diff --git "a/onlyflow/models/unet.py" "b/onlyflow/models/unet.py"
new file mode 100644--- /dev/null
+++ "b/onlyflow/models/unet.py"
@@ -0,0 +1,2503 @@
+from dataclasses import dataclass
+from dataclasses import dataclass
+from typing import Optional, Tuple, Union, Dict, Any
+
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+import torch.utils.checkpoint
+from diffusers import UNet2DConditionModel
+from diffusers.configuration_utils import ConfigMixin, register_to_config, FrozenDict
+from diffusers.loaders import UNet2DConditionLoadersMixin, PeftAdapterMixin, FromOriginalModelMixin
+from diffusers.models import DualTransformer2DModel
+from diffusers.models.attention_processor import IPAdapterAttnProcessor, IPAdapterAttnProcessor2_0, \
+ AttnProcessor, AttentionProcessor, ADDED_KV_ATTENTION_PROCESSORS, AttnAddedKVProcessor, CROSS_ATTENTION_PROCESSORS, \
+ FusedAttnProcessor2_0
+from diffusers.models.downsampling import Downsample2D
+from diffusers.models.embeddings import TimestepEmbedding, Timesteps
+from diffusers.models.modeling_utils import ModelMixin
+from diffusers.models.resnet import ResnetBlock2D
+from diffusers.models.upsampling import Upsample2D
+from diffusers.utils import BaseOutput, logging, deprecate
+from diffusers.utils.torch_utils import apply_freeu
+from einops import rearrange
+
+from . import attention_processor
+from onlyflow.models.attention import BasicTransformerBlock
+from onlyflow.models.attention_processor import (
+ FlowAdaptorAttnProcessor, Attention, AttnProcessor2_0
+)
+from onlyflow.models.transformer_2d import Transformer2DModel
+
+logger = logging.get_logger(__name__) # pylint: disable=invalid-name
+
+
+@dataclass
+class UNetMotionOutput(BaseOutput):
+ """
+ The output of [`UNetMotionOutput`].
+
+ Args:
+ sample (`torch.Tensor` of shape `(batch_size, num_channels, num_frames, height, width)`):
+ The hidden states output conditioned on `encoder_hidden_states` input. Output of last layer of model.
+ """
+
+ sample: torch.Tensor
+
+
+class AnimateDiffTransformer3D(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**).
+ 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 _ in range(num_layers)
+ ]
+ )
+
+ self.proj_out = nn.Linear(inner_dim, in_channels)
+
+ def forward(
+ self,
+ hidden_states: torch.Tensor,
+ encoder_hidden_states: Optional[torch.LongTensor] = None,
+ timestep: Optional[torch.LongTensor] = None,
+ class_labels: Optional[torch.LongTensor] = None,
+ num_frames: int = 1,
+ cross_attention_kwargs: Optional[Dict[str, Any]] = None,
+ ) -> torch.Tensor:
+ """
+ The [`AnimateDiffTransformer3D`] forward method.
+
+ Args:
+ hidden_states (`torch.LongTensor` of shape `(batch size, num latent pixels)` if discrete, `torch.Tensor` 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).
+
+ Returns:
+ torch.Tensor:
+ The output 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(input=hidden_states)
+
+ # 2. Blocks
+ for block in self.transformer_blocks:
+ hidden_states = block(
+ hidden_states=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(input=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
+ return output
+
+
+class DownBlockMotion(nn.Module):
+ def __init__(
+ self,
+ in_channels: int,
+ out_channels: int,
+ temb_channels: int,
+ dropout: float = 0.0,
+ num_layers: int = 1,
+ resnet_eps: float = 1e-6,
+ resnet_time_scale_shift: str = "default",
+ resnet_act_fn: str = "swish",
+ resnet_groups: int = 32,
+ resnet_pre_norm: bool = True,
+ output_scale_factor: float = 1.0,
+ add_downsample: bool = True,
+ downsample_padding: int = 1,
+ temporal_num_attention_heads: Union[int, Tuple[int]] = 1,
+ temporal_cross_attention_dim: Optional[int] = None,
+ temporal_max_seq_length: int = 32,
+ temporal_transformer_layers_per_block: Union[int, Tuple[int]] = 1,
+ temporal_double_self_attention: bool = True,
+ ):
+ super().__init__()
+ resnets = []
+ motion_modules = []
+
+ # support for variable transformer layers per temporal block
+ if isinstance(temporal_transformer_layers_per_block, int):
+ temporal_transformer_layers_per_block = (temporal_transformer_layers_per_block,) * num_layers
+ elif len(temporal_transformer_layers_per_block) != num_layers:
+ raise ValueError(
+ f"`temporal_transformer_layers_per_block` must be an integer or a tuple of integers of length {num_layers}"
+ )
+
+ # support for variable number of attention head per temporal layers
+ if isinstance(temporal_num_attention_heads, int):
+ temporal_num_attention_heads = (temporal_num_attention_heads,) * num_layers
+ elif len(temporal_num_attention_heads) != num_layers:
+ raise ValueError(
+ f"`temporal_num_attention_heads` must be an integer or a tuple of integers of length {num_layers}"
+ )
+
+ for i in range(num_layers):
+ in_channels = in_channels if i == 0 else out_channels
+ resnets.append(
+ ResnetBlock2D(
+ in_channels=in_channels,
+ out_channels=out_channels,
+ temb_channels=temb_channels,
+ eps=resnet_eps,
+ groups=resnet_groups,
+ dropout=dropout,
+ time_embedding_norm=resnet_time_scale_shift,
+ non_linearity=resnet_act_fn,
+ output_scale_factor=output_scale_factor,
+ pre_norm=resnet_pre_norm,
+ )
+ )
+ motion_modules.append(
+ AnimateDiffTransformer3D(
+ num_attention_heads=temporal_num_attention_heads[i],
+ in_channels=out_channels,
+ num_layers=temporal_transformer_layers_per_block[i],
+ norm_num_groups=resnet_groups,
+ cross_attention_dim=temporal_cross_attention_dim,
+ attention_bias=False,
+ activation_fn="geglu",
+ positional_embeddings="sinusoidal",
+ num_positional_embeddings=temporal_max_seq_length,
+ attention_head_dim=out_channels // temporal_num_attention_heads[i],
+ double_self_attention=temporal_double_self_attention,
+ )
+ )
+
+ self.resnets = nn.ModuleList(resnets)
+ self.motion_modules = nn.ModuleList(motion_modules)
+
+ if add_downsample:
+ self.downsamplers = nn.ModuleList(
+ [
+ Downsample2D(
+ out_channels,
+ use_conv=True,
+ out_channels=out_channels,
+ padding=downsample_padding,
+ name="op",
+ )
+ ]
+ )
+ else:
+ self.downsamplers = None
+
+ self.gradient_checkpointing = False
+
+ def forward(
+ self,
+ hidden_states: torch.Tensor,
+ temb: Optional[torch.Tensor] = None,
+ num_frames: int = 1,
+ motion_cross_attention_kwargs: Optional[Dict[str, Any]] = None,
+ *args,
+ **kwargs,
+ ) -> Union[torch.Tensor, Tuple[torch.Tensor, ...]]:
+ if len(args) > 0 or kwargs.get("scale", None) is not None:
+ deprecation_message = "The `scale` argument is deprecated and will be ignored. Please remove it, as passing it will raise an error in the future. `scale` should directly be passed while calling the underlying pipeline component i.e., via `cross_attention_kwargs`."
+ deprecate("scale", "1.0.0", deprecation_message)
+
+ output_states = ()
+
+ blocks = zip(self.resnets, self.motion_modules)
+ for resnet, motion_module in blocks:
+ if self.training and self.gradient_checkpointing:
+
+ hidden_states = torch.utils.checkpoint.checkpoint(
+ resnet,
+ hidden_states,
+ temb,
+ use_reentrant=False,
+ )
+
+ hidden_states = torch.utils.checkpoint.checkpoint(
+ motion_module,
+ hidden_states,
+ num_frames=num_frames,
+ cross_attention_kwargs=motion_cross_attention_kwargs,
+ use_reentrant=False,
+ )
+
+ else:
+ hidden_states = resnet(input_tensor=hidden_states, temb=temb)
+ hidden_states = motion_module(hidden_states, num_frames=num_frames,
+ cross_attention_kwargs=motion_cross_attention_kwargs)
+
+ output_states = output_states + (hidden_states,)
+
+ if self.downsamplers is not None:
+ for downsampler in self.downsamplers:
+ hidden_states = downsampler(hidden_states)
+
+ output_states = output_states + (hidden_states,)
+
+ return hidden_states, output_states
+
+
+class CrossAttnDownBlockMotion(nn.Module):
+ def __init__(
+ self,
+ in_channels: int,
+ out_channels: int,
+ temb_channels: int,
+ dropout: float = 0.0,
+ num_layers: int = 1,
+ transformer_layers_per_block: Union[int, Tuple[int]] = 1,
+ resnet_eps: float = 1e-6,
+ resnet_time_scale_shift: str = "default",
+ resnet_act_fn: str = "swish",
+ resnet_groups: int = 32,
+ resnet_pre_norm: bool = True,
+ num_attention_heads: int = 1,
+ cross_attention_dim: int = 1280,
+ output_scale_factor: float = 1.0,
+ downsample_padding: int = 1,
+ add_downsample: bool = True,
+ dual_cross_attention: bool = False,
+ use_linear_projection: bool = False,
+ only_cross_attention: bool = False,
+ upcast_attention: bool = False,
+ attention_type: str = "default",
+ temporal_cross_attention_dim: Optional[int] = None,
+ temporal_num_attention_heads: int = 8,
+ temporal_max_seq_length: int = 32,
+ temporal_transformer_layers_per_block: Union[int, Tuple[int]] = 1,
+ temporal_double_self_attention: bool = True,
+ ):
+ super().__init__()
+ resnets = []
+ attentions = []
+ motion_modules = []
+
+ self.has_cross_attention = True
+ self.num_attention_heads = num_attention_heads
+
+ # support for variable transformer layers per block
+ if isinstance(transformer_layers_per_block, int):
+ transformer_layers_per_block = (transformer_layers_per_block,) * num_layers
+ elif len(transformer_layers_per_block) != num_layers:
+ raise ValueError(
+ f"transformer_layers_per_block must be an integer or a list of integers of length {num_layers}"
+ )
+
+ # support for variable transformer layers per temporal block
+ if isinstance(temporal_transformer_layers_per_block, int):
+ temporal_transformer_layers_per_block = (temporal_transformer_layers_per_block,) * num_layers
+ elif len(temporal_transformer_layers_per_block) != num_layers:
+ raise ValueError(
+ f"temporal_transformer_layers_per_block must be an integer or a list of integers of length {num_layers}"
+ )
+
+ for i in range(num_layers):
+ in_channels = in_channels if i == 0 else out_channels
+ resnets.append(
+ ResnetBlock2D(
+ in_channels=in_channels,
+ out_channels=out_channels,
+ temb_channels=temb_channels,
+ eps=resnet_eps,
+ groups=resnet_groups,
+ dropout=dropout,
+ time_embedding_norm=resnet_time_scale_shift,
+ non_linearity=resnet_act_fn,
+ output_scale_factor=output_scale_factor,
+ pre_norm=resnet_pre_norm,
+ )
+ )
+
+ if not dual_cross_attention:
+ attentions.append(
+ Transformer2DModel(
+ num_attention_heads,
+ out_channels // num_attention_heads,
+ in_channels=out_channels,
+ num_layers=transformer_layers_per_block[i],
+ cross_attention_dim=cross_attention_dim,
+ norm_num_groups=resnet_groups,
+ use_linear_projection=use_linear_projection,
+ only_cross_attention=only_cross_attention,
+ upcast_attention=upcast_attention,
+ attention_type=attention_type,
+ )
+ )
+ else:
+ attentions.append(
+ DualTransformer2DModel(
+ num_attention_heads,
+ out_channels // num_attention_heads,
+ in_channels=out_channels,
+ num_layers=1,
+ cross_attention_dim=cross_attention_dim,
+ norm_num_groups=resnet_groups,
+ )
+ )
+
+ motion_modules.append(
+ AnimateDiffTransformer3D(
+ num_attention_heads=temporal_num_attention_heads,
+ in_channels=out_channels,
+ num_layers=temporal_transformer_layers_per_block[i],
+ norm_num_groups=resnet_groups,
+ cross_attention_dim=temporal_cross_attention_dim,
+ attention_bias=False,
+ activation_fn="geglu",
+ positional_embeddings="sinusoidal",
+ num_positional_embeddings=temporal_max_seq_length,
+ attention_head_dim=out_channels // temporal_num_attention_heads,
+ double_self_attention=temporal_double_self_attention,
+ )
+ )
+
+ self.attentions = nn.ModuleList(attentions)
+ self.resnets = nn.ModuleList(resnets)
+ self.motion_modules = nn.ModuleList(motion_modules)
+
+ if add_downsample:
+ self.downsamplers = nn.ModuleList(
+ [
+ Downsample2D(
+ out_channels,
+ use_conv=True,
+ out_channels=out_channels,
+ padding=downsample_padding,
+ name="op",
+ )
+ ]
+ )
+ else:
+ self.downsamplers = None
+
+ self.gradient_checkpointing = False
+
+ def forward(
+ self,
+ hidden_states: torch.Tensor,
+ temb: Optional[torch.Tensor] = None,
+ encoder_hidden_states: Optional[torch.Tensor] = None,
+ attention_mask: Optional[torch.Tensor] = None,
+ num_frames: int = 1,
+ encoder_attention_mask: Optional[torch.Tensor] = None,
+ cross_attention_kwargs: Optional[Dict[str, Any]] = None,
+ motion_cross_attention_kwargs: Optional[Dict[str, Any]] = None,
+ additional_residuals: Optional[torch.Tensor] = None,
+ ):
+ if cross_attention_kwargs is not None:
+ if cross_attention_kwargs.get("scale", None) is not None:
+ logger.warning("Passing `scale` to `cross_attention_kwargs` is deprecated. `scale` will be ignored.")
