# 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 os

import warnings

from collections import defaultdict

from pathlib import Path

from typing import Callable, Dict, List, Optional, Union

import torch

from huggingface_hub import hf_hub_download

from .models.attention_processor import (

AttnAddedKVProcessor,

AttnAddedKVProcessor2_0,

CustomDiffusionAttnProcessor,

CustomDiffusionXFormersAttnProcessor,

LoRAAttnAddedKVProcessor,

LoRAAttnProcessor,

SlicedAttnAddedKVProcessor,

)

from .utils import (

DIFFUSERS_CACHE,

HF_HUB_OFFLINE,

TEXT_ENCODER_TARGET_MODULES,

_get_model_file,

deprecate,

is_safetensors_available,

is_transformers_available,

logging,

)

if is_safetensors_available():

import safetensors

if is_transformers_available():

from transformers import PreTrainedModel, PreTrainedTokenizer

logger = logging.get_logger(__name__)

TEXT_ENCODER_NAME = "text_encoder"

UNET_NAME = "unet"

LORA_WEIGHT_NAME = "pytorch_lora_weights.bin"

LORA_WEIGHT_NAME_SAFE = "pytorch_lora_weights.safetensors"

TEXT_INVERSION_NAME = "learned_embeds.bin"

TEXT_INVERSION_NAME_SAFE = "learned_embeds.safetensors"

CUSTOM_DIFFUSION_WEIGHT_NAME = "pytorch_custom_diffusion_weights.bin"

CUSTOM_DIFFUSION_WEIGHT_NAME_SAFE = "pytorch_custom_diffusion_weights.safetensors"

class AttnProcsLayers(torch.nn.Module):

def __init__(self, state_dict: Dict[str, torch.Tensor]):

super().__init__()

self.layers = torch.nn.ModuleList(state_dict.values())

self.mapping = dict(enumerate(state_dict.keys()))

self.rev_mapping = {v: k for k, v in enumerate(state_dict.keys())}

# we add a hook to state_dict() and load_state_dict() so that the

# naming fits with `unet.attn_processors`

def map_to(module, state_dict, *args, **kwargs):

new_state_dict = {}

for key, value in state_dict.items():

num = int(key.split(".")[1]) # 0 is always "layers"

new_key = key.replace(f"layers.{num}", module.mapping[num])

new_state_dict[new_key] = value

return new_state_dict

def map_from(module, state_dict, *args, **kwargs):

all_keys = list(state_dict.keys())

for key in all_keys:

replace_key = key.split(".processor")[0] + ".processor"

new_key = key.replace(replace_key, f"layers.{module.rev_mapping[replace_key]}")

state_dict[new_key] = state_dict[key]

del state_dict[key]

self._register_state_dict_hook(map_to)

self._register_load_state_dict_pre_hook(map_from, with_module=True)

class UNet2DConditionLoadersMixin:

text_encoder_name = TEXT_ENCODER_NAME

unet_name = UNET_NAME

def load_attn_procs(self, pretrained_model_name_or_path_or_dict: Union[str, Dict[str, torch.Tensor]], **kwargs):

r"""

Load pretrained attention processor layers into `UNet2DConditionModel`. Attention processor layers have to be

defined in

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

and be a `torch.nn.Module` class.

<Tip warning={true}>

This function is experimental and might change in the future.

</Tip>

Parameters:

pretrained_model_name_or_path_or_dict (`str` or `os.PathLike` or `dict`):

Can be either:

- A string, the *model id* of a pretrained model hosted inside a model repo on huggingface.co.

Valid model ids should have an organization name, like `google/ddpm-celebahq-256`.

- A path to a *directory* containing model weights saved using [`~ModelMixin.save_config`], e.g.,

`./my_model_directory/`.

- A [torch state

dict](https://pytorch.org/tutorials/beginner/saving_loading_models.html#what-is-a-state-dict).

cache_dir (`Union[str, os.PathLike]`, *optional*):

Path to a directory in which a downloaded pretrained model configuration should be cached if the

standard cache should not be used.

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

Whether or not to force the (re-)download of the model weights and configuration files, overriding the

cached versions if they exist.

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

Whether or not to delete incompletely received files. Will attempt to resume the download if such a

file exists.

proxies (`Dict[str, str]`, *optional*):

A dictionary of proxy servers to use by protocol or endpoint, e.g., `{'http': 'foo.bar:3128',

'http://hostname': 'foo.bar:4012'}`. The proxies are used on each request.

