# coding=utf-8

# Copyright 2024 The HuggingFace Inc. team.

# Copyright (c) 2022, NVIDIA CORPORATION. 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 copy

import inspect

import itertools

import json

import os

import re

from collections import OrderedDict

from functools import partial, wraps

from pathlib import Path

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

import safetensors

import torch

from huggingface_hub import create_repo, split_torch_state_dict_into_shards

from huggingface_hub.utils import validate_hf_hub_args

from torch import Tensor, nn

from .. import __version__

from ..quantizers import DiffusersAutoQuantizer, DiffusersQuantizer

from ..quantizers.quantization_config import QuantizationMethod

from ..utils import (

CONFIG_NAME,

FLAX_WEIGHTS_NAME,

SAFE_WEIGHTS_INDEX_NAME,

SAFETENSORS_WEIGHTS_NAME,

WEIGHTS_INDEX_NAME,

WEIGHTS_NAME,

_add_variant,

_get_checkpoint_shard_files,

_get_model_file,

deprecate,

is_accelerate_available,

is_bitsandbytes_available,

is_bitsandbytes_version,

is_torch_version,

logging,

)

from ..utils.hub_utils import (

PushToHubMixin,

load_or_create_model_card,

populate_model_card,

)

from .model_loading_utils import (

_determine_device_map,

_fetch_index_file,

_fetch_index_file_legacy,

_load_state_dict_into_model,

_merge_sharded_checkpoints,

load_model_dict_into_meta,

load_state_dict,

)

logger = logging.get_logger(__name__)

_REGEX_SHARD = re.compile(r"(.*?)-\d{5}-of-\d{5}")

if is_torch_version(">=", "1.9.0"):

_LOW_CPU_MEM_USAGE_DEFAULT = True

else:

_LOW_CPU_MEM_USAGE_DEFAULT = False

if is_accelerate_available():

import accelerate

def get_parameter_device(parameter: torch.nn.Module) -> torch.device:

try:

parameters_and_buffers = itertools.chain(parameter.parameters(), parameter.buffers())

return next(parameters_and_buffers).device

except StopIteration:

# For torch.nn.DataParallel compatibility in PyTorch 1.5

def find_tensor_attributes(module: torch.nn.Module) -> List[Tuple[str, Tensor]]:

tuples = [(k, v) for k, v in module.__dict__.items() if torch.is_tensor(v)]

return tuples

gen = parameter._named_members(get_members_fn=find_tensor_attributes)

first_tuple = next(gen)

return first_tuple[1].device

def get_parameter_dtype(parameter: torch.nn.Module) -> torch.dtype:

try:

return next(parameter.parameters()).dtype

except StopIteration:

try:

return next(parameter.buffers()).dtype

except StopIteration:

# For torch.nn.DataParallel compatibility in PyTorch 1.5

def find_tensor_attributes(module: torch.nn.Module) -> List[Tuple[str, Tensor]]:

tuples = [(k, v) for k, v in module.__dict__.items() if torch.is_tensor(v)]

return tuples

gen = parameter._named_members(get_members_fn=find_tensor_attributes)

first_tuple = next(gen)

return first_tuple[1].dtype

class ModelMixin(torch.nn.Module, PushToHubMixin):

r"""

Base class for all models.

[`ModelMixin`] takes care of storing the model configuration and provides methods for loading, downloading and

saving models.

- **config_name** ([`str`]) -- Filename to save a model to when calling [`~models.ModelMixin.save_pretrained`].

"""

config_name = CONFIG_NAME

_automatically_saved_args = ["_diffusers_version", "_class_name", "_name_or_path"]

_supports_gradient_checkpointing = False

_keys_to_ignore_on_load_unexpected = None

_no_split_modules = None

_keep_in_fp32_modules = None

def __init__(self):

super().__init__()

def __getattr__(self, name: str) -> Any:

"""The only reason we overwrite `getattr` here is to gracefully deprecate accessing

config attributes directly. See https://github.com/huggingface/diffusers/pull/3129 We need to overwrite

__getattr__ here in addition so that we don't trigger `torch.nn.Module`'s __getattr__':

https://pytorch.org/docs/stable/_modules/torch/nn/modules/module.html#Module

"""

is_in_config = "_internal_dict" in self.__dict__ and hasattr(self.__dict__["_internal_dict"], name)

is_attribute = name in self.__dict__

if is_in_config and not is_attribute:

deprecation_message = f"Accessing config attribute `{name}` directly via '{type(self).__name__}' object attribute is deprecated. Please access '{name}' over '{type(self).__name__}'s config object instead, e.g. 'unet.config.{name}'."

deprecate("direct config name access", "1.0.0", deprecation_message, standard_warn=False, stacklevel=3)

return self._internal_dict[name]

# call PyTorch's https://pytorch.org/docs/stable/_modules/torch/nn/modules/module.html#Module

return super().__getattr__(name)

@property

def is_gradient_checkpointing(self) -> bool:

"""

Whether gradient checkpointing is activated for this model or not.

