# Copyright 2023 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 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 .activations import get_activation

from .attention_processor import AttentionProcessor, AttnProcessor

from .embeddings import (

GaussianFourierProjection,

TextImageProjection,

TextImageTimeEmbedding,

TextTimeEmbedding,

TimestepEmbedding,

Timesteps,

)

from .modeling_utils import ModelMixin

from .unet_2d_blocks import (

CrossAttnDownBlock2D,

CrossAttnUpBlock2D,

DownBlock2D,

UNetMidBlock2DCrossAttn,

UNetMidBlock2DSimpleCrossAttn,

UpBlock2D,

get_down_block,

get_up_block,

)

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

@dataclass

class UNet2DConditionOutput(BaseOutput):

"""

Args:

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

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

"""

sample: torch.FloatTensor

class UNet2DConditionModel(ModelMixin, ConfigMixin, UNet2DConditionLoadersMixin):

r"""

UNet2DConditionModel 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.

center_input_sample (`bool`, *optional*, defaults to `False`): Whether to center the input sample.

flip_sin_to_cos (`bool`, *optional*, defaults to `False`):

Whether to flip the sin to cos in the time embedding.

freq_shift (`int`, *optional*, defaults to 0): The frequency shift to apply to the time embedding.

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

The tuple of downsample blocks to use.

mid_block_type (`str`, *optional*, defaults to `"UNetMidBlock2DCrossAttn"`):

The mid block type. Choose from `UNetMidBlock2DCrossAttn` or `UNetMidBlock2DSimpleCrossAttn`, will skip the

mid block layer if `None`.

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

The tuple of upsample blocks to use.

only_cross_attention(`bool` or `Tuple[bool]`, *optional*, default to `False`):

Whether to include self-attention in the basic transformer blocks, see

[`~models.attention.BasicTransformerBlock`].

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` or `Tuple[int]`, *optional*, defaults to 1280):

The dimension of the cross attention features.

encoder_hid_dim (`int`, *optional*, defaults to None):

If `encoder_hid_dim_type` is defined, `encoder_hidden_states` will be projected from `encoder_hid_dim`

dimension to `cross_attention_dim`.

encoder_hid_dim_type (`str`, *optional*, defaults to None):

If given, the `encoder_hidden_states` and potentially other embeddings will be down-projected to text

embeddings of dimension `cross_attention` according to `encoder_hid_dim_type`.

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

resnet_time_scale_shift (`str`, *optional*, defaults to `"default"`): Time scale shift config

for resnet blocks, see [`~models.resnet.ResnetBlock2D`]. Choose from `default` or `scale_shift`.

class_embed_type (`str`, *optional*, defaults to None):

The type of class embedding to use which is ultimately summed with the time embeddings. Choose from `None`,

`"timestep"`, `"identity"`, `"projection"`, or `"simple_projection"`.

addition_embed_type (`str`, *optional*, defaults to None):

Configures an optional embedding which will be summed with the time embeddings. Choose from `None` or

"text". "text" will use the `TextTimeEmbedding` layer.

num_class_embeds (`int`, *optional*, defaults to None):

Input dimension of the learnable embedding matrix to be projected to `time_embed_dim`, when performing

class conditioning with `class_embed_type` equal to `None`.

time_embedding_type (`str`, *optional*, default to `positional`):

The type of position embedding to use for timesteps. Choose from `positional` or `fourier`.

time_embedding_dim (`int`, *optional*, default to `None`):

An optional override for the dimension of the projected time embedding.

time_embedding_act_fn (`str`, *optional*, default to `None`):

Optional activation function to use on the time embeddings only one time before they as passed to the rest

of the unet. Choose from `silu`, `mish`, `gelu`, and `swish`.

timestep_post_act (`str, *optional*, default to `None`):

The second activation function to use in timestep embedding. Choose from `silu`, `mish` and `gelu`.

time_cond_proj_dim (`int`, *optional*, default to `None`):

The dimension of `cond_proj` layer in timestep embedding.

conv_in_kernel (`int`, *optional*, default to `3`): The kernel size of `conv_in` layer.

conv_out_kernel (`int`, *optional*, default to `3`): The kernel size of `conv_out` layer.

projection_class_embeddings_input_dim (`int`, *optional*): The dimension of the `class_labels` input when

using the "projection" `class_embed_type`. Required when using the "projection" `class_embed_type`.

class_embeddings_concat (`bool`, *optional*, defaults to `False`): Whether to concatenate the time

embeddings with the class embeddings.

mid_block_only_cross_attention (`bool`, *optional*, defaults to `None`):

Whether to use cross attention with the mid block when using the `UNetMidBlock2DSimpleCrossAttn`. If

`only_cross_attention` is given as a single boolean and `mid_block_only_cross_attention` is None, the

`only_cross_attention` value will be used as the value for `mid_block_only_cross_attention`. Else, it will

default to `False`.