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

Whether or not to only look at local files (i.e., do not try to download the model).

use_auth_token (`str` or *bool*, *optional*):

The token to use as HTTP bearer authorization for remote files. If `True`, will use the token generated

when running `diffusers-cli login` (stored in `~/.huggingface`).

revision (`str`, *optional*, defaults to `"main"`):

The specific model version to use. It can be a branch name, a tag name, or a commit id, since we use a

git-based system for storing models and other artifacts on huggingface.co, so `revision` can be any

identifier allowed by git.

subfolder (`str`, *optional*, defaults to `""`):

In case the relevant files are located inside a subfolder of the model repo (either remote in

huggingface.co or downloaded locally), you can specify the folder name here.

mirror (`str`, *optional*):

Mirror source to accelerate downloads in China. If you are from China and have an accessibility

problem, you can set this option to resolve it. Note that we do not guarantee the timeliness or safety.

Please refer to the mirror site for more information.

<Tip>

It is required to be logged in (`huggingface-cli login`) when you want to use private or [gated

models](https://huggingface.co/docs/hub/models-gated#gated-models).

</Tip>

"""

cache_dir = kwargs.pop("cache_dir", DIFFUSERS_CACHE)

force_download = kwargs.pop("force_download", False)

resume_download = kwargs.pop("resume_download", False)

proxies = kwargs.pop("proxies", None)

local_files_only = kwargs.pop("local_files_only", HF_HUB_OFFLINE)

use_auth_token = kwargs.pop("use_auth_token", None)

revision = kwargs.pop("revision", None)

subfolder = kwargs.pop("subfolder", None)

weight_name = kwargs.pop("weight_name", None)

use_safetensors = kwargs.pop("use_safetensors", None)

if use_safetensors and not is_safetensors_available():

raise ValueError(

"`use_safetensors`=True but safetensors is not installed. Please install safetensors with `pip install safetenstors"

)

allow_pickle = False

if use_safetensors is None:

use_safetensors = is_safetensors_available()

allow_pickle = True

user_agent = {

"file_type": "attn_procs_weights",

"framework": "pytorch",

}

model_file = None

if not isinstance(pretrained_model_name_or_path_or_dict, dict):

# Let's first try to load .safetensors weights

if (use_safetensors and weight_name is None) or (

weight_name is not None and weight_name.endswith(".safetensors")

):

try:

model_file = _get_model_file(

pretrained_model_name_or_path_or_dict,

weights_name=weight_name or LORA_WEIGHT_NAME_SAFE,

cache_dir=cache_dir,

force_download=force_download,

resume_download=resume_download,

proxies=proxies,

local_files_only=local_files_only,

use_auth_token=use_auth_token,

revision=revision,

subfolder=subfolder,

user_agent=user_agent,

)

state_dict = safetensors.torch.load_file(model_file, device="cpu")

except IOError as e:

if not allow_pickle:

raise e

# try loading non-safetensors weights

pass

if model_file is None:

model_file = _get_model_file(

pretrained_model_name_or_path_or_dict,

weights_name=weight_name or LORA_WEIGHT_NAME,

cache_dir=cache_dir,

force_download=force_download,

resume_download=resume_download,

proxies=proxies,

local_files_only=local_files_only,

use_auth_token=use_auth_token,

revision=revision,

subfolder=subfolder,

user_agent=user_agent,

)

state_dict = torch.load(model_file, map_location="cpu")

else:

state_dict = pretrained_model_name_or_path_or_dict

# fill attn processors

attn_processors = {}

is_lora = all("lora" in k for k in state_dict.keys())

is_custom_diffusion = any("custom_diffusion" in k for k in state_dict.keys())

if is_lora:

is_new_lora_format = all(

key.startswith(self.unet_name) or key.startswith(self.text_encoder_name) for key in state_dict.keys()

)

if is_new_lora_format:

# Strip the `"unet"` prefix.

is_text_encoder_present = any(key.startswith(self.text_encoder_name) for key in state_dict.keys())

if is_text_encoder_present:

warn_message = "The state_dict contains LoRA params corresponding to the text encoder which are not being used here. To use both UNet and text encoder related LoRA params, use [`pipe.load_lora_weights()`](https://huggingface.co/docs/diffusers/main/en/api/loaders#diffusers.loaders.LoraLoaderMixin.load_lora_weights)."

warnings.warn(warn_message)

unet_keys = [k for k in state_dict.keys() if k.startswith(self.unet_name)]

state_dict = {k.replace(f"{self.unet_name}.", ""): v for k, v in state_dict.items() if k in unet_keys}

lora_grouped_dict = defaultdict(dict)

for key, value in state_dict.items():

attn_processor_key, sub_key = ".".join(key.split(".")[:-3]), ".".join(key.split(".")[-3:])

lora_grouped_dict[attn_processor_key][sub_key] = value

for key, value_dict in lora_grouped_dict.items():

rank = value_dict["to_k_lora.down.weight"].shape[0]

hidden_size = value_dict["to_k_lora.up.weight"].shape[0]

attn_processor = self

for sub_key in key.split("."):

attn_processor = getattr(attn_processor, sub_key)

if isinstance(

attn_processor, (AttnAddedKVProcessor, SlicedAttnAddedKVProcessor, AttnAddedKVProcessor2_0)

