# 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.

import warnings

from typing import Callable, Optional, Union

import torch

import torch.nn.functional as F

from torch import nn

from ..utils import deprecate, logging, maybe_allow_in_graph

from ..utils.import_utils import is_xformers_available

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

if is_xformers_available():

import xformers

import xformers.ops

else:

xformers = None

@maybe_allow_in_graph

class Attention(nn.Module):

r"""

A cross attention layer.

Parameters:

query_dim (`int`): The number of channels in the query.

cross_attention_dim (`int`, *optional*):

The number of channels in the encoder_hidden_states. If not given, defaults to `query_dim`.

heads (`int`, *optional*, defaults to 8): The number of heads to use for multi-head attention.

dim_head (`int`, *optional*, defaults to 64): The number of channels in each head.

dropout (`float`, *optional*, defaults to 0.0): The dropout probability to use.

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

Set to `True` for the query, key, and value linear layers to contain a bias parameter.

"""

def __init__(

self,

query_dim: int,

cross_attention_dim: Optional[int] = None,

heads: int = 8,

dim_head: int = 64,

dropout: float = 0.0,

bias=False,

upcast_attention: bool = False,

upcast_softmax: bool = False,

cross_attention_norm: Optional[str] = None,

cross_attention_norm_num_groups: int = 32,

added_kv_proj_dim: Optional[int] = None,

norm_num_groups: Optional[int] = None,

spatial_norm_dim: Optional[int] = None,

out_bias: bool = True,

scale_qk: bool = True,

only_cross_attention: bool = False,

eps: float = 1e-5,

rescale_output_factor: float = 1.0,

residual_connection: bool = False,

_from_deprecated_attn_block=False,

processor: Optional["AttnProcessor"] = None,

):

super().__init__()

inner_dim = dim_head * heads

cross_attention_dim = cross_attention_dim if cross_attention_dim is not None else query_dim

self.upcast_attention = upcast_attention

self.upcast_softmax = upcast_softmax

self.rescale_output_factor = rescale_output_factor

self.residual_connection = residual_connection

# we make use of this private variable to know whether this class is loaded

# with an deprecated state dict so that we can convert it on the fly

self._from_deprecated_attn_block = _from_deprecated_attn_block

self.scale_qk = scale_qk

self.scale = dim_head**-0.5 if self.scale_qk else 1.0

self.heads = heads

# for slice_size > 0 the attention score computation

# is split across the batch axis to save memory

# You can set slice_size with `set_attention_slice`

self.sliceable_head_dim = heads

self.added_kv_proj_dim = added_kv_proj_dim

self.only_cross_attention = only_cross_attention

if self.added_kv_proj_dim is None and self.only_cross_attention:

raise ValueError(

"`only_cross_attention` can only be set to True if `added_kv_proj_dim` is not None. Make sure to set either `only_cross_attention=False` or define `added_kv_proj_dim`."

)

if norm_num_groups is not None:

self.group_norm = nn.GroupNorm(num_channels=query_dim, num_groups=norm_num_groups, eps=eps, affine=True)

else:

self.group_norm = None

if spatial_norm_dim is not None:

self.spatial_norm = SpatialNorm(f_channels=query_dim, zq_channels=spatial_norm_dim)

else:

self.spatial_norm = None

if cross_attention_norm is None:

self.norm_cross = None

elif cross_attention_norm == "layer_norm":

self.norm_cross = nn.LayerNorm(cross_attention_dim)

elif cross_attention_norm == "group_norm":

if self.added_kv_proj_dim is not None:

# The given `encoder_hidden_states` are initially of shape

# (batch_size, seq_len, added_kv_proj_dim) before being projected

# to (batch_size, seq_len, cross_attention_dim). The norm is applied

# before the projection, so we need to use `added_kv_proj_dim` as

# the number of channels for the group norm.

norm_cross_num_channels = added_kv_proj_dim

else:

norm_cross_num_channels = cross_attention_dim

self.norm_cross = nn.GroupNorm(

num_channels=norm_cross_num_channels, num_groups=cross_attention_norm_num_groups, eps=1e-5, affine=True

)

else:

raise ValueError(

f"unknown cross_attention_norm: {cross_attention_norm}. Should be None, 'layer_norm' or 'group_norm'"

)

self.to_q = nn.Linear(query_dim, inner_dim, bias=bias)

if not self.only_cross_attention:

# only relevant for the `AddedKVProcessor` classes

self.to_k = nn.Linear(cross_attention_dim, inner_dim, bias=bias)

self.to_v = nn.Linear(cross_attention_dim, inner_dim, bias=bias)

else:

self.to_k = None

self.to_v = None

if self.added_kv_proj_dim is not None:

self.add_k_proj = nn.Linear(added_kv_proj_dim, inner_dim)

self.add_v_proj = nn.Linear(added_kv_proj_dim, inner_dim)

self.to_out = nn.ModuleList([])

self.to_out.append(nn.Linear(inner_dim, query_dim, bias=out_bias))

self.to_out.append(nn.Dropout(dropout))

# set attention processor

# We use the AttnProcessor2_0 by default when torch 2.x is used which uses

# torch.nn.functional.scaled_dot_product_attention for native Flash/memory_efficient_attention

# but only if it has the default `scale` argument. TODO remove scale_qk check when we move to torch 2.1

if processor is None:

processor = (

AttnProcessor2_0() if hasattr(F, "scaled_dot_product_attention") and self.scale_qk else AttnProcessor()

)

self.set_processor(processor)

def set_use_memory_efficient_attention_xformers(

self, use_memory_efficient_attention_xformers: bool, attention_op: Optional[Callable] = None

):

is_lora = hasattr(self, "processor") and isinstance(

self.processor, (LoRAAttnProcessor, LoRAXFormersAttnProcessor)

)

is_custom_diffusion = hasattr(self, "processor") and isinstance(

self.processor, (CustomDiffusionAttnProcessor, CustomDiffusionXFormersAttnProcessor)

)

if use_memory_efficient_attention_xformers:

if self.added_kv_proj_dim is not None:

# TODO(Anton, Patrick, Suraj, William) - currently xformers doesn't work for UnCLIP

# which uses this type of cross attention ONLY because the attention mask of format

# [0, ..., -10.000, ..., 0, ...,] is not supported

raise NotImplementedError(

"Memory efficient attention with `xformers` is currently not supported when"

" `self.added_kv_proj_dim` is defined."

)

elif not is_xformers_available():

raise ModuleNotFoundError(

(

"Refer to https://github.com/facebookresearch/xformers for more information on how to install"

" xformers"

),

name="xformers",

)

elif not torch.cuda.is_available():

raise ValueError(

"torch.cuda.is_available() should be True but is False. xformers' memory efficient attention is"

" only available for GPU "

)

elif hasattr(F, "scaled_dot_product_attention") and self.scale_qk:

warnings.warn(

"You have specified using flash attention using xFormers but you have PyTorch 2.0 already installed. "

"We will default to PyTorch's native efficient flash attention implementation (`F.scaled_dot_product_attention`) "

"introduced in PyTorch 2.0. In case you are using LoRA or Custom Diffusion, we will fall "

"back to their respective attention processors i.e., we will NOT use the PyTorch 2.0 "

"native efficient flash attention."

)

else:

try:

# Make sure we can run the memory efficient attention

_ = xformers.ops.memory_efficient_attention(

torch.randn((1, 2, 40), device="cuda"),

torch.randn((1, 2, 40), device="cuda"),

torch.randn((1, 2, 40), device="cuda"),

)

except Exception as e:

raise e

if is_lora:

processor = LoRAXFormersAttnProcessor(

hidden_size=self.processor.hidden_size,

cross_attention_dim=self.processor.cross_attention_dim,

rank=self.processor.rank,

attention_op=attention_op,

)

processor.load_state_dict(self.processor.state_dict())

processor.to(self.processor.to_q_lora.up.weight.device)

elif is_custom_diffusion:

processor = CustomDiffusionXFormersAttnProcessor(

train_kv=self.processor.train_kv,

train_q_out=self.processor.train_q_out,

hidden_size=self.processor.hidden_size,

cross_attention_dim=self.processor.cross_attention_dim,

attention_op=attention_op,

)

processor.load_state_dict(self.processor.state_dict())

if hasattr(self.processor, "to_k_custom_diffusion"):

processor.to(self.processor.to_k_custom_diffusion.weight.device)

else:

processor = XFormersAttnProcessor(attention_op=attention_op)

else:

if is_lora:

processor = LoRAAttnProcessor(

hidden_size=self.processor.hidden_size,

cross_attention_dim=self.processor.cross_attention_dim,

rank=self.processor.rank,

)

processor.load_state_dict(self.processor.state_dict())

processor.to(self.processor.to_q_lora.up.weight.device)

elif is_custom_diffusion:

processor = CustomDiffusionAttnProcessor(

train_kv=self.processor.train_kv,

train_q_out=self.processor.train_q_out,

hidden_size=self.processor.hidden_size,

cross_attention_dim=self.processor.cross_attention_dim,

)

processor.load_state_dict(self.processor.state_dict())

if hasattr(self.processor, "to_k_custom_diffusion"):

processor.to(self.processor.to_k_custom_diffusion.weight.device)

else:

