add local diffusers

diffusers/commands/diffusers_cli.py

@@ -0,0 +1,43 @@
+#!/usr/bin/env python
+# Copyright 2024 The HuggingFace Team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+from argparse import ArgumentParser
+
+from .env import EnvironmentCommand
+from .fp16_safetensors import FP16SafetensorsCommand
+
+
+def main():
+    parser = ArgumentParser("Diffusers CLI tool", usage="diffusers-cli <command> [<args>]")
+    commands_parser = parser.add_subparsers(help="diffusers-cli command helpers")
+
+    # Register commands
+    EnvironmentCommand.register_subcommand(commands_parser)
+    FP16SafetensorsCommand.register_subcommand(commands_parser)
+
+    # Let's go
+    args = parser.parse_args()
+
+    if not hasattr(args, "func"):
+        parser.print_help()
+        exit(1)
+
+    # Run
+    service = args.func(args)
+    service.run()
+
+
+if __name__ == "__main__":
+    main()

diffusers/commands/env.py

@@ -0,0 +1,84 @@
+# Copyright 2024 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 platform
+from argparse import ArgumentParser
+
+import huggingface_hub
+
+from .. import __version__ as version
+from ..utils import is_accelerate_available, is_torch_available, is_transformers_available, is_xformers_available
+from . import BaseDiffusersCLICommand
+
+
+def info_command_factory(_):
+    return EnvironmentCommand()
+
+
+class EnvironmentCommand(BaseDiffusersCLICommand):
+    @staticmethod
+    def register_subcommand(parser: ArgumentParser):
+        download_parser = parser.add_parser("env")
+        download_parser.set_defaults(func=info_command_factory)
+
+    def run(self):
+        hub_version = huggingface_hub.__version__
+
+        pt_version = "not installed"
+        pt_cuda_available = "NA"
+        if is_torch_available():
+            import torch
+
+            pt_version = torch.__version__
+            pt_cuda_available = torch.cuda.is_available()
+
+        transformers_version = "not installed"
+        if is_transformers_available():
+            import transformers
+
+            transformers_version = transformers.__version__
+
+        accelerate_version = "not installed"
+        if is_accelerate_available():
+            import accelerate
+
+            accelerate_version = accelerate.__version__
+
+        xformers_version = "not installed"
+        if is_xformers_available():
+            import xformers
+
+            xformers_version = xformers.__version__
+
+        info = {
+            "`diffusers` version": version,
+            "Platform": platform.platform(),
+            "Python version": platform.python_version(),
+            "PyTorch version (GPU?)": f"{pt_version} ({pt_cuda_available})",
+            "Huggingface_hub version": hub_version,
+            "Transformers version": transformers_version,
+            "Accelerate version": accelerate_version,
+            "xFormers version": xformers_version,
+            "Using GPU in script?": "<fill in>",
+            "Using distributed or parallel set-up in script?": "<fill in>",
+        }
+
+        print("\nCopy-and-paste the text below in your GitHub issue and FILL OUT the two last points.\n")
+        print(self.format_dict(info))
+
+        return info
+
+    @staticmethod
+    def format_dict(d):
+        return "\n".join([f"- {prop}: {val}" for prop, val in d.items()]) + "\n"

diffusers/commands/fp16_safetensors.py

@@ -0,0 +1,132 @@
+# Copyright 2024 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.
+
+"""
+Usage example:
+    diffusers-cli fp16_safetensors --ckpt_id=openai/shap-e --fp16 --use_safetensors
+"""
+
+import glob
+import json
+import warnings
+from argparse import ArgumentParser, Namespace
+from importlib import import_module
+
+import huggingface_hub
+import torch
+from huggingface_hub import hf_hub_download
+from packaging import version
+
+from ..utils import logging
+from . import BaseDiffusersCLICommand
+
+
+def conversion_command_factory(args: Namespace):
+    if args.use_auth_token:
+        warnings.warn(
+            "The `--use_auth_token` flag is deprecated and will be removed in a future version. Authentication is now"
+            " handled automatically if user is logged in."
+        )
+    return FP16SafetensorsCommand(args.ckpt_id, args.fp16, args.use_safetensors)
+
+
+class FP16SafetensorsCommand(BaseDiffusersCLICommand):
+    @staticmethod
+    def register_subcommand(parser: ArgumentParser):
+        conversion_parser = parser.add_parser("fp16_safetensors")
+        conversion_parser.add_argument(
+            "--ckpt_id",
+            type=str,
+            help="Repo id of the checkpoints on which to run the conversion. Example: 'openai/shap-e'.",
+        )
+        conversion_parser.add_argument(
+            "--fp16", action="store_true", help="If serializing the variables in FP16 precision."
+        )
+        conversion_parser.add_argument(
+            "--use_safetensors", action="store_true", help="If serializing in the safetensors format."
+        )
+        conversion_parser.add_argument(
+            "--use_auth_token",
+            action="store_true",
+            help="When working with checkpoints having private visibility. When used `huggingface-cli login` needs to be run beforehand.",
+        )
+        conversion_parser.set_defaults(func=conversion_command_factory)
+
+    def __init__(self, ckpt_id: str, fp16: bool, use_safetensors: bool):
+        self.logger = logging.get_logger("diffusers-cli/fp16_safetensors")
+        self.ckpt_id = ckpt_id
+        self.local_ckpt_dir = f"/tmp/{ckpt_id}"
+        self.fp16 = fp16
+
+        self.use_safetensors = use_safetensors
+
+        if not self.use_safetensors and not self.fp16:
+            raise NotImplementedError(
+                "When `use_safetensors` and `fp16` both are False, then this command is of no use."
+            )
+
+    def run(self):
+        if version.parse(huggingface_hub.__version__) < version.parse("0.9.0"):
+            raise ImportError(
+                "The huggingface_hub version must be >= 0.9.0 to use this command. Please update your huggingface_hub"
+                " installation."
+            )
+        else:
+            from huggingface_hub import create_commit
+            from huggingface_hub._commit_api import CommitOperationAdd
+
+        model_index = hf_hub_download(repo_id=self.ckpt_id, filename="model_index.json")
+        with open(model_index, "r") as f:
+            pipeline_class_name = json.load(f)["_class_name"]
+        pipeline_class = getattr(import_module("diffusers"), pipeline_class_name)
+        self.logger.info(f"Pipeline class imported: {pipeline_class_name}.")
+
+        # Load the appropriate pipeline. We could have use `DiffusionPipeline`
+        # here, but just to avoid any rough edge cases.
+        pipeline = pipeline_class.from_pretrained(
+            self.ckpt_id, torch_dtype=torch.float16 if self.fp16 else torch.float32
+        )
+        pipeline.save_pretrained(
+            self.local_ckpt_dir,
+            safe_serialization=True if self.use_safetensors else False,
+            variant="fp16" if self.fp16 else None,
+        )
+        self.logger.info(f"Pipeline locally saved to {self.local_ckpt_dir}.")
+
+        # Fetch all the paths.
+        if self.fp16:
+            modified_paths = glob.glob(f"{self.local_ckpt_dir}/*/*.fp16.*")
+        elif self.use_safetensors:
+            modified_paths = glob.glob(f"{self.local_ckpt_dir}/*/*.safetensors")
+
+        # Prepare for the PR.
+        commit_message = f"Serialize variables with FP16: {self.fp16} and safetensors: {self.use_safetensors}."
+        operations = []
+        for path in modified_paths:
+            operations.append(CommitOperationAdd(path_in_repo="/".join(path.split("/")[4:]), path_or_fileobj=path))
+
+        # Open the PR.
+        commit_description = (
+            "Variables converted by the [`diffusers`' `fp16_safetensors`"
+            " CLI](https://github.com/huggingface/diffusers/blob/main/src/diffusers/commands/fp16_safetensors.py)."
+        )
+        hub_pr_url = create_commit(
+            repo_id=self.ckpt_id,
+            operations=operations,
+            commit_message=commit_message,
+            commit_description=commit_description,
+            repo_type="model",
+            create_pr=True,
+        ).pr_url
+        self.logger.info(f"PR created here: {hub_pr_url}.")