):

cross_attention_dim = value_dict["add_k_proj_lora.down.weight"].shape[1]

attn_processor_class = LoRAAttnAddedKVProcessor

else:

cross_attention_dim = value_dict["to_k_lora.down.weight"].shape[1]

attn_processor_class = LoRAAttnProcessor

attn_processors[key] = attn_processor_class(

hidden_size=hidden_size, cross_attention_dim=cross_attention_dim, rank=rank

)

attn_processors[key].load_state_dict(value_dict)

elif is_custom_diffusion:

custom_diffusion_grouped_dict = defaultdict(dict)

for key, value in state_dict.items():

if len(value) == 0:

custom_diffusion_grouped_dict[key] = {}

else:

if "to_out" in key:

attn_processor_key, sub_key = ".".join(key.split(".")[:-3]), ".".join(key.split(".")[-3:])

else:

attn_processor_key, sub_key = ".".join(key.split(".")[:-2]), ".".join(key.split(".")[-2:])

custom_diffusion_grouped_dict[attn_processor_key][sub_key] = value

for key, value_dict in custom_diffusion_grouped_dict.items():

if len(value_dict) == 0:

attn_processors[key] = CustomDiffusionAttnProcessor(

train_kv=False, train_q_out=False, hidden_size=None, cross_attention_dim=None

)

else:

cross_attention_dim = value_dict["to_k_custom_diffusion.weight"].shape[1]

hidden_size = value_dict["to_k_custom_diffusion.weight"].shape[0]

train_q_out = True if "to_q_custom_diffusion.weight" in value_dict else False

attn_processors[key] = CustomDiffusionAttnProcessor(

train_kv=True,

train_q_out=train_q_out,

hidden_size=hidden_size,

cross_attention_dim=cross_attention_dim,

)

attn_processors[key].load_state_dict(value_dict)

else:

raise ValueError(

f"{model_file} does not seem to be in the correct format expected by LoRA or Custom Diffusion training."

)

# set correct dtype & device

attn_processors = {k: v.to(device=self.device, dtype=self.dtype) for k, v in attn_processors.items()}

# set layers

self.set_attn_processor(attn_processors)

def save_attn_procs(

self,

save_directory: Union[str, os.PathLike],

is_main_process: bool = True,

weight_name: str = None,

save_function: Callable = None,

safe_serialization: bool = False,

**kwargs,

):

r"""

Save an attention processor to a directory, so that it can be re-loaded using the

[`~loaders.UNet2DConditionLoadersMixin.load_attn_procs`] method.

Arguments:

save_directory (`str` or `os.PathLike`):

Directory to which to save. Will be created if it doesn't exist.

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

Whether the process calling this is the main process or not. Useful when in distributed training like

TPUs and need to call this function on all processes. In this case, set `is_main_process=True` only on

the main process to avoid race conditions.

save_function (`Callable`):

The function to use to save the state dictionary. Useful on distributed training like TPUs when one

need to replace `torch.save` by another method. Can be configured with the environment variable

`DIFFUSERS_SAVE_MODE`.

"""

weight_name = weight_name or deprecate(

"weights_name",

"0.18.0",

"`weights_name` is deprecated, please use `weight_name` instead.",

take_from=kwargs,

)

if os.path.isfile(save_directory):

logger.error(f"Provided path ({save_directory}) should be a directory, not a file")

return

if save_function is None:

if safe_serialization:

def save_function(weights, filename):

return safetensors.torch.save_file(weights, filename, metadata={"format": "pt"})

else:

save_function = torch.save

os.makedirs(save_directory, exist_ok=True)

is_custom_diffusion = any(

isinstance(x, (CustomDiffusionAttnProcessor, CustomDiffusionXFormersAttnProcessor))

for (_, x) in self.attn_processors.items()

)

if is_custom_diffusion:

model_to_save = AttnProcsLayers(

{

y: x

for (y, x) in self.attn_processors.items()

if isinstance(x, (CustomDiffusionAttnProcessor, CustomDiffusionXFormersAttnProcessor))

}

)

state_dict = model_to_save.state_dict()

for name, attn in self.attn_processors.items():

if len(attn.state_dict()) == 0:

state_dict[name] = {}

else:

model_to_save = AttnProcsLayers(self.attn_processors)

state_dict = model_to_save.state_dict()

if weight_name is None:

if safe_serialization:

weight_name = CUSTOM_DIFFUSION_WEIGHT_NAME_SAFE if is_custom_diffusion else LORA_WEIGHT_NAME_SAFE

else:

weight_name = CUSTOM_DIFFUSION_WEIGHT_NAME if is_custom_diffusion else LORA_WEIGHT_NAME

# Save the model

save_function(state_dict, os.path.join(save_directory, weight_name))

logger.info(f"Model weights saved in {os.path.join(save_directory, weight_name)}")

class TextualInversionLoaderMixin:

r"""

Mixin class for loading textual inversion tokens and embeddings to the tokenizer and text encoder.