+
+ output_states = ()
+
+ blocks = list(zip(self.resnets, self.attentions, self.motion_modules))
+ for i, (resnet, attn, motion_module) in enumerate(blocks):
+ if self.training and self.gradient_checkpointing:
+
+ hidden_states = torch.utils.checkpoint.checkpoint(
+ resnet,
+ hidden_states,
+ temb,
+ use_reentrant=False,
+ )
+
+ hidden_states = torch.utils.checkpoint.checkpoint(
+ attn,
+ hidden_states,
+ encoder_hidden_states=encoder_hidden_states,
+ cross_attention_kwargs=cross_attention_kwargs,
+ attention_mask=attention_mask,
+ encoder_attention_mask=encoder_attention_mask,
+ return_dict=False,
+ use_reentrant=False,
+ )[0]
+
+ hidden_states = torch.utils.checkpoint.checkpoint(
+ motion_module,
+ hidden_states,
+ num_frames=num_frames,
+ cross_attention_kwargs=motion_cross_attention_kwargs,
+ use_reentrant=False,
+ )
+
+ else:
+ hidden_states = resnet(input_tensor=hidden_states, temb=temb)
+
+ hidden_states = attn(
+ hidden_states=hidden_states,
+ encoder_hidden_states=encoder_hidden_states,
+ cross_attention_kwargs=cross_attention_kwargs,
+ attention_mask=attention_mask,
+ encoder_attention_mask=encoder_attention_mask,
+ return_dict=False,
+ )[0]
+
+ hidden_states = motion_module(
+ hidden_states=hidden_states,
+ num_frames=num_frames,
+ cross_attention_kwargs=motion_cross_attention_kwargs,
+ )
+
+ # apply additional residuals to the output of the last pair of resnet and attention blocks
+ if i == len(blocks) - 1 and additional_residuals is not None:
+ hidden_states = hidden_states + additional_residuals
+
+ output_states = output_states + (hidden_states,)
+
+ if self.downsamplers is not None:
+ for downsampler in self.downsamplers:
+ hidden_states = downsampler(hidden_states=hidden_states)
+
+ output_states = output_states + (hidden_states,)
+
+ return hidden_states, output_states
+
+
+class CrossAttnUpBlockMotion(nn.Module):
+ def __init__(
+ self,
+ in_channels: int,
+ out_channels: int,
+ prev_output_channel: int,
+ temb_channels: int,
+ resolution_idx: Optional[int] = None,
+ dropout: float = 0.0,
+ num_layers: int = 1,
+ transformer_layers_per_block: Union[int, Tuple[int]] = 1,
+ resnet_eps: float = 1e-6,
+ resnet_time_scale_shift: str = "default",
+ resnet_act_fn: str = "swish",
+ resnet_groups: int = 32,
+ resnet_pre_norm: bool = True,
+ num_attention_heads: int = 1,
+ cross_attention_dim: int = 1280,
+ output_scale_factor: float = 1.0,
+ add_upsample: bool = True,
+ dual_cross_attention: bool = False,
+ use_linear_projection: bool = False,
+ only_cross_attention: bool = False,
+ upcast_attention: bool = False,
+ attention_type: str = "default",
+ temporal_cross_attention_dim: Optional[int] = None,
+ temporal_num_attention_heads: int = 8,
+ temporal_max_seq_length: int = 32,
+ temporal_transformer_layers_per_block: Union[int, Tuple[int]] = 1,
+ ):
+ super().__init__()
+ resnets = []
+ attentions = []
+ motion_modules = []
+
+ self.has_cross_attention = True
+ self.num_attention_heads = num_attention_heads
+
+ # support for variable transformer layers per block
+ if isinstance(transformer_layers_per_block, int):
+ transformer_layers_per_block = (transformer_layers_per_block,) * num_layers
+ elif len(transformer_layers_per_block) != num_layers:
+ raise ValueError(
+ f"transformer_layers_per_block must be an integer or a list of integers of length {num_layers}, got {len(transformer_layers_per_block)}"
+ )
+
+ # support for variable transformer layers per temporal block
+ if isinstance(temporal_transformer_layers_per_block, int):
+ temporal_transformer_layers_per_block = (temporal_transformer_layers_per_block,) * num_layers
+ elif len(temporal_transformer_layers_per_block) != num_layers:
+ raise ValueError(
+ f"temporal_transformer_layers_per_block must be an integer or a list of integers of length {num_layers}, got {len(temporal_transformer_layers_per_block)}"
+ )
+
+ for i in range(num_layers):
+ res_skip_channels = in_channels if (i == num_layers - 1) else out_channels
+ resnet_in_channels = prev_output_channel if i == 0 else out_channels
+
+ resnets.append(
+ ResnetBlock2D(
+ in_channels=resnet_in_channels + res_skip_channels,
+ out_channels=out_channels,
+ temb_channels=temb_channels,
+ eps=resnet_eps,
+ groups=resnet_groups,
+ dropout=dropout,
+ time_embedding_norm=resnet_time_scale_shift,
+ non_linearity=resnet_act_fn,
+ output_scale_factor=output_scale_factor,
+ pre_norm=resnet_pre_norm,
+ )
+ )
+
+ if not dual_cross_attention:
+ attentions.append(
+ Transformer2DModel(
+ num_attention_heads,
+ out_channels // num_attention_heads,
+ in_channels=out_channels,
+ num_layers=transformer_layers_per_block[i],
+ cross_attention_dim=cross_attention_dim,
+ norm_num_groups=resnet_groups,
+ use_linear_projection=use_linear_projection,
+ only_cross_attention=only_cross_attention,
+ upcast_attention=upcast_attention,
+ attention_type=attention_type,
+ )
+ )
+ else:
+ attentions.append(
+ DualTransformer2DModel(
+ num_attention_heads,
+ out_channels // num_attention_heads,
+ in_channels=out_channels,
+ num_layers=1,
+ cross_attention_dim=cross_attention_dim,
+ norm_num_groups=resnet_groups,
+ )
+ )
+ motion_modules.append(
+ AnimateDiffTransformer3D(
+ num_attention_heads=temporal_num_attention_heads,
+ in_channels=out_channels,
+ num_layers=temporal_transformer_layers_per_block[i],
+ norm_num_groups=resnet_groups,
+ cross_attention_dim=temporal_cross_attention_dim,
+ attention_bias=False,
+ activation_fn="geglu",
+ positional_embeddings="sinusoidal",
+ num_positional_embeddings=temporal_max_seq_length,
+ attention_head_dim=out_channels // temporal_num_attention_heads,
+ )
+ )
+
+ self.attentions = nn.ModuleList(attentions)
+ self.resnets = nn.ModuleList(resnets)
+ self.motion_modules = nn.ModuleList(motion_modules)
+
+ if add_upsample:
+ self.upsamplers = nn.ModuleList([Upsample2D(out_channels, use_conv=True, out_channels=out_channels)])
+ else:
+ self.upsamplers = None
+
+ self.gradient_checkpointing = False
+ self.resolution_idx = resolution_idx
+
+ def forward(
+ self,
+ hidden_states: torch.Tensor,
+ res_hidden_states_tuple: Tuple[torch.Tensor, ...],
+ temb: Optional[torch.Tensor] = None,
+ encoder_hidden_states: Optional[torch.Tensor] = None,
+ cross_attention_kwargs: Optional[Dict[str, Any]] = None,
+ motion_cross_attention_kwargs: Optional[Dict[str, Any]] = None,
+ upsample_size: Optional[int] = None,
+ attention_mask: Optional[torch.Tensor] = None,
+ encoder_attention_mask: Optional[torch.Tensor] = None,
+ num_frames: int = 1,
+ ) -> torch.Tensor:
+ if cross_attention_kwargs is not None:
+ if cross_attention_kwargs.get("scale", None) is not None:
+ logger.warning("Passing `scale` to `cross_attention_kwargs` is deprecated. `scale` will be ignored.")