diffusers/configuration_utils.py

@@ -0,0 +1,704 @@
+# 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.
+"""ConfigMixin base class and utilities."""
+
+import dataclasses
+import functools
+import importlib
+import inspect
+import json
+import os
+import re
+from collections import OrderedDict
+from pathlib import PosixPath
+from typing import Any, Dict, Tuple, Union
+
+import numpy as np
+from huggingface_hub import create_repo, hf_hub_download
+from huggingface_hub.utils import (
+    EntryNotFoundError,
+    RepositoryNotFoundError,
+    RevisionNotFoundError,
+    validate_hf_hub_args,
+)
+from requests import HTTPError
+
+from . import __version__
+from .utils import (
+    HUGGINGFACE_CO_RESOLVE_ENDPOINT,
+    DummyObject,
+    deprecate,
+    extract_commit_hash,
+    http_user_agent,
+    logging,
+)
+
+
+logger = logging.get_logger(__name__)
+
+_re_configuration_file = re.compile(r"config\.(.*)\.json")
+
+
+class FrozenDict(OrderedDict):
+    def __init__(self, *args, **kwargs):
+        super().__init__(*args, **kwargs)
+
+        for key, value in self.items():
+            setattr(self, key, value)
+
+        self.__frozen = True
+
+    def __delitem__(self, *args, **kwargs):
+        raise Exception(f"You cannot use ``__delitem__`` on a {self.__class__.__name__} instance.")
+
+    def setdefault(self, *args, **kwargs):
+        raise Exception(f"You cannot use ``setdefault`` on a {self.__class__.__name__} instance.")
+
+    def pop(self, *args, **kwargs):
+        raise Exception(f"You cannot use ``pop`` on a {self.__class__.__name__} instance.")
+
+    def update(self, *args, **kwargs):
+        raise Exception(f"You cannot use ``update`` on a {self.__class__.__name__} instance.")
+
+    def __setattr__(self, name, value):
+        if hasattr(self, "__frozen") and self.__frozen:
+            raise Exception(f"You cannot use ``__setattr__`` on a {self.__class__.__name__} instance.")
+        super().__setattr__(name, value)
+
+    def __setitem__(self, name, value):
+        if hasattr(self, "__frozen") and self.__frozen:
+            raise Exception(f"You cannot use ``__setattr__`` on a {self.__class__.__name__} instance.")
+        super().__setitem__(name, value)
+
+
+class ConfigMixin:
+    r"""
+    Base class for all configuration classes. All configuration parameters are stored under `self.config`. Also
+    provides the [`~ConfigMixin.from_config`] and [`~ConfigMixin.save_config`] methods for loading, downloading, and
+    saving classes that inherit from [`ConfigMixin`].
+
+    Class attributes:
+        - **config_name** (`str`) -- A filename under which the config should stored when calling
+          [`~ConfigMixin.save_config`] (should be overridden by parent class).
+        - **ignore_for_config** (`List[str]`) -- A list of attributes that should not be saved in the config (should be
+          overridden by subclass).
+        - **has_compatibles** (`bool`) -- Whether the class has compatible classes (should be overridden by subclass).
+        - **_deprecated_kwargs** (`List[str]`) -- Keyword arguments that are deprecated. Note that the `init` function
+          should only have a `kwargs` argument if at least one argument is deprecated (should be overridden by
+          subclass).
+    """
+
+    config_name = None
+    ignore_for_config = []
+    has_compatibles = False
+
+    _deprecated_kwargs = []
+
+    def register_to_config(self, **kwargs):
+        if self.config_name is None:
+            raise NotImplementedError(f"Make sure that {self.__class__} has defined a class name `config_name`")
+        # Special case for `kwargs` used in deprecation warning added to schedulers
+        # TODO: remove this when we remove the deprecation warning, and the `kwargs` argument,
+        # or solve in a more general way.
+        kwargs.pop("kwargs", None)
+
+        if not hasattr(self, "_internal_dict"):
+            internal_dict = kwargs
+        else:
+            previous_dict = dict(self._internal_dict)
+            internal_dict = {**self._internal_dict, **kwargs}
+            logger.debug(f"Updating config from {previous_dict} to {internal_dict}")
+
+        self._internal_dict = FrozenDict(internal_dict)
+
+    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
+
+        This function is mostly copied from PyTorch's __getattr__ overwrite:
+        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. 'scheduler.config.{name}'."
+            deprecate("direct config name access", "1.0.0", deprecation_message, standard_warn=False)
+            return self._internal_dict[name]
+
+        raise AttributeError(f"'{type(self).__name__}' object has no attribute '{name}'")
+
+    def save_config(self, save_directory: Union[str, os.PathLike], push_to_hub: bool = False, **kwargs):
+        """
+        Save a configuration object to the directory specified in `save_directory` so that it can be reloaded using the
+        [`~ConfigMixin.from_config`] class method.
+
+        Args:
+            save_directory (`str` or `os.PathLike`):
+                Directory where the configuration JSON file is saved (will be created if it does not exist).
+            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):
+            raise AssertionError(f"Provided path ({save_directory}) should be a directory, not a file")
+
+        os.makedirs(save_directory, exist_ok=True)
+
+        # If we save using the predefined names, we can load using `from_config`
+        output_config_file = os.path.join(save_directory, self.config_name)
+
+        self.to_json_file(output_config_file)
+        logger.info(f"Configuration saved in {output_config_file}")
+
+        if push_to_hub:
+            commit_message = kwargs.pop("commit_message", None)
+            private = kwargs.pop("private", False)
+            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
+
+            self._upload_folder(
+                save_directory,
+                repo_id,
+                token=token,
+                commit_message=commit_message,
+                create_pr=create_pr,
+            )
+
+    @classmethod
+    def from_config(cls, config: Union[FrozenDict, Dict[str, Any]] = None, return_unused_kwargs=False, **kwargs):
+        r"""
+        Instantiate a Python class from a config dictionary.
+
+        Parameters:
+            config (`Dict[str, Any]`):
+                A config dictionary from which the Python class is instantiated. Make sure to only load configuration
+                files of compatible classes.
+            return_unused_kwargs (`bool`, *optional*, defaults to `False`):
+                Whether kwargs that are not consumed by the Python class should be returned or not.
+            kwargs (remaining dictionary of keyword arguments, *optional*):
+                Can be used to update the configuration object (after it is loaded) and initiate the Python class.
+                `**kwargs` are passed directly to the underlying scheduler/model's `__init__` method and eventually
+                overwrite the same named arguments in `config`.
+
+        Returns:
+            [`ModelMixin`] or [`SchedulerMixin`]:
+                A model or scheduler object instantiated from a config dictionary.
