_states
if self.use_audio_module:
residual_audio = encoder_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:
if self.training and self.gradient_checkpointing:
if isinstance(block, TemporalBasicTransformerBlock):
hidden_states = torch.utils.checkpoint.checkpoint(
create_custom_forward(block),
hidden_states,
ref_img_feature,
None, # attention_mask
encoder_hidden_states,
timestep,
None, # cross_attention_kwargs
video_length,
uc_mask,
)
elif isinstance(block, JointAudioTemporalBasicTransformerBlock):
(
hidden_states,
encoder_hidden_states,
) = torch.utils.checkpoint.checkpoint(
create_custom_forward(block),
hidden_states,
encoder_hidden_states,
attention_mask,
emotion,
)
else:
hidden_states = torch.utils.checkpoint.checkpoint(
create_custom_forward(block),
hidden_states,
encoder_hidden_states,
timestep,
attention_mask,
video_length,
)
else:
if isinstance(block, TemporalBasicTransformerBlock):
hidden_states = block(
hidden_states=hidden_states,
ref_img_feature=ref_img_feature,
encoder_hidden_states=encoder_hidden_states,
timestep=timestep,
video_length=video_length,
uc_mask=uc_mask,
)
elif isinstance(block, JointAudioTemporalBasicTransformerBlock):
hidden_states, encoder_hidden_states = block(
hidden_states, # shape [2, 4096, 320]
encoder_hidden_states=encoder_hidden_states, # shape [2, 20, 640]
attention_mask=attention_mask,
emotion=emotion,
)
else:
hidden_states = block(
hidden_states, # shape [2, 4096, 320]
encoder_hidden_states=encoder_hidden_states, # shape [2, 20, 640]
attention_mask=attention_mask,
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
if self.use_audio_module and not self.is_final_block:
audio_output = encoder_hidden_states + residual_audio
else:
audio_output = None
output = rearrange(output, "(b f) c h w -> b c f h w", f=video_length)
if not return_dict:
if self.use_audio_module:
return output, audio_output
else:
return output
if self.use_audio_module:
return output, audio_output
else:
return output
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