# set attention processor

# We use the AttnProcessor2_0 by default when torch 2.x is used which uses

# torch.nn.functional.scaled_dot_product_attention for native Flash/memory_efficient_attention

# but only if it has the default `scale` argument. TODO remove scale_qk check when we move to torch 2.1

processor = (

AttnProcessor2_0()

if hasattr(F, "scaled_dot_product_attention") and self.scale_qk

else AttnProcessor()

)

self.set_processor(processor)

def set_attention_slice(self, slice_size):

if slice_size is not None and slice_size > self.sliceable_head_dim:

raise ValueError(f"slice_size {slice_size} has to be smaller or equal to {self.sliceable_head_dim}.")

if slice_size is not None and self.added_kv_proj_dim is not None:

processor = SlicedAttnAddedKVProcessor(slice_size)

elif slice_size is not None:

processor = SlicedAttnProcessor(slice_size)

elif self.added_kv_proj_dim is not None:

processor = AttnAddedKVProcessor()

else:

# set attention processor

# We use the AttnProcessor2_0 by default when torch 2.x is used which uses

# torch.nn.functional.scaled_dot_product_attention for native Flash/memory_efficient_attention

# but only if it has the default `scale` argument. TODO remove scale_qk check when we move to torch 2.1

processor = (

AttnProcessor2_0() if hasattr(F, "scaled_dot_product_attention") and self.scale_qk else AttnProcessor()

)

self.set_processor(processor)

def set_processor(self, processor: "AttnProcessor"):

# if current processor is in `self._modules` and if passed `processor` is not, we need to

# pop `processor` from `self._modules`

if (

hasattr(self, "processor")

and isinstance(self.processor, torch.nn.Module)

and not isinstance(processor, torch.nn.Module)

):

logger.info(f"You are removing possibly trained weights of {self.processor} with {processor}")

self._modules.pop("processor")

self.processor = processor

def forward(self, hidden_states, encoder_hidden_states=None, attention_mask=None, **cross_attention_kwargs):

# The `Attention` class can call different attention processors / attention functions

# here we simply pass along all tensors to the selected processor class

# For standard processors that are defined here, `**cross_attention_kwargs` is empty

return self.processor(

self,

hidden_states,

encoder_hidden_states=encoder_hidden_states,

attention_mask=attention_mask,

**cross_attention_kwargs,

)

def batch_to_head_dim(self, tensor):

head_size = self.heads

batch_size, seq_len, dim = tensor.shape

tensor = tensor.reshape(batch_size // head_size, head_size, seq_len, dim)

tensor = tensor.permute(0, 2, 1, 3).reshape(batch_size // head_size, seq_len, dim * head_size)

return tensor

def head_to_batch_dim(self, tensor, out_dim=3):

head_size = self.heads

batch_size, seq_len, dim = tensor.shape

tensor = tensor.reshape(batch_size, seq_len, head_size, dim // head_size)

tensor = tensor.permute(0, 2, 1, 3)

if out_dim == 3:

tensor = tensor.reshape(batch_size * head_size, seq_len, dim // head_size)

return tensor

def get_attention_scores(self, query, key, attention_mask=None):

dtype = query.dtype

if self.upcast_attention:

query = query.float()

key = key.float()

if attention_mask is None:

baddbmm_input = torch.empty(

query.shape[0], query.shape[1], key.shape[1], dtype=query.dtype, device=query.device

)

beta = 0

else:

baddbmm_input = attention_mask

beta = 1

attention_scores = torch.baddbmm(

baddbmm_input,

query,

key.transpose(-1, -2),

beta=beta,

alpha=self.scale,

)

del baddbmm_input

if self.upcast_softmax:

attention_scores = attention_scores.float()

attention_probs = attention_scores.softmax(dim=-1)

del attention_scores

attention_probs = attention_probs.to(dtype)

return attention_probs

def prepare_attention_mask(self, attention_mask, target_length, batch_size=None, out_dim=3):

if batch_size is None:

deprecate(

"batch_size=None",

"0.0.15",

(

"Not passing the `batch_size` parameter to `prepare_attention_mask` can lead to incorrect"

" attention mask preparation and is deprecated behavior. Please make sure to pass `batch_size` to"

" `prepare_attention_mask` when preparing the attention_mask."

