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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 | |
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. | |
""" | |
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) | |