from dataclasses import dataclass from typing import Any, Dict, Optional import torch from diffusers.configuration_utils import ConfigMixin, register_to_config from diffusers.models.attention import BasicTransformerBlock, TemporalBasicTransformerBlock from diffusers.models.embeddings import TimestepEmbedding, Timesteps from diffusers.models.modeling_utils import ModelMixin from diffusers.models.resnet import AlphaBlender from diffusers.utils import BaseOutput from torch import nn @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.unets.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 = torch.utils.checkpoint.checkpoint(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.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 if self.gradient_checkpointing: hidden_states_mix = torch.utils.checkpoint.checkpoint( temporal_block, hidden_states_mix, num_frames, time_context, ) hidden_states = self.time_mixer( x_spatial=hidden_states, x_temporal=hidden_states_mix, image_only_indicator=image_only_indicator, ) else: 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 = torch.utils.checkpoint.checkpoint(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)