+
+ is_freeu_enabled = (
+ getattr(self, "s1", None)
+ and getattr(self, "s2", None)
+ and getattr(self, "b1", None)
+ and getattr(self, "b2", None)
+ )
+
+ blocks = zip(self.resnets, self.attentions, self.motion_modules)
+ for resnet, attn, motion_module in blocks:
+ # pop res hidden states
+ res_hidden_states = res_hidden_states_tuple[-1]
+ res_hidden_states_tuple = res_hidden_states_tuple[:-1]
+
+ # FreeU: Only operate on the first two stages
+ if is_freeu_enabled:
+ hidden_states, res_hidden_states = apply_freeu(
+ self.resolution_idx,
+ hidden_states,
+ res_hidden_states,
+ s1=self.s1,
+ s2=self.s2,
+ b1=self.b1,
+ b2=self.b2,
+ )
+
+ hidden_states = torch.cat([hidden_states, res_hidden_states], dim=1)
+
+ if self.training and self.gradient_checkpointing:
+
+ hidden_states = torch.utils.checkpoint.checkpoint(
+ resnet,
+ hidden_states,
+ temb,
+ use_reentrant=False,
+ )
+
+ hidden_states = torch.utils.checkpoint.checkpoint(
+ attn,
+ hidden_states,
+ encoder_hidden_states=encoder_hidden_states,
+ cross_attention_kwargs=cross_attention_kwargs,
+ attention_mask=attention_mask,
+ encoder_attention_mask=encoder_attention_mask,
+ return_dict=False,
+ use_reentrant=False,
+ )[0]
+
+ hidden_states = torch.utils.checkpoint.checkpoint(
+ motion_module,
+ hidden_states,
+ num_frames=num_frames,
+ cross_attention_kwargs=motion_cross_attention_kwargs,
+ use_reentrant=False,
+ )
+
+ else:
+ hidden_states = resnet(input_tensor=hidden_states, temb=temb)
+
+ hidden_states = attn(
+ hidden_states=hidden_states,
+ encoder_hidden_states=encoder_hidden_states,
+ cross_attention_kwargs=cross_attention_kwargs,
+ attention_mask=attention_mask,
+ encoder_attention_mask=encoder_attention_mask,
+ return_dict=False,
+ )[0]
+
+ hidden_states = motion_module(
+ hidden_states=hidden_states,
+ num_frames=num_frames,
+ cross_attention_kwargs=motion_cross_attention_kwargs,
+ )
+
+ if self.upsamplers is not None:
+ for upsampler in self.upsamplers:
+ hidden_states = upsampler(hidden_states=hidden_states, output_size=upsample_size)
+
+ return hidden_states
+
+
+class UpBlockMotion(nn.Module):
+ def __init__(
+ self,
+ in_channels: int,
+ prev_output_channel: int,
+ out_channels: int,
+ temb_channels: int,
+ resolution_idx: Optional[int] = None,
+ dropout: float = 0.0,
+ num_layers: int = 1,
+ resnet_eps: float = 1e-6,
+ resnet_time_scale_shift: str = "default",
+ resnet_act_fn: str = "swish",
+ resnet_groups: int = 32,
+ resnet_pre_norm: bool = True,
+ output_scale_factor: float = 1.0,
+ add_upsample: bool = True,
+ temporal_cross_attention_dim: Optional[int] = None,
+ temporal_num_attention_heads: int = 8,
+ temporal_max_seq_length: int = 32,
+ temporal_transformer_layers_per_block: Union[int, Tuple[int]] = 1,
+ ):
+ super().__init__()
+ resnets = []
+ motion_modules = []
+
+ # support for variable transformer layers per temporal block
+ if isinstance(temporal_transformer_layers_per_block, int):
+ temporal_transformer_layers_per_block = (temporal_transformer_layers_per_block,) * num_layers
+ elif len(temporal_transformer_layers_per_block) != num_layers:
+ raise ValueError(
+ f"temporal_transformer_layers_per_block must be an integer or a list of integers of length {num_layers}"
+ )
+
+ for i in range(num_layers):
+ res_skip_channels = in_channels if (i == num_layers - 1) else out_channels
+ resnet_in_channels = prev_output_channel if i == 0 else out_channels
+
+ resnets.append(
+ ResnetBlock2D(
+ in_channels=resnet_in_channels + res_skip_channels,
+ out_channels=out_channels,
+ temb_channels=temb_channels,
+ eps=resnet_eps,
+ groups=resnet_groups,
+ dropout=dropout,
+ time_embedding_norm=resnet_time_scale_shift,
+ non_linearity=resnet_act_fn,
+ output_scale_factor=output_scale_factor,
+ pre_norm=resnet_pre_norm,
+ )
+ )
+
+ motion_modules.append(
+ AnimateDiffTransformer3D(
+ num_attention_heads=temporal_num_attention_heads,
+ in_channels=out_channels,
+ num_layers=temporal_transformer_layers_per_block[i],
+ norm_num_groups=resnet_groups,
+ cross_attention_dim=temporal_cross_attention_dim,
+ attention_bias=False,
+ activation_fn="geglu",
+ positional_embeddings="sinusoidal",
+ num_positional_embeddings=temporal_max_seq_length,
+ attention_head_dim=out_channels // temporal_num_attention_heads,
+ )
+ )
+
+ self.resnets = nn.ModuleList(resnets)
+ self.motion_modules = nn.ModuleList(motion_modules)
+
+ if add_upsample:
+ self.upsamplers = nn.ModuleList([Upsample2D(out_channels, use_conv=True, out_channels=out_channels)])
+ else:
+ self.upsamplers = None
+
+ self.gradient_checkpointing = False
+ self.resolution_idx = resolution_idx
+
+ def forward(
+ self,
+ hidden_states: torch.Tensor,
+ res_hidden_states_tuple: Tuple[torch.Tensor, ...],
+ temb: Optional[torch.Tensor] = None,
+ upsample_size=None,
+ num_frames: int = 1,
+ motion_cross_attention_kwargs: Optional[Dict[str, Any]] = None,
+ *args,
+ **kwargs,
+ ) -> torch.Tensor:
+ if len(args) > 0 or kwargs.get("scale", None) is not None:
+ deprecation_message = "The `scale` argument is deprecated and will be ignored. Please remove it, as passing it will raise an error in the future. `scale` should directly be passed while calling the underlying pipeline component i.e., via `cross_attention_kwargs`."
+ deprecate("scale", "1.0.0", deprecation_message)
+
+ is_freeu_enabled = (
+ getattr(self, "s1", None)
+ and getattr(self, "s2", None)
+ and getattr(self, "b1", None)
+ and getattr(self, "b2", None)
+ )
+
+ blocks = zip(self.resnets, self.motion_modules)
+
+ for resnet, motion_module in blocks:
+ # pop res hidden states
+ res_hidden_states = res_hidden_states_tuple[-1]
+ res_hidden_states_tuple = res_hidden_states_tuple[:-1]
+
+ # FreeU: Only operate on the first two stages
+ if is_freeu_enabled:
+ hidden_states, res_hidden_states = apply_freeu(
+ self.resolution_idx,
+ hidden_states,
+ res_hidden_states,
+ s1=self.s1,
+ s2=self.s2,
+ b1=self.b1,
+ b2=self.b2,
+ )
+
+ hidden_states = torch.cat([hidden_states, res_hidden_states], dim=1)
+
+ if self.training and self.gradient_checkpointing:
+
+ hidden_states = torch.utils.checkpoint.checkpoint(
+ resnet,
+ hidden_states,
+ temb,
+ use_reentrant=False,
+ )
+
+ hidden_states = torch.utils.checkpoint.checkpoint(
+ motion_module,
+ hidden_states,
+ num_frames=num_frames,
+ cross_attention_kwargs=motion_cross_attention_kwargs,
+ use_reentrant=False,
+ )
+ else:
+ hidden_states = resnet(input_tensor=hidden_states, temb=temb)
+
+ hidden_states = motion_module(
+ hidden_states=hidden_states,
+ num_frames=num_frames,
+ cross_attention_kwargs=motion_cross_attention_kwargs)
+
+ if self.upsamplers is not None:
+ for upsampler in self.upsamplers:
+ hidden_states = upsampler(hidden_states=hidden_states, output_size=upsample_size)
+
+ return hidden_states
+
+
+class UNetMidBlockCrossAttnMotion(nn.Module):
+ def __init__(
+ self,
+ in_channels: int,
+ temb_channels: int,
+ dropout: float = 0.0,
+ num_layers: int = 1,
+ transformer_layers_per_block: Union[int, Tuple[int]] = 1,
+ resnet_eps: float = 1e-6,
+ resnet_time_scale_shift: str = "default",
+ resnet_act_fn: str = "swish",
+ resnet_groups: int = 32,
+ resnet_pre_norm: bool = True,
+ num_attention_heads: int = 1,
+ output_scale_factor: float = 1.0,
+ cross_attention_dim: int = 1280,
+ dual_cross_attention: bool = False,
+ use_linear_projection: bool = False,
+ upcast_attention: bool = False,
+ attention_type: str = "default",
+ temporal_num_attention_heads: int = 1,
+ temporal_cross_attention_dim: Optional[int] = None,
+ temporal_max_seq_length: int = 32,
+ temporal_transformer_layers_per_block: Union[int, Tuple[int]] = 1,
+ ):
+ super().__init__()
+
+ self.has_cross_attention = True
+ self.num_attention_heads = num_attention_heads
+ resnet_groups = resnet_groups if resnet_groups is not None else min(in_channels // 4, 32)
+
+ # support for variable transformer layers per block
+ if isinstance(transformer_layers_per_block, int):
+ transformer_layers_per_block = (transformer_layers_per_block,) * num_layers
+ elif len(transformer_layers_per_block) != num_layers:
+ raise ValueError(
+ f"`transformer_layers_per_block` should be an integer or a list of integers of length {num_layers}."
+ )
+
+ # support for variable transformer layers per temporal block
+ if isinstance(temporal_transformer_layers_per_block, int):
+ temporal_transformer_layers_per_block = (temporal_transformer_layers_per_block,) * num_layers
+ elif len(temporal_transformer_layers_per_block) != num_layers:
+ raise ValueError(
+ f"`temporal_transformer_layers_per_block` should be an integer or a list of integers of length {num_layers}."
+ )
+
+ # there is always at least one resnet
+ resnets = [
+ ResnetBlock2D(
+ in_channels=in_channels,
+ out_channels=in_channels,
+ temb_channels=temb_channels,
+ eps=resnet_eps,
+ groups=resnet_groups,
+ dropout=dropout,
+ time_embedding_norm=resnet_time_scale_shift,
+ non_linearity=resnet_act_fn,
+ output_scale_factor=output_scale_factor,
+ pre_norm=resnet_pre_norm,
+ )
+ ]
+ attentions = []
+ motion_modules = []
+
+ for i in range(num_layers):
+ if not dual_cross_attention:
+ attentions.append(
+ Transformer2DModel(
+ num_attention_heads,
+ in_channels // num_attention_heads,
+ in_channels=in_channels,
+ num_layers=transformer_layers_per_block[i],
+ cross_attention_dim=cross_attention_dim,
+ norm_num_groups=resnet_groups,
+ use_linear_projection=use_linear_projection,
+ upcast_attention=upcast_attention,
+ attention_type=attention_type,
+ )
+ )
+ else:
+ attentions.append(
+ DualTransformer2DModel(
+ num_attention_heads,
+ in_channels // num_attention_heads,
+ in_channels=in_channels,
+ num_layers=1,
+ cross_attention_dim=cross_attention_dim,
+ norm_num_groups=resnet_groups,
+ )
+ )
+ resnets.append(
+ ResnetBlock2D(
+ in_channels=in_channels,
+ out_channels=in_channels,
+ temb_channels=temb_channels,
+ eps=resnet_eps,
+ groups=resnet_groups,
+ dropout=dropout,
+ time_embedding_norm=resnet_time_scale_shift,
+ non_linearity=resnet_act_fn,
+ output_scale_factor=output_scale_factor,
+ pre_norm=resnet_pre_norm,
+ )
+ )
+ motion_modules.append(
+ AnimateDiffTransformer3D(
+ num_attention_heads=temporal_num_attention_heads,
+ attention_head_dim=in_channels // temporal_num_attention_heads,
+ in_channels=in_channels,
+ num_layers=temporal_transformer_layers_per_block[i],
+ norm_num_groups=resnet_groups,
+ cross_attention_dim=temporal_cross_attention_dim,
+ attention_bias=False,
+ positional_embeddings="sinusoidal",
+ num_positional_embeddings=temporal_max_seq_length,
+ activation_fn="geglu",
+ )
+ )
+
+ self.attentions = nn.ModuleList(attentions)
+ self.resnets = nn.ModuleList(resnets)
+ self.motion_modules = nn.ModuleList(motion_modules)
+
+ self.gradient_checkpointing = False
+
+ def forward(
+ self,
+ hidden_states: torch.Tensor,
+ temb: Optional[torch.Tensor] = None,
+ encoder_hidden_states: Optional[torch.Tensor] = None,
+ attention_mask: Optional[torch.Tensor] = None,
+ cross_attention_kwargs: Optional[Dict[str, Any]] = None,
+ motion_cross_attention_kwargs: Optional[Dict[str, Any]] = None,
+ encoder_attention_mask: Optional[torch.Tensor] = None,
+ num_frames: int = 1,
+ ) -> torch.Tensor:
+ if cross_attention_kwargs is not None:
+ if cross_attention_kwargs.get("scale", None) is not None:
+ logger.warning("Passing `scale` to `cross_attention_kwargs` is deprecated. `scale` will be ignored.")
