# Copyright 2024 The HuggingFace Team. All rights reserved.

#

# Licensed under the Apache License, Version 2.0 (the "License");

# you may not use this file except in compliance with the License.

# You may obtain a copy of the License at

#

# http://www.apache.org/licenses/LICENSE-2.0

#

# Unless required by applicable law or agreed to in writing, software

# distributed under the License is distributed on an "AS IS" BASIS,

# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.

# See the License for the specific language governing permissions and

# limitations under the License.

from typing import Any, Dict, Optional, Tuple, Union

import torch

import torch.nn as nn

import torch.utils.checkpoint

from ...configuration_utils import ConfigMixin, FrozenDict, register_to_config

from ...loaders import UNet2DConditionLoadersMixin

from ...utils import logging

from ..attention_processor import (

ADDED_KV_ATTENTION_PROCESSORS,

CROSS_ATTENTION_PROCESSORS,

Attention,

AttentionProcessor,

AttnAddedKVProcessor,

AttnProcessor,

)

from ..embeddings import TimestepEmbedding, Timesteps

from ..modeling_utils import ModelMixin

from ..transformers.transformer_temporal import TransformerTemporalModel

from .unet_2d_blocks import UNetMidBlock2DCrossAttn

from .unet_2d_condition import UNet2DConditionModel

from .unet_3d_blocks import (

CrossAttnDownBlockMotion,

CrossAttnUpBlockMotion,

DownBlockMotion,

UNetMidBlockCrossAttnMotion,

UpBlockMotion,

get_down_block,

get_up_block,

)

from .unet_3d_condition import UNet3DConditionOutput

logger = logging.get_logger(__name__) # pylint: disable=invalid-name

class MotionModules(nn.Module):

def __init__(

self,

in_channels: int,

layers_per_block: int = 2,

num_attention_heads: 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([])

for i in range(layers_per_block):

self.motion_modules.append(

TransformerTemporalModel(

in_channels=in_channels,

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):

@register_to_config

def __init__(

self,

block_out_channels: Tuple[int, ...] = (320, 640, 1280, 1280),

motion_layers_per_block: int = 2,

motion_mid_block_layers_per_block: int = 1,

motion_num_attention_heads: 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`, *optional*, defaults to 2):

The number of motion layers per UNet block.

motion_mid_block_layers_per_block (`int`, *optional*, defaults to 1):

The number of motion layers in the middle UNet block.

motion_num_attention_heads (`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 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,

max_seq_length=motion_max_seq_length,

layers_per_block=motion_layers_per_block,

)

)

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,

layers_per_block=motion_mid_block_layers_per_block,

max_seq_length=motion_max_seq_length,

)

else:

self.mid_block = None

reversed_block_out_channels = list(reversed(block_out_channels))

output_channel = reversed_block_out_channels[0]

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=motion_num_attention_heads,

max_seq_length=motion_max_seq_length,

layers_per_block=motion_layers_per_block + 1,

)

)

self.down_blocks = nn.ModuleList(down_blocks)

self.up_blocks = nn.ModuleList(up_blocks)

def forward(self, sample):

pass

class UNetMotionModel(ModelMixin, ConfigMixin, UNet2DConditionLoadersMixin):

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: 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,

use_linear_projection: bool = False,

num_attention_heads: Union[int, Tuple[int, ...]] = 8,

motion_max_seq_length: int = 32,

motion_num_attention_heads: int = 8,

use_motion_mid_block: int = True,

encoder_hid_dim: Optional[int] = None,

encoder_hid_dim_type: Optional[str] = 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}."

)

# 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

# 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)

# 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,

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,

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,

temporal_max_seq_length=motion_max_seq_length,

)

self.down_blocks.append(down_block)

# mid

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,

num_attention_heads=num_attention_heads[-1],

resnet_groups=norm_num_groups,

dual_cross_attention=False,

use_linear_projection=use_linear_projection,

temporal_num_attention_heads=motion_num_attention_heads,

temporal_max_seq_length=motion_max_seq_length,

)

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,

num_attention_heads=num_attention_heads[-1],

resnet_groups=norm_num_groups,

dual_cross_attention=False,

use_linear_projection=use_linear_projection,

)

# 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))

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=layers_per_block + 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=cross_attention_dim,

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=motion_num_attention_heads,

temporal_max_seq_length=motion_max_seq_length,

)

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)

# 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"]

# 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"]

config = FrozenDict(config)

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())

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(return_deprecated_lora=True)

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)

# 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:

"""

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)

# 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:

"""

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.

<Tip warning={true}>

This API is 🧪 experimental.

</Tip>

"""

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.

<Tip warning={true}>

This API is 🧪 experimental.

</Tip>

"""

if self.original_attn_processors is not None:

self.set_attn_processor(self.original_attn_processors)

def forward(

self,

sample: torch.FloatTensor,

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"""

The [`UNetMotionModel`] forward method.

Args:

sample (`torch.FloatTensor`):

The noisy input tensor with the following shape `(batch, num_frames, channel, height, width`.

timestep (`torch.FloatTensor` or `float` or `int`): The number of timesteps to denoise an input.

encoder_hidden_states (`torch.FloatTensor`):

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.unet_3d_condition.UNet3DConditionOutput`] instead of a plain

tuple.

Returns:

[`~models.unet_3d_condition.UNet3DConditionOutput`] or `tuple`:

If `return_dict` is True, an [`~models.unet_3d_condition.UNet3DConditionOutput`] 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)

emb = emb.repeat_interleave(repeats=num_frames, dim=0)

encoder_hidden_states = encoder_hidden_states.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)

# 3. down

down_block_res_samples = (sample,)

for downsample_block in 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=cross_attention_kwargs,

)

else:

sample, res_samples = downsample_block(hidden_states=sample, temb=emb, num_frames=num_frames)

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=cross_attention_kwargs,

)

else:

sample = self.mid_block(

sample,

emb,

encoder_hidden_states=encoder_hidden_states,

attention_mask=attention_mask,

cross_attention_kwargs=cross_attention_kwargs,

)

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=cross_attention_kwargs,

)

else:

sample = upsample_block(

hidden_states=sample,

temb=emb,

res_hidden_states_tuple=res_samples,

upsample_size=upsample_size,

num_frames=num_frames,

)

# 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 UNet3DConditionOutput(sample=sample)