"""

return any(hasattr(m, "gradient_checkpointing") and m.gradient_checkpointing for m in self.modules())

def enable_gradient_checkpointing(self) -> None:

"""

Activates gradient checkpointing for the current model (may be referred to as *activation checkpointing* or

*checkpoint activations* in other frameworks).

"""

if not self._supports_gradient_checkpointing:

raise ValueError(f"{self.__class__.__name__} does not support gradient checkpointing.")

self.apply(partial(self._set_gradient_checkpointing, value=True))

def disable_gradient_checkpointing(self) -> None:

"""

Deactivates gradient checkpointing for the current model (may be referred to as *activation checkpointing* or

*checkpoint activations* in other frameworks).

"""

if self._supports_gradient_checkpointing:

self.apply(partial(self._set_gradient_checkpointing, value=False))

def set_use_npu_flash_attention(self, valid: bool) -> None:

r"""

Set the switch for the npu flash attention.

"""

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

if hasattr(module, "set_use_npu_flash_attention"):

module.set_use_npu_flash_attention(valid)

for child in module.children():

fn_recursive_set_npu_flash_attention(child)

for module in self.children():

if isinstance(module, torch.nn.Module):

fn_recursive_set_npu_flash_attention(module)

def enable_npu_flash_attention(self) -> None:

r"""

Enable npu flash attention from torch_npu

"""

self.set_use_npu_flash_attention(True)

def disable_npu_flash_attention(self) -> None:

r"""

disable npu flash attention from torch_npu

"""

self.set_use_npu_flash_attention(False)

def set_use_xla_flash_attention(

self, use_xla_flash_attention: bool, partition_spec: Optional[Callable] = None

) -> None:

# Recursively walk through all the children.

# Any children which exposes the set_use_xla_flash_attention method

# gets the message

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

if hasattr(module, "set_use_xla_flash_attention"):

module.set_use_xla_flash_attention(use_xla_flash_attention, partition_spec)

for child in module.children():

fn_recursive_set_flash_attention(child)

for module in self.children():

if isinstance(module, torch.nn.Module):

fn_recursive_set_flash_attention(module)

def enable_xla_flash_attention(self, partition_spec: Optional[Callable] = None):

r"""

Enable the flash attention pallals kernel for torch_xla.

"""

self.set_use_xla_flash_attention(True, partition_spec)

def disable_xla_flash_attention(self):

r"""

Disable the flash attention pallals kernel for torch_xla.

"""

self.set_use_xla_flash_attention(False)

def set_use_memory_efficient_attention_xformers(

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

) -> None:

# Recursively walk through all the children.

# Any children which exposes the set_use_memory_efficient_attention_xformers method

# gets the message

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

if hasattr(module, "set_use_memory_efficient_attention_xformers"):

module.set_use_memory_efficient_attention_xformers(valid, attention_op)

for child in module.children():

fn_recursive_set_mem_eff(child)

for module in self.children():

if isinstance(module, torch.nn.Module):

fn_recursive_set_mem_eff(module)

def enable_xformers_memory_efficient_attention(self, attention_op: Optional[Callable] = None) -> None:

r"""

Enable memory efficient attention from [xFormers](https://facebookresearch.github.io/xformers/).

When this option is enabled, you should observe lower GPU memory usage and a potential speed up during

inference. Speed up during training is not guaranteed.

<Tip warning={true}>

⚠️ When memory efficient attention and sliced attention are both enabled, memory efficient attention takes

precedent.

</Tip>

Parameters:

attention_op (`Callable`, *optional*):

Override the default `None` operator for use as `op` argument to the

[`memory_efficient_attention()`](https://facebookresearch.github.io/xformers/components/ops.html#xformers.ops.memory_efficient_attention)

function of xFormers.