),

)

batch_size = 1

head_size = self.heads

if attention_mask is None:

return attention_mask

current_length: int = attention_mask.shape[-1]

if current_length != target_length:

if attention_mask.device.type == "mps":

# HACK: MPS: Does not support padding by greater than dimension of input tensor.

# Instead, we can manually construct the padding tensor.

padding_shape = (attention_mask.shape[0], attention_mask.shape[1], target_length)

padding = torch.zeros(padding_shape, dtype=attention_mask.dtype, device=attention_mask.device)

attention_mask = torch.cat([attention_mask, padding], dim=2)

else:

# TODO: for pipelines such as stable-diffusion, padding cross-attn mask:

# we want to instead pad by (0, remaining_length), where remaining_length is:

# remaining_length: int = target_length - current_length

# TODO: re-enable tests/models/test_models_unet_2d_condition.py#test_model_xattn_padding

attention_mask = F.pad(attention_mask, (0, target_length), value=0.0)

if out_dim == 3:

if attention_mask.shape[0] < batch_size * head_size:

attention_mask = attention_mask.repeat_interleave(head_size, dim=0)

elif out_dim == 4:

attention_mask = attention_mask.unsqueeze(1)

attention_mask = attention_mask.repeat_interleave(head_size, dim=1)

return attention_mask

def norm_encoder_hidden_states(self, encoder_hidden_states):

assert self.norm_cross is not None, "self.norm_cross must be defined to call self.norm_encoder_hidden_states"

if isinstance(self.norm_cross, nn.LayerNorm):

encoder_hidden_states = self.norm_cross(encoder_hidden_states)

elif isinstance(self.norm_cross, nn.GroupNorm):

# Group norm norms along the channels dimension and expects

# input to be in the shape of (N, C, *). In this case, we want

# to norm along the hidden dimension, so we need to move

# (batch_size, sequence_length, hidden_size) ->

# (batch_size, hidden_size, sequence_length)

encoder_hidden_states = encoder_hidden_states.transpose(1, 2)

encoder_hidden_states = self.norm_cross(encoder_hidden_states)

encoder_hidden_states = encoder_hidden_states.transpose(1, 2)

else:

assert False

return encoder_hidden_states

class AttnProcessor:

r"""

Default processor for performing attention-related computations.

"""

def __call__(

self,

attn: Attention,

hidden_states,

encoder_hidden_states=None,

attention_mask=None,

temb=None,

):

residual = hidden_states

if attn.spatial_norm is not None:

hidden_states = attn.spatial_norm(hidden_states, temb)

input_ndim = hidden_states.ndim

if input_ndim == 4:

batch_size, channel, height, width = hidden_states.shape

hidden_states = hidden_states.view(batch_size, channel, height * width).transpose(1, 2)

batch_size, sequence_length, _ = (

hidden_states.shape if encoder_hidden_states is None else encoder_hidden_states.shape

)

attention_mask = attn.prepare_attention_mask(attention_mask, sequence_length, batch_size)

if attn.group_norm is not None:

hidden_states = attn.group_norm(hidden_states.transpose(1, 2)).transpose(1, 2)

query = attn.to_q(hidden_states)

if encoder_hidden_states is None:

encoder_hidden_states = hidden_states

elif attn.norm_cross:

encoder_hidden_states = attn.norm_encoder_hidden_states(encoder_hidden_states)

key = attn.to_k(encoder_hidden_states)

value = attn.to_v(encoder_hidden_states)

query = attn.head_to_batch_dim(query)

key = attn.head_to_batch_dim(key)

value = attn.head_to_batch_dim(value)

attention_probs = attn.get_attention_scores(query, key, attention_mask)

hidden_states = torch.bmm(attention_probs, value)

hidden_states = attn.batch_to_head_dim(hidden_states)

# linear proj

hidden_states = attn.to_out[0](hidden_states)

# dropout

hidden_states = attn.to_out[1](hidden_states)

if input_ndim == 4:

hidden_states = hidden_states.transpose(-1, -2).reshape(batch_size, channel, height, width)

if attn.residual_connection:

hidden_states = hidden_states + residual

hidden_states = hidden_states / attn.rescale_output_factor

return hidden_states

class LoRALinearLayer(nn.Module):

def __init__(self, in_features, out_features, rank=4):

super().__init__()

if rank > min(in_features, out_features):

raise ValueError(f"LoRA rank {rank} must be less or equal than {min(in_features, out_features)}")

self.down = nn.Linear(in_features, rank, bias=False)

self.up = nn.Linear(rank, out_features, bias=False)

nn.init.normal_(self.down.weight, std=1 / rank)

nn.init.zeros_(self.up.weight)

def forward(self, hidden_states):

orig_dtype = hidden_states.dtype

dtype = self.down.weight.dtype

down_hidden_states = self.down(hidden_states.to(dtype))

up_hidden_states = self.up(down_hidden_states)

return up_hidden_states.to(orig_dtype)

class LoRAAttnProcessor(nn.Module):

r"""

Processor for implementing the LoRA attention mechanism.

Args:

hidden_size (`int`, *optional*):

The hidden size of the attention layer.

cross_attention_dim (`int`, *optional*):

The number of channels in the `encoder_hidden_states`.

rank (`int`, defaults to 4):

The dimension of the LoRA update matrices.