+
+        Examples:
+
+        ```python
+        >>> from diffusers import DDPMScheduler, DDIMScheduler, PNDMScheduler
+
+        >>> # Download scheduler from huggingface.co and cache.
+        >>> scheduler = DDPMScheduler.from_pretrained("google/ddpm-cifar10-32")
+
+        >>> # Instantiate DDIM scheduler class with same config as DDPM
+        >>> scheduler = DDIMScheduler.from_config(scheduler.config)
+
+        >>> # Instantiate PNDM scheduler class with same config as DDPM
+        >>> scheduler = PNDMScheduler.from_config(scheduler.config)
+        ```
+        """
+        # <===== TO BE REMOVED WITH DEPRECATION
+        # TODO(Patrick) - make sure to remove the following lines when config=="model_path" is deprecated
+        if "pretrained_model_name_or_path" in kwargs:
+            config = kwargs.pop("pretrained_model_name_or_path")
+
+        if config is None:
+            raise ValueError("Please make sure to provide a config as the first positional argument.")
+        # ======>
+
+        if not isinstance(config, dict):
+            deprecation_message = "It is deprecated to pass a pretrained model name or path to `from_config`."
+            if "Scheduler" in cls.__name__:
+                deprecation_message += (
+                    f"If you were trying to load a scheduler, please use {cls}.from_pretrained(...) instead."
+                    " Otherwise, please make sure to pass a configuration dictionary instead. This functionality will"
+                    " be removed in v1.0.0."
+                )
+            elif "Model" in cls.__name__:
+                deprecation_message += (
+                    f"If you were trying to load a model, please use {cls}.load_config(...) followed by"
+                    f" {cls}.from_config(...) instead. Otherwise, please make sure to pass a configuration dictionary"
+                    " instead. This functionality will be removed in v1.0.0."
+                )
+            deprecate("config-passed-as-path", "1.0.0", deprecation_message, standard_warn=False)
+            config, kwargs = cls.load_config(pretrained_model_name_or_path=config, return_unused_kwargs=True, **kwargs)
+
+        init_dict, unused_kwargs, hidden_dict = cls.extract_init_dict(config, **kwargs)
+
+        # Allow dtype to be specified on initialization
+        if "dtype" in unused_kwargs:
+            init_dict["dtype"] = unused_kwargs.pop("dtype")
+
+        # add possible deprecated kwargs
+        for deprecated_kwarg in cls._deprecated_kwargs:
+            if deprecated_kwarg in unused_kwargs:
+                init_dict[deprecated_kwarg] = unused_kwargs.pop(deprecated_kwarg)
+
+        # Return model and optionally state and/or unused_kwargs
+        model = cls(**init_dict)
+
+        # make sure to also save config parameters that might be used for compatible classes
+        # update _class_name
+        if "_class_name" in hidden_dict:
+            hidden_dict["_class_name"] = cls.__name__
+
+        model.register_to_config(**hidden_dict)
+
+        # add hidden kwargs of compatible classes to unused_kwargs
+        unused_kwargs = {**unused_kwargs, **hidden_dict}
+
+        if return_unused_kwargs:
+            return (model, unused_kwargs)
+        else:
+            return model
+
+    @classmethod
+    def get_config_dict(cls, *args, **kwargs):
+        deprecation_message = (
+            f" The function get_config_dict is deprecated. Please use {cls}.load_config instead. This function will be"
+            " removed in version v1.0.0"
+        )
+        deprecate("get_config_dict", "1.0.0", deprecation_message, standard_warn=False)
+        return cls.load_config(*args, **kwargs)
+
+    @classmethod
+    @validate_hf_hub_args
+    def load_config(
+        cls,
+        pretrained_model_name_or_path: Union[str, os.PathLike],
+        return_unused_kwargs=False,
+        return_commit_hash=False,
+        **kwargs,
+    ) -> Tuple[Dict[str, Any], Dict[str, Any]]:
+        r"""
+        Load a model or scheduler configuration.
+
+        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 model weights saved with
+                      [`~ConfigMixin.save_config`].
+
+            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.
+            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:
+                Deprecated and ignored. All downloads are now resumed by default when possible. Will be removed in v1
+                of Diffusers.
+            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.
+            subfolder (`str`, *optional*, defaults to `""`):
+                The subfolder location of a model file within a larger model repository on the Hub or locally.
+            return_unused_kwargs (`bool`, *optional*, defaults to `False):
+                Whether unused keyword arguments of the config are returned.
+            return_commit_hash (`bool`, *optional*, defaults to `False):
+                Whether the `commit_hash` of the loaded configuration are returned.
+
+        Returns:
+            `dict`:
+                A dictionary of all the parameters stored in a JSON configuration file.
+
+        """
+        cache_dir = kwargs.pop("cache_dir", None)
+        force_download = kwargs.pop("force_download", False)
+        resume_download = kwargs.pop("resume_download", None)
+        proxies = kwargs.pop("proxies", None)
+        token = kwargs.pop("token", None)
+        local_files_only = kwargs.pop("local_files_only", False)
+        revision = kwargs.pop("revision", None)
+        _ = kwargs.pop("mirror", None)
+        subfolder = kwargs.pop("subfolder", None)
+        user_agent = kwargs.pop("user_agent", {})
+
+        user_agent = {**user_agent, "file_type": "config"}
+        user_agent = http_user_agent(user_agent)
+
+        pretrained_model_name_or_path = str(pretrained_model_name_or_path)
+
+        if cls.config_name is None:
+            raise ValueError(
+                "`self.config_name` is not defined. Note that one should not load a config from "
+                "`ConfigMixin`. Please make sure to define `config_name` in a class inheriting from `ConfigMixin`"
+            )
+
+        if os.path.isfile(pretrained_model_name_or_path):
+            config_file = pretrained_model_name_or_path
+        elif os.path.isdir(pretrained_model_name_or_path):
+            if os.path.isfile(os.path.join(pretrained_model_name_or_path, cls.config_name)):
+                # Load from a PyTorch checkpoint
+                config_file = os.path.join(pretrained_model_name_or_path, cls.config_name)
+            elif subfolder is not None and os.path.isfile(
+                os.path.join(pretrained_model_name_or_path, subfolder, cls.config_name)
+            ):
+                config_file = os.path.join(pretrained_model_name_or_path, subfolder, cls.config_name)
+            else:
+                raise EnvironmentError(
+                    f"Error no file named {cls.config_name} found in directory {pretrained_model_name_or_path}."
+                )
+        else:
+            try:
+                # Load from URL or cache if already cached