Examples:

```py

>>> import torch

>>> from diffusers import UNet2DConditionModel

>>> from xformers.ops import MemoryEfficientAttentionFlashAttentionOp

>>> model = UNet2DConditionModel.from_pretrained(

... "stabilityai/stable-diffusion-2-1", subfolder="unet", torch_dtype=torch.float16

... )

>>> model = model.to("cuda")

>>> model.enable_xformers_memory_efficient_attention(attention_op=MemoryEfficientAttentionFlashAttentionOp)

```

"""

self.set_use_memory_efficient_attention_xformers(True, attention_op)

def disable_xformers_memory_efficient_attention(self) -> None:

r"""

Disable memory efficient attention from [xFormers](https://facebookresearch.github.io/xformers/).

"""

self.set_use_memory_efficient_attention_xformers(False)

def save_pretrained(

self,

save_directory: Union[str, os.PathLike],

is_main_process: bool = True,

save_function: Optional[Callable] = None,

safe_serialization: bool = True,

variant: Optional[str] = None,

max_shard_size: Union[int, str] = "10GB",

push_to_hub: bool = False,

**kwargs,

):

"""

Save a model and its configuration file to a directory so that it can be reloaded using the

[`~models.ModelMixin.from_pretrained`] class method.

Arguments:

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

Directory to save a model and its configuration file to. 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 during distributed training and you

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 during distributed training when you need to

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

`DIFFUSERS_SAVE_MODE`.

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

Whether to save the model using `safetensors` or the traditional PyTorch way with `pickle`.

variant (`str`, *optional*):

If specified, weights are saved in the format `pytorch_model.<variant>.bin`.

max_shard_size (`int` or `str`, defaults to `"10GB"`):

The maximum size for a checkpoint before being sharded. Checkpoints shard will then be each of size

lower than this size. If expressed as a string, needs to be digits followed by a unit (like `"5GB"`).

If expressed as an integer, the unit is bytes. Note that this limit will be decreased after a certain

period of time (starting from Oct 2024) to allow users to upgrade to the latest version of `diffusers`.

This is to establish a common default size for this argument across different libraries in the Hugging

Face ecosystem (`transformers`, and `accelerate`, for example).

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

Whether or not to push your model to the Hugging Face Hub after saving it. You can specify the

repository you want to push to with `repo_id` (will default to the name of `save_directory` in your

namespace).

kwargs (`Dict[str, Any]`, *optional*):

Additional keyword arguments passed along to the [`~utils.PushToHubMixin.push_to_hub`] method.

"""

if os.path.isfile(save_directory):

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

return

hf_quantizer = getattr(self, "hf_quantizer", None)

if hf_quantizer is not None:

quantization_serializable = (

hf_quantizer is not None

and isinstance(hf_quantizer, DiffusersQuantizer)

and hf_quantizer.is_serializable

)

if not quantization_serializable:

raise ValueError(

f"The model is quantized with {hf_quantizer.quantization_config.quant_method} and is not serializable - check out the warnings from"

" the logger on the traceback to understand the reason why the quantized model is not serializable."

)

weights_name = SAFETENSORS_WEIGHTS_NAME if safe_serialization else WEIGHTS_NAME

weights_name = _add_variant(weights_name, variant)

weights_name_pattern = weights_name.replace(".bin", "{suffix}.bin").replace(

".safetensors", "{suffix}.safetensors"

)

os.makedirs(save_directory, exist_ok=True)

if push_to_hub:

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

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

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

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

repo_id = kwargs.pop("repo_id", save_directory.split(os.path.sep)[-1])

repo_id = create_repo(repo_id, exist_ok=True, private=private, token=token).repo_id

# Only save the model itself if we are using distributed training

model_to_save = self

# Attach architecture to the config

# Save the config

if is_main_process:

model_to_save.save_config(save_directory)

# Save the model

state_dict = model_to_save.state_dict()

# Save the model

state_dict_split = split_torch_state_dict_into_shards(

state_dict, max_shard_size=max_shard_size, filename_pattern=weights_name_pattern

)

# Clean the folder from a previous save

if is_main_process:

for filename in os.listdir(save_directory):

if filename in state_dict_split.filename_to_tensors.keys():

continue

full_filename = os.path.join(save_directory, filename)

if not os.path.isfile(full_filename):

continue

weights_without_ext = weights_name_pattern.replace(".bin", "").replace(".safetensors", "")

weights_without_ext = weights_without_ext.replace("{suffix}", "")

filename_without_ext = filename.replace(".bin", "").replace(".safetensors", "")

