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| # Copyright 2024 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 diffusers.configuration_utils import ConfigMixin, register_to_config | |
| from diffusers.utils import BaseOutput | |
| from diffusers.models.embeddings import TimestepEmbedding, Timesteps | |
| from diffusers.models.modeling_utils import ModelMixin | |
| from ..resnet import AlphaBlender | |
| from ..attention import ( | |
| BasicTransformerBlock, | |
| TemporalRopeBasicTransformerBlock, | |
| ) | |
| from ..embeddings import rope | |
| 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 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( | |
| [ | |
| TemporalRopeBasicTransformerBlock( | |
| inner_dim, | |
| time_mix_inner_dim, | |
| num_attention_heads, | |
| attention_head_dim, | |
| cross_attention_dim=cross_attention_dim, | |
| ) | |
| for _ in range(num_layers) | |
| ] | |
| ) | |
| 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, | |
| position_ids: Optional[torch.Tensor] = None, | |
| ): | |
| """ | |
| 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 | |
| # (B*F, 1, C) | |
| time_context = encoder_hidden_states | |
| # (B, 1, C) | |
| time_context_first_timestep = time_context[None, :].reshape( | |
| batch_size, num_frames, -1, time_context.shape[-1] | |
| )[:, 0] | |
| # (B*N, 1, C) | |
| time_context = time_context_first_timestep.repeat_interleave( | |
| height * width, dim=0 | |
| ) | |
| 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) | |
| if position_ids is None: | |
| # (B, F) | |
| frame_rotary_emb = torch.arange(num_frames, device=hidden_states.device) | |
| frame_rotary_emb = frame_rotary_emb[None, :].repeat(batch_size, 1) | |
| else: | |
| frame_rotary_emb = position_ids | |
| # (B, 1, F, d/2, 2, 2) | |
| frame_rotary_emb = rope(frame_rotary_emb, self.attention_head_dim) | |
| # (B*N, 1, F, d/2, 2, 2) | |
| frame_rotary_emb = frame_rotary_emb.repeat_interleave(height * width, dim=0) | |
| # 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 = temporal_block( | |
| hidden_states, | |
| num_frames=num_frames, | |
| encoder_hidden_states=time_context, | |
| frame_rotary_emb=frame_rotary_emb, | |
| ) | |
| 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) | |