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# Copyright 2023 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from dataclasses import dataclass
from typing import Any, Dict, Optional
import torch
from torch import nn
from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput
from .attention import BasicTransformerBlock, TemporalBasicTransformerBlock
from .embeddings import TimestepEmbedding, Timesteps
from .modeling_utils import ModelMixin
from .resnet import AlphaBlender
@dataclass
class TransformerTemporalModelOutput(BaseOutput):
"""
The output of [`TransformerTemporalModel`].
Args:
sample (`torch.FloatTensor` of shape `(batch_size x num_frames, num_channels, height, width)`):
The hidden states output conditioned on `encoder_hidden_states` input.
"""
sample: torch.FloatTensor
class TransformerTemporalModel(ModelMixin, ConfigMixin):
"""
A Transformer model for video-like data.
Parameters:
num_attention_heads (`int`, *optional*, defaults to 16): The number of heads to use for multi-head attention.
attention_head_dim (`int`, *optional*, defaults to 88): The number of channels in each head.
in_channels (`int`, *optional*):
The number of channels in the input and output (specify if the input is **continuous**).
num_layers (`int`, *optional*, defaults to 1): The number of layers of Transformer blocks to use.
dropout (`float`, *optional*, defaults to 0.0): The dropout probability to use.
cross_attention_dim (`int`, *optional*): The number of `encoder_hidden_states` dimensions to use.
attention_bias (`bool`, *optional*):
Configure if the `TransformerBlock` attention should contain a bias parameter.
sample_size (`int`, *optional*): The width of the latent images (specify if the input is **discrete**).
This is fixed during training since it is used to learn a number of position embeddings.
activation_fn (`str`, *optional*, defaults to `"geglu"`):
Activation function to use in feed-forward. See `diffusers.models.activations.get_activation` for supported
activation functions.
norm_elementwise_affine (`bool`, *optional*):
Configure if the `TransformerBlock` should use learnable elementwise affine parameters for normalization.
double_self_attention (`bool`, *optional*):
Configure if each `TransformerBlock` should contain two self-attention layers.
positional_embeddings: (`str`, *optional*):
The type of positional embeddings to apply to the sequence input before passing use.
num_positional_embeddings: (`int`, *optional*):
The maximum length of the sequence over which to apply positional embeddings.
"""
@register_to_config
def __init__(
self,
num_attention_heads: int = 16,
attention_head_dim: int = 88,
in_channels: Optional[int] = None,
out_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,
sample_size: Optional[int] = None,
activation_fn: str = "geglu",
norm_elementwise_affine: bool = True,
double_self_attention: bool = True,
positional_embeddings: Optional[str] = None,
num_positional_embeddings: Optional[int] = None,
):
super().__init__()
self.num_attention_heads = num_attention_heads
self.attention_head_dim = attention_head_dim
inner_dim = num_attention_heads * attention_head_dim
self.in_channels = in_channels
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)
# 3. 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,
attention_bias=attention_bias,
double_self_attention=double_self_attention,
norm_elementwise_affine=norm_elementwise_affine,
positional_embeddings=positional_embeddings,
num_positional_embeddings=num_positional_embeddings,
)
for d in range(num_layers)
]
)
self.proj_out = nn.Linear(inner_dim, in_channels)
def forward(
self,
hidden_states: torch.FloatTensor,
encoder_hidden_states: Optional[torch.LongTensor] = None,
timestep: Optional[torch.LongTensor] = None,
class_labels: torch.LongTensor = None,
num_frames: int = 1,
cross_attention_kwargs: Optional[Dict[str, Any]] = None,
return_dict: bool = True,
) -> TransformerTemporalModelOutput:
"""
The [`TransformerTemporal`] forward method.