# make sure that file to be deleted matches format of sharded file, e.g. pytorch_model-00001-of-00005

if (

filename.startswith(weights_without_ext)

and _REGEX_SHARD.fullmatch(filename_without_ext) is not None

):

os.remove(full_filename)

for filename, tensors in state_dict_split.filename_to_tensors.items():

shard = {tensor: state_dict[tensor] for tensor in tensors}

filepath = os.path.join(save_directory, filename)

if safe_serialization:

# At some point we will need to deal better with save_function (used for TPU and other distributed

# joyfulness), but for now this enough.

safetensors.torch.save_file(shard, filepath, metadata={"format": "pt"})

else:

torch.save(shard, filepath)

if state_dict_split.is_sharded:

index = {

"metadata": state_dict_split.metadata,

"weight_map": state_dict_split.tensor_to_filename,

}

save_index_file = SAFE_WEIGHTS_INDEX_NAME if safe_serialization else WEIGHTS_INDEX_NAME

save_index_file = os.path.join(save_directory, _add_variant(save_index_file, variant))

# Save the index as well

with open(save_index_file, "w", encoding="utf-8") as f:

content = json.dumps(index, indent=2, sort_keys=True) + "\n"

f.write(content)

logger.info(

f"The model is bigger than the maximum size per checkpoint ({max_shard_size}) and is going to be "

f"split in {len(state_dict_split.filename_to_tensors)} checkpoint shards. You can find where each parameters has been saved in the "

f"index located at {save_index_file}."

)

else:

path_to_weights = os.path.join(save_directory, weights_name)

logger.info(f"Model weights saved in {path_to_weights}")

if push_to_hub:

# Create a new empty model card and eventually tag it

model_card = load_or_create_model_card(repo_id, token=token)

model_card = populate_model_card(model_card)

model_card.save(Path(save_directory, "README.md").as_posix())

self._upload_folder(

save_directory,

repo_id,

token=token,

commit_message=commit_message,

create_pr=create_pr,

)

def dequantize(self):

"""

Potentially dequantize the model in case it has been quantized by a quantization method that support

dequantization.

"""

hf_quantizer = getattr(self, "hf_quantizer", None)

if hf_quantizer is None:

raise ValueError("You need to first quantize your model in order to dequantize it")

return hf_quantizer.dequantize(self)

@classmethod

@validate_hf_hub_args

def from_pretrained(cls, pretrained_model_name_or_path: Optional[Union[str, os.PathLike]], **kwargs):

r"""

Instantiate a pretrained PyTorch model from a pretrained model configuration.

The model is set in evaluation mode - `model.eval()` - by default, and dropout modules are deactivated. To

train the model, set it back in training mode with `model.train()`.

Parameters:

pretrained_model_name_or_path (`str` or `os.PathLike`, *optional*):

Can be either:

- A string, the *model id* (for example `google/ddpm-celebahq-256`) of a pretrained model hosted on

the Hub.

- A path to a *directory* (for example `./my_model_directory`) containing the model weights saved

with [`~ModelMixin.save_pretrained`].

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

Path to a directory where a downloaded pretrained model configuration is cached if the standard cache

is not used.

torch_dtype (`str` or `torch.dtype`, *optional*):

Override the default `torch.dtype` and load the model with another dtype. If `"auto"` is passed, the

dtype is automatically derived from the model's weights.

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.

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

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

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

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

Whether or not to also return a dictionary containing missing keys, unexpected keys and error messages.

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

Whether to only load local model weights and configuration files or not. If set to `True`, the model

won't be downloaded from the Hub.

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

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

`diffusers-cli login` (stored in `~/.huggingface`) is used.

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

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

allowed by Git.

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

Load the model weights from a Flax checkpoint save file.

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

The subfolder location of a model file within a larger model repository on the Hub or locally.

mirror (`str`, *optional*):

Mirror source to resolve accessibility issues if you're downloading a model in China. We do not

guarantee the timeliness or safety of the source, and you should refer to the mirror site for more

information.

device_map (`str` or `Dict[str, Union[int, str, torch.device]]`, *optional*):

A map that specifies where each submodule should go. It doesn't need to be defined for each

parameter/buffer name; once a given module name is inside, every submodule of it will be sent to the

same device. Defaults to `None`, meaning that the model will be loaded on CPU.