+
+ hidden_states = self.resnets[0](hidden_states, temb)
+
+ blocks = zip(self.attentions, self.resnets[1:], self.motion_modules)
+ for attn, resnet, motion_module in blocks:
+ if self.training and self.gradient_checkpointing:
+
+ hidden_states = torch.utils.checkpoint.checkpoint(
+ attn,
+ hidden_states,
+ encoder_hidden_states=encoder_hidden_states,
+ cross_attention_kwargs=cross_attention_kwargs,
+ attention_mask=attention_mask,
+ encoder_attention_mask=encoder_attention_mask,
+ return_dict=False,
+ use_reentrant=False,
+ )[0]
+
+ hidden_states = torch.utils.checkpoint.checkpoint(
+ motion_module,
+ hidden_states,
+ temb,
+ cross_attention_kwargs=motion_cross_attention_kwargs,
+ use_reentrant=False,
+ )
+ hidden_states = torch.utils.checkpoint.checkpoint(
+ resnet,
+ hidden_states,
+ temb,
+ use_reentrant=False,
+ )
+ else:
+ hidden_states = attn(
+ hidden_states,
+ encoder_hidden_states=encoder_hidden_states,
+ cross_attention_kwargs=cross_attention_kwargs,
+ attention_mask=attention_mask,
+ encoder_attention_mask=encoder_attention_mask,
+ return_dict=False,
+ )[0]
+ hidden_states = motion_module(
+ hidden_states=hidden_states,
+ num_frames=num_frames,
+ cross_attention_kwargs=motion_cross_attention_kwargs,
+ )
+ hidden_states = resnet(input_tensor=hidden_states, temb=temb)
+
+ return hidden_states
+
+
+class UNetMidBlock2DCrossAttn(nn.Module):
+ def __init__(
+ self,
+ in_channels: int,
+ temb_channels: int,
+ out_channels: Optional[int] = None,
+ dropout: float = 0.0,
+ num_layers: int = 1,
+ transformer_layers_per_block: Union[int, Tuple[int]] = 1,
+ resnet_eps: float = 1e-6,
+ resnet_time_scale_shift: str = "default",
+ resnet_act_fn: str = "swish",
+ resnet_groups: int = 32,
+ resnet_groups_out: Optional[int] = None,
+ resnet_pre_norm: bool = True,
+ num_attention_heads: int = 1,
+ output_scale_factor: float = 1.0,
+ cross_attention_dim: int = 1280,
+ dual_cross_attention: bool = False,
+ use_linear_projection: bool = False,
+ upcast_attention: bool = False,
+ attention_type: str = "default",
+ ):
+ super().__init__()
+
+ out_channels = out_channels or in_channels
+ self.in_channels = in_channels
+ self.out_channels = out_channels
+
+ self.has_cross_attention = True
+ self.num_attention_heads = num_attention_heads
+ resnet_groups = resnet_groups if resnet_groups is not None else min(in_channels // 4, 32)
+
+ # support for variable transformer layers per block
+ if isinstance(transformer_layers_per_block, int):
+ transformer_layers_per_block = [transformer_layers_per_block] * num_layers
+
+ resnet_groups_out = resnet_groups_out or resnet_groups
+
+ # there is always at least one resnet
+ resnets = [
+ ResnetBlock2D(
+ in_channels=in_channels,
+ out_channels=out_channels,
+ temb_channels=temb_channels,
+ eps=resnet_eps,
+ groups=resnet_groups,
+ groups_out=resnet_groups_out,
+ dropout=dropout,
+ time_embedding_norm=resnet_time_scale_shift,
+ non_linearity=resnet_act_fn,
+ output_scale_factor=output_scale_factor,
+ pre_norm=resnet_pre_norm,
+ )
+ ]
+ attentions = []
+
+ for i in range(num_layers):
+ if not dual_cross_attention:
+ attentions.append(
+ Transformer2DModel(
+ num_attention_heads,
+ out_channels // num_attention_heads,
+ in_channels=out_channels,
+ num_layers=transformer_layers_per_block[i],
+ cross_attention_dim=cross_attention_dim,
+ norm_num_groups=resnet_groups_out,
+ use_linear_projection=use_linear_projection,
+ upcast_attention=upcast_attention,
+ attention_type=attention_type,
+ )
+ )
+ else:
+ attentions.append(
+ DualTransformer2DModel(
+ num_attention_heads,
+ out_channels // num_attention_heads,
+ in_channels=out_channels,
+ num_layers=1,
+ cross_attention_dim=cross_attention_dim,
+ norm_num_groups=resnet_groups,
+ )
+ )
+ resnets.append(
+ ResnetBlock2D(
+ in_channels=out_channels,
+ out_channels=out_channels,
+ temb_channels=temb_channels,
+ eps=resnet_eps,
+ groups=resnet_groups_out,
+ dropout=dropout,
+ time_embedding_norm=resnet_time_scale_shift,
+ non_linearity=resnet_act_fn,
+ output_scale_factor=output_scale_factor,
+ pre_norm=resnet_pre_norm,
+ )
+ )
+
+ self.attentions = nn.ModuleList(attentions)
+ self.resnets = nn.ModuleList(resnets)
+
+ self.gradient_checkpointing = False
+
+ def forward(
+ self,
+ hidden_states: torch.Tensor,
+ temb: Optional[torch.Tensor] = None,
+ encoder_hidden_states: Optional[torch.Tensor] = None,
+ attention_mask: Optional[torch.Tensor] = None,
+ cross_attention_kwargs: Optional[Dict[str, Any]] = None,
+ encoder_attention_mask: Optional[torch.Tensor] = None,
+ ) -> torch.Tensor:
+ if cross_attention_kwargs is not None:
+ if cross_attention_kwargs.get("scale", None) is not None:
+ logger.warning("Passing `scale` to `cross_attention_kwargs` is deprecated. `scale` will be ignored.")
+
+ hidden_states = self.resnets[0](input_tensor=hidden_states, temb=temb)
+ for attn, resnet in zip(self.attentions, self.resnets[1:]):
+ if self.training and self.gradient_checkpointing:
+
+ hidden_states = torch.utils.checkpoint.checkpoint(
+ attn,
+ hidden_states,
+ encoder_hidden_states=encoder_hidden_states,
+ cross_attention_kwargs=cross_attention_kwargs,
+ attention_mask=attention_mask,
+ encoder_attention_mask=encoder_attention_mask,
+ return_dict=False,
+ use_reentrant=False,
+ )[0]
+
+ hidden_states = torch.utils.checkpoint.checkpoint(
+ resnet,
+ hidden_states,
+ temb,
+ use_reentrant=False,
+ )
+ else:
+ hidden_states = attn(
+ hidden_states=hidden_states,
+ encoder_hidden_states=encoder_hidden_states,
+ cross_attention_kwargs=cross_attention_kwargs,
+ attention_mask=attention_mask,
+ encoder_attention_mask=encoder_attention_mask,
+ return_dict=False,
+ )[0]
+ hidden_states = resnet(input_tensor=hidden_states, temb=temb)
+
+ return hidden_states
+
+
+class MotionModules(nn.Module):
+ def __init__(
+ self,
+ in_channels: int,
+ layers_per_block: int = 2,
+ transformer_layers_per_block: Union[int, Tuple[int]] = 8,
+ num_attention_heads: Union[int, Tuple[int]] = 8,
+ attention_bias: bool = False,
+ cross_attention_dim: Optional[int] = None,
+ activation_fn: str = "geglu",
+ norm_num_groups: int = 32,
+ max_seq_length: int = 32,
+ ):
+ super().__init__()
+ self.motion_modules = nn.ModuleList([])
+
+ if isinstance(transformer_layers_per_block, int):
+ transformer_layers_per_block = (transformer_layers_per_block,) * layers_per_block
+ elif len(transformer_layers_per_block) != layers_per_block:
+ raise ValueError(
+ f"The number of transformer layers per block must match the number of layers per block, "
+ f"got {layers_per_block} and {len(transformer_layers_per_block)}"
+ )
+
+ for i in range(layers_per_block):
+ self.motion_modules.append(
+ AnimateDiffTransformer3D(
+ in_channels=in_channels,
+ num_layers=transformer_layers_per_block[i],
+ norm_num_groups=norm_num_groups,
+ cross_attention_dim=cross_attention_dim,
+ activation_fn=activation_fn,
+ attention_bias=attention_bias,
+ num_attention_heads=num_attention_heads,
+ attention_head_dim=in_channels // num_attention_heads,
+ positional_embeddings="sinusoidal",
+ num_positional_embeddings=max_seq_length,
+ )
+ )
+
+
+class MotionAdapter(ModelMixin, ConfigMixin, FromOriginalModelMixin):
+ @register_to_config
+ def __init__(
+ self,
+ block_out_channels: Tuple[int, ...] = (320, 640, 1280, 1280),
+ motion_layers_per_block: Union[int, Tuple[int]] = 2,
+ motion_transformer_layers_per_block: Union[int, Tuple[int], Tuple[Tuple[int]]] = 1,
+ motion_mid_block_layers_per_block: int = 1,
+ motion_transformer_layers_per_mid_block: Union[int, Tuple[int]] = 1,
+ motion_num_attention_heads: Union[int, Tuple[int]] = 8,
+ motion_norm_num_groups: int = 32,
+ motion_max_seq_length: int = 32,
+ use_motion_mid_block: bool = True,
+ conv_in_channels: Optional[int] = None,
+ ):
+ """Container to store AnimateDiff Motion Modules
+
+ Args:
+ block_out_channels (`Tuple[int]`, *optional*, defaults to `(320, 640, 1280, 1280)`):
+ The tuple of output channels for each UNet block.
+ motion_layers_per_block (`int` or `Tuple[int]`, *optional*, defaults to 2):
+ The number of motion layers per UNet block.
+ motion_transformer_layers_per_block (`int`, `Tuple[int]`, or `Tuple[Tuple[int]]`, *optional*, defaults to 1):
+ The number of transformer layers to use in each motion layer in each block.
+ motion_mid_block_layers_per_block (`int`, *optional*, defaults to 1):
+ The number of motion layers in the middle UNet block.
+ motion_transformer_layers_per_mid_block (`int` or `Tuple[int]`, *optional*, defaults to 1):
+ The number of transformer layers to use in each motion layer in the middle block.
+ motion_num_attention_heads (`int` or `Tuple[int]`, *optional*, defaults to 8):
+ The number of heads to use in each attention layer of the motion module.
+ motion_norm_num_groups (`int`, *optional*, defaults to 32):
+ The number of groups to use in each group normalization layer of the motion module.
+ motion_max_seq_length (`int`, *optional*, defaults to 32):
+ The maximum sequence length to use in the motion module.
+ use_motion_mid_block (`bool`, *optional*, defaults to True):
+ Whether to use a motion module in the middle of the UNet.
+ """
+
+ super().__init__()
+ down_blocks = []
+ up_blocks = []
+
+ if isinstance(motion_layers_per_block, int):
+ motion_layers_per_block = (motion_layers_per_block,) * len(block_out_channels)
+ elif len(motion_layers_per_block) != len(block_out_channels):
+ raise ValueError(
+ f"The number of motion layers per block must match the number of blocks, "
+ f"got {len(block_out_channels)} and {len(motion_layers_per_block)}"
+ )
+
+ if isinstance(motion_transformer_layers_per_block, int):
+ motion_transformer_layers_per_block = (motion_transformer_layers_per_block,) * len(block_out_channels)
+
+ if isinstance(motion_transformer_layers_per_mid_block, int):
+ motion_transformer_layers_per_mid_block = (
+ motion_transformer_layers_per_mid_block,
+ ) * motion_mid_block_layers_per_block
+ elif len(motion_transformer_layers_per_mid_block) != motion_mid_block_layers_per_block:
+ raise ValueError(
+ f"The number of layers per mid block ({motion_mid_block_layers_per_block}) "
+ f"must match the length of motion_transformer_layers_per_mid_block ({len(motion_transformer_layers_per_mid_block)})"
+ )
+
+ if isinstance(motion_num_attention_heads, int):
+ motion_num_attention_heads = (motion_num_attention_heads,) * len(block_out_channels)
+ elif len(motion_num_attention_heads) != len(block_out_channels):
+ raise ValueError(
+ f"The length of the attention head number tuple in the motion module must match the "
+ f"number of block, got {len(motion_num_attention_heads)} and {len(block_out_channels)}"
+ )
+
+ if conv_in_channels:
+ # input
+ self.conv_in = nn.Conv2d(conv_in_channels, block_out_channels[0], kernel_size=3, padding=1)
+ else:
+ self.conv_in = None
+
+ for i, channel in enumerate(block_out_channels):
+ output_channel = block_out_channels[i]
+ down_blocks.append(
+ MotionModules(
+ in_channels=output_channel,
+ norm_num_groups=motion_norm_num_groups,
+ cross_attention_dim=None,
+ activation_fn="geglu",
+ attention_bias=False,
+ num_attention_heads=motion_num_attention_heads[i],
+ max_seq_length=motion_max_seq_length,
+ layers_per_block=motion_layers_per_block[i],
+ transformer_layers_per_block=motion_transformer_layers_per_block[i],
+ )
+ )
+
+ if use_motion_mid_block:
+ self.mid_block = MotionModules(
+ in_channels=block_out_channels[-1],
+ norm_num_groups=motion_norm_num_groups,
+ cross_attention_dim=None,
+ activation_fn="geglu",
+ attention_bias=False,
+ num_attention_heads=motion_num_attention_heads[-1],
+ max_seq_length=motion_max_seq_length,
+ layers_per_block=motion_mid_block_layers_per_block,
+ transformer_layers_per_block=motion_transformer_layers_per_mid_block,
+ )
+ else:
+ self.mid_block = None
+
+ reversed_block_out_channels = list(reversed(block_out_channels))
+ output_channel = reversed_block_out_channels[0]
+
+ reversed_motion_layers_per_block = list(reversed(motion_layers_per_block))
+ reversed_motion_transformer_layers_per_block = list(reversed(motion_transformer_layers_per_block))
+ reversed_motion_num_attention_heads = list(reversed(motion_num_attention_heads))
+ for i, channel in enumerate(reversed_block_out_channels):
+ output_channel = reversed_block_out_channels[i]
+ up_blocks.append(
+ MotionModules(
+ in_channels=output_channel,
+ norm_num_groups=motion_norm_num_groups,
+ cross_attention_dim=None,
+ activation_fn="geglu",
+ attention_bias=False,
+ num_attention_heads=reversed_motion_num_attention_heads[i],
+ max_seq_length=motion_max_seq_length,
+ layers_per_block=reversed_motion_layers_per_block[i] + 1,
+ transformer_layers_per_block=reversed_motion_transformer_layers_per_block[i],
+ )
+ )
+
+ self.down_blocks = nn.ModuleList(down_blocks)
+ self.up_blocks = nn.ModuleList(up_blocks)
+
+ def forward(self, sample):
+ pass
+
+
+class UNetMotionModel(ModelMixin, ConfigMixin, UNet2DConditionLoadersMixin, PeftAdapterMixin):
+ r"""
+ A modified conditional 2D UNet model that takes a noisy sample, conditional state, and a timestep and returns a
+ sample shaped output.