Args:
hidden_states (`torch.LongTensor` of shape `(batch size, num latent pixels)` if discrete, `torch.FloatTensor` of shape `(batch size, channel, height, width)` if continuous):
Input hidden_states.
encoder_hidden_states ( `torch.LongTensor` of shape `(batch size, encoder_hidden_states dim)`, *optional*):
Conditional embeddings for cross attention layer. If not given, cross-attention defaults to
self-attention.
timestep ( `torch.LongTensor`, *optional*):
Used to indicate denoising step. Optional timestep to be applied as an embedding in `AdaLayerNorm`.
class_labels ( `torch.LongTensor` of shape `(batch size, num classes)`, *optional*):
Used to indicate class labels conditioning. Optional class labels to be applied as an embedding in
`AdaLayerZeroNorm`.
num_frames (`int`, *optional*, defaults to 1):
The number of frames to be processed per batch. This is used to reshape the hidden states.
cross_attention_kwargs (`dict`, *optional*):
A kwargs dictionary that if specified is passed along to the `AttentionProcessor` as defined under
`self.processor` in
[diffusers.models.attention_processor](https://github.com/huggingface/diffusers/blob/main/src/diffusers/models/attention_processor.py).
return_dict (`bool`, *optional*, defaults to `True`):
Whether or not to return a [`~models.unet_2d_condition.UNet2DConditionOutput`] instead of a plain
tuple.
Returns:
[`~models.transformer_temporal.TransformerTemporalModelOutput`] or `tuple`:
If `return_dict` is True, an [`~models.transformer_temporal.TransformerTemporalModelOutput`] is
returned, otherwise a `tuple` where the first element is the sample tensor.
"""
# 1. Input
batch_frames, channel, height, width = hidden_states.shape
batch_size = batch_frames // num_frames
residual = hidden_states
hidden_states = hidden_states[None, :].reshape(batch_size, num_frames, channel, height, width)
hidden_states = hidden_states.permute(0, 2, 1, 3, 4)
hidden_states = self.norm(hidden_states)
hidden_states = hidden_states.permute(0, 3, 4, 2, 1).reshape(batch_size * height * width, num_frames, channel)
hidden_states = self.proj_in(hidden_states)
# 2. Blocks
for block in self.transformer_blocks:
hidden_states = block(
hidden_states,
encoder_hidden_states=encoder_hidden_states,
timestep=timestep,
cross_attention_kwargs=cross_attention_kwargs,
class_labels=class_labels,
)
# 3. Output
hidden_states = self.proj_out(hidden_states)
hidden_states = (
hidden_states[None, None, :]
.reshape(batch_size, height, width, num_frames, channel)
.permute(0, 3, 4, 1, 2)
.contiguous()
)
hidden_states = hidden_states.reshape(batch_frames, channel, height, width)
output = hidden_states + residual
if not return_dict:
return (output,)
return TransformerTemporalModelOutput(sample=output)
class TransformerSpatioTemporalModel(nn.Module):
"""
A Transformer model for video-like data.
Parameters:
num_attention_heads (`int`, *optional*, defaults to 16): The number of heads to use for multi-head attention.
attention_head_dim (`int`, *optional*, defaults to 88): The number of channels in each head.
in_channels (`int`, *optional*):
The number of channels in the input and output (specify if the input is **continuous**).
out_channels (`int`, *optional*):
The number of channels in the output (specify if the input is **continuous**).
num_layers (`int`, *optional*, defaults to 1): The number of layers of Transformer blocks to use.
cross_attention_dim (`int`, *optional*): The number of `encoder_hidden_states` dimensions to use.
"""
def __init__(
self,
num_attention_heads: int = 16,
attention_head_dim: int = 88,
in_channels: int = 320,
out_channels: Optional[int] = None,
num_layers: int = 1,
cross_attention_dim: Optional[int] = None,
):
super().__init__()
self.num_attention_heads = num_attention_heads
self.attention_head_dim = attention_head_dim
inner_dim = num_attention_heads * attention_head_dim
self.inner_dim = inner_dim
# 2. Define input layers
self.in_channels = in_channels
self.norm = torch.nn.GroupNorm(num_groups=32, num_channels=in_channels, eps=1e-6)
self.proj_in = nn.Linear(in_channels, inner_dim)
# 3. Define transformers blocks
self.transformer_blocks = nn.ModuleList(
[
BasicTransformerBlock(
inner_dim,
num_attention_heads,
attention_head_dim,
cross_attention_dim=cross_attention_dim,
)
for d in range(num_layers)
]
)
time_mix_inner_dim = inner_dim
self.temporal_transformer_blocks = nn.ModuleList(
[
TemporalBasicTransformerBlock(
inner_dim,
time_mix_inner_dim,
num_attention_heads,
attention_head_dim,
cross_attention_dim=cross_attention_dim,
)
for _ in range(num_layers)
]
)
time_embed_dim = in_channels * 4
self.time_pos_embed = TimestepEmbedding(in_channels, time_embed_dim, out_dim=in_channels)
self.time_proj = Timesteps(in_channels, True, 0)
self.time_mixer = AlphaBlender(alpha=0.5, merge_strategy="learned_with_images")
# 4. Define output layers
self.out_channels = in_channels if out_channels is None else out_channels
# TODO: should use out_channels for continuous projections
self.proj_out = nn.Linear(inner_dim, in_channels)
self.gradient_checkpointing = False
def forward(
self,
hidden_states: torch.Tensor,
encoder_hidden_states: Optional[torch.Tensor] = None,
image_only_indicator: Optional[torch.Tensor] = None,
return_dict: bool = True,
):
"""
Args:
hidden_states (`torch.FloatTensor` of shape `(batch size, channel, height, width)`):
Input hidden_states.