Set `device_map="auto"` to have 🤗 Accelerate automatically compute the most optimized `device_map`. For

more information about each option see [designing a device

map](https://hf.co/docs/accelerate/main/en/usage_guides/big_modeling#designing-a-device-map).

max_memory (`Dict`, *optional*):

A dictionary device identifier for the maximum memory. Will default to the maximum memory available for

each GPU and the available CPU RAM if unset.

offload_folder (`str` or `os.PathLike`, *optional*):

The path to offload weights if `device_map` contains the value `"disk"`.

offload_state_dict (`bool`, *optional*):

If `True`, temporarily offloads the CPU state dict to the hard drive to avoid running out of CPU RAM if

the weight of the CPU state dict + the biggest shard of the checkpoint does not fit. Defaults to `True`

when there is some disk offload.

low_cpu_mem_usage (`bool`, *optional*, defaults to `True` if torch version >= 1.9.0 else `False`):

Speed up model loading only loading the pretrained weights and not initializing the weights. This also

tries to not use more than 1x model size in CPU memory (including peak memory) while loading the model.

Only supported for PyTorch >= 1.9.0. If you are using an older version of PyTorch, setting this

argument to `True` will raise an error.

variant (`str`, *optional*):

Load weights from a specified `variant` filename such as `"fp16"` or `"ema"`. This is ignored when

loading `from_flax`.

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

If set to `None`, the `safetensors` weights are downloaded if they're available **and** if the

`safetensors` library is installed. If set to `True`, the model is forcibly loaded from `safetensors`

weights. If set to `False`, `safetensors` weights are not loaded.

<Tip>

To use private or [gated models](https://huggingface.co/docs/hub/models-gated#gated-models), log-in with

`huggingface-cli login`. You can also activate the special

["offline-mode"](https://huggingface.co/diffusers/installation.html#offline-mode) to use this method in a

firewalled environment.

</Tip>

Example:

```py

from diffusers import UNet2DConditionModel

unet = UNet2DConditionModel.from_pretrained("runwayml/stable-diffusion-v1-5", subfolder="unet")

```

If you get the error message below, you need to finetune the weights for your downstream task:

```bash

Some weights of UNet2DConditionModel were not initialized from the model checkpoint at runwayml/stable-diffusion-v1-5 and are newly initialized because the shapes did not match:

- conv_in.weight: found shape torch.Size([320, 4, 3, 3]) in the checkpoint and torch.Size([320, 9, 3, 3]) in the model instantiated

You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.

```

"""

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

low_cpu_mem_usage = kwargs.pop("low_cpu_mem_usage", _LOW_CPU_MEM_USAGE_DEFAULT)

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

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

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

allow_pickle = False

if use_safetensors is None:

use_safetensors = True

allow_pickle = True

if low_cpu_mem_usage and not is_accelerate_available():

low_cpu_mem_usage = False

logger.warning(

"Cannot initialize model with low cpu memory usage because `accelerate` was not found in the"

" environment. Defaulting to `low_cpu_mem_usage=False`. It is strongly recommended to install"

" `accelerate` for faster and less memory-intense model loading. You can do so with: \n```\npip"

" install accelerate\n```\n."

)

if device_map is not None and not is_accelerate_available():

raise NotImplementedError(

"Loading and dispatching requires `accelerate`. Please make sure to install accelerate or set"

" `device_map=None`. You can install accelerate with `pip install accelerate`."

)

# Check if we can handle device_map and dispatching the weights

if device_map is not None and not is_torch_version(">=", "1.9.0"):

raise NotImplementedError(

"Loading and dispatching requires torch >= 1.9.0. Please either update your PyTorch version or set"

" `device_map=None`."

)

if low_cpu_mem_usage is True and not is_torch_version(">=", "1.9.0"):

raise NotImplementedError(

"Low memory initialization requires torch >= 1.9.0. Please either update your PyTorch version or set"

" `low_cpu_mem_usage=False`."

)

if low_cpu_mem_usage is False and device_map is not None:

raise ValueError(

f"You cannot set `low_cpu_mem_usage` to `False` while using device_map={device_map} for loading and"

" dispatching. Please make sure to set `low_cpu_mem_usage=True`."

)

# change device_map into a map if we passed an int, a str or a torch.device

if isinstance(device_map, torch.device):

device_map = {"": device_map}

elif isinstance(device_map, str) and device_map not in ["auto", "balanced", "balanced_low_0", "sequential"]:

try:

device_map = {"": torch.device(device_map)}

except RuntimeError:

raise ValueError(

"When passing device_map as a string, the value needs to be a device name (e.g. cpu, cuda:0) or "

f"'auto', 'balanced', 'balanced_low_0', 'sequential' but found {device_map}."

