# 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,

ImageHintTimeEmbedding,

ImageProjection,

ImageTimeEmbedding,

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):

"""

The output of [`UNet2DConditionModel`].

Args:

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

The hidden states output conditioned on `encoder_hidden_states` input. Output of last layer of model.

"""

sample: torch.FloatTensor = None

class UNet2DConditionModel(ModelMixin, ConfigMixin, UNet2DConditionLoadersMixin):

r"""

A 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).

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): Number of channels in the input sample.

out_channels (`int`, *optional*, defaults to 4): 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"`):

Block type for middle of UNet, it can be either `UNetMidBlock2DCrossAttn` or

`UNetMidBlock2DSimpleCrossAttn`. If `None`, the mid block layer is skipped.

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`, normalization and activation layers is skipped 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.

transformer_layers_per_block (`int` or `Tuple[int]`, *optional*, defaults to 1):

The number of transformer blocks of type [`~models.attention.BasicTransformerBlock`]. Only relevant for

[`~models.unet_2d_blocks.CrossAttnDownBlock2D`], [`~models.unet_2d_blocks.CrossAttnUpBlock2D`],

[`~models.unet_2d_blocks.UNetMidBlock2DCrossAttn`].

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 are 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.

num_attention_heads (`int`, *optional*):

The number of attention heads. If not defined, defaults to `attention_head_dim`

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.

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

Dimension for the timestep embeddings.

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*, defaults to `positional`):

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

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

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

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

Optional activation function to use only once on the time embeddings before they are passed to the rest of

the UNet. Choose from `silu`, `mish`, `gelu`, and `swish`.

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

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

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

The dimension of `cond_proj` layer in the 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

`class_embed_type="projection"`. Required when `class_embed_type="projection"`.

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 is used as the value for `mid_block_only_cross_attention`. Default to `False`

otherwise.

"""

_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,

transformer_layers_per_block: Union[int, Tuple[int]] = 1,

encoder_hid_dim: Optional[int] = None,

encoder_hid_dim_type: Optional[str] = None,

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

num_attention_heads: Optional[Union[int, Tuple[int]]] = None,

dual_cross_attention: bool = False,

use_linear_projection: bool = False,

class_embed_type: Optional[str] = None,

addition_embed_type: Optional[str] = None,

addition_time_embed_dim: Optional[int] = 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

if num_attention_heads is not None:

raise ValueError(

"At the moment it is not possible to define the number of attention heads via `num_attention_heads` because of a naming issue as described in https://github.com/huggingface/diffusers/issues/2011#issuecomment-1547958131. Passing `num_attention_heads` will only be supported in diffusers v0.19."

)

# If `num_attention_heads` is not defined (which is the case for most models)

# it will default to `attention_head_dim`. This looks weird upon first reading it and it is.

# The reason for this behavior is to correct for incorrectly named variables that were introduced

# when this library was created. The incorrect naming was only discovered much later in https://github.com/huggingface/diffusers/issues/2011#issuecomment-1547958131

# Changing `attention_head_dim` to `num_attention_heads` for 40,000+ configurations is too backwards breaking

# which is why we correct for the naming here.

num_attention_heads = num_attention_heads or attention_head_dim

# 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(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 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 == "image_proj":

# Kandinsky 2.2

self.encoder_hid_proj = ImageProjection(

image_embed_dim=encoder_hid_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 == "text_time":

self.add_time_proj = Timesteps(addition_time_embed_dim, flip_sin_to_cos, freq_shift)

self.add_embedding = TimestepEmbedding(projection_class_embeddings_input_dim, time_embed_dim)

elif addition_embed_type == "image":

# Kandinsky 2.2

self.add_embedding = ImageTimeEmbedding(image_embed_dim=encoder_hid_dim, time_embed_dim=time_embed_dim)

elif addition_embed_type == "image_hint":

# Kandinsky 2.2 ControlNet

self.add_embedding = ImageHintTimeEmbedding(image_embed_dim=encoder_hid_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(num_attention_heads, int):

num_attention_heads = (num_attention_heads,) * len(down_block_types)

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 isinstance(transformer_layers_per_block, int):

transformer_layers_per_block = [transformer_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],

transformer_layers_per_block=transformer_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],

num_attention_heads=num_attention_heads[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,

attention_head_dim=attention_head_dim[i] if attention_head_dim[i] is not None else output_channel,