+                config_file = hf_hub_download(
+                    pretrained_model_name_or_path,
+                    filename=cls.config_name,
+                    cache_dir=cache_dir,
+                    force_download=force_download,
+                    proxies=proxies,
+                    resume_download=resume_download,
+                    local_files_only=local_files_only,
+                    token=token,
+                    user_agent=user_agent,
+                    subfolder=subfolder,
+                    revision=revision,
+                )
+            except RepositoryNotFoundError:
+                raise EnvironmentError(
+                    f"{pretrained_model_name_or_path} is not a local folder and is not a valid model identifier"
+                    " listed on 'https://huggingface.co/models'\nIf this is a private repository, make sure to pass a"
+                    " token having permission to this repo with `token` or log in with `huggingface-cli login`."
+                )
+            except RevisionNotFoundError:
+                raise EnvironmentError(
+                    f"{revision} is not a valid git identifier (branch name, tag name or commit id) that exists for"
+                    " this model name. Check the model page at"
+                    f" 'https://huggingface.co/{pretrained_model_name_or_path}' for available revisions."
+                )
+            except EntryNotFoundError:
+                raise EnvironmentError(
+                    f"{pretrained_model_name_or_path} does not appear to have a file named {cls.config_name}."
+                )
+            except HTTPError as err:
+                raise EnvironmentError(
+                    "There was a specific connection error when trying to load"
+                    f" {pretrained_model_name_or_path}:\n{err}"
+                )
+            except ValueError:
+                raise EnvironmentError(
+                    f"We couldn't connect to '{HUGGINGFACE_CO_RESOLVE_ENDPOINT}' to load this model, couldn't find it"
+                    f" in the cached files and it looks like {pretrained_model_name_or_path} is not the path to a"
+                    f" directory containing a {cls.config_name} file.\nCheckout your internet connection or see how to"
+                    " run the library in offline mode at"
+                    " 'https://huggingface.co/docs/diffusers/installation#offline-mode'."
+                )
+            except EnvironmentError:
+                raise EnvironmentError(
+                    f"Can't load config for '{pretrained_model_name_or_path}'. If you were trying to load it from "
+                    "'https://huggingface.co/models', make sure you don't have a local directory with the same name. "
+                    f"Otherwise, make sure '{pretrained_model_name_or_path}' is the correct path to a directory "
+                    f"containing a {cls.config_name} file"
+                )
+
+        try:
+            # Load config dict
+            config_dict = cls._dict_from_json_file(config_file)
+
+            commit_hash = extract_commit_hash(config_file)
+        except (json.JSONDecodeError, UnicodeDecodeError):
+            raise EnvironmentError(f"It looks like the config file at '{config_file}' is not a valid JSON file.")
+
+        if not (return_unused_kwargs or return_commit_hash):
+            return config_dict
+
+        outputs = (config_dict,)
+
+        if return_unused_kwargs:
+            outputs += (kwargs,)
+
+        if return_commit_hash:
+            outputs += (commit_hash,)
+
+        return outputs
+
+    @staticmethod
+    def _get_init_keys(input_class):
+        return set(dict(inspect.signature(input_class.__init__).parameters).keys())
+
+    @classmethod
+    def extract_init_dict(cls, config_dict, **kwargs):
+        # Skip keys that were not present in the original config, so default __init__ values were used
+        used_defaults = config_dict.get("_use_default_values", [])
+        config_dict = {k: v for k, v in config_dict.items() if k not in used_defaults and k != "_use_default_values"}
+
+        # 0. Copy origin config dict
+        original_dict = dict(config_dict.items())
+
+        # 1. Retrieve expected config attributes from __init__ signature
+        expected_keys = cls._get_init_keys(cls)
+        expected_keys.remove("self")
+        # remove general kwargs if present in dict
+        if "kwargs" in expected_keys:
+            expected_keys.remove("kwargs")
+        # remove flax internal keys
+        if hasattr(cls, "_flax_internal_args"):
+            for arg in cls._flax_internal_args:
+                expected_keys.remove(arg)
+
+        # 2. Remove attributes that cannot be expected from expected config attributes
+        # remove keys to be ignored
+        if len(cls.ignore_for_config) > 0:
+            expected_keys = expected_keys - set(cls.ignore_for_config)
+
+        # load diffusers library to import compatible and original scheduler
+        diffusers_library = importlib.import_module(__name__.split(".")[0])
+
+        if cls.has_compatibles:
+            compatible_classes = [c for c in cls._get_compatibles() if not isinstance(c, DummyObject)]
+        else:
+            compatible_classes = []
+
+        expected_keys_comp_cls = set()
+        for c in compatible_classes:
+            expected_keys_c = cls._get_init_keys(c)
+            expected_keys_comp_cls = expected_keys_comp_cls.union(expected_keys_c)
+        expected_keys_comp_cls = expected_keys_comp_cls - cls._get_init_keys(cls)
+        config_dict = {k: v for k, v in config_dict.items() if k not in expected_keys_comp_cls}
+
+        # remove attributes from orig class that cannot be expected
+        orig_cls_name = config_dict.pop("_class_name", cls.__name__)
+        if (
+            isinstance(orig_cls_name, str)
+            and orig_cls_name != cls.__name__
+            and hasattr(diffusers_library, orig_cls_name)
+        ):
+            orig_cls = getattr(diffusers_library, orig_cls_name)
+            unexpected_keys_from_orig = cls._get_init_keys(orig_cls) - expected_keys
+            config_dict = {k: v for k, v in config_dict.items() if k not in unexpected_keys_from_orig}
+        elif not isinstance(orig_cls_name, str) and not isinstance(orig_cls_name, (list, tuple)):
+            raise ValueError(
+                "Make sure that the `_class_name` is of type string or list of string (for custom pipelines)."
+            )
+
+        # remove private attributes
+        config_dict = {k: v for k, v in config_dict.items() if not k.startswith("_")}
+
+        # 3. Create keyword arguments that will be passed to __init__ from expected keyword arguments
+        init_dict = {}
+        for key in expected_keys:
+            # if config param is passed to kwarg and is present in config dict
+            # it should overwrite existing config dict key
+            if key in kwargs and key in config_dict:
+                config_dict[key] = kwargs.pop(key)
+
+            if key in kwargs:
+                # overwrite key
+                init_dict[key] = kwargs.pop(key)
+            elif key in config_dict:
+                # use value from config dict