+
+ This model inherits from [`ModelMixin`]. Check the superclass documentation for it's generic methods implemented
+ for all models (such as downloading or saving).
+ """
+
+ _supports_gradient_checkpointing = True
+
+ @register_to_config
+ def __init__(
+ self,
+ sample_size: Optional[int] = None,
+ in_channels: int = 4,
+ out_channels: int = 4,
+ down_block_types: Tuple[str, ...] = (
+ "CrossAttnDownBlockMotion",
+ "CrossAttnDownBlockMotion",
+ "CrossAttnDownBlockMotion",
+ "DownBlockMotion",
+ ),
+ up_block_types: Tuple[str, ...] = (
+ "UpBlockMotion",
+ "CrossAttnUpBlockMotion",
+ "CrossAttnUpBlockMotion",
+ "CrossAttnUpBlockMotion",
+ ),
+ block_out_channels: Tuple[int, ...] = (320, 640, 1280, 1280),
+ layers_per_block: Union[int, Tuple[int]] = 2,
+ downsample_padding: int = 1,
+ mid_block_scale_factor: float = 1,
+ act_fn: str = "silu",
+ norm_num_groups: int = 32,
+ norm_eps: float = 1e-5,
+ cross_attention_dim: int = 1280,
+ transformer_layers_per_block: Union[int, Tuple[int], Tuple[Tuple]] = 1,
+ reverse_transformer_layers_per_block: Optional[Union[int, Tuple[int], Tuple[Tuple]]] = None,
+ temporal_transformer_layers_per_block: Union[int, Tuple[int], Tuple[Tuple]] = 1,
+ reverse_temporal_transformer_layers_per_block: Optional[Union[int, Tuple[int], Tuple[Tuple]]] = None,
+ transformer_layers_per_mid_block: Optional[Union[int, Tuple[int]]] = None,
+ temporal_transformer_layers_per_mid_block: Optional[Union[int, Tuple[int]]] = 1,
+ use_linear_projection: bool = False,
+ num_attention_heads: Union[int, Tuple[int, ...]] = 8,
+ motion_max_seq_length: int = 32,
+ motion_num_attention_heads: Union[int, Tuple[int, ...]] = 8,
+ reverse_motion_num_attention_heads: Optional[
+ Union[int, Tuple[int, ...], Tuple[Tuple[int, ...], ...]]] = None,
+ use_motion_mid_block: bool = True,
+ mid_block_layers: int = 1,
+ encoder_hid_dim: Optional[int] = None,
+ encoder_hid_dim_type: Optional[str] = None,
+ addition_embed_type: Optional[str] = None,
+ addition_time_embed_dim: Optional[int] = None,
+ projection_class_embeddings_input_dim: Optional[int] = None,
+ time_cond_proj_dim: Optional[int] = None,
+ ):
+ super().__init__()
+
+ self.sample_size = sample_size
+
+ # Check inputs
+ if len(down_block_types) != len(up_block_types):
+ raise ValueError(
+ f"Must provide the same number of `down_block_types` as `up_block_types`. `down_block_types`: {down_block_types}. `up_block_types`: {up_block_types}."
+ )
+
+ if len(block_out_channels) != len(down_block_types):
+ raise ValueError(
+ f"Must provide the same number of `block_out_channels` as `down_block_types`. `block_out_channels`: {block_out_channels}. `down_block_types`: {down_block_types}."
+ )
+
+ if not isinstance(num_attention_heads, int) and len(num_attention_heads) != len(down_block_types):
+ raise ValueError(
+ f"Must provide the same number of `num_attention_heads` as `down_block_types`. `num_attention_heads`: {num_attention_heads}. `down_block_types`: {down_block_types}."
+ )
+
+ if isinstance(cross_attention_dim, list) and len(cross_attention_dim) != len(down_block_types):
+ raise ValueError(
+ f"Must provide the same number of `cross_attention_dim` as `down_block_types`. `cross_attention_dim`: {cross_attention_dim}. `down_block_types`: {down_block_types}."
+ )
+
+ if not isinstance(layers_per_block, int) and len(layers_per_block) != len(down_block_types):
+ raise ValueError(
+ f"Must provide the same number of `layers_per_block` as `down_block_types`. `layers_per_block`: {layers_per_block}. `down_block_types`: {down_block_types}."
+ )
+
+ if isinstance(transformer_layers_per_block, list) and reverse_transformer_layers_per_block is None:
+ for layer_number_per_block in transformer_layers_per_block:
+ if isinstance(layer_number_per_block, list):
+ raise ValueError("Must provide 'reverse_transformer_layers_per_block` if using asymmetrical UNet.")
+
+ if (
+ isinstance(temporal_transformer_layers_per_block, list)
+ and reverse_temporal_transformer_layers_per_block is None
+ ):
+ for layer_number_per_block in temporal_transformer_layers_per_block:
+ if isinstance(layer_number_per_block, list):
+ raise ValueError(
+ "Must provide 'reverse_temporal_transformer_layers_per_block` if using asymmetrical motion module in UNet."
+ )
+
+ # input
+ conv_in_kernel = 3
+ conv_out_kernel = 3
+ conv_in_padding = (conv_in_kernel - 1) // 2
+ self.conv_in = nn.Conv2d(
+ in_channels, block_out_channels[0], kernel_size=conv_in_kernel, padding=conv_in_padding
+ )
+
+ # time
+ time_embed_dim = block_out_channels[0] * 4
+ self.time_proj = Timesteps(block_out_channels[0], True, 0)
+ timestep_input_dim = block_out_channels[0]
+
+ self.time_embedding = TimestepEmbedding(
+ timestep_input_dim, time_embed_dim, act_fn=act_fn, cond_proj_dim=time_cond_proj_dim
+ )
+
+ if encoder_hid_dim_type is None:
+ self.encoder_hid_proj = None
+
+ if addition_embed_type == "text_time":
+ self.add_time_proj = Timesteps(addition_time_embed_dim, True, 0)
+ self.add_embedding = TimestepEmbedding(projection_class_embeddings_input_dim, time_embed_dim)
+
+ # class embedding
+ self.down_blocks = nn.ModuleList([])
+ self.up_blocks = nn.ModuleList([])
+
+ if isinstance(num_attention_heads, int):
+ num_attention_heads = (num_attention_heads,) * len(down_block_types)
+
+ if isinstance(cross_attention_dim, int):
+ cross_attention_dim = (cross_attention_dim,) * len(down_block_types)
+
+ if isinstance(layers_per_block, int):
+ layers_per_block = [layers_per_block] * len(down_block_types)
+
+ if isinstance(transformer_layers_per_block, int):
+ transformer_layers_per_block = [transformer_layers_per_block] * len(down_block_types)
+
+ if isinstance(reverse_transformer_layers_per_block, int):
+ reverse_transformer_layers_per_block = [reverse_transformer_layers_per_block] * len(down_block_types)
+
+ if isinstance(temporal_transformer_layers_per_block, int):
+ temporal_transformer_layers_per_block = [temporal_transformer_layers_per_block] * len(down_block_types)
+
+ if isinstance(reverse_temporal_transformer_layers_per_block, int):
+ reverse_temporal_transformer_layers_per_block = [reverse_temporal_transformer_layers_per_block] * len(
+ down_block_types
+ )
+
+ if isinstance(motion_num_attention_heads, int):
+ motion_num_attention_heads = (motion_num_attention_heads,) * len(down_block_types)
+
+ # down
+ output_channel = block_out_channels[0]
+ for i, down_block_type in enumerate(down_block_types):
+ input_channel = output_channel
+ output_channel = block_out_channels[i]
+ is_final_block = i == len(block_out_channels) - 1
+
+ if down_block_type == "CrossAttnDownBlockMotion":
+ down_block = CrossAttnDownBlockMotion(
+ in_channels=input_channel,
+ out_channels=output_channel,
+ temb_channels=time_embed_dim,
+ num_layers=layers_per_block[i],
+ transformer_layers_per_block=transformer_layers_per_block[i],
+ resnet_eps=norm_eps,
+ resnet_act_fn=act_fn,
+ resnet_groups=norm_num_groups,
+ num_attention_heads=num_attention_heads[i],
+ cross_attention_dim=cross_attention_dim[i],
+ downsample_padding=downsample_padding,
+ add_downsample=not is_final_block,
+ use_linear_projection=use_linear_projection,
+ temporal_num_attention_heads=motion_num_attention_heads[i],
+ temporal_max_seq_length=motion_max_seq_length,
+ temporal_transformer_layers_per_block=temporal_transformer_layers_per_block[i],
+ )
+ elif down_block_type == "DownBlockMotion":
+ down_block = DownBlockMotion(
+ in_channels=input_channel,
+ out_channels=output_channel,
+ temb_channels=time_embed_dim,
+ num_layers=layers_per_block[i],
+ resnet_eps=norm_eps,
+ resnet_act_fn=act_fn,
+ resnet_groups=norm_num_groups,
+ add_downsample=not is_final_block,
+ downsample_padding=downsample_padding,
+ temporal_num_attention_heads=motion_num_attention_heads[i],
+ temporal_max_seq_length=motion_max_seq_length,
+ temporal_transformer_layers_per_block=temporal_transformer_layers_per_block[i],
+ )
+ else:
+ raise ValueError(
+ "Invalid `down_block_type` encountered. Must be one of `CrossAttnDownBlockMotion` or `DownBlockMotion`"
+ )
+
+ self.down_blocks.append(down_block)
+
+ # mid
+ if transformer_layers_per_mid_block is None:
+ transformer_layers_per_mid_block = (
+ transformer_layers_per_block[-1] if isinstance(transformer_layers_per_block[-1], int) else 1
+ )
+
+ if use_motion_mid_block:
+ self.mid_block = UNetMidBlockCrossAttnMotion(
+ in_channels=block_out_channels[-1],
+ temb_channels=time_embed_dim,
+ resnet_eps=norm_eps,
+ resnet_act_fn=act_fn,
+ output_scale_factor=mid_block_scale_factor,
+ cross_attention_dim=cross_attention_dim[-1],
+ num_attention_heads=num_attention_heads[-1],
+ resnet_groups=norm_num_groups,
+ dual_cross_attention=False,
+ use_linear_projection=use_linear_projection,
+ num_layers=mid_block_layers,
+ temporal_num_attention_heads=motion_num_attention_heads[-1],
+ temporal_max_seq_length=motion_max_seq_length,
+ transformer_layers_per_block=transformer_layers_per_mid_block,
+ temporal_transformer_layers_per_block=temporal_transformer_layers_per_mid_block,
+ )
+
+ else:
+ self.mid_block = UNetMidBlock2DCrossAttn(
+ in_channels=block_out_channels[-1],
+ temb_channels=time_embed_dim,
+ resnet_eps=norm_eps,
+ resnet_act_fn=act_fn,
+ output_scale_factor=mid_block_scale_factor,
+ cross_attention_dim=cross_attention_dim[-1],
+ num_attention_heads=num_attention_heads[-1],
+ resnet_groups=norm_num_groups,
+ dual_cross_attention=False,
+ use_linear_projection=use_linear_projection,
+ num_layers=mid_block_layers,
+ transformer_layers_per_block=transformer_layers_per_mid_block,
+ )
+
+ # count how many layers upsample the images
+ self.num_upsamplers = 0
+
+ # up
+ reversed_block_out_channels = list(reversed(block_out_channels))
+ reversed_num_attention_heads = list(reversed(num_attention_heads))