"""

_supports_gradient_checkpointing = True

@register_to_config

def __init__(

self,

sample_size: Optional[int] = None,

in_channels: int = 4,

out_channels: int = 4,

center_input_sample: bool = False,

flip_sin_to_cos: bool = True,

freq_shift: int = 0,

down_block_types: Tuple[str] = (

"CrossAttnDownBlock2D",

"CrossAttnDownBlock2D",

"CrossAttnDownBlock2D",

"DownBlock2D",

),

mid_block_type: Optional[str] = "UNetMidBlock2DCrossAttn",

up_block_types: Tuple[str] = ("UpBlock2D", "CrossAttnUpBlock2D", "CrossAttnUpBlock2D", "CrossAttnUpBlock2D"),

only_cross_attention: Union[bool, Tuple[bool]] = False,

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: Optional[int] = 32,

norm_eps: float = 1e-5,

cross_attention_dim: Union[int, Tuple[int]] = 1280,

encoder_hid_dim: Optional[int] = None,

encoder_hid_dim_type: Optional[str] = None,

attention_head_dim: Union[int, Tuple[int]] = 8,

dual_cross_attention: bool = False,

use_linear_projection: bool = False,

class_embed_type: Optional[str] = None,

addition_embed_type: Optional[str] = None,

num_class_embeds: Optional[int] = None,

upcast_attention: bool = False,

resnet_time_scale_shift: str = "default",

resnet_skip_time_act: bool = False,

resnet_out_scale_factor: int = 1.0,

time_embedding_type: str = "positional",

time_embedding_dim: Optional[int] = None,

time_embedding_act_fn: Optional[str] = None,

timestep_post_act: Optional[str] = None,

time_cond_proj_dim: Optional[int] = None,

conv_in_kernel: int = 3,

conv_out_kernel: int = 3,

projection_class_embeddings_input_dim: Optional[int] = None,

class_embeddings_concat: bool = False,

mid_block_only_cross_attention: Optional[bool] = None,

cross_attention_norm: Optional[str] = None,

addition_embed_type_num_heads=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(only_cross_attention, bool) and len(only_cross_attention) != len(down_block_types):

raise ValueError(

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

)

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}."

)

# input

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

if time_embedding_type == "fourier":

time_embed_dim = time_embedding_dim or block_out_channels[0] * 2

if time_embed_dim % 2 != 0:

raise ValueError(f"`time_embed_dim` should be divisible by 2, but is {time_embed_dim}.")

self.time_proj = GaussianFourierProjection(

time_embed_dim // 2, set_W_to_weight=False, log=False, flip_sin_to_cos=flip_sin_to_cos

)

timestep_input_dim = time_embed_dim

elif time_embedding_type == "positional":

time_embed_dim = time_embedding_dim or block_out_channels[0] * 4

self.time_proj = Timesteps(block_out_channels[0], flip_sin_to_cos, freq_shift)

timestep_input_dim = block_out_channels[0]

else:

raise ValueError(

f"{time_embedding_type} does not exist. Please make sure to use one of `fourier` or `positional`."

)

self.time_embedding = TimestepEmbedding(

timestep_input_dim,

time_embed_dim,

act_fn=act_fn,

post_act_fn=timestep_post_act,

cond_proj_dim=time_cond_proj_dim,

)

if encoder_hid_dim_type is None and encoder_hid_dim is not None:

encoder_hid_dim_type = "text_proj"

self.register_to_config(encoder_hid_dim_type=encoder_hid_dim_type)

logger.info("encoder_hid_dim_type defaults to 'text_proj' as `encoder_hid_dim` is defined.")

if encoder_hid_dim is None and encoder_hid_dim_type is not None:

raise ValueError(

f"`encoder_hid_dim` has to be defined when `encoder_hid_dim_type` is set to {encoder_hid_dim_type}."