+                init_dict[key] = config_dict.pop(key)
+
+        # 4. Give nice warning if unexpected values have been passed
+        if len(config_dict) > 0:
+            logger.warning(
+                f"The config attributes {config_dict} were passed to {cls.__name__}, "
+                "but are not expected and will be ignored. Please verify your "
+                f"{cls.config_name} configuration file."
+            )
+
+        # 5. Give nice info if config attributes are initialized to default because they have not been passed
+        passed_keys = set(init_dict.keys())
+        if len(expected_keys - passed_keys) > 0:
+            logger.info(
+                f"{expected_keys - passed_keys} was not found in config. Values will be initialized to default values."
+            )
+
+        # 6. Define unused keyword arguments
+        unused_kwargs = {**config_dict, **kwargs}
+
+        # 7. Define "hidden" config parameters that were saved for compatible classes
+        hidden_config_dict = {k: v for k, v in original_dict.items() if k not in init_dict}
+
+        return init_dict, unused_kwargs, hidden_config_dict
+
+    @classmethod
+    def _dict_from_json_file(cls, json_file: Union[str, os.PathLike]):
+        with open(json_file, "r", encoding="utf-8") as reader:
+            text = reader.read()
+        return json.loads(text)
+
+    def __repr__(self):
+        return f"{self.__class__.__name__} {self.to_json_string()}"
+
+    @property
+    def config(self) -> Dict[str, Any]:
+        """
+        Returns the config of the class as a frozen dictionary
+
+        Returns:
+            `Dict[str, Any]`: Config of the class.
+        """
+        return self._internal_dict
+
+    def to_json_string(self) -> str:
+        """
+        Serializes the configuration instance to a JSON string.
+
+        Returns:
+            `str`:
+                String containing all the attributes that make up the configuration instance in JSON format.
+        """
+        config_dict = self._internal_dict if hasattr(self, "_internal_dict") else {}
+        config_dict["_class_name"] = self.__class__.__name__
+        config_dict["_diffusers_version"] = __version__
+
+        def to_json_saveable(value):
+            if isinstance(value, np.ndarray):
+                value = value.tolist()
+            elif isinstance(value, PosixPath):
+                value = str(value)
+            return value
+
+        config_dict = {k: to_json_saveable(v) for k, v in config_dict.items()}
+        # Don't save "_ignore_files" or "_use_default_values"
+        config_dict.pop("_ignore_files", None)
+        config_dict.pop("_use_default_values", None)
+
+        return json.dumps(config_dict, indent=2, sort_keys=True) + "\n"
+
+    def to_json_file(self, json_file_path: Union[str, os.PathLike]):
+        """
+        Save the configuration instance's parameters to a JSON file.
+
+        Args:
+            json_file_path (`str` or `os.PathLike`):
+                Path to the JSON file to save a configuration instance's parameters.
+        """
+        with open(json_file_path, "w", encoding="utf-8") as writer:
+            writer.write(self.to_json_string())
+
+
+def register_to_config(init):
+    r"""
+    Decorator to apply on the init of classes inheriting from [`ConfigMixin`] so that all the arguments are
+    automatically sent to `self.register_for_config`. To ignore a specific argument accepted by the init but that
+    shouldn't be registered in the config, use the `ignore_for_config` class variable
+
+    Warning: Once decorated, all private arguments (beginning with an underscore) are trashed and not sent to the init!
+    """
+
+    @functools.wraps(init)
+    def inner_init(self, *args, **kwargs):
+        # Ignore private kwargs in the init.
+        init_kwargs = {k: v for k, v in kwargs.items() if not k.startswith("_")}
+        config_init_kwargs = {k: v for k, v in kwargs.items() if k.startswith("_")}
+        if not isinstance(self, ConfigMixin):
+            raise RuntimeError(
+                f"`@register_for_config` was applied to {self.__class__.__name__} init method, but this class does "
+                "not inherit from `ConfigMixin`."
+            )
+
+        ignore = getattr(self, "ignore_for_config", [])
+        # Get positional arguments aligned with kwargs
+        new_kwargs = {}
+        signature = inspect.signature(init)
+        parameters = {
+            name: p.default for i, (name, p) in enumerate(signature.parameters.items()) if i > 0 and name not in ignore
+        }
+        for arg, name in zip(args, parameters.keys()):
+            new_kwargs[name] = arg
+
+        # Then add all kwargs
+        new_kwargs.update(
+            {
+                k: init_kwargs.get(k, default)
+                for k, default in parameters.items()
+                if k not in ignore and k not in new_kwargs
+            }
+        )
+
+        # Take note of the parameters that were not present in the loaded config
+        if len(set(new_kwargs.keys()) - set(init_kwargs)) > 0:
+            new_kwargs["_use_default_values"] = list(set(new_kwargs.keys()) - set(init_kwargs))
+
+        new_kwargs = {**config_init_kwargs, **new_kwargs}
+        getattr(self, "register_to_config")(**new_kwargs)
+        init(self, *args, **init_kwargs)
+
+    return inner_init
+
+
+def flax_register_to_config(cls):
+    original_init = cls.__init__
+
+    @functools.wraps(original_init)
+    def init(self, *args, **kwargs):
+        if not isinstance(self, ConfigMixin):
+            raise RuntimeError(
+                f"`@register_for_config` was applied to {self.__class__.__name__} init method, but this class does "
+                "not inherit from `ConfigMixin`."
+            )
+
+        # Ignore private kwargs in the init. Retrieve all passed attributes
+        init_kwargs = dict(kwargs.items())
+
+        # Retrieve default values
+        fields = dataclasses.fields(self)
+        default_kwargs = {}
+        for field in fields:
+            # ignore flax specific attributes
+            if field.name in self._flax_internal_args:
+                continue
+            if type(field.default) == dataclasses._MISSING_TYPE:
+                default_kwargs[field.name] = None
+            else:
+                default_kwargs[field.name] = getattr(self, field.name)
+
+        # Make sure init_kwargs override default kwargs
+        new_kwargs = {**default_kwargs, **init_kwargs}
+        # dtype should be part of `init_kwargs`, but not `new_kwargs`
+        if "dtype" in new_kwargs:
+            new_kwargs.pop("dtype")
+
+        # Get positional arguments aligned with kwargs
+        for i, arg in enumerate(args):
+            name = fields[i].name
+            new_kwargs[name] = arg
+
+        # Take note of the parameters that were not present in the loaded config
+        if len(set(new_kwargs.keys()) - set(init_kwargs)) > 0:
+            new_kwargs["_use_default_values"] = list(set(new_kwargs.keys()) - set(init_kwargs))
+
+        getattr(self, "register_to_config")(**new_kwargs)
+        original_init(self, *args, **kwargs)
+
+    cls.__init__ = init
+    return cls