"""

def maybe_convert_prompt(self, prompt: Union[str, List[str]], tokenizer: "PreTrainedTokenizer"):

r"""

Maybe convert a prompt into a "multi vector"-compatible prompt. If the prompt includes a token that corresponds

to a multi-vector textual inversion embedding, this function will process the prompt so that the special token

is replaced with multiple special tokens each corresponding to one of the vectors. If the prompt has no textual

inversion token or a textual inversion token that is a single vector, the input prompt is simply returned.

Parameters:

prompt (`str` or list of `str`):

The prompt or prompts to guide the image generation.

tokenizer (`PreTrainedTokenizer`):

The tokenizer responsible for encoding the prompt into input tokens.

Returns:

`str` or list of `str`: The converted prompt

"""

if not isinstance(prompt, List):

prompts = [prompt]

else:

prompts = prompt

prompts = [self._maybe_convert_prompt(p, tokenizer) for p in prompts]

if not isinstance(prompt, List):

return prompts[0]

return prompts

def _maybe_convert_prompt(self, prompt: str, tokenizer: "PreTrainedTokenizer"):

r"""

Maybe convert a prompt into a "multi vector"-compatible prompt. If the prompt includes a token that corresponds

to a multi-vector textual inversion embedding, this function will process the prompt so that the special token

is replaced with multiple special tokens each corresponding to one of the vectors. If the prompt has no textual

inversion token or a textual inversion token that is a single vector, the input prompt is simply returned.

Parameters:

prompt (`str`):

The prompt to guide the image generation.

tokenizer (`PreTrainedTokenizer`):

The tokenizer responsible for encoding the prompt into input tokens.

Returns:

`str`: The converted prompt

"""

tokens = tokenizer.tokenize(prompt)

for token in tokens:

if token in tokenizer.added_tokens_encoder:

replacement = token

i = 1

while f"{token}_{i}" in tokenizer.added_tokens_encoder:

replacement += f" {token}_{i}"

i += 1

prompt = prompt.replace(token, replacement)

return prompt

def load_textual_inversion(

self,

pretrained_model_name_or_path: Union[str, List[str]],

token: Optional[Union[str, List[str]]] = None,

**kwargs,

):

r"""

Load textual inversion embeddings into the text encoder of stable diffusion pipelines. Both `diffusers` and

`Automatic1111` formats are supported (see example below).

<Tip warning={true}>

This function is experimental and might change in the future.

</Tip>

Parameters:

pretrained_model_name_or_path (`str` or `os.PathLike` or `List[str or os.PathLike]`):

Can be either:

- A string, the *model id* of a pretrained model hosted inside a model repo on huggingface.co.

Valid model ids should have an organization name, like

`"sd-concepts-library/low-poly-hd-logos-icons"`.

- A path to a *directory* containing textual inversion weights, e.g.

`./my_text_inversion_directory/`.

- A path to a *file* containing textual inversion weights, e.g. `./my_text_inversions.pt`.

Or a list of those elements.

token (`str` or `List[str]`, *optional*):

Override the token to use for the textual inversion weights. If `pretrained_model_name_or_path` is a

list, then `token` must also be a list of equal length.

weight_name (`str`, *optional*):

Name of a custom weight file. This should be used in two cases:

- The saved textual inversion file is in `diffusers` format, but was saved under a specific weight

name, such as `text_inv.bin`.

- The saved textual inversion file is in the "Automatic1111" form.

cache_dir (`Union[str, os.PathLike]`, *optional*):

Path to a directory in which a downloaded pretrained model configuration should be cached if the

standard cache should not be used.

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

Whether or not to force the (re-)download of the model weights and configuration files, overriding the

cached versions if they exist.

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

Whether or not to delete incompletely received files. Will attempt to resume the download if such a

file exists.

proxies (`Dict[str, str]`, *optional*):

A dictionary of proxy servers to use by protocol or endpoint, e.g., `{'http': 'foo.bar:3128',

'http://hostname': 'foo.bar:4012'}`. The proxies are used on each request.

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

Whether or not to only look at local files (i.e., do not try to download the model).

use_auth_token (`str` or *bool*, *optional*):

The token to use as HTTP bearer authorization for remote files. If `True`, will use the token generated

when running `diffusers-cli login` (stored in `~/.huggingface`).

revision (`str`, *optional*, defaults to `"main"`):

The specific model version to use. It can be a branch name, a tag name, or a commit id, since we use a

git-based system for storing models and other artifacts on huggingface.co, so `revision` can be any

identifier allowed by git.

subfolder (`str`, *optional*, defaults to `""`):

In case the relevant files are located inside a subfolder of the model repo (either remote in

huggingface.co or downloaded locally), you can specify the folder name here.

mirror (`str`, *optional*):

Mirror source to accelerate downloads in China. If you are from China and have an accessibility

problem, you can set this option to resolve it. Note that we do not guarantee the timeliness or safety.