)

elif isinstance(device_map, int):

if device_map < 0:

raise ValueError(

"You can't pass device_map as a negative int. If you want to put the model on the cpu, pass device_map = 'cpu' "

)

else:

device_map = {"": device_map}

if device_map is not None:

if low_cpu_mem_usage is None:

low_cpu_mem_usage = True

elif not low_cpu_mem_usage:

raise ValueError("Passing along a `device_map` requires `low_cpu_mem_usage=True`")

if low_cpu_mem_usage:

if device_map is not None and not is_torch_version(">=", "1.10"):

# The max memory utils require PyTorch >= 1.10 to have torch.cuda.mem_get_info.

raise ValueError("`low_cpu_mem_usage` and `device_map` require PyTorch >= 1.10.")

# Load config if we don't provide a configuration

config_path = pretrained_model_name_or_path

user_agent = {

"diffusers": __version__,

"file_type": "model",

"framework": "pytorch",

}

# load config

config, unused_kwargs, commit_hash = cls.load_config(

config_path,

cache_dir=cache_dir,

return_unused_kwargs=True,

return_commit_hash=True,

force_download=force_download,

proxies=proxies,

local_files_only=local_files_only,

token=token,

revision=revision,

subfolder=subfolder,

user_agent=user_agent,

**kwargs,

)

# no in-place modification of the original config.

config = copy.deepcopy(config)

# determine initial quantization config.

#######################################

pre_quantized = "quantization_config" in config and config["quantization_config"] is not None

if pre_quantized or quantization_config is not None:

if pre_quantized:

config["quantization_config"] = DiffusersAutoQuantizer.merge_quantization_configs(

config["quantization_config"], quantization_config

)

else:

config["quantization_config"] = quantization_config

hf_quantizer = DiffusersAutoQuantizer.from_config(

config["quantization_config"], pre_quantized=pre_quantized

)

else:

hf_quantizer = None

if hf_quantizer is not None:

is_bnb_quantization_method = hf_quantizer.quantization_config.quant_method.value == "bitsandbytes"

if is_bnb_quantization_method and device_map is not None:

raise NotImplementedError(

"Currently, `device_map` is automatically inferred for quantized bitsandbytes models. Support for providing `device_map` as an input will be added in the future."

)

hf_quantizer.validate_environment(torch_dtype=torch_dtype, from_flax=from_flax, device_map=device_map)

torch_dtype = hf_quantizer.update_torch_dtype(torch_dtype)

# In order to ensure popular quantization methods are supported. Can be disable with `disable_telemetry`

user_agent["quant"] = hf_quantizer.quantization_config.quant_method.value

# Force-set to `True` for more mem efficiency

if low_cpu_mem_usage is None:

low_cpu_mem_usage = True

logger.info("Set `low_cpu_mem_usage` to True as `hf_quantizer` is not None.")

elif not low_cpu_mem_usage:

raise ValueError("`low_cpu_mem_usage` cannot be False or None when using quantization.")

# Check if `_keep_in_fp32_modules` is not None

use_keep_in_fp32_modules = (cls._keep_in_fp32_modules is not None) and (

(torch_dtype == torch.float16) or hasattr(hf_quantizer, "use_keep_in_fp32_modules")

)

if use_keep_in_fp32_modules:

keep_in_fp32_modules = cls._keep_in_fp32_modules

if not isinstance(keep_in_fp32_modules, list):

keep_in_fp32_modules = [keep_in_fp32_modules]

if low_cpu_mem_usage is None:

low_cpu_mem_usage = True

logger.info("Set `low_cpu_mem_usage` to True as `_keep_in_fp32_modules` is not None.")

elif not low_cpu_mem_usage:

raise ValueError("`low_cpu_mem_usage` cannot be False when `keep_in_fp32_modules` is True.")

else:

keep_in_fp32_modules = []

#######################################

# Determine if we're loading from a directory of sharded checkpoints.

is_sharded = False

index_file = None

is_local = os.path.isdir(pretrained_model_name_or_path)

index_file_kwargs = {

"is_local": is_local,

"pretrained_model_name_or_path": pretrained_model_name_or_path,

"subfolder": subfolder or "",

"use_safetensors": use_safetensors,

"cache_dir": cache_dir,

"variant": variant,

"force_download": force_download,

"proxies": proxies,

"local_files_only": local_files_only,

"token": token,

"revision": revision,

"user_agent": user_agent,

"commit_hash": commit_hash,

}

index_file = _fetch_index_file(**index_file_kwargs)

# In case the index file was not found we still have to consider the legacy format.

# this becomes applicable when the variant is not None.

if variant is not None and (index_file is None or not os.path.exists(index_file)):

index_file = _fetch_index_file_legacy(**index_file_kwargs)

if index_file is not None and index_file.is_file():

is_sharded = True

if is_sharded and from_flax:

raise ValueError("Loading of sharded checkpoints is not supported when `from_flax=True`.")