diffusers/dependency_versions_check.py

@@ -0,0 +1,34 @@
+# Copyright 2024 The HuggingFace Team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+from .dependency_versions_table import deps
+from .utils.versions import require_version, require_version_core
+
+
+# define which module versions we always want to check at run time
+# (usually the ones defined in `install_requires` in setup.py)
+#
+# order specific notes:
+# - tqdm must be checked before tokenizers
+
+pkgs_to_check_at_runtime = "python requests filelock numpy".split()
+for pkg in pkgs_to_check_at_runtime:
+    if pkg in deps:
+        require_version_core(deps[pkg])
+    else:
+        raise ValueError(f"can't find {pkg} in {deps.keys()}, check dependency_versions_table.py")
+
+
+def dep_version_check(pkg, hint=None):
+    require_version(deps[pkg], hint)

diffusers/dependency_versions_table.py

@@ -0,0 +1,46 @@
+# THIS FILE HAS BEEN AUTOGENERATED. To update:
+# 1. modify the `_deps` dict in setup.py
+# 2. run `make deps_table_update`
+deps = {
+    "Pillow": "Pillow",
+    "accelerate": "accelerate>=0.29.3",
+    "compel": "compel==0.1.8",
+    "datasets": "datasets",
+    "filelock": "filelock",
+    "flax": "flax>=0.4.1",
+    "hf-doc-builder": "hf-doc-builder>=0.3.0",
+    "huggingface-hub": "huggingface-hub>=0.20.2",
+    "requests-mock": "requests-mock==1.10.0",
+    "importlib_metadata": "importlib_metadata",
+    "invisible-watermark": "invisible-watermark>=0.2.0",
+    "isort": "isort>=5.5.4",
+    "jax": "jax>=0.4.1",
+    "jaxlib": "jaxlib>=0.4.1",
+    "Jinja2": "Jinja2",
+    "k-diffusion": "k-diffusion>=0.0.12",
+    "torchsde": "torchsde",
+    "note_seq": "note_seq",
+    "librosa": "librosa",
+    "numpy": "numpy",
+    "parameterized": "parameterized",
+    "peft": "peft>=0.6.0",
+    "protobuf": "protobuf>=3.20.3,<4",
+    "pytest": "pytest",
+    "pytest-timeout": "pytest-timeout",
+    "pytest-xdist": "pytest-xdist",
+    "python": "python>=3.8.0",
+    "ruff": "ruff==0.1.5",
+    "safetensors": "safetensors>=0.3.1",
+    "sentencepiece": "sentencepiece>=0.1.91,!=0.1.92",
+    "GitPython": "GitPython<3.1.19",
+    "scipy": "scipy",
+    "onnx": "onnx",
+    "regex": "regex!=2019.12.17",
+    "requests": "requests",
+    "tensorboard": "tensorboard",
+    "torch": "torch>=1.4",
+    "torchvision": "torchvision",
+    "transformers": "transformers>=4.25.1",
+    "urllib3": "urllib3<=2.0.0",
+    "black": "black",
+}

diffusers/experimental/README.md

@@ -0,0 +1,5 @@
+# 🧨 Diffusers Experimental
+
+We are adding experimental code to support novel applications and usages of the Diffusers library.
+Currently, the following experiments are supported:
+* Reinforcement learning via an implementation of the [Diffuser](https://arxiv.org/abs/2205.09991) model.

