# Copyright 2023 Alibaba DAMO-VILAB and The HuggingFace Team. All rights reserved.

# Copyright 2023 The ModelScope Team.

#

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

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

# You may obtain a copy of the License at

#

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

#

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

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

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

# See the License for the specific language governing permissions and

# limitations under the License.

from dataclasses import dataclass

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

import torch

import torch.nn as nn

import torch.utils.checkpoint

from ..configuration_utils import ConfigMixin, register_to_config

from ..loaders import UNet2DConditionLoadersMixin

from ..utils import BaseOutput, logging

from .attention_processor import AttentionProcessor, AttnProcessor

from .embeddings import TimestepEmbedding, Timesteps

from .modeling_utils import ModelMixin

from .transformer_temporal import TransformerTemporalModel

from .unet_3d_blocks import (

CrossAttnDownBlock3D,

CrossAttnUpBlock3D,

DownBlock3D,

UNetMidBlock3DCrossAttn,

UpBlock3D,

get_down_block,

get_up_block,

)

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

@dataclass

class UNet3DConditionOutput(BaseOutput):

"""

Args:

sample (`torch.FloatTensor` of shape `(batch_size, num_frames, num_channels, height, width)`):

Hidden states conditioned on `encoder_hidden_states` input. Output of last layer of model.

"""

sample: torch.FloatTensor

class UNet3DConditionModel(ModelMixin, ConfigMixin, UNet2DConditionLoadersMixin):

r"""

UNet3DConditionModel is a conditional 2D UNet model that takes in a noisy sample, conditional state, and a timestep

and returns sample shaped output.

This model inherits from [`ModelMixin`]. Check the superclass documentation for the generic methods the library

implements for all the models (such as downloading or saving, etc.)

Parameters:

sample_size (`int` or `Tuple[int, int]`, *optional*, defaults to `None`):

Height and width of input/output sample.

in_channels (`int`, *optional*, defaults to 4): The number of channels in the input sample.

out_channels (`int`, *optional*, defaults to 4): The number of channels in the output.

down_block_types (`Tuple[str]`, *optional*, defaults to `("CrossAttnDownBlock2D", "CrossAttnDownBlock2D", "CrossAttnDownBlock2D", "DownBlock2D")`):

The tuple of downsample blocks to use.

up_block_types (`Tuple[str]`, *optional*, defaults to `("UpBlock2D", "CrossAttnUpBlock2D", "CrossAttnUpBlock2D", "CrossAttnUpBlock2D",)`):

The tuple of upsample blocks to use.

block_out_channels (`Tuple[int]`, *optional*, defaults to `(320, 640, 1280, 1280)`):

The tuple of output channels for each block.

layers_per_block (`int`, *optional*, defaults to 2): The number of layers per block.

downsample_padding (`int`, *optional*, defaults to 1): The padding to use for the downsampling convolution.

mid_block_scale_factor (`float`, *optional*, defaults to 1.0): The scale factor to use for the mid block.

act_fn (`str`, *optional*, defaults to `"silu"`): The activation function to use.

norm_num_groups (`int`, *optional*, defaults to 32): The number of groups to use for the normalization.

If `None`, it will skip the normalization and activation layers in post-processing

norm_eps (`float`, *optional*, defaults to 1e-5): The epsilon to use for the normalization.

cross_attention_dim (`int`, *optional*, defaults to 1280): The dimension of the cross attention features.

attention_head_dim (`int`, *optional*, defaults to 8): The dimension of the attention heads.

"""

_supports_gradient_checkpointing = False

@register_to_config

def __init__(

self,

sample_size: Optional[int] = None,

in_channels: int = 4,

out_channels: int = 4,

down_block_types: Tuple[str] = (

"CrossAttnDownBlock3D",

"CrossAttnDownBlock3D",

"CrossAttnDownBlock3D",

"DownBlock3D",

),

up_block_types: Tuple[str] = ("UpBlock3D", "CrossAttnUpBlock3D", "CrossAttnUpBlock3D", "CrossAttnUpBlock3D"),

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: Optional[int] = 32,

norm_eps: float = 1e-5,

cross_attention_dim: int = 1024,

attention_head_dim: Union[int, Tuple[int]] = 64,

):

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(attention_head_dim, int) and len(attention_head_dim) != len(down_block_types):

raise ValueError(

f"Must provide the same number of `attention_head_dim` as `down_block_types`. `attention_head_dim`: {attention_head_dim}. `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,

)

self.transformer_in = TransformerTemporalModel(

num_attention_heads=8,

attention_head_dim=attention_head_dim,

in_channels=block_out_channels[0],

num_layers=1,

)

# class embedding

self.down_blocks = nn.ModuleList([])

self.up_blocks = nn.ModuleList([])

if isinstance(attention_head_dim, int):

attention_head_dim = (attention_head_dim,) * 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,

attn_num_head_channels=attention_head_dim[i],

downsample_padding=downsample_padding,

dual_cross_attention=False,

)

self.down_blocks.append(down_block)

# mid

self.mid_block = UNetMidBlock3DCrossAttn(

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,

attn_num_head_channels=attention_head_dim[-1],

resnet_groups=norm_num_groups,

dual_cross_attention=False,

)

# count how many layers upsample the images

self.num_upsamplers = 0

# up

reversed_block_out_channels = list(reversed(block_out_channels))

reversed_attention_head_dim = list(reversed(attention_head_dim))

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,

attn_num_head_channels=reversed_attention_head_dim[i],

dual_cross_attention=False,

)

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

)

@property

# Copied from diffusers.models.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, "set_processor"):

processors[f"{name}.processor"] = module.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.unet_2d_condition.UNet2DConditionModel.set_attention_slice

def set_attention_slice(self, slice_size):

r"""

Enable sliced attention computation.