# load model

model_file = None

if from_flax:

model_file = _get_model_file(

pretrained_model_name_or_path,

weights_name=FLAX_WEIGHTS_NAME,

cache_dir=cache_dir,

force_download=force_download,

proxies=proxies,

local_files_only=local_files_only,

token=token,

revision=revision,

subfolder=subfolder,

user_agent=user_agent,

commit_hash=commit_hash,

)

model = cls.from_config(config, **unused_kwargs)

# Convert the weights

from .modeling_pytorch_flax_utils import load_flax_checkpoint_in_pytorch_model

model = load_flax_checkpoint_in_pytorch_model(model, model_file)

else:

if is_sharded:

sharded_ckpt_cached_folder, sharded_metadata = _get_checkpoint_shard_files(

pretrained_model_name_or_path,

index_file,

cache_dir=cache_dir,

proxies=proxies,

local_files_only=local_files_only,

token=token,

user_agent=user_agent,

revision=revision,

subfolder=subfolder or "",

)

if hf_quantizer is not None:

model_file = _merge_sharded_checkpoints(sharded_ckpt_cached_folder, sharded_metadata)

logger.info("Merged sharded checkpoints as `hf_quantizer` is not None.")

is_sharded = False

elif use_safetensors and not is_sharded:

try:

model_file = _get_model_file(

pretrained_model_name_or_path,

weights_name=_add_variant(SAFETENSORS_WEIGHTS_NAME, variant),

cache_dir=cache_dir,

force_download=force_download,

proxies=proxies,

local_files_only=local_files_only,

token=token,

revision=revision,

subfolder=subfolder,

user_agent=user_agent,

commit_hash=commit_hash,

)

except IOError as e:

logger.error(f"An error occurred while trying to fetch {pretrained_model_name_or_path}: {e}")

if not allow_pickle:

raise

logger.warning(

"Defaulting to unsafe serialization. Pass `allow_pickle=False` to raise an error instead."

)

if model_file is None and not is_sharded:

model_file = _get_model_file(

pretrained_model_name_or_path,

weights_name=_add_variant(WEIGHTS_NAME, variant),

cache_dir=cache_dir,

force_download=force_download,

proxies=proxies,

local_files_only=local_files_only,

token=token,

revision=revision,

subfolder=subfolder,

user_agent=user_agent,

commit_hash=commit_hash,

)

if low_cpu_mem_usage:

# Instantiate model with empty weights

with accelerate.init_empty_weights():

model = cls.from_config(config, **unused_kwargs)

if hf_quantizer is not None:

hf_quantizer.preprocess_model(

model=model, device_map=device_map, keep_in_fp32_modules=keep_in_fp32_modules

)

# if device_map is None, load the state dict and move the params from meta device to the cpu

if device_map is None and not is_sharded:

# `torch.cuda.current_device()` is fine here when `hf_quantizer` is not None.

# It would error out during the `validate_environment()` call above in the absence of cuda.

if hf_quantizer is None:

param_device = "cpu"

# TODO (sayakpaul, SunMarc): remove this after model loading refactor

else:

param_device = torch.device(torch.cuda.current_device())

state_dict = load_state_dict(model_file, variant=variant)

model._convert_deprecated_attention_blocks(state_dict)

# move the params from meta device to cpu

missing_keys = set(model.state_dict().keys()) - set(state_dict.keys())

if hf_quantizer is not None:

missing_keys = hf_quantizer.update_missing_keys(model, missing_keys, prefix="")

if len(missing_keys) > 0:

raise ValueError(

f"Cannot load {cls} from {pretrained_model_name_or_path} because the following keys are"

f" missing: \n {', '.join(missing_keys)}. \n Please make sure to pass"

" `low_cpu_mem_usage=False` and `device_map=None` if you want to randomly initialize"

" those weights or else make sure your checkpoint file is correct."