+ reversed_layers_per_block = list(reversed(layers_per_block))
+ reversed_cross_attention_dim = list(reversed(cross_attention_dim))
+ reversed_motion_num_attention_heads = list(reversed(motion_num_attention_heads))
+
+ if reverse_transformer_layers_per_block is None:
+ reverse_transformer_layers_per_block = list(reversed(transformer_layers_per_block))
+
+ if reverse_temporal_transformer_layers_per_block is None:
+ reverse_temporal_transformer_layers_per_block = list(reversed(temporal_transformer_layers_per_block))
+
+ output_channel = reversed_block_out_channels[0]
+ for i, up_block_type in enumerate(up_block_types):
+ is_final_block = i == len(block_out_channels) - 1
+
+ prev_output_channel = output_channel
+ output_channel = reversed_block_out_channels[i]
+ input_channel = reversed_block_out_channels[min(i + 1, len(block_out_channels) - 1)]
+
+ # add upsample block for all BUT final layer
+ if not is_final_block:
+ add_upsample = True
+ self.num_upsamplers += 1
+ else:
+ add_upsample = False
+
+ if up_block_type == "CrossAttnUpBlockMotion":
+ up_block = CrossAttnUpBlockMotion(
+ in_channels=input_channel,
+ out_channels=output_channel,
+ prev_output_channel=prev_output_channel,
+ temb_channels=time_embed_dim,
+ resolution_idx=i,
+ num_layers=reversed_layers_per_block[i] + 1,
+ transformer_layers_per_block=reverse_transformer_layers_per_block[i],
+ resnet_eps=norm_eps,
+ resnet_act_fn=act_fn,
+ resnet_groups=norm_num_groups,
+ num_attention_heads=reversed_num_attention_heads[i],
+ cross_attention_dim=reversed_cross_attention_dim[i],
+ add_upsample=add_upsample,
+ use_linear_projection=use_linear_projection,
+ temporal_num_attention_heads=reversed_motion_num_attention_heads[i],
+ temporal_max_seq_length=motion_max_seq_length,
+ temporal_transformer_layers_per_block=reverse_temporal_transformer_layers_per_block[i],
+ )
+ elif up_block_type == "UpBlockMotion":
+ up_block = UpBlockMotion(
+ in_channels=input_channel,
+ prev_output_channel=prev_output_channel,
+ out_channels=output_channel,
+ temb_channels=time_embed_dim,
+ resolution_idx=i,
+ num_layers=reversed_layers_per_block[i] + 1,
+ resnet_eps=norm_eps,
+ resnet_act_fn=act_fn,
+ resnet_groups=norm_num_groups,
+ add_upsample=add_upsample,
+ temporal_num_attention_heads=reversed_motion_num_attention_heads[i],
+ temporal_max_seq_length=motion_max_seq_length,
+ temporal_transformer_layers_per_block=reverse_temporal_transformer_layers_per_block[i],
+ )
+ else:
+ raise ValueError(
+ "Invalid `up_block_type` encountered. Must be one of `CrossAttnUpBlockMotion` or `UpBlockMotion`"
+ )
+
+ self.up_blocks.append(up_block)
+ prev_output_channel = output_channel
+
+ # out
+ if norm_num_groups is not None:
+ self.conv_norm_out = nn.GroupNorm(
+ num_channels=block_out_channels[0], num_groups=norm_num_groups, eps=norm_eps
+ )
+ self.conv_act = nn.SiLU()
+ else:
+ self.conv_norm_out = None
+ self.conv_act = None
+
+ conv_out_padding = (conv_out_kernel - 1) // 2
+ self.conv_out = nn.Conv2d(
+ block_out_channels[0], out_channels, kernel_size=conv_out_kernel, padding=conv_out_padding
+ )
+
+ @classmethod
+ def from_unet2d(
+ cls,
+ unet: UNet2DConditionModel,
+ motion_adapter: Optional[MotionAdapter] = None,
+ load_weights: bool = True,
+ ):
+ has_motion_adapter = motion_adapter is not None
+
+ if has_motion_adapter:
+ motion_adapter.to(device=unet.device)
+
+ # check compatibility of number of blocks
+ if len(unet.config["down_block_types"]) != len(motion_adapter.config["block_out_channels"]):
+ raise ValueError("Incompatible Motion Adapter, got different number of blocks")
+
+ # check layers compatibility for each block
+ if isinstance(unet.config["layers_per_block"], int):
+ expanded_layers_per_block = [unet.config["layers_per_block"]] * len(unet.config["down_block_types"])
+ else:
+ expanded_layers_per_block = list(unet.config["layers_per_block"])
+ if isinstance(motion_adapter.config["motion_layers_per_block"], int):
+ expanded_adapter_layers_per_block = [motion_adapter.config["motion_layers_per_block"]] * len(
+ motion_adapter.config["block_out_channels"]
+ )
+ else:
+ expanded_adapter_layers_per_block = list(motion_adapter.config["motion_layers_per_block"])
+ if expanded_layers_per_block != expanded_adapter_layers_per_block:
+ raise ValueError("Incompatible Motion Adapter, got different number of layers per block")
+
+ # based on https://github.com/guoyww/AnimateDiff/blob/895f3220c06318ea0760131ec70408b466c49333/animatediff/models/unet.py#L459
+ config = dict(unet.config)
+ config["_class_name"] = cls.__name__
+
+ down_blocks = []
+ for down_blocks_type in config["down_block_types"]:
+ if "CrossAttn" in down_blocks_type:
+ down_blocks.append("CrossAttnDownBlockMotion")
+ else:
+ down_blocks.append("DownBlockMotion")
+ config["down_block_types"] = down_blocks
+
+ up_blocks = []
+ for down_blocks_type in config["up_block_types"]:
+ if "CrossAttn" in down_blocks_type:
+ up_blocks.append("CrossAttnUpBlockMotion")
+ else:
+ up_blocks.append("UpBlockMotion")
+ config["up_block_types"] = up_blocks
+
+ if has_motion_adapter:
+ config["motion_num_attention_heads"] = motion_adapter.config["motion_num_attention_heads"]
+ config["motion_max_seq_length"] = motion_adapter.config["motion_max_seq_length"]
+ config["use_motion_mid_block"] = motion_adapter.config["use_motion_mid_block"]
+ config["layers_per_block"] = motion_adapter.config["motion_layers_per_block"]
+ config["temporal_transformer_layers_per_mid_block"] = motion_adapter.config[
+ "motion_transformer_layers_per_mid_block"
+ ]
+ config["temporal_transformer_layers_per_block"] = motion_adapter.config[
+ "motion_transformer_layers_per_block"
+ ]
+ config["motion_num_attention_heads"] = motion_adapter.config["motion_num_attention_heads"]
+
+ # For PIA UNets we need to set the number input channels to 9
+ if motion_adapter.config["conv_in_channels"]:
+ config["in_channels"] = motion_adapter.config["conv_in_channels"]
+
+ # Need this for backwards compatibility with UNet2DConditionModel checkpoints
+ if not config.get("num_attention_heads"):
+ config["num_attention_heads"] = config["attention_head_dim"]
+
+ expected_kwargs, optional_kwargs = cls._get_signature_keys(cls)
+ config = FrozenDict({k: config.get(k) for k in config if k in expected_kwargs or k in optional_kwargs})
+ config["_class_name"] = cls.__name__
+ model = cls.from_config(config)
+
+ if not load_weights:
+ return model
+
+ # Logic for loading PIA UNets which allow the first 4 channels to be any UNet2DConditionModel conv_in weight
+ # while the last 5 channels must be PIA conv_in weights.
+ if has_motion_adapter and motion_adapter.config["conv_in_channels"]:
+ model.conv_in = motion_adapter.conv_in
+ updated_conv_in_weight = torch.cat(
+ [unet.conv_in.weight, motion_adapter.conv_in.weight[:, 4:, :, :]], dim=1
+ )
+ model.conv_in.load_state_dict({"weight": updated_conv_in_weight, "bias": unet.conv_in.bias})
+ else:
+ model.conv_in.load_state_dict(unet.conv_in.state_dict())
+
+ model.time_proj.load_state_dict(unet.time_proj.state_dict())
+ model.time_embedding.load_state_dict(unet.time_embedding.state_dict())
+
+ if any(
+ isinstance(proc, (IPAdapterAttnProcessor, IPAdapterAttnProcessor2_0))
+ for proc in unet.attn_processors.values()
+ ):
+ attn_procs = {}
+ for name, processor in unet.attn_processors.items():
+ if name.endswith("attn1.processor"):
+ attn_processor_class = (
+ AttnProcessor2_0 if hasattr(F, "scaled_dot_product_attention") else AttnProcessor
+ )
+ attn_procs[name] = attn_processor_class()
+ else:
+ attn_processor_class = (
+ attention_processor.IPAdapterAttnProcessor2_0
+ if hasattr(F, "scaled_dot_product_attention")
+ else IPAdapterAttnProcessor
+ )
+ attn_procs[name] = attn_processor_class(
+ hidden_size=processor.hidden_size,
+ cross_attention_dim=processor.cross_attention_dim,
+ scale=processor.scale,
+ num_tokens=processor.num_tokens,
+ )
+ for name, processor in model.attn_processors.items():
+ if name not in attn_procs:
+ attn_procs[name] = processor.__class__()
+ model.set_attn_processor(attn_procs)
+ model.config.encoder_hid_dim_type = "ip_image_proj"
+ model.encoder_hid_proj = unet.encoder_hid_proj
+
+ for i, down_block in enumerate(unet.down_blocks):
+ model.down_blocks[i].resnets.load_state_dict(down_block.resnets.state_dict())
+ if hasattr(model.down_blocks[i], "attentions"):
+ model.down_blocks[i].attentions.load_state_dict(down_block.attentions.state_dict())
+ if model.down_blocks[i].downsamplers:
+ model.down_blocks[i].downsamplers.load_state_dict(down_block.downsamplers.state_dict())
+
+ for i, up_block in enumerate(unet.up_blocks):
+ model.up_blocks[i].resnets.load_state_dict(up_block.resnets.state_dict())
+ if hasattr(model.up_blocks[i], "attentions"):
+ model.up_blocks[i].attentions.load_state_dict(up_block.attentions.state_dict())
+ if model.up_blocks[i].upsamplers:
+ model.up_blocks[i].upsamplers.load_state_dict(up_block.upsamplers.state_dict())
+
+ model.mid_block.resnets.load_state_dict(unet.mid_block.resnets.state_dict())
+ model.mid_block.attentions.load_state_dict(unet.mid_block.attentions.state_dict())
+
+ if unet.conv_norm_out is not None:
+ model.conv_norm_out.load_state_dict(unet.conv_norm_out.state_dict())
+ if unet.conv_act is not None:
+ model.conv_act.load_state_dict(unet.conv_act.state_dict())
+ model.conv_out.load_state_dict(unet.conv_out.state_dict())
+
+ if has_motion_adapter:
+ model.load_motion_modules(motion_adapter)
+
+ # ensure that the Motion UNet is the same dtype as the UNet2DConditionModel
+ model.to(unet.dtype)
+
+ return model
+
+ def freeze_unet2d_params(self) -> None:
+ """Freeze the weights of just the UNet2DConditionModel, and leave the motion modules
+ unfrozen for fine tuning.