"""

def __init__(self, hidden_size, cross_attention_dim=None, rank=4):

super().__init__()

self.hidden_size = hidden_size

self.cross_attention_dim = cross_attention_dim

self.rank = rank

self.to_q_lora = LoRALinearLayer(hidden_size, hidden_size, rank)

self.to_k_lora = LoRALinearLayer(cross_attention_dim or hidden_size, hidden_size, rank)

self.to_v_lora = LoRALinearLayer(cross_attention_dim or hidden_size, hidden_size, rank)

self.to_out_lora = LoRALinearLayer(hidden_size, hidden_size, rank)

def __call__(

self, attn: Attention, hidden_states, encoder_hidden_states=None, attention_mask=None, scale=1.0, temb=None

):

residual = hidden_states

if attn.spatial_norm is not None:

hidden_states = attn.spatial_norm(hidden_states, temb)

input_ndim = hidden_states.ndim

if input_ndim == 4:

batch_size, channel, height, width = hidden_states.shape

hidden_states = hidden_states.view(batch_size, channel, height * width).transpose(1, 2)

batch_size, sequence_length, _ = (

hidden_states.shape if encoder_hidden_states is None else encoder_hidden_states.shape

)

attention_mask = attn.prepare_attention_mask(attention_mask, sequence_length, batch_size)

if attn.group_norm is not None:

hidden_states = attn.group_norm(hidden_states.transpose(1, 2)).transpose(1, 2)

query = attn.to_q(hidden_states) + scale * self.to_q_lora(hidden_states)

query = attn.head_to_batch_dim(query)

if encoder_hidden_states is None:

encoder_hidden_states = hidden_states

elif attn.norm_cross:

encoder_hidden_states = attn.norm_encoder_hidden_states(encoder_hidden_states)

key = attn.to_k(encoder_hidden_states) + scale * self.to_k_lora(encoder_hidden_states)

value = attn.to_v(encoder_hidden_states) + scale * self.to_v_lora(encoder_hidden_states)

key = attn.head_to_batch_dim(key)

value = attn.head_to_batch_dim(value)

attention_probs = attn.get_attention_scores(query, key, attention_mask)

hidden_states = torch.bmm(attention_probs, value)

hidden_states = attn.batch_to_head_dim(hidden_states)

# linear proj

hidden_states = attn.to_out[0](hidden_states) + scale * self.to_out_lora(hidden_states)

# dropout

hidden_states = attn.to_out[1](hidden_states)

if input_ndim == 4:

hidden_states = hidden_states.transpose(-1, -2).reshape(batch_size, channel, height, width)

if attn.residual_connection:

hidden_states = hidden_states + residual

hidden_states = hidden_states / attn.rescale_output_factor

return hidden_states

class CustomDiffusionAttnProcessor(nn.Module):

r"""

Processor for implementing attention for the Custom Diffusion method.

Args:

train_kv (`bool`, defaults to `True`):

Whether to newly train the key and value matrices corresponding to the text features.

train_q_out (`bool`, defaults to `True`):

Whether to newly train query matrices corresponding to the latent image features.

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

The hidden size of the attention layer.

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

The number of channels in the `encoder_hidden_states`.

out_bias (`bool`, defaults to `True`):

Whether to include the bias parameter in `train_q_out`.

dropout (`float`, *optional*, defaults to 0.0):

The dropout probability to use.

"""

def __init__(

self,

train_kv=True,

train_q_out=True,

hidden_size=None,

cross_attention_dim=None,

out_bias=True,

dropout=0.0,

):

super().__init__()

self.train_kv = train_kv

self.train_q_out = train_q_out

self.hidden_size = hidden_size

self.cross_attention_dim = cross_attention_dim

# `_custom_diffusion` id for easy serialization and loading.

if self.train_kv:

self.to_k_custom_diffusion = nn.Linear(cross_attention_dim or hidden_size, hidden_size, bias=False)

self.to_v_custom_diffusion = nn.Linear(cross_attention_dim or hidden_size, hidden_size, bias=False)

if self.train_q_out:

self.to_q_custom_diffusion = nn.Linear(hidden_size, hidden_size, bias=False)

self.to_out_custom_diffusion = nn.ModuleList([])

self.to_out_custom_diffusion.append(nn.Linear(hidden_size, hidden_size, bias=out_bias))

self.to_out_custom_diffusion.append(nn.Dropout(dropout))

def __call__(self, attn: Attention, hidden_states, encoder_hidden_states=None, attention_mask=None):

batch_size, sequence_length, _ = hidden_states.shape

attention_mask = attn.prepare_attention_mask(attention_mask, sequence_length, batch_size)