num_frames (`int`):
The number of frames to be processed per batch. This is used to reshape the hidden states.
encoder_hidden_states ( `torch.LongTensor` of shape `(batch size, encoder_hidden_states dim)`, *optional*):
Conditional embeddings for cross attention layer. If not given, cross-attention defaults to
self-attention.
image_only_indicator (`torch.LongTensor` of shape `(batch size, num_frames)`, *optional*):
A tensor indicating whether the input contains only images. 1 indicates that the input contains only
images, 0 indicates that the input contains video frames.
return_dict (`bool`, *optional*, defaults to `True`):
Whether or not to return a [`~models.transformer_temporal.TransformerTemporalModelOutput`] instead of a plain
tuple.
Returns:
[`~models.transformer_temporal.TransformerTemporalModelOutput`] or `tuple`:
If `return_dict` is True, an [`~models.transformer_temporal.TransformerTemporalModelOutput`] is
returned, otherwise a `tuple` where the first element is the sample tensor.
"""
# 1. Input
batch_frames, _, height, width = hidden_states.shape
num_frames = image_only_indicator.shape[-1]
batch_size = batch_frames // num_frames
time_context = encoder_hidden_states
time_context_first_timestep = time_context[None, :].reshape(
batch_size, num_frames, -1, time_context.shape[-1]
)[:, 0]
time_context = time_context_first_timestep[None, :].broadcast_to(
height * width, batch_size, 1, time_context.shape[-1]
)
time_context = time_context.reshape(height * width * batch_size, 1, time_context.shape[-1])
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_frames, height * width, inner_dim)
hidden_states = self.proj_in(hidden_states)
num_frames_emb = torch.arange(num_frames, device=hidden_states.device)
num_frames_emb = num_frames_emb.repeat(batch_size, 1)
num_frames_emb = num_frames_emb.reshape(-1)
t_emb = self.time_proj(num_frames_emb)
# `Timesteps` does not contain any weights and will always return f32 tensors
# but time_embedding might actually be running in fp16. so we need to cast here.
# there might be better ways to encapsulate this.
t_emb = t_emb.to(dtype=hidden_states.dtype)
emb = self.time_pos_embed(t_emb)
emb = emb[:, None, :]
# 2. Blocks
for block, temporal_block in zip(self.transformer_blocks, self.temporal_transformer_blocks):
if self.training and self.gradient_checkpointing:
hidden_states = torch.utils.checkpoint.checkpoint(
block,
hidden_states,
None,
encoder_hidden_states,
None,
use_reentrant=False,
)
else:
hidden_states = block(
hidden_states,
encoder_hidden_states=encoder_hidden_states,
)
hidden_states_mix = hidden_states
hidden_states_mix = hidden_states_mix + emb
hidden_states_mix = temporal_block(
hidden_states_mix,
num_frames=num_frames,
encoder_hidden_states=time_context,
)
hidden_states = self.time_mixer(
x_spatial=hidden_states,
x_temporal=hidden_states_mix,
image_only_indicator=image_only_indicator,
)
# 3. Output
hidden_states = self.proj_out(hidden_states)
hidden_states = hidden_states.reshape(batch_frames, height, width, inner_dim).permute(0, 3, 1, 2).contiguous()
output = hidden_states + residual
if not return_dict:
return (output,)
return TransformerTemporalModelOutput(sample=output)
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