Please refer to the mirror site for more information.

<Tip>

It is required to be logged in (`huggingface-cli login`) when you want to use private or [gated

models](https://huggingface.co/docs/hub/models-gated#gated-models).

</Tip>

Example:

To load a textual inversion embedding vector in `diffusers` format:

```py

from diffusers import StableDiffusionPipeline

import torch

model_id = "runwayml/stable-diffusion-v1-5"

pipe = StableDiffusionPipeline.from_pretrained(model_id, torch_dtype=torch.float16).to("cuda")

pipe.load_textual_inversion("sd-concepts-library/cat-toy")

prompt = "A <cat-toy> backpack"

image = pipe(prompt, num_inference_steps=50).images[0]

image.save("cat-backpack.png")

```

To load a textual inversion embedding vector in Automatic1111 format, make sure to first download the vector,

e.g. from [civitAI](https://civitai.com/models/3036?modelVersionId=9857) and then load the vector locally:

```py

from diffusers import StableDiffusionPipeline

import torch

model_id = "runwayml/stable-diffusion-v1-5"

pipe = StableDiffusionPipeline.from_pretrained(model_id, torch_dtype=torch.float16).to("cuda")

pipe.load_textual_inversion("./charturnerv2.pt", token="charturnerv2")

prompt = "charturnerv2, multiple views of the same character in the same outfit, a character turnaround of a woman wearing a black jacket and red shirt, best quality, intricate details."

image = pipe(prompt, num_inference_steps=50).images[0]

image.save("character.png")

```

"""

if not hasattr(self, "tokenizer") or not isinstance(self.tokenizer, PreTrainedTokenizer):

raise ValueError(

f"{self.__class__.__name__} requires `self.tokenizer` of type `PreTrainedTokenizer` for calling"

f" `{self.load_textual_inversion.__name__}`"

)

if not hasattr(self, "text_encoder") or not isinstance(self.text_encoder, PreTrainedModel):

raise ValueError(

f"{self.__class__.__name__} requires `self.text_encoder` of type `PreTrainedModel` for calling"

f" `{self.load_textual_inversion.__name__}`"

)

cache_dir = kwargs.pop("cache_dir", DIFFUSERS_CACHE)

force_download = kwargs.pop("force_download", False)

resume_download = kwargs.pop("resume_download", False)

proxies = kwargs.pop("proxies", None)

local_files_only = kwargs.pop("local_files_only", HF_HUB_OFFLINE)

use_auth_token = kwargs.pop("use_auth_token", None)

revision = kwargs.pop("revision", None)

subfolder = kwargs.pop("subfolder", None)

weight_name = kwargs.pop("weight_name", None)

use_safetensors = kwargs.pop("use_safetensors", None)

if use_safetensors and not is_safetensors_available():

raise ValueError(

"`use_safetensors`=True but safetensors is not installed. Please install safetensors with `pip install safetenstors"

)

allow_pickle = False

if use_safetensors is None:

use_safetensors = is_safetensors_available()

allow_pickle = True

user_agent = {

"file_type": "text_inversion",

"framework": "pytorch",

}

if isinstance(pretrained_model_name_or_path, str):

pretrained_model_name_or_paths = [pretrained_model_name_or_path]

else:

pretrained_model_name_or_paths = pretrained_model_name_or_path

if isinstance(token, str):

tokens = [token]

elif token is None:

tokens = [None] * len(pretrained_model_name_or_paths)

else:

tokens = token

if len(pretrained_model_name_or_paths) != len(tokens):

raise ValueError(

f"You have passed a list of models of length {len(pretrained_model_name_or_paths)}, and list of tokens of length {len(tokens)}"

f"Make sure both lists have the same length."

)

valid_tokens = [t for t in tokens if t is not None]

if len(set(valid_tokens)) < len(valid_tokens):

raise ValueError(f"You have passed a list of tokens that contains duplicates: {tokens}")

token_ids_and_embeddings = []

for pretrained_model_name_or_path, token in zip(pretrained_model_name_or_paths, tokens):

# 1. Load textual inversion file

model_file = None

# Let's first try to load .safetensors weights

if (use_safetensors and weight_name is None) or (

weight_name is not None and weight_name.endswith(".safetensors")