if self.train_q_out:

query = self.to_q_custom_diffusion(hidden_states)

else:

query = attn.to_q(hidden_states)

if encoder_hidden_states is None:

crossattn = False

encoder_hidden_states = hidden_states

else:

crossattn = True

if attn.norm_cross:

encoder_hidden_states = attn.norm_encoder_hidden_states(encoder_hidden_states)

if self.train_kv:

key = self.to_k_custom_diffusion(encoder_hidden_states)

value = self.to_v_custom_diffusion(encoder_hidden_states)

else:

key = attn.to_k(encoder_hidden_states)

value = attn.to_v(encoder_hidden_states)

if crossattn:

detach = torch.ones_like(key)

detach[:, :1, :] = detach[:, :1, :] * 0.0

key = detach * key + (1 - detach) * key.detach()

value = detach * value + (1 - detach) * value.detach()

query = attn.head_to_batch_dim(query)

key = attn.head_to_batch_dim(key)

value = attn.head_to_batch_dim(value)

attention_probs = attn.get_attention_scores(query, key, attention_mask)

hidden_states = torch.bmm(attention_probs, value)

hidden_states = attn.batch_to_head_dim(hidden_states)

if self.train_q_out:

# linear proj

hidden_states = self.to_out_custom_diffusion[0](hidden_states)

# dropout

hidden_states = self.to_out_custom_diffusion[1](hidden_states)

else:

# linear proj

hidden_states = attn.to_out[0](hidden_states)

# dropout

hidden_states = attn.to_out[1](hidden_states)

return hidden_states

class AttnAddedKVProcessor:

r"""

Processor for performing attention-related computations with extra learnable key and value matrices for the text

encoder.

"""

def __call__(self, attn: Attention, hidden_states, encoder_hidden_states=None, attention_mask=None):

residual = hidden_states

hidden_states = hidden_states.view(hidden_states.shape[0], hidden_states.shape[1], -1).transpose(1, 2)

batch_size, sequence_length, _ = hidden_states.shape

attention_mask = attn.prepare_attention_mask(attention_mask, sequence_length, batch_size)

if encoder_hidden_states is None:

encoder_hidden_states = hidden_states

elif attn.norm_cross:

encoder_hidden_states = attn.norm_encoder_hidden_states(encoder_hidden_states)

hidden_states = attn.group_norm(hidden_states.transpose(1, 2)).transpose(1, 2)

query = attn.to_q(hidden_states)

query = attn.head_to_batch_dim(query)

encoder_hidden_states_key_proj = attn.add_k_proj(encoder_hidden_states)

encoder_hidden_states_value_proj = attn.add_v_proj(encoder_hidden_states)

encoder_hidden_states_key_proj = attn.head_to_batch_dim(encoder_hidden_states_key_proj)

encoder_hidden_states_value_proj = attn.head_to_batch_dim(encoder_hidden_states_value_proj)

if not attn.only_cross_attention:

key = attn.to_k(hidden_states)

value = attn.to_v(hidden_states)

key = attn.head_to_batch_dim(key)

value = attn.head_to_batch_dim(value)

key = torch.cat([encoder_hidden_states_key_proj, key], dim=1)

value = torch.cat([encoder_hidden_states_value_proj, value], dim=1)

else:

key = encoder_hidden_states_key_proj

value = encoder_hidden_states_value_proj

attention_probs = attn.get_attention_scores(query, key, attention_mask)

hidden_states = torch.bmm(attention_probs, value)

hidden_states = attn.batch_to_head_dim(hidden_states)

# linear proj

hidden_states = attn.to_out[0](hidden_states)

# dropout

hidden_states = attn.to_out[1](hidden_states)

hidden_states = hidden_states.transpose(-1, -2).reshape(residual.shape)

hidden_states = hidden_states + residual

return hidden_states

class AttnAddedKVProcessor2_0:

r"""

Processor for performing scaled dot-product attention (enabled by default if you're using PyTorch 2.0), with extra

learnable key and value matrices for the text encoder.

"""

def __init__(self):

if not hasattr(F, "scaled_dot_product_attention"):

raise ImportError(

"AttnAddedKVProcessor2_0 requires PyTorch 2.0, to use it, please upgrade PyTorch to 2.0."