)

self.down_blocks.append(down_block)

# mid

if mid_block_type == "UNetMidBlock2DCrossAttn":

self.mid_block = UNetMidBlock2DCrossAttn(

transformer_layers_per_block=transformer_layers_per_block[-1],

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],

num_attention_heads=num_attention_heads[-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],

attention_head_dim=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_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_transformer_layers_per_block = list(reversed(transformer_layers_per_block))

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,

transformer_layers_per_block=reversed_transformer_layers_per_block[i],

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],

num_attention_heads=reversed_num_attention_heads[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,

attention_head_dim=attention_head_dim[i] if attention_head_dim[i] is not None else output_channel,

)

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"""

Sets the attention processor to use to compute attention.

Parameters:

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

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

for **all** `Attention` layers.

If `processor` is a dict, the key needs to define the path to the corresponding cross attention

processor. This is strongly recommended when setting trainable attention processors.

"""

count = len(self.attn_processors.keys())

if isinstance(processor, dict) and len(processor) != count:

raise ValueError(

f"A dict of processors was passed, but the number of processors {len(processor)} does not match the"

f" number of attention layers: {count}. Please make sure to pass {count} processor classes."

)

def fn_recursive_attn_processor(name: str, module: torch.nn.Module, processor):

if hasattr(module, "set_processor"):

if not isinstance(processor, dict):

module.set_processor(processor)

else:

module.set_processor(processor.pop(f"{name}.processor"))

for sub_name, child in module.named_children():

fn_recursive_attn_processor(f"{name}.{sub_name}", child, processor)

for name, module in self.named_children():

fn_recursive_attn_processor(name, module, processor)

def set_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 splits the input tensor in slices to compute attention in

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

Args:

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

When `"auto"`, input to the attention heads is halved, so attention is computed in two steps. If

`"max"`, maximum amount of memory is 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"""

The [`UNet2DConditionModel`] forward method.

Args:

sample (`torch.FloatTensor`):

The noisy input tensor with the following shape `(batch, 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)`.

encoder_attention_mask (`torch.Tensor`):

A cross-attention mask of shape `(batch, sequence_length)` is applied to `encoder_hidden_states`. If

`True` the mask is kept, otherwise if `False` it is discarded. Mask will be converted into a bias,

which adds large negative values to the 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 [`AttnProcessor`].

added_cond_kwargs: (`dict`, *optional*):

A kwargs dictionary containin additional embeddings that if specified are added to the embeddings that

are passed along to the UNet blocks.

Returns:

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

If `return_dict` is True, an [`~models.unet_2d_condition.UNet2DConditionOutput`] 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 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)

aug_emb = None

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)

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

# Kandinsky 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)

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

if "text_embeds" not in added_cond_kwargs:

raise ValueError(

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

)

text_embeds = added_cond_kwargs.get("text_embeds")

if "time_ids" not in added_cond_kwargs:

raise ValueError(

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

)

time_ids = added_cond_kwargs.get("time_ids")

time_embeds = self.add_time_proj(time_ids.flatten())

time_embeds = time_embeds.reshape((text_embeds.shape[0], -1))

add_embeds = torch.concat([text_embeds, time_embeds], dim=-1)

add_embeds = add_embeds.to(emb.dtype)

aug_emb = self.add_embedding(add_embeds)

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

# Kandinsky 2.2 - style

if "image_embeds" not in added_cond_kwargs:

raise ValueError(

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

)

image_embs = added_cond_kwargs.get("image_embeds")

aug_emb = self.add_embedding(image_embs)

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

# Kandinsky 2.2 - style

if "image_embeds" not in added_cond_kwargs or "hint" not in added_cond_kwargs:

raise ValueError(

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

)

image_embs = added_cond_kwargs.get("image_embeds")

hint = added_cond_kwargs.get("hint")

aug_emb, hint = self.add_embedding(image_embs, hint)

sample = torch.cat([sample, hint], dim=1)

emb = emb + aug_emb if aug_emb is not None else 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)

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

# Kandinsky 2.2 - style

if "image_embeds" not in added_cond_kwargs:

raise ValueError(

f"{self.__class__} has the config param `encoder_hid_dim_type` set to '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(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)