):

try:

model_file = _get_model_file(

pretrained_model_name_or_path,

weights_name=weight_name or TEXT_INVERSION_NAME_SAFE,

cache_dir=cache_dir,

force_download=force_download,

resume_download=resume_download,

proxies=proxies,

local_files_only=local_files_only,

use_auth_token=use_auth_token,

revision=revision,

subfolder=subfolder,

user_agent=user_agent,

)

state_dict = safetensors.torch.load_file(model_file, device="cpu")

except Exception as e:

if not allow_pickle:

raise e

model_file = None

if model_file is None:

model_file = _get_model_file(

pretrained_model_name_or_path,

weights_name=weight_name or TEXT_INVERSION_NAME,

cache_dir=cache_dir,

force_download=force_download,

resume_download=resume_download,

proxies=proxies,

local_files_only=local_files_only,

use_auth_token=use_auth_token,

revision=revision,

subfolder=subfolder,

user_agent=user_agent,

)

state_dict = torch.load(model_file, map_location="cpu")

# 2. Load token and embedding correcly from file

if isinstance(state_dict, torch.Tensor):

if token is None:

raise ValueError(

"You are trying to load a textual inversion embedding that has been saved as a PyTorch tensor. Make sure to pass the name of the corresponding token in this case: `token=...`."

)

embedding = state_dict

elif len(state_dict) == 1:

# diffusers

loaded_token, embedding = next(iter(state_dict.items()))

elif "string_to_param" in state_dict:

# A1111

loaded_token = state_dict["name"]

embedding = state_dict["string_to_param"]["*"]

if token is not None and loaded_token != token:

logger.info(f"The loaded token: {loaded_token} is overwritten by the passed token {token}.")

else:

token = loaded_token

embedding = embedding.to(dtype=self.text_encoder.dtype, device=self.text_encoder.device)

# 3. Make sure we don't mess up the tokenizer or text encoder

vocab = self.tokenizer.get_vocab()

if token in vocab:

raise ValueError(

f"Token {token} already in tokenizer vocabulary. Please choose a different token name or remove {token} and embedding from the tokenizer and text encoder."

)

elif f"{token}_1" in vocab:

multi_vector_tokens = [token]

i = 1

while f"{token}_{i}" in self.tokenizer.added_tokens_encoder:

multi_vector_tokens.append(f"{token}_{i}")

i += 1

raise ValueError(

f"Multi-vector Token {multi_vector_tokens} already in tokenizer vocabulary. Please choose a different token name or remove the {multi_vector_tokens} and embedding from the tokenizer and text encoder."

)

is_multi_vector = len(embedding.shape) > 1 and embedding.shape[0] > 1

if is_multi_vector:

tokens = [token] + [f"{token}_{i}" for i in range(1, embedding.shape[0])]

embeddings = [e for e in embedding] # noqa: C416

else:

tokens = [token]

embeddings = [embedding[0]] if len(embedding.shape) > 1 else [embedding]

# add tokens and get ids

self.tokenizer.add_tokens(tokens)

token_ids = self.tokenizer.convert_tokens_to_ids(tokens)

token_ids_and_embeddings += zip(token_ids, embeddings)

logger.info(f"Loaded textual inversion embedding for {token}.")

# resize token embeddings and set all new embeddings

self.text_encoder.resize_token_embeddings(len(self.tokenizer))

for token_id, embedding in token_ids_and_embeddings:

self.text_encoder.get_input_embeddings().weight.data[token_id] = embedding

class LoraLoaderMixin:

r"""

Utility class for handling the loading LoRA layers into UNet (of class [`UNet2DConditionModel`]) and Text Encoder

(of class [`CLIPTextModel`](https://huggingface.co/docs/transformers/model_doc/clip#transformers.CLIPTextModel)).

<Tip warning={true}>

This function is experimental and might change in the future.

</Tip>

"""

text_encoder_name = TEXT_ENCODER_NAME

unet_name = UNET_NAME

def load_lora_weights(self, pretrained_model_name_or_path_or_dict: Union[str, Dict[str, torch.Tensor]], **kwargs):

r"""

Load pretrained attention processor layers (such as LoRA) into [`UNet2DConditionModel`] and

[`CLIPTextModel`](https://huggingface.co/docs/transformers/model_doc/clip#transformers.CLIPTextModel)).

<Tip warning={true}>

This function is experimental and might change in the future.

</Tip>

Parameters:

pretrained_model_name_or_path_or_dict (`str` or `os.PathLike` or `dict`):

Can be either:

- A string, the *model id* of a pretrained model hosted inside a model repo on huggingface.co.

Valid model ids should have an organization name, like `google/ddpm-celebahq-256`.

- A path to a *directory* containing model weights saved using [`~ModelMixin.save_config`], e.g.,

`./my_model_directory/`.

- A [torch state

dict](https://pytorch.org/tutorials/beginner/saving_loading_models.html#what-is-a-state-dict).

cache_dir (`Union[str, os.PathLike]`, *optional*):

Path to a directory in which a downloaded pretrained model configuration should be cached if the

standard cache should not be used.

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

Whether or not to force the (re-)download of the model weights and configuration files, overriding the

cached versions if they exist.