)

def __call__(self, attn: Attention, hidden_states, encoder_hidden_states=None, attention_mask=None):

residual = hidden_states

hidden_states = hidden_states.view(hidden_states.shape[0], hidden_states.shape[1], -1).transpose(1, 2)

batch_size, sequence_length, _ = hidden_states.shape

attention_mask = attn.prepare_attention_mask(attention_mask, sequence_length, batch_size, out_dim=4)

if encoder_hidden_states is None:

encoder_hidden_states = hidden_states

elif attn.norm_cross:

encoder_hidden_states = attn.norm_encoder_hidden_states(encoder_hidden_states)

hidden_states = attn.group_norm(hidden_states.transpose(1, 2)).transpose(1, 2)

query = attn.to_q(hidden_states)

query = attn.head_to_batch_dim(query, out_dim=4)

encoder_hidden_states_key_proj = attn.add_k_proj(encoder_hidden_states)

encoder_hidden_states_value_proj = attn.add_v_proj(encoder_hidden_states)

encoder_hidden_states_key_proj = attn.head_to_batch_dim(encoder_hidden_states_key_proj, out_dim=4)

encoder_hidden_states_value_proj = attn.head_to_batch_dim(encoder_hidden_states_value_proj, out_dim=4)

if not attn.only_cross_attention:

key = attn.to_k(hidden_states)

value = attn.to_v(hidden_states)

key = attn.head_to_batch_dim(key, out_dim=4)

value = attn.head_to_batch_dim(value, out_dim=4)

key = torch.cat([encoder_hidden_states_key_proj, key], dim=2)

value = torch.cat([encoder_hidden_states_value_proj, value], dim=2)

else:

key = encoder_hidden_states_key_proj

value = encoder_hidden_states_value_proj

# the output of sdp = (batch, num_heads, seq_len, head_dim)

# TODO: add support for attn.scale when we move to Torch 2.1

hidden_states = F.scaled_dot_product_attention(

query, key, value, attn_mask=attention_mask, dropout_p=0.0, is_causal=False

)

hidden_states = hidden_states.transpose(1, 2).reshape(batch_size, -1, residual.shape[1])

# linear proj

hidden_states = attn.to_out[0](hidden_states)

# dropout

hidden_states = attn.to_out[1](hidden_states)

hidden_states = hidden_states.transpose(-1, -2).reshape(residual.shape)

hidden_states = hidden_states + residual

return hidden_states

class LoRAAttnAddedKVProcessor(nn.Module):

r"""

Processor for implementing the LoRA attention mechanism with extra learnable key and value matrices for the text

encoder.

Args:

hidden_size (`int`, *optional*):

The hidden size of the attention layer.

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

The number of channels in the `encoder_hidden_states`.

rank (`int`, defaults to 4):

The dimension of the LoRA update matrices.

"""

def __init__(self, hidden_size, cross_attention_dim=None, rank=4):

super().__init__()

self.hidden_size = hidden_size

self.cross_attention_dim = cross_attention_dim

self.rank = rank

self.to_q_lora = LoRALinearLayer(hidden_size, hidden_size, rank)

self.add_k_proj_lora = LoRALinearLayer(cross_attention_dim or hidden_size, hidden_size, rank)

self.add_v_proj_lora = LoRALinearLayer(cross_attention_dim or hidden_size, hidden_size, rank)

self.to_k_lora = LoRALinearLayer(hidden_size, hidden_size, rank)

self.to_v_lora = LoRALinearLayer(hidden_size, hidden_size, rank)

self.to_out_lora = LoRALinearLayer(hidden_size, hidden_size, rank)

def __call__(self, attn: Attention, hidden_states, encoder_hidden_states=None, attention_mask=None, scale=1.0):

residual = hidden_states

hidden_states = hidden_states.view(hidden_states.shape[0], hidden_states.shape[1], -1).transpose(1, 2)

batch_size, sequence_length, _ = hidden_states.shape

attention_mask = attn.prepare_attention_mask(attention_mask, sequence_length, batch_size)

if encoder_hidden_states is None:

encoder_hidden_states = hidden_states

elif attn.norm_cross:

encoder_hidden_states = attn.norm_encoder_hidden_states(encoder_hidden_states)

hidden_states = attn.group_norm(hidden_states.transpose(1, 2)).transpose(1, 2)

query = attn.to_q(hidden_states) + scale * self.to_q_lora(hidden_states)

query = attn.head_to_batch_dim(query)

encoder_hidden_states_key_proj = attn.add_k_proj(encoder_hidden_states) + scale * self.add_k_proj_lora(

encoder_hidden_states

)

encoder_hidden_states_value_proj = attn.add_v_proj(encoder_hidden_states) + scale * self.add_v_proj_lora(

encoder_hidden_states

)

encoder_hidden_states_key_proj = attn.head_to_batch_dim(encoder_hidden_states_key_proj)

encoder_hidden_states_value_proj = attn.head_to_batch_dim(encoder_hidden_states_value_proj)

if not attn.only_cross_attention:

key = attn.to_k(hidden_states) + scale * self.to_k_lora(hidden_states)

value = attn.to_v(hidden_states) + scale * self.to_v_lora(hidden_states)

key = attn.head_to_batch_dim(key)

value = attn.head_to_batch_dim(value)

key = torch.cat([encoder_hidden_states_key_proj, key], dim=1)

value = torch.cat([encoder_hidden_states_value_proj, value], dim=1)

else:

key = encoder_hidden_states_key_proj

value = encoder_hidden_states_value_proj

attention_probs = attn.get_attention_scores(query, key, attention_mask)

hidden_states = torch.bmm(attention_probs, value)

hidden_states = attn.batch_to_head_dim(hidden_states)