+ """
+ # Freeze everything
+ for param in self.parameters():
+ param.requires_grad = False
+
+ # Unfreeze Motion Modules
+ for down_block in self.down_blocks:
+ motion_modules = down_block.motion_modules
+ for param in motion_modules.parameters():
+ param.requires_grad = True
+
+ for up_block in self.up_blocks:
+ motion_modules = up_block.motion_modules
+ for param in motion_modules.parameters():
+ param.requires_grad = True
+
+ if hasattr(self.mid_block, "motion_modules"):
+ motion_modules = self.mid_block.motion_modules
+ for param in motion_modules.parameters():
+ param.requires_grad = True
+
+ def load_motion_modules(self, motion_adapter: Optional[MotionAdapter]) -> None:
+ for i, down_block in enumerate(motion_adapter.down_blocks):
+ self.down_blocks[i].motion_modules.load_state_dict(down_block.motion_modules.state_dict())
+ for i, up_block in enumerate(motion_adapter.up_blocks):
+ self.up_blocks[i].motion_modules.load_state_dict(up_block.motion_modules.state_dict())
+
+ # to support older motion modules that don't have a mid_block
+ if hasattr(self.mid_block, "motion_modules"):
+ self.mid_block.motion_modules.load_state_dict(motion_adapter.mid_block.motion_modules.state_dict())
+
+ def save_motion_modules(
+ self,
+ save_directory: str,
+ is_main_process: bool = True,
+ safe_serialization: bool = True,
+ variant: Optional[str] = None,
+ push_to_hub: bool = False,
+ **kwargs,
+ ) -> None:
+ state_dict = self.state_dict()
+
+ # Extract all motion modules
+ motion_state_dict = {}
+ for k, v in state_dict.items():
+ if "motion_modules" in k:
+ motion_state_dict[k] = v
+
+ adapter = MotionAdapter(
+ block_out_channels=self.config["block_out_channels"],
+ motion_layers_per_block=self.config["layers_per_block"],
+ motion_norm_num_groups=self.config["norm_num_groups"],
+ motion_num_attention_heads=self.config["motion_num_attention_heads"],
+ motion_max_seq_length=self.config["motion_max_seq_length"],
+ use_motion_mid_block=self.config["use_motion_mid_block"],
+ )
+ adapter.load_state_dict(motion_state_dict)
+ adapter.save_pretrained(
+ save_directory=save_directory,
+ is_main_process=is_main_process,
+ safe_serialization=safe_serialization,
+ variant=variant,
+ push_to_hub=push_to_hub,
+ **kwargs,
+ )
+
+ @property
+ # Copied from diffusers.models.unets.unet_2d_condition.UNet2DConditionModel.attn_processors
+ def attn_processors(self) -> Dict[str, AttentionProcessor]:
+ r"""
+ Returns:
+ `dict` of attention processors: A dictionary containing all attention processors used in the model with
+ indexed by its weight name.
+ """
+ # set recursively
+ processors = {}
+
+ def fn_recursive_add_processors(name: str, module: torch.nn.Module, processors: Dict[str, AttentionProcessor]):
+ if hasattr(module, "get_processor"):
+ processors[f"{name}.processor"] = module.get_processor()
+
+ for sub_name, child in module.named_children():
+ fn_recursive_add_processors(f"{name}.{sub_name}", child, processors)
+
+ return processors
+
+ for name, module in self.named_children():
+ fn_recursive_add_processors(name, module, processors)
+
+ return processors
+
+ # Copied from diffusers.models.unets.unet_2d_condition.UNet2DConditionModel.set_attn_processor
+ def set_attn_processor(self, processor: Union[AttentionProcessor, Dict[str, AttentionProcessor]]):
+ r"""
+ Sets the attention processor to use to compute attention.
+
+ Parameters:
+ processor (`dict` of `AttentionProcessor` or only `AttentionProcessor`):
+ The instantiated processor class or a dictionary of processor classes that will be set as the processor
+ for **all** `Attention` layers.
+
+ If `processor` is a dict, the key needs to define the path to the corresponding cross attention
+ processor. This is strongly recommended when setting trainable attention processors.
+
+ """
+ count = len(self.attn_processors.keys())
+
+ if isinstance(processor, dict) and len(processor) != count:
+ raise ValueError(
+ f"A dict of processors was passed, but the number of processors {len(processor)} does not match the"
+ f" number of attention layers: {count}. Please make sure to pass {count} processor classes."
+ )
+
+ def fn_recursive_attn_processor(name: str, module: torch.nn.Module, processor):
+ if hasattr(module, "set_processor"):
+ if not isinstance(processor, dict):
+ module.set_processor(processor)
+ else:
+ module.set_processor(processor.pop(f"{name}.processor"))
+
+ for sub_name, child in module.named_children():
+ fn_recursive_attn_processor(f"{name}.{sub_name}", child, processor)
+
+ for name, module in self.named_children():
+ fn_recursive_attn_processor(name, module, processor)
+
+ def set_all_attn(self, add_spatial, add_temporal, encoder_only, flow_channels, **kwargs):
+ """
+ Args:
+ add_spatial: Add spatial attention processor for FlowEncoder
+ add_temporal: Add temporal attention processor for FlowEncoder
+ **kwargs: Passed to the attention processor
+
+ Returns:
+
+ """
+ if len(flow_channels) != len(self.down_blocks):
+ raise ValueError("Flow channels must be the same length as down_blocks")
+
+ attn_procs = {}
+ for name, original_attn in self.attn_processors.items():
+
+ block = name.split('.')[0] # wether it is mid_block, up_blocks or down_blocks
+ attention_cross_type = name.split('.')[-2] # wether it is first or second attention (often self or cross)
+ if block == 'mid_block':
+ attention_domain_type = name.split('.')[1] # wether it is spatial or temporal
+ layer = int(name.split('.')[2]) # the layer number
+ hidden_dim = getattr(self, f"{block}").resnets[layer].in_channels
+ flow_channel = flow_channels[-1]
+ else:
+ attention_domain_type = name.split('.')[2] # wether it is spatial or temporal
+ block_num = int(name.split('.')[1]) # the block number
+ layer = int(name.split('.')[3]) # the layer number
+ hidden_dim = getattr(self, f"{block}")[block_num].resnets[layer].out_channels
+ flow_channel = flow_channels[block_num if 'down' in block else - (block_num + 1)]
+
+ add_adaptor = attention_cross_type == 'attn1' and ('down' in block or not encoder_only)
+
+ if attention_domain_type == 'attentions' and add_spatial and add_adaptor:
+ new_proc = FlowAdaptorAttnProcessor(type='spatial', hidden_size=hidden_dim,
+ flow_feature_dim=flow_channel, **kwargs)
+ elif attention_domain_type == 'motion_modules' and add_temporal and add_adaptor:
+ new_proc = FlowAdaptorAttnProcessor(type='temporal', hidden_size=hidden_dim,
+ flow_feature_dim=flow_channel, **kwargs)
+ else:
+ new_proc = original_attn
+
+ attn_procs[name] = new_proc
+
+ self.set_attn_processor(attn_procs)
+
+ def enable_forward_chunking(self, chunk_size: Optional[int] = None, dim: int = 0) -> None:
+ """
+ Sets the attention processor to use [feed forward
+ chunking](https://huggingface.co/blog/reformer#2-chunked-feed-forward-layers).
+
+ Parameters:
+ chunk_size (`int`, *optional*):
+ The chunk size of the feed-forward layers. If not specified, will run feed-forward layer individually
+ over each tensor of dim=`dim`.
+ dim (`int`, *optional*, defaults to `0`):
+ The dimension over which the feed-forward computation should be chunked. Choose between dim=0 (batch)
+ or dim=1 (sequence length).
+ """
+ if dim not in [0, 1]:
+ raise ValueError(f"Make sure to set `dim` to either 0 or 1, not {dim}")
+
+ # By default chunk size is 1
+ chunk_size = chunk_size or 1
+
+ def fn_recursive_feed_forward(module: torch.nn.Module, chunk_size: int, dim: int):
+ if hasattr(module, "set_chunk_feed_forward"):
+ module.set_chunk_feed_forward(chunk_size=chunk_size, dim=dim)
+
+ for child in module.children():
+ fn_recursive_feed_forward(child, chunk_size, dim)
+
+ for module in self.children():
+ fn_recursive_feed_forward(module, chunk_size, dim)
+
+ def disable_forward_chunking(self) -> None:
+ def fn_recursive_feed_forward(module: torch.nn.Module, chunk_size: int, dim: int):
+ if hasattr(module, "set_chunk_feed_forward"):
+ module.set_chunk_feed_forward(chunk_size=chunk_size, dim=dim)
+
+ for child in module.children():
+ fn_recursive_feed_forward(child, chunk_size, dim)
+
+ for module in self.children():
+ fn_recursive_feed_forward(module, None, 0)
+
+ # Copied from diffusers.models.unets.unet_2d_condition.UNet2DConditionModel.set_default_attn_processor
+ def set_default_attn_processor(self) -> None:
+ """
+ Disables custom attention processors and sets the default attention implementation.
+ """
+ if all(proc.__class__ in ADDED_KV_ATTENTION_PROCESSORS for proc in self.attn_processors.values()):
+ processor = AttnAddedKVProcessor()
+ elif all(proc.__class__ in CROSS_ATTENTION_PROCESSORS for proc in self.attn_processors.values()):
+ processor = AttnProcessor()
+ else:
+ raise ValueError(
+ f"Cannot call `set_default_attn_processor` when attention processors are of type {next(iter(self.attn_processors.values()))}"
+ )
+
+ self.set_attn_processor(processor)
+
+ def _set_gradient_checkpointing(self, module, value: bool = False) -> None:
+ if isinstance(module, (CrossAttnDownBlockMotion, DownBlockMotion, CrossAttnUpBlockMotion, UpBlockMotion)):
+ module.gradient_checkpointing = value
+
+ # Copied from diffusers.models.unets.unet_2d_condition.UNet2DConditionModel.enable_freeu
+ def enable_freeu(self, s1: float, s2: float, b1: float, b2: float) -> None:
+ r"""Enables the FreeU mechanism from https://arxiv.org/abs/2309.11497.
+
+ The suffixes after the scaling factors represent the stage blocks where they are being applied.
+
+ Please refer to the [official repository](https://github.com/ChenyangSi/FreeU) for combinations of values that
+ are known to work well for different pipelines such as Stable Diffusion v1, v2, and Stable Diffusion XL.
+
+ Args:
+ s1 (`float`):
+ Scaling factor for stage 1 to attenuate the contributions of the skip features. This is done to
+ mitigate the "oversmoothing effect" in the enhanced denoising process.
+ s2 (`float`):
+ Scaling factor for stage 2 to attenuate the contributions of the skip features. This is done to
+ mitigate the "oversmoothing effect" in the enhanced denoising process.
+ b1 (`float`): Scaling factor for stage 1 to amplify the contributions of backbone features.
+ b2 (`float`): Scaling factor for stage 2 to amplify the contributions of backbone features.
+ """
+ for i, upsample_block in enumerate(self.up_blocks):
+ setattr(upsample_block, "s1", s1)
+ setattr(upsample_block, "s2", s2)
+ setattr(upsample_block, "b1", b1)
+ setattr(upsample_block, "b2", b2)
+
+ # Copied from diffusers.models.unets.unet_2d_condition.UNet2DConditionModel.disable_freeu
+ def disable_freeu(self) -> None:
+ """Disables the FreeU mechanism."""
+ freeu_keys = {"s1", "s2", "b1", "b2"}
+ for i, upsample_block in enumerate(self.up_blocks):
+ for k in freeu_keys:
+ if hasattr(upsample_block, k) or getattr(upsample_block, k, None) is not None:
+ setattr(upsample_block, k, None)
+
+ # Copied from diffusers.models.unets.unet_2d_condition.UNet2DConditionModel.fuse_qkv_projections
+ def fuse_qkv_projections(self):
+ """
+ Enables fused QKV projections. For self-attention modules, all projection matrices (i.e., query, key, value)
+ are fused. For cross-attention modules, key and value projection matrices are fused.
+
+
+
+ This API is 🧪 experimental.
+
+
+ """
+ self.original_attn_processors = None
+
+ for _, attn_processor in self.attn_processors.items():
+ if "Added" in str(attn_processor.__class__.__name__):
+ raise ValueError("`fuse_qkv_projections()` is not supported for models having added KV projections.")
