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(

TransformerTemporalModel(

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,

)

@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

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):

r"""

_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

down_block = get_down_block(

down_block_type,

num_layers=layers_per_block[i],

in_channels=input_channel,

out_channels=output_channel,

temb_channels=time_embed_dim,

add_downsample=not is_final_block,

resnet_eps=norm_eps,

resnet_act_fn=act_fn,

resnet_groups=norm_num_groups,

cross_attention_dim=cross_attention_dim[i],

num_attention_heads=num_attention_heads[i],

downsample_padding=downsample_padding,

use_linear_projection=use_linear_projection,

dual_cross_attention=False,

temporal_num_attention_heads=motion_num_attention_heads[i],

temporal_max_seq_length=motion_max_seq_length,

transformer_layers_per_block=transformer_layers_per_block[i],

temporal_transformer_layers_per_block=temporal_transformer_layers_per_block[i],

)

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

up_block = get_up_block(

up_block_type,

num_layers=reversed_layers_per_block[i] + 1,

in_channels=input_channel,

out_channels=output_channel,

prev_output_channel=prev_output_channel,

temb_channels=time_embed_dim,

add_upsample=add_upsample,

resnet_eps=norm_eps,

resnet_act_fn=act_fn,

resnet_groups=norm_num_groups,

cross_attention_dim=reversed_cross_attention_dim[i],

num_attention_heads=reversed_num_attention_heads[i],

dual_cross_attention=False,

resolution_idx=i,

use_linear_projection=use_linear_projection,

temporal_num_attention_heads=reversed_motion_num_attention_heads[i],

temporal_max_seq_length=motion_max_seq_length,

transformer_layers_per_block=reverse_transformer_layers_per_block[i],

temporal_transformer_layers_per_block=reverse_temporal_transformer_layers_per_block[i],

)

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 = (

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

# 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"""

# 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"""

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)

# Copied from diffusers.models.unets.unet_3d_condition.UNet3DConditionModel.enable_forward_chunking

def enable_forward_chunking(self, chunk_size: Optional[int] = None, dim: int = 0) -> None:

"""

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)

# Copied from diffusers.models.unets.unet_3d_condition.UNet3DConditionModel.disable_forward_chunking

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:

"""

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.

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):

"""

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)

# Copied from diffusers.models.unets.unet_2d_condition.UNet2DConditionModel.unfuse_qkv_projections

def unfuse_qkv_projections(self):

"""Disables the fused QKV projection if enabled.

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,

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[UNet3DConditionOutput, Tuple[torch.Tensor]]:

r"""

# 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