# linear proj

hidden_states = attn.to_out[0](hidden_states) + scale * self.to_out_lora(hidden_states)

# dropout

hidden_states = attn.to_out[1](hidden_states)

hidden_states = hidden_states.transpose(-1, -2).reshape(residual.shape)

hidden_states = hidden_states + residual

return hidden_states

class XFormersAttnProcessor:

r"""

Processor for implementing memory efficient attention using xFormers.

Args:

attention_op (`Callable`, *optional*, defaults to `None`):

The base

[operator](https://facebookresearch.github.io/xformers/components/ops.html#xformers.ops.AttentionOpBase) to

use as the attention operator. It is recommended to set to `None`, and allow xFormers to choose the best

operator.

"""

def __init__(self, attention_op: Optional[Callable] = None):

self.attention_op = attention_op

def __call__(

self,

attn: Attention,

hidden_states: torch.FloatTensor,

encoder_hidden_states: Optional[torch.FloatTensor] = None,

attention_mask: Optional[torch.FloatTensor] = None,

temb: Optional[torch.FloatTensor] = None,

):

residual = hidden_states

if attn.spatial_norm is not None:

hidden_states = attn.spatial_norm(hidden_states, temb)

input_ndim = hidden_states.ndim

if input_ndim == 4:

batch_size, channel, height, width = hidden_states.shape

hidden_states = hidden_states.view(batch_size, channel, height * width).transpose(1, 2)

batch_size, key_tokens, _ = (

hidden_states.shape if encoder_hidden_states is None else encoder_hidden_states.shape

)

attention_mask = attn.prepare_attention_mask(attention_mask, key_tokens, batch_size)

if attention_mask is not None:

# expand our mask's singleton query_tokens dimension:

# [batch*heads, 1, key_tokens] ->

# [batch*heads, query_tokens, key_tokens]

# so that it can be added as a bias onto the attention scores that xformers computes:

# [batch*heads, query_tokens, key_tokens]

# we do this explicitly because xformers doesn't broadcast the singleton dimension for us.

_, query_tokens, _ = hidden_states.shape

attention_mask = attention_mask.expand(-1, query_tokens, -1)

if attn.group_norm is not None:

hidden_states = attn.group_norm(hidden_states.transpose(1, 2)).transpose(1, 2)

query = attn.to_q(hidden_states)

if encoder_hidden_states is None:

encoder_hidden_states = hidden_states

elif attn.norm_cross:

encoder_hidden_states = attn.norm_encoder_hidden_states(encoder_hidden_states)

key = attn.to_k(encoder_hidden_states)

value = attn.to_v(encoder_hidden_states)

query = attn.head_to_batch_dim(query).contiguous()

key = attn.head_to_batch_dim(key).contiguous()

value = attn.head_to_batch_dim(value).contiguous()

hidden_states = xformers.ops.memory_efficient_attention(

query, key, value, attn_bias=attention_mask, op=self.attention_op, scale=attn.scale

)

hidden_states = hidden_states.to(query.dtype)

hidden_states = attn.batch_to_head_dim(hidden_states)

# linear proj

hidden_states = attn.to_out[0](hidden_states)

# dropout

hidden_states = attn.to_out[1](hidden_states)

if input_ndim == 4:

hidden_states = hidden_states.transpose(-1, -2).reshape(batch_size, channel, height, width)

if attn.residual_connection:

hidden_states = hidden_states + residual

hidden_states = hidden_states / attn.rescale_output_factor

return hidden_states

class AttnProcessor2_0:

r"""

Processor for implementing scaled dot-product attention (enabled by default if you're using PyTorch 2.0).

"""

def __init__(self):

if not hasattr(F, "scaled_dot_product_attention"):

raise ImportError("AttnProcessor2_0 requires PyTorch 2.0, to use it, please upgrade PyTorch to 2.0.")

def __call__(

self,

attn: Attention,

hidden_states,

encoder_hidden_states=None,

attention_mask=None,

temb=None,

):

residual = hidden_states

if attn.spatial_norm is not None:

hidden_states = attn.spatial_norm(hidden_states, temb)