When this option is enabled, the attention module will split the input tensor in slices, to compute attention

in several steps. This is useful to save some memory in exchange for a small speed decrease.

Args:

slice_size (`str` or `int` or `list(int)`, *optional*, defaults to `"auto"`):

When `"auto"`, halves the input to the attention heads, so attention will be computed in two steps. If

`"max"`, maximum amount of memory will be saved by running only one slice at a time. If a number is

provided, uses as many slices as `attention_head_dim // slice_size`. In this case, `attention_head_dim`

must be a multiple of `slice_size`.

"""

sliceable_head_dims = []

def fn_recursive_retrieve_sliceable_dims(module: torch.nn.Module):

if hasattr(module, "set_attention_slice"):

sliceable_head_dims.append(module.sliceable_head_dim)

for child in module.children():

fn_recursive_retrieve_sliceable_dims(child)

# retrieve number of attention layers

for module in self.children():

fn_recursive_retrieve_sliceable_dims(module)

num_sliceable_layers = len(sliceable_head_dims)

if slice_size == "auto":

# half the attention head size is usually a good trade-off between

# speed and memory

slice_size = [dim // 2 for dim in sliceable_head_dims]

elif slice_size == "max":

# make smallest slice possible

slice_size = num_sliceable_layers * [1]

slice_size = num_sliceable_layers * [slice_size] if not isinstance(slice_size, list) else slice_size

if len(slice_size) != len(sliceable_head_dims):

raise ValueError(

f"You have provided {len(slice_size)}, but {self.config} has {len(sliceable_head_dims)} different"

f" attention layers. Make sure to match `len(slice_size)` to be {len(sliceable_head_dims)}."

)

for i in range(len(slice_size)):

size = slice_size[i]

dim = sliceable_head_dims[i]

if size is not None and size > dim:

raise ValueError(f"size {size} has to be smaller or equal to {dim}.")

# Recursively walk through all the children.

# Any children which exposes the set_attention_slice method

# gets the message

def fn_recursive_set_attention_slice(module: torch.nn.Module, slice_size: List[int]):

if hasattr(module, "set_attention_slice"):

module.set_attention_slice(slice_size.pop())

for child in module.children():

fn_recursive_set_attention_slice(child, slice_size)

reversed_slice_size = list(reversed(slice_size))

for module in self.children():

fn_recursive_set_attention_slice(module, reversed_slice_size)

# Copied from diffusers.models.unet_2d_condition.UNet2DConditionModel.set_attn_processor

def set_attn_processor(self, processor: Union[AttentionProcessor, Dict[str, AttentionProcessor]]):

r"""

Parameters:

`processor (`dict` of `AttentionProcessor` or `AttentionProcessor`):

The instantiated processor class or a dictionary of processor classes that will be set as the processor

of **all** `Attention` layers.

In case `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.unet_2d_condition.UNet2DConditionModel.set_default_attn_processor

def set_default_attn_processor(self):

"""

Disables custom attention processors and sets the default attention implementation.

"""

self.set_attn_processor(AttnProcessor())

def _set_gradient_checkpointing(self, module, value=False):

if isinstance(module, (CrossAttnDownBlock3D, DownBlock3D, CrossAttnUpBlock3D, UpBlock3D)):

module.gradient_checkpointing = value

def forward(

self,

sample: torch.FloatTensor,

timestep: Union[torch.Tensor, float, int],

encoder_hidden_states: torch.Tensor,

class_labels: Optional[torch.Tensor] = None,

timestep_cond: Optional[torch.Tensor] = None,

attention_mask: Optional[torch.Tensor] = None,

cross_attention_kwargs: Optional[Dict[str, Any]] = 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]:

r"""

Args:

sample (`torch.FloatTensor`): (batch, num_frames, channel, height, width) noisy inputs tensor

timestep (`torch.FloatTensor` or `float` or `int`): (batch) timesteps

encoder_hidden_states (`torch.FloatTensor`): (batch, sequence_length, feature_dim) encoder hidden states

return_dict (`bool`, *optional*, defaults to `True`):

Whether or not to return a [`models.unet_2d_condition.UNet3DConditionOutput`] instead of a plain tuple.

cross_attention_kwargs (`dict`, *optional*):

A kwargs dictionary that if specified is passed along to the `AttentionProcessor` as defined under

`self.processor` in

[diffusers.cross_attention](https://github.com/huggingface/diffusers/blob/main/src/diffusers/models/cross_attention.py).

Returns:

[`~models.unet_2d_condition.UNet3DConditionOutput`] or `tuple`:

[`~models.unet_2d_condition.UNet3DConditionOutput`] if `return_dict` is True, otherwise a `tuple`. When

returning a tuple, 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)

# 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)

sample = self.transformer_in(

sample, num_frames=num_frames, cross_attention_kwargs=cross_attention_kwargs

).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:

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,

)

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)