diffusers/experimental/rl/value_guided_sampling.py

@@ -0,0 +1,153 @@
+# Copyright 2024 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 numpy as np
+import torch
+import tqdm
+
+from ...models.unets.unet_1d import UNet1DModel
+from ...pipelines import DiffusionPipeline
+from ...utils.dummy_pt_objects import DDPMScheduler
+from ...utils.torch_utils import randn_tensor
+
+
+class ValueGuidedRLPipeline(DiffusionPipeline):
+    r"""
+    Pipeline for value-guided sampling from a diffusion model trained to predict sequences of states.
+
+    This model inherits from [`DiffusionPipeline`]. Check the superclass documentation for the generic methods
+    implemented for all pipelines (downloading, saving, running on a particular device, etc.).
+
+    Parameters:
+        value_function ([`UNet1DModel`]):
+            A specialized UNet for fine-tuning trajectories base on reward.
+        unet ([`UNet1DModel`]):
+            UNet architecture to denoise the encoded trajectories.
+        scheduler ([`SchedulerMixin`]):
+            A scheduler to be used in combination with `unet` to denoise the encoded trajectories. Default for this
+            application is [`DDPMScheduler`].
+        env ():
+            An environment following the OpenAI gym API to act in. For now only Hopper has pretrained models.
+    """
+
+    def __init__(
+        self,
+        value_function: UNet1DModel,
+        unet: UNet1DModel,
+        scheduler: DDPMScheduler,
+        env,
+    ):
+        super().__init__()
+
+        self.register_modules(value_function=value_function, unet=unet, scheduler=scheduler, env=env)
+
+        self.data = env.get_dataset()
+        self.means = {}
+        for key in self.data.keys():
+            try:
+                self.means[key] = self.data[key].mean()
+            except:  # noqa: E722
+                pass
+        self.stds = {}
+        for key in self.data.keys():
+            try:
+                self.stds[key] = self.data[key].std()
+            except:  # noqa: E722
+                pass
+        self.state_dim = env.observation_space.shape[0]
+        self.action_dim = env.action_space.shape[0]
+
+    def normalize(self, x_in, key):
+        return (x_in - self.means[key]) / self.stds[key]
+
+    def de_normalize(self, x_in, key):
+        return x_in * self.stds[key] + self.means[key]
+
+    def to_torch(self, x_in):
+        if isinstance(x_in, dict):
+            return {k: self.to_torch(v) for k, v in x_in.items()}
+        elif torch.is_tensor(x_in):
+            return x_in.to(self.unet.device)
+        return torch.tensor(x_in, device=self.unet.device)
+
+    def reset_x0(self, x_in, cond, act_dim):
+        for key, val in cond.items():
+            x_in[:, key, act_dim:] = val.clone()
+        return x_in
+
+    def run_diffusion(self, x, conditions, n_guide_steps, scale):
+        batch_size = x.shape[0]
+        y = None
+        for i in tqdm.tqdm(self.scheduler.timesteps):
+            # create batch of timesteps to pass into model
+            timesteps = torch.full((batch_size,), i, device=self.unet.device, dtype=torch.long)
+            for _ in range(n_guide_steps):
+                with torch.enable_grad():
+                    x.requires_grad_()
+
+                    # permute to match dimension for pre-trained models
+                    y = self.value_function(x.permute(0, 2, 1), timesteps).sample
+                    grad = torch.autograd.grad([y.sum()], [x])[0]
+
+                    posterior_variance = self.scheduler._get_variance(i)
+                    model_std = torch.exp(0.5 * posterior_variance)
+                    grad = model_std * grad
+
+                grad[timesteps < 2] = 0
+                x = x.detach()
+                x = x + scale * grad
+                x = self.reset_x0(x, conditions, self.action_dim)
+
+            prev_x = self.unet(x.permute(0, 2, 1), timesteps).sample.permute(0, 2, 1)
+
+            # TODO: verify deprecation of this kwarg
+            x = self.scheduler.step(prev_x, i, x)["prev_sample"]
+
+            # apply conditions to the trajectory (set the initial state)
+            x = self.reset_x0(x, conditions, self.action_dim)
+            x = self.to_torch(x)
+        return x, y
+
+    def __call__(self, obs, batch_size=64, planning_horizon=32, n_guide_steps=2, scale=0.1):
+        # normalize the observations and create  batch dimension
+        obs = self.normalize(obs, "observations")
+        obs = obs[None].repeat(batch_size, axis=0)
+
+        conditions = {0: self.to_torch(obs)}
+        shape = (batch_size, planning_horizon, self.state_dim + self.action_dim)
+
+        # generate initial noise and apply our conditions (to make the trajectories start at current state)
+        x1 = randn_tensor(shape, device=self.unet.device)
+        x = self.reset_x0(x1, conditions, self.action_dim)
+        x = self.to_torch(x)
+
+        # run the diffusion process
+        x, y = self.run_diffusion(x, conditions, n_guide_steps, scale)
+
+        # sort output trajectories by value
+        sorted_idx = y.argsort(0, descending=True).squeeze()
+        sorted_values = x[sorted_idx]
+        actions = sorted_values[:, :, : self.action_dim]
+        actions = actions.detach().cpu().numpy()
+        denorm_actions = self.de_normalize(actions, key="actions")
+
+        # select the action with the highest value
+        if y is not None:
+            selected_index = 0
+        else:
+            # if we didn't run value guiding, select a random action
+            selected_index = np.random.randint(0, batch_size)
+
+        denorm_actions = denorm_actions[selected_index, 0]
+        return denorm_actions