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

Whether or not to delete incompletely received files. Will attempt to resume the download if such a

file exists.

proxies (`Dict[str, str]`, *optional*):

A dictionary of proxy servers to use by protocol or endpoint, e.g., `{'http': 'foo.bar:3128',

'http://hostname': 'foo.bar:4012'}`. The proxies are used on each request.

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

Whether or not to only look at local files (i.e., do not try to download the model).

use_auth_token (`str` or *bool*, *optional*):

The token to use as HTTP bearer authorization for remote files. If `True`, will use the token generated

when running `diffusers-cli login` (stored in `~/.huggingface`).

revision (`str`, *optional*, defaults to `"main"`):

The specific model version to use. It can be a branch name, a tag name, or a commit id, since we use a

git-based system for storing models and other artifacts on huggingface.co, so `revision` can be any

identifier allowed by git.

subfolder (`str`, *optional*, defaults to `""`):

In case the relevant files are located inside a subfolder of the model repo (either remote in

huggingface.co or downloaded locally), you can specify the folder name here.

mirror (`str`, *optional*):

Mirror source to accelerate downloads in China. If you are from China and have an accessibility

problem, you can set this option to resolve it. Note that we do not guarantee the timeliness or safety.

Please refer to the mirror site for more information.

<Tip>

It is required to be logged in (`huggingface-cli login`) when you want to use private or [gated

models](https://huggingface.co/docs/hub/models-gated#gated-models).

</Tip>

"""

# Load the main state dict first which has the LoRA layers for either of

# UNet and text encoder or both.

cache_dir = kwargs.pop("cache_dir", DIFFUSERS_CACHE)

force_download = kwargs.pop("force_download", False)

resume_download = kwargs.pop("resume_download", False)

proxies = kwargs.pop("proxies", None)

local_files_only = kwargs.pop("local_files_only", HF_HUB_OFFLINE)

use_auth_token = kwargs.pop("use_auth_token", None)

revision = kwargs.pop("revision", None)

subfolder = kwargs.pop("subfolder", None)

weight_name = kwargs.pop("weight_name", None)

use_safetensors = kwargs.pop("use_safetensors", None)

if use_safetensors and not is_safetensors_available():

raise ValueError(

"`use_safetensors`=True but safetensors is not installed. Please install safetensors with `pip install safetenstors"

)

allow_pickle = False

if use_safetensors is None:

use_safetensors = is_safetensors_available()

allow_pickle = True

user_agent = {

"file_type": "attn_procs_weights",

"framework": "pytorch",

}

model_file = None

if not isinstance(pretrained_model_name_or_path_or_dict, dict):

# Let's first try to load .safetensors weights

if (use_safetensors and weight_name is None) or (

weight_name is not None and weight_name.endswith(".safetensors")

):

try:

model_file = _get_model_file(

pretrained_model_name_or_path_or_dict,

weights_name=weight_name or LORA_WEIGHT_NAME_SAFE,

cache_dir=cache_dir,

force_download=force_download,

resume_download=resume_download,

proxies=proxies,

local_files_only=local_files_only,

use_auth_token=use_auth_token,

revision=revision,

subfolder=subfolder,

user_agent=user_agent,

)

state_dict = safetensors.torch.load_file(model_file, device="cpu")

except IOError as e:

if not allow_pickle:

raise e

# try loading non-safetensors weights

pass

if model_file is None:

model_file = _get_model_file(

pretrained_model_name_or_path_or_dict,

weights_name=weight_name or LORA_WEIGHT_NAME,

cache_dir=cache_dir,

force_download=force_download,

resume_download=resume_download,

proxies=proxies,

local_files_only=local_files_only,

use_auth_token=use_auth_token,

revision=revision,

subfolder=subfolder,

user_agent=user_agent,

)

state_dict = torch.load(model_file, map_location="cpu")

else:

state_dict = pretrained_model_name_or_path_or_dict

# If the serialization format is new (introduced in https://github.com/huggingface/diffusers/pull/2918),

# then the `state_dict` keys should have `self.unet_name` and/or `self.text_encoder_name` as

# their prefixes.

keys = list(state_dict.keys())

if all(key.startswith(self.unet_name) or key.startswith(self.text_encoder_name) for key in keys):

# Load the layers corresponding to UNet.

unet_keys = [k for k in keys if k.startswith(self.unet_name)]

logger.info(f"Loading {self.unet_name}.")

unet_lora_state_dict = {

k.replace(f"{self.unet_name}.", ""): v for k, v in state_dict.items() if k in unet_keys

}

self.unet.load_attn_procs(unet_lora_state_dict)

# Load the layers corresponding to text encoder and make necessary adjustments.

text_encoder_keys = [k for k in keys if k.startswith(self.text_encoder_name)]

logger.info(f"Loading {self.text_encoder_name}.")