)

if encoder_hid_dim_type == "text_proj":

self.encoder_hid_proj = nn.Linear(encoder_hid_dim, cross_attention_dim)

elif encoder_hid_dim_type == "text_image_proj":

# image_embed_dim DOESN'T have to be `cross_attention_dim`. To not clutter the __init__ too much

# they are set to `cross_attention_dim` here as this is exactly the required dimension for the currently only use

# case when `addition_embed_type == "text_image_proj"` (Kadinsky 2.1)`

self.encoder_hid_proj = TextImageProjection(

text_embed_dim=encoder_hid_dim,

image_embed_dim=cross_attention_dim,

cross_attention_dim=cross_attention_dim,

)

elif encoder_hid_dim_type is not None:

raise ValueError(

f"encoder_hid_dim_type: {encoder_hid_dim_type} must be None, 'text_proj' or 'text_image_proj'."

)

else:

self.encoder_hid_proj = None

# class embedding

if class_embed_type is None and num_class_embeds is not None:

self.class_embedding = nn.Embedding(num_class_embeds, time_embed_dim)

elif class_embed_type == "timestep":

self.class_embedding = TimestepEmbedding(timestep_input_dim, time_embed_dim, act_fn=act_fn)

elif class_embed_type == "identity":

self.class_embedding = nn.Identity(time_embed_dim, time_embed_dim)

elif class_embed_type == "projection":

if projection_class_embeddings_input_dim is None:

raise ValueError(

"`class_embed_type`: 'projection' requires `projection_class_embeddings_input_dim` be set"

)

# The projection `class_embed_type` is the same as the timestep `class_embed_type` except

# 1. the `class_labels` inputs are not first converted to sinusoidal embeddings

# 2. it projects from an arbitrary input dimension.

#

# Note that `TimestepEmbedding` is quite general, being mainly linear layers and activations.

# When used for embedding actual timesteps, the timesteps are first converted to sinusoidal embeddings.

# As a result, `TimestepEmbedding` can be passed arbitrary vectors.

self.class_embedding = TimestepEmbedding(projection_class_embeddings_input_dim, time_embed_dim)

elif class_embed_type == "simple_projection":

if projection_class_embeddings_input_dim is None:

raise ValueError(

"`class_embed_type`: 'simple_projection' requires `projection_class_embeddings_input_dim` be set"

)

self.class_embedding = nn.Linear(projection_class_embeddings_input_dim, time_embed_dim)

else:

self.class_embedding = None

if addition_embed_type == "text":

if encoder_hid_dim is not None:

text_time_embedding_from_dim = encoder_hid_dim

else:

text_time_embedding_from_dim = cross_attention_dim

self.add_embedding = TextTimeEmbedding(

text_time_embedding_from_dim, time_embed_dim, num_heads=addition_embed_type_num_heads

)

elif addition_embed_type == "text_image":

# text_embed_dim and image_embed_dim DON'T have to be `cross_attention_dim`. To not clutter the __init__ too much

# they are set to `cross_attention_dim` here as this is exactly the required dimension for the currently only use

# case when `addition_embed_type == "text_image"` (Kadinsky 2.1)`

self.add_embedding = TextImageTimeEmbedding(

text_embed_dim=cross_attention_dim, image_embed_dim=cross_attention_dim, time_embed_dim=time_embed_dim

)

elif addition_embed_type is not None:

raise ValueError(f"addition_embed_type: {addition_embed_type} must be None, 'text' or 'text_image'.")

if time_embedding_act_fn is None:

self.time_embed_act = None

else:

self.time_embed_act = get_activation(time_embedding_act_fn)

self.down_blocks = nn.ModuleList([])

self.up_blocks = nn.ModuleList([])

if isinstance(only_cross_attention, bool):

if mid_block_only_cross_attention is None:

mid_block_only_cross_attention = only_cross_attention

only_cross_attention = [only_cross_attention] * len(down_block_types)

if mid_block_only_cross_attention is None:

mid_block_only_cross_attention = False

if isinstance(attention_head_dim, int):

attention_head_dim = (attention_head_dim,) * 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 class_embeddings_concat:

# The time embeddings are concatenated with the class embeddings. The dimension of the

# time embeddings passed to the down, middle, and up blocks is twice the dimension of the

# regular time embeddings

blocks_time_embed_dim = time_embed_dim * 2

else:

blocks_time_embed_dim = time_embed_dim

# 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=blocks_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],

attn_num_head_channels=attention_head_dim[i],

downsample_padding=downsample_padding,

dual_cross_attention=dual_cross_attention,

use_linear_projection=use_linear_projection,

only_cross_attention=only_cross_attention[i],

upcast_attention=upcast_attention,

resnet_time_scale_shift=resnet_time_scale_shift,

resnet_skip_time_act=resnet_skip_time_act,

resnet_out_scale_factor=resnet_out_scale_factor,

cross_attention_norm=cross_attention_norm,

)

self.down_blocks.append(down_block)

# mid

if mid_block_type == "UNetMidBlock2DCrossAttn":

self.mid_block = UNetMidBlock2DCrossAttn(

in_channels=block_out_channels[-1],

temb_channels=blocks_time_embed_dim,

resnet_eps=norm_eps,

resnet_act_fn=act_fn,

output_scale_factor=mid_block_scale_factor,

resnet_time_scale_shift=resnet_time_scale_shift,

cross_attention_dim=cross_attention_dim[-1],

attn_num_head_channels=attention_head_dim[-1],

resnet_groups=norm_num_groups,

dual_cross_attention=dual_cross_attention,

use_linear_projection=use_linear_projection,

upcast_attention=upcast_attention,

)

elif mid_block_type == "UNetMidBlock2DSimpleCrossAttn":

self.mid_block = UNetMidBlock2DSimpleCrossAttn(

in_channels=block_out_channels[-1],

temb_channels=blocks_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],

attn_num_head_channels=attention_head_dim[-1],

resnet_groups=norm_num_groups,

resnet_time_scale_shift=resnet_time_scale_shift,

skip_time_act=resnet_skip_time_act,

only_cross_attention=mid_block_only_cross_attention,

cross_attention_norm=cross_attention_norm,

)

elif mid_block_type is None:

self.mid_block = None

else:

raise ValueError(f"unknown mid_block_type : {mid_block_type}")

# 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))

reversed_layers_per_block = list(reversed(layers_per_block))

reversed_cross_attention_dim = list(reversed(cross_attention_dim))

only_cross_attention = list(reversed(only_cross_attention))

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=blocks_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],

attn_num_head_channels=reversed_attention_head_dim[i],

dual_cross_attention=dual_cross_attention,

use_linear_projection=use_linear_projection,

only_cross_attention=only_cross_attention[i],

upcast_attention=upcast_attention,

resnet_time_scale_shift=resnet_time_scale_shift,

resnet_skip_time_act=resnet_skip_time_act,

resnet_out_scale_factor=resnet_out_scale_factor,

cross_attention_norm=cross_attention_norm,

)

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 = get_activation(act_fn)

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

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

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)

def set_default_attn_processor(self):

"""

Disables custom attention processors and sets the default attention implementation.

"""

self.set_attn_processor(AttnProcessor())

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)

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

if isinstance(module, (CrossAttnDownBlock2D, DownBlock2D, CrossAttnUpBlock2D, UpBlock2D)):

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,

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,

encoder_attention_mask: Optional[torch.Tensor] = None,

return_dict: bool = True,

) -> Union[UNet2DConditionOutput, Tuple]:

r"""

Args:

sample (`torch.FloatTensor`): (batch, 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

encoder_attention_mask (`torch.Tensor`):

(batch, sequence_length) cross-attention mask, applied to encoder_hidden_states. True = keep, False =

discard. Mask will be converted into a bias, which adds large negative values to attention scores

corresponding to "discard" tokens.

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

Whether or not to return a [`models.unet_2d_condition.UNet2DConditionOutput`] 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).

added_cond_kwargs (`dict`, *optional*):

A kwargs dictionary that if specified includes additonal conditions that can be used for additonal time

embeddings or encoder hidden states projections. See the configurations `encoder_hid_dim_type` and

`addition_embed_type` for more information.

Returns:

[`~models.unet_2d_condition.UNet2DConditionOutput`] or `tuple`:

[`~models.unet_2d_condition.UNet2DConditionOutput`] 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 layers).