)

unexpected_keys = load_model_dict_into_meta(

model,

state_dict,

device=param_device,

dtype=torch_dtype,

model_name_or_path=pretrained_model_name_or_path,

hf_quantizer=hf_quantizer,

keep_in_fp32_modules=keep_in_fp32_modules,

)

if cls._keys_to_ignore_on_load_unexpected is not None:

for pat in cls._keys_to_ignore_on_load_unexpected:

unexpected_keys = [k for k in unexpected_keys if re.search(pat, k) is None]

if len(unexpected_keys) > 0:

logger.warning(

f"Some weights of the model checkpoint were not used when initializing {cls.__name__}: \n {[', '.join(unexpected_keys)]}"

)

else: # else let accelerate handle loading and dispatching.

# Load weights and dispatch according to the device_map

# by default the device_map is None and the weights are loaded on the CPU

force_hook = True

device_map = _determine_device_map(

model, device_map, max_memory, torch_dtype, keep_in_fp32_modules, hf_quantizer

)

if device_map is None and is_sharded:

# we load the parameters on the cpu

device_map = {"": "cpu"}

force_hook = False

try:

accelerate.load_checkpoint_and_dispatch(

model,

model_file if not is_sharded else index_file,

device_map,

max_memory=max_memory,

offload_folder=offload_folder,

offload_state_dict=offload_state_dict,

dtype=torch_dtype,

force_hooks=force_hook,

strict=True,

)

except AttributeError as e:

# When using accelerate loading, we do not have the ability to load the state

# dict and rename the weight names manually. Additionally, accelerate skips

# torch loading conventions and directly writes into `module.{_buffers, _parameters}`

# (which look like they should be private variables?), so we can't use the standard hooks

# to rename parameters on load. We need to mimic the original weight names so the correct

# attributes are available. After we have loaded the weights, we convert the deprecated

# names to the new non-deprecated names. Then we _greatly encourage_ the user to convert

# the weights so we don't have to do this again.

if "'Attention' object has no attribute" in str(e):

logger.warning(

f"Taking `{str(e)}` while using `accelerate.load_checkpoint_and_dispatch` to mean {pretrained_model_name_or_path}"

" was saved with deprecated attention block weight names. We will load it with the deprecated attention block"

" names and convert them on the fly to the new attention block format. Please re-save the model after this conversion,"

" so we don't have to do the on the fly renaming in the future. If the model is from a hub checkpoint,"

" please also re-upload it or open a PR on the original repository."

)

model._temp_convert_self_to_deprecated_attention_blocks()

accelerate.load_checkpoint_and_dispatch(

model,

model_file if not is_sharded else index_file,

device_map,

max_memory=max_memory,

offload_folder=offload_folder,

offload_state_dict=offload_state_dict,

dtype=torch_dtype,

force_hooks=force_hook,

strict=True,

)

model._undo_temp_convert_self_to_deprecated_attention_blocks()

else:

raise e

loading_info = {

"missing_keys": [],

"unexpected_keys": [],

"mismatched_keys": [],

"error_msgs": [],

}

else:

model = cls.from_config(config, **unused_kwargs)

state_dict = load_state_dict(model_file, variant=variant)

model._convert_deprecated_attention_blocks(state_dict)

model, missing_keys, unexpected_keys, mismatched_keys, error_msgs = cls._load_pretrained_model(

model,

state_dict,

model_file,

pretrained_model_name_or_path,

ignore_mismatched_sizes=ignore_mismatched_sizes,

)

loading_info = {

"missing_keys": missing_keys,

"unexpected_keys": unexpected_keys,

"mismatched_keys": mismatched_keys,

"error_msgs": error_msgs,

}

if hf_quantizer is not None:

hf_quantizer.postprocess_model(model)

model.hf_quantizer = hf_quantizer

if torch_dtype is not None and not isinstance(torch_dtype, torch.dtype):

raise ValueError(

f"{torch_dtype} needs to be of type `torch.dtype`, e.g. `torch.float16`, but is {type(torch_dtype)}."

)

# When using `use_keep_in_fp32_modules` if we do a global `to()` here, then we will

# completely lose the effectivity of `use_keep_in_fp32_modules`.

elif torch_dtype is not None and hf_quantizer is None and not use_keep_in_fp32_modules:

model = model.to(torch_dtype)

if hf_quantizer is not None:

# We also make sure to purge `_pre_quantization_dtype` when we serialize