text_encoder_lora_state_dict = {

k.replace(f"{self.text_encoder_name}.", ""): v for k, v in state_dict.items() if k in text_encoder_keys

}

if len(text_encoder_lora_state_dict) > 0:

attn_procs_text_encoder = self._load_text_encoder_attn_procs(text_encoder_lora_state_dict)

self._modify_text_encoder(attn_procs_text_encoder)

# Otherwise, we're dealing with the old format. This means the `state_dict` should only

# contain the module names of the `unet` as its keys WITHOUT any prefix.

elif not all(

key.startswith(self.unet_name) or key.startswith(self.text_encoder_name) for key in state_dict.keys()

):

self.unet.load_attn_procs(state_dict)

warn_message = "You have saved the LoRA weights using the old format. To convert the old LoRA weights to the new format, you can first load them in a dictionary and then create a new dictionary like the following: `new_state_dict = {f'unet'.{module_name}: params for module_name, params in old_state_dict.items()}`."

warnings.warn(warn_message)

def _modify_text_encoder(self, attn_processors: Dict[str, LoRAAttnProcessor]):

r"""

Monkey-patches the forward passes of attention modules of the text encoder.

Parameters:

attn_processors: Dict[str, `LoRAAttnProcessor`]:

A dictionary mapping the module names and their corresponding [`~LoRAAttnProcessor`].

"""

# Loop over the original attention modules.

for name, _ in self.text_encoder.named_modules():

if any(x in name for x in TEXT_ENCODER_TARGET_MODULES):

# Retrieve the module and its corresponding LoRA processor.

module = self.text_encoder.get_submodule(name)

# Construct a new function that performs the LoRA merging. We will monkey patch

# this forward pass.

lora_layer = getattr(attn_processors[name], self._get_lora_layer_attribute(name))

old_forward = module.forward

def new_forward(x):

return old_forward(x) + lora_layer(x)

# Monkey-patch.

module.forward = new_forward

def _get_lora_layer_attribute(self, name: str) -> str:

if "q_proj" in name:

return "to_q_lora"

elif "v_proj" in name:

return "to_v_lora"

elif "k_proj" in name:

return "to_k_lora"

else:

return "to_out_lora"

def _load_text_encoder_attn_procs(

self, pretrained_model_name_or_path_or_dict: Union[str, Dict[str, torch.Tensor]], **kwargs

):

r"""

Load pretrained attention processor layers for

[`CLIPTextModel`](https://huggingface.co/docs/transformers/model_doc/clip#transformers.CLIPTextModel).

<Tip warning={true}>

This function is experimental and might change in the future.

</Tip>

Parameters:

pretrained_model_name_or_path_or_dict (`str` or `os.PathLike` or `dict`):

Can be either:

- A string, the *model id* of a pretrained model hosted inside a model repo on huggingface.co.

Valid model ids should have an organization name, like `google/ddpm-celebahq-256`.

- A path to a *directory* containing model weights saved using [`~ModelMixin.save_config`], e.g.,

`./my_model_directory/`.

- A [torch state

dict](https://pytorch.org/tutorials/beginner/saving_loading_models.html#what-is-a-state-dict).

cache_dir (`Union[str, os.PathLike]`, *optional*):

Path to a directory in which a downloaded pretrained model configuration should be cached if the

standard cache should not be used.

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

Whether or not to force the (re-)download of the model weights and configuration files, overriding the

cached versions if they exist.

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

Whether or not to delete incompletely received files. Will attempt to resume the download if such a

file exists.

proxies (`Dict[str, str]`, *optional*):

A dictionary of proxy servers to use by protocol or endpoint, e.g., `{'http': 'foo.bar:3128',

'http://hostname': 'foo.bar:4012'}`. The proxies are used on each request.

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

Whether or not to only look at local files (i.e., do not try to download the model).

use_auth_token (`str` or *bool*, *optional*):

The token to use as HTTP bearer authorization for remote files. If `True`, will use the token generated

when running `diffusers-cli login` (stored in `~/.huggingface`).

revision (`str`, *optional*, defaults to `"main"`):

The specific model version to use. It can be a branch name, a tag name, or a commit id, since we use a

git-based system for storing models and other artifacts on huggingface.co, so `revision` can be any

identifier allowed by git.

subfolder (`str`, *optional*, defaults to `""`):

In case the relevant files are located inside a subfolder of the model repo (either remote in

huggingface.co or downloaded locally), you can specify the folder name here.

mirror (`str`, *optional*):

Mirror source to accelerate downloads in China. If you are from China and have an accessibility

problem, you can set this option to resolve it. Note that we do not guarantee the timeliness or safety.

Please refer to the mirror site for more information.

Returns:

`Dict[name, LoRAAttnProcessor]`: Mapping between the module names and their corresponding

[`LoRAAttnProcessor`].