# 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

# ensure attention_mask is a bias, and give it a singleton query_tokens dimension

# expects mask of shape:

# [batch, key_tokens]

# adds singleton query_tokens dimension:

# [batch, 1, key_tokens]

# this helps to broadcast it as a bias over attention scores, which will be in one of the following shapes:

# [batch, heads, query_tokens, key_tokens] (e.g. torch sdp attn)

# [batch * heads, query_tokens, key_tokens] (e.g. xformers or classic attn)

if attention_mask is not None:

# assume that mask is expressed as:

# (1 = keep, 0 = discard)

# convert mask into a bias that can be added to attention scores:

# (keep = +0, discard = -10000.0)

attention_mask = (1 - attention_mask.to(sample.dtype)) * -10000.0

attention_mask = attention_mask.unsqueeze(1)

# convert encoder_attention_mask to a bias the same way we do for attention_mask

if encoder_attention_mask is not None:

encoder_attention_mask = (1 - encoder_attention_mask.to(sample.dtype)) * -10000.0

encoder_attention_mask = encoder_attention_mask.unsqueeze(1)

# 0. center input if necessary

if self.config.center_input_sample:

sample = 2 * sample - 1.0

# 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

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=sample.dtype)

emb = self.time_embedding(t_emb, timestep_cond)

if self.class_embedding is not None:

if class_labels is None:

raise ValueError("class_labels should be provided when num_class_embeds > 0")

if self.config.class_embed_type == "timestep":

class_labels = self.time_proj(class_labels)

# `Timesteps` does not contain any weights and will always return f32 tensors

# there might be better ways to encapsulate this.

class_labels = class_labels.to(dtype=sample.dtype)

class_emb = self.class_embedding(class_labels).to(dtype=sample.dtype)

if self.config.class_embeddings_concat:

emb = torch.cat([emb, class_emb], dim=-1)

else:

emb = emb + class_emb

if self.config.addition_embed_type == "text":

aug_emb = self.add_embedding(encoder_hidden_states)

emb = emb + aug_emb

elif self.config.addition_embed_type == "text_image":

# Kadinsky 2.1 - style

if "image_embeds" not in added_cond_kwargs:

raise ValueError(

f"{self.__class__} has the config param `addition_embed_type` set to 'text_image' which requires the keyword argument `image_embeds` to be passed in `added_cond_kwargs`"

)

image_embs = added_cond_kwargs.get("image_embeds")

text_embs = added_cond_kwargs.get("text_embeds", encoder_hidden_states)

aug_emb = self.add_embedding(text_embs, image_embs)

emb = emb + aug_emb

if self.time_embed_act is not None:

emb = self.time_embed_act(emb)

if self.encoder_hid_proj is not None and self.config.encoder_hid_dim_type == "text_proj":

encoder_hidden_states = self.encoder_hid_proj(encoder_hidden_states)

elif self.encoder_hid_proj is not None and self.config.encoder_hid_dim_type == "text_image_proj":

# Kadinsky 2.1 - style

if "image_embeds" not in added_cond_kwargs:

raise ValueError(

f"{self.__class__} has the config param `encoder_hid_dim_type` set to 'text_image_proj' which requires the keyword argument `image_embeds` to be passed in `added_conditions`"

)

image_embeds = added_cond_kwargs.get("image_embeds")

encoder_hidden_states = self.encoder_hid_proj(encoder_hidden_states, image_embeds)

# 2. pre-process

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,

cross_attention_kwargs=cross_attention_kwargs,

encoder_attention_mask=encoder_attention_mask,

)

else:

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

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 = 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,

cross_attention_kwargs=cross_attention_kwargs,

encoder_attention_mask=encoder_attention_mask,

)

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,

cross_attention_kwargs=cross_attention_kwargs,

upsample_size=upsample_size,

attention_mask=attention_mask,

encoder_attention_mask=encoder_attention_mask,

)

else:

sample = upsample_block(

hidden_states=sample, temb=emb, res_hidden_states_tuple=res_samples, upsample_size=upsample_size

)

# 6. post-process

if self.conv_norm_out:

sample = self.conv_norm_out(sample)

sample = self.conv_act(sample)

sample = self.conv_out(sample)

if not return_dict:

return (sample,)

return UNet2DConditionOutput(sample=sample)