+
+ self.original_attn_processors = self.attn_processors
+
+ for module in self.modules():
+ if isinstance(module, Attention):
+ module.fuse_projections(fuse=True)
+
+ self.set_attn_processor(FusedAttnProcessor2_0())
+
+ # Copied from diffusers.models.unets.unet_2d_condition.UNet2DConditionModel.unfuse_qkv_projections
+ def unfuse_qkv_projections(self):
+ """Disables the fused QKV projection if enabled.
+
+
+
+ This API is 🧪 experimental.
+
+
+
+ """
+ if self.original_attn_processors is not None:
+ self.set_attn_processor(self.original_attn_processors)
+
+ def forward(
+ self,
+ sample: torch.Tensor,
+ timestep: Union[torch.Tensor, float, int],
+ encoder_hidden_states: torch.Tensor,
+ timestep_cond: Optional[torch.Tensor] = None,
+ attention_mask: Optional[torch.Tensor] = None,
+ cross_attention_kwargs: Optional[Dict[str, Any]] = None,
+ motion_cross_attention_kwargs: Optional[Dict[str, Any]] = None,
+ added_cond_kwargs: Optional[Dict[str, torch.Tensor]] = None,
+ down_block_additional_residuals: Optional[Tuple[torch.Tensor]] = None,
+ mid_block_additional_residual: Optional[torch.Tensor] = None,
+ return_dict: bool = True,
+ ) -> Union[UNetMotionOutput, Tuple[torch.Tensor]]:
+ r"""
+ The [`UNetMotionModel`] forward method.
+
+ Args:
+ sample (`torch.Tensor`):
+ The noisy input tensor with the following shape `(batch, num_frames, channel, height, width`.
+ timestep (`torch.Tensor` or `float` or `int`): The number of timesteps to denoise an input.
+ encoder_hidden_states (`torch.Tensor`):
+ The encoder hidden states with shape `(batch, sequence_length, feature_dim)`.
+ timestep_cond: (`torch.Tensor`, *optional*, defaults to `None`):
+ Conditional embeddings for timestep. If provided, the embeddings will be summed with the samples passed
+ through the `self.time_embedding` layer to obtain the timestep embeddings.
+ attention_mask (`torch.Tensor`, *optional*, defaults to `None`):
+ An attention mask of shape `(batch, key_tokens)` is applied to `encoder_hidden_states`. If `1` the mask
+ is kept, otherwise if `0` it is discarded. Mask will be converted into a bias, which adds large
+ negative values to the attention scores corresponding to "discard" tokens.
+ 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).
+ down_block_additional_residuals: (`tuple` of `torch.Tensor`, *optional*):
+ A tuple of tensors that if specified are added to the residuals of down unet blocks.
+ mid_block_additional_residual: (`torch.Tensor`, *optional*):
+ A tensor that if specified is added to the residual of the middle unet block.
+ return_dict (`bool`, *optional*, defaults to `True`):
+ Whether or not to return a [`~models.unets.unet_motion_model.UNetMotionOutput`] instead of a plain
+ tuple.
+
+ Returns:
+ [`~models.unets.unet_motion_model.UNetMotionOutput`] or `tuple`:
+ If `return_dict` is True, an [`~models.unets.unet_motion_model.UNetMotionOutput`] is returned,
+ otherwise a `tuple` is returned where the first element is the sample tensor.
+ """
+ # By default samples have to be AT least a multiple of the overall upsampling factor.
+ # The overall upsampling factor is equal to 2 ** (# num of upsampling layears).
+ # However, the upsampling interpolation output size can be forced to fit any upsampling size
+ # on the fly if necessary.
+ default_overall_up_factor = 2 ** self.num_upsamplers
+
+ # upsample size should be forwarded when sample is not a multiple of `default_overall_up_factor`
+ forward_upsample_size = False
+ upsample_size = None
+
+ if any(s % default_overall_up_factor != 0 for s in sample.shape[-2:]):
+ logger.info("Forward upsample size to force interpolation output size.")
+ forward_upsample_size = True
+
+ # prepare attention_mask
+ if attention_mask is not None:
+ attention_mask = (1 - attention_mask.to(sample.dtype)) * -10000.0
+ attention_mask = attention_mask.unsqueeze(1)
+
+ # 1. time
+ timesteps = timestep
+ if not torch.is_tensor(timesteps):
+ # TODO: this requires sync between CPU and GPU. So try to pass timesteps as tensors if you can
+ # This would be a good case for the `match` statement (Python 3.10+)
+ is_mps = sample.device.type == "mps"
+ if isinstance(timestep, float):
+ dtype = torch.float32 if is_mps else torch.float64
+ else:
+ dtype = torch.int32 if is_mps else torch.int64
+ timesteps = torch.tensor([timesteps], dtype=dtype, device=sample.device)
+ elif len(timesteps.shape) == 0:
+ timesteps = timesteps[None].to(sample.device)
+
+ # broadcast to batch dimension in a way that's compatible with ONNX/Core ML
+ num_frames = sample.shape[2]
+ timesteps = timesteps.expand(sample.shape[0])
+
+ t_emb = self.time_proj(timesteps)
+
+ # 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=self.dtype)
+
+ emb = self.time_embedding(t_emb, timestep_cond)
+ aug_emb = None
+
+ if self.config.addition_embed_type == "text_time":
+ if "text_embeds" not in added_cond_kwargs:
+ raise ValueError(
+ f"{self.__class__} has the config param `addition_embed_type` set to 'text_time' which requires the keyword argument `text_embeds` to be passed in `added_cond_kwargs`"
+ )
+
+ text_embeds = added_cond_kwargs.get("text_embeds")
+ if "time_ids" not in added_cond_kwargs:
+ raise ValueError(
+ f"{self.__class__} has the config param `addition_embed_type` set to 'text_time' which requires the keyword argument `time_ids` to be passed in `added_cond_kwargs`"
+ )
+ time_ids = added_cond_kwargs.get("time_ids")
+ time_embeds = self.add_time_proj(time_ids.flatten())
+ time_embeds = time_embeds.reshape((text_embeds.shape[0], -1))
+
+ add_embeds = torch.concat([text_embeds, time_embeds], dim=-1)
+ add_embeds = add_embeds.to(emb.dtype)
+ aug_emb = self.add_embedding(add_embeds)
+
+ emb = emb if aug_emb is None else emb + aug_emb
+ emb = emb.repeat_interleave(repeats=num_frames, dim=0)
+
+ if self.encoder_hid_proj is not None and self.config.encoder_hid_dim_type == "ip_image_proj":
+ if "image_embeds" not in added_cond_kwargs:
+ raise ValueError(
+ f"{self.__class__} has the config param `encoder_hid_dim_type` set to 'ip_image_proj' which requires the keyword argument `image_embeds` to be passed in `added_conditions`"
+ )
+ image_embeds = added_cond_kwargs.get("image_embeds")
+ image_embeds = self.encoder_hid_proj(image_embeds)
+ image_embeds = [image_embed.repeat_interleave(repeats=num_frames, dim=0) for image_embed in image_embeds]
+ encoder_hidden_states = (encoder_hidden_states, image_embeds)
+
+ # 2. pre-process
+ sample = sample.permute(0, 2, 1, 3, 4).reshape((sample.shape[0] * num_frames, -1) + sample.shape[3:])
+ sample = self.conv_in(sample)
+
+ flow_feature_spatial = [rearrange(flow, "b c f h w -> (b f) (h w) c") for flow in
+ added_cond_kwargs["flow_embedding_features"]]
+ flow_feature_temporal = [rearrange(flow, "b c f h w -> (b h w) f c") for flow in
+ added_cond_kwargs["flow_embedding_features"]]
+
+ merged_cross_attention_kwargs = [{"flow_feature": flow_feature} for flow_feature in flow_feature_spatial]
+ merged_motion_cross_attention_kwargs = [{"flow_feature": flow_feature} for flow_feature in
+ flow_feature_temporal]
+
+ for cak, mcak in zip(merged_cross_attention_kwargs, merged_motion_cross_attention_kwargs):
+ if cross_attention_kwargs is not None: cak.update(cross_attention_kwargs)
+ if motion_cross_attention_kwargs is not None: mcak.update(motion_cross_attention_kwargs)
+
+ # 3. down
+ down_block_res_samples = (sample,)
+ for i, downsample_block in enumerate(self.down_blocks):
+
+ if hasattr(downsample_block, "has_cross_attention") and downsample_block.has_cross_attention:
+ sample, res_samples = downsample_block(
+ hidden_states=sample,
+ temb=emb,
+ encoder_hidden_states=encoder_hidden_states,
+ attention_mask=attention_mask,
+ num_frames=num_frames,
+ cross_attention_kwargs=merged_cross_attention_kwargs[i],
+ motion_cross_attention_kwargs=merged_motion_cross_attention_kwargs[i]
+ )
+ else:
+ sample, res_samples = downsample_block(
+ hidden_states=sample,
+ temb=emb,
+ num_frames=num_frames,
+ motion_cross_attention_kwargs=merged_motion_cross_attention_kwargs[i]
+ )
+
+ down_block_res_samples += res_samples
+
+ if down_block_additional_residuals is not None:
+ new_down_block_res_samples = ()
+
+ for down_block_res_sample, down_block_additional_residual in zip(
+ down_block_res_samples, down_block_additional_residuals
+ ):
+ down_block_res_sample = down_block_res_sample + down_block_additional_residual
+ new_down_block_res_samples += (down_block_res_sample,)
+
+ down_block_res_samples = new_down_block_res_samples
+
+ # 4. mid
+ if self.mid_block is not None:
+ # To support older versions of motion modules that don't have a mid_block
+
+ if hasattr(self.mid_block, "motion_modules"):
+ sample = self.mid_block(
+ sample,
+ emb,
+ encoder_hidden_states=encoder_hidden_states,
+ attention_mask=attention_mask,
+ num_frames=num_frames,
+ cross_attention_kwargs=merged_cross_attention_kwargs[-1],
+ motion_cross_attention_kwargs=merged_motion_cross_attention_kwargs[-1]
+ )
+ else:
+ sample = self.mid_block(
+ sample,
+ emb,
+ encoder_hidden_states=encoder_hidden_states,
+ attention_mask=attention_mask,
+ cross_attention_kwargs=merged_cross_attention_kwargs[-1],
+ )
+
+ if mid_block_additional_residual is not None:
+ sample = sample + mid_block_additional_residual
+
+ # 5. up
+ for i, upsample_block in enumerate(self.up_blocks):
+ is_final_block = i == len(self.up_blocks) - 1
+
+ res_samples = down_block_res_samples[-len(upsample_block.resnets):]
+ down_block_res_samples = down_block_res_samples[: -len(upsample_block.resnets)]
+
+ # if we have not reached the final block and need to forward the
+ # upsample size, we do it here
+ if not is_final_block and forward_upsample_size:
+ upsample_size = down_block_res_samples[-1].shape[2:]
+
+ if hasattr(upsample_block, "has_cross_attention") and upsample_block.has_cross_attention:
+ sample = upsample_block(
+ hidden_states=sample,
+ temb=emb,
+ res_hidden_states_tuple=res_samples,
+ encoder_hidden_states=encoder_hidden_states,
+ upsample_size=upsample_size,
+ attention_mask=attention_mask,
+ num_frames=num_frames,
+ cross_attention_kwargs=merged_cross_attention_kwargs[-(i + 1)],
+ motion_cross_attention_kwargs=merged_motion_cross_attention_kwargs[-(i + 1)]
+ )
+ else:
+ sample = upsample_block(
+ hidden_states=sample,
+ temb=emb,
+ res_hidden_states_tuple=res_samples,
+ upsample_size=upsample_size,
+ num_frames=num_frames,
+ motion_cross_attention_kwargs=merged_motion_cross_attention_kwargs[-(i + 1)]
+ )
+
+ # 6. post-process
+ if self.conv_norm_out:
+ sample = self.conv_norm_out(sample)
+ sample = self.conv_act(sample)
+
+ sample = self.conv_out(sample)
+
+ # reshape to (batch, channel, framerate, width, height)
+ sample = sample[None, :].reshape((-1, num_frames) + sample.shape[1:]).permute(0, 2, 1, 3, 4)
+
+ if not return_dict:
+ return (sample,)
+
+ return UNetMotionOutput(sample=sample)