diffusers/image_processor.py

@@ -0,0 +1,1070 @@
+# Copyright 2024 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 math
+import warnings
+from typing import List, Optional, Tuple, Union
+
+import numpy as np
+import PIL.Image
+import torch
+import torch.nn.functional as F
+from PIL import Image, ImageFilter, ImageOps
+
+from .configuration_utils import ConfigMixin, register_to_config
+from .utils import CONFIG_NAME, PIL_INTERPOLATION, deprecate
+
+
+PipelineImageInput = Union[
+    PIL.Image.Image,
+    np.ndarray,
+    torch.FloatTensor,
+    List[PIL.Image.Image],
+    List[np.ndarray],
+    List[torch.FloatTensor],
+]
+
+PipelineDepthInput = PipelineImageInput
+
+
+class VaeImageProcessor(ConfigMixin):
+    """
+    Image processor for VAE.
+
+    Args:
+        do_resize (`bool`, *optional*, defaults to `True`):
+            Whether to downscale the image's (height, width) dimensions to multiples of `vae_scale_factor`. Can accept
+            `height` and `width` arguments from [`image_processor.VaeImageProcessor.preprocess`] method.
+        vae_scale_factor (`int`, *optional*, defaults to `8`):
+            VAE scale factor. If `do_resize` is `True`, the image is automatically resized to multiples of this factor.
+        resample (`str`, *optional*, defaults to `lanczos`):
+            Resampling filter to use when resizing the image.
+        do_normalize (`bool`, *optional*, defaults to `True`):
+            Whether to normalize the image to [-1,1].
+        do_binarize (`bool`, *optional*, defaults to `False`):
+            Whether to binarize the image to 0/1.
+        do_convert_rgb (`bool`, *optional*, defaults to be `False`):
+            Whether to convert the images to RGB format.
+        do_convert_grayscale (`bool`, *optional*, defaults to be `False`):
+            Whether to convert the images to grayscale format.
+    """
+
+    config_name = CONFIG_NAME
+
+    @register_to_config
+    def __init__(
+        self,
+        do_resize: bool = True,
+        vae_scale_factor: int = 8,
+        resample: str = "lanczos",
+        do_normalize: bool = True,
+        do_binarize: bool = False,
+        do_convert_rgb: bool = False,
+        do_convert_grayscale: bool = False,
+    ):
+        super().__init__()
+        if do_convert_rgb and do_convert_grayscale:
+            raise ValueError(
+                "`do_convert_rgb` and `do_convert_grayscale` can not both be set to `True`,"
+                " if you intended to convert the image into RGB format, please set `do_convert_grayscale = False`.",
+                " if you intended to convert the image into grayscale format, please set `do_convert_rgb = False`",
+            )
+            self.config.do_convert_rgb = False
+
+    @staticmethod
+    def numpy_to_pil(images: np.ndarray) -> List[PIL.Image.Image]:
+        """
+        Convert a numpy image or a batch of images to a PIL image.
+        """
+        if images.ndim == 3:
+            images = images[None, ...]
+        images = (images * 255).round().astype("uint8")
+        if images.shape[-1] == 1:
+            # special case for grayscale (single channel) images
+            pil_images = [Image.fromarray(image.squeeze(), mode="L") for image in images]
+        else:
+            pil_images = [Image.fromarray(image) for image in images]
+
+        return pil_images
+
+    @staticmethod
+    def pil_to_numpy(images: Union[List[PIL.Image.Image], PIL.Image.Image]) -> np.ndarray:
+        """
+        Convert a PIL image or a list of PIL images to NumPy arrays.
+        """
+        if not isinstance(images, list):
+            images = [images]
+        images = [np.array(image).astype(np.float32) / 255.0 for image in images]
+        images = np.stack(images, axis=0)
+
+        return images
+
+    @staticmethod
+    def numpy_to_pt(images: np.ndarray) -> torch.FloatTensor:
+        """
+        Convert a NumPy image to a PyTorch tensor.
+        """
+        if images.ndim == 3:
+            images = images[..., None]
+
+        images = torch.from_numpy(images.transpose(0, 3, 1, 2))
+        return images
+
+    @staticmethod
+    def pt_to_numpy(images: torch.FloatTensor) -> np.ndarray:
+        """
+        Convert a PyTorch tensor to a NumPy image.
+        """
+        images = images.cpu().permute(0, 2, 3, 1).float().numpy()
+        return images
+
+    @staticmethod
+    def normalize(images: Union[np.ndarray, torch.Tensor]) -> Union[np.ndarray, torch.Tensor]:
+        """
+        Normalize an image array to [-1,1].
+        """
+        return 2.0 * images - 1.0
+
+    @staticmethod
+    def denormalize(images: Union[np.ndarray, torch.Tensor]) -> Union[np.ndarray, torch.Tensor]:
+        """
+        Denormalize an image array to [0,1].
+        """
+        return (images / 2 + 0.5).clamp(0, 1)
+
+    @staticmethod
+    def convert_to_rgb(image: PIL.Image.Image) -> PIL.Image.Image:
+        """
+        Converts a PIL image to RGB format.
+        """
+        image = image.convert("RGB")
+
+        return image
+
+    @staticmethod
+    def convert_to_grayscale(image: PIL.Image.Image) -> PIL.Image.Image:
+        """
+        Converts a PIL image to grayscale format.
+        """
+        image = image.convert("L")
+
+        return image
+
+    @staticmethod
+    def blur(image: PIL.Image.Image, blur_factor: int = 4) -> PIL.Image.Image:
+        """
+        Applies Gaussian blur to an image.
+        """
+        image = image.filter(ImageFilter.GaussianBlur(blur_factor))
+
+        return image
+
+    @staticmethod
+    def get_crop_region(mask_image: PIL.Image.Image, width: int, height: int, pad=0):
+        """
+        Finds a rectangular region that contains all masked ares in an image, and expands region to match the aspect
+        ratio of the original image; for example, if user drew mask in a 128x32 region, and the dimensions for
+        processing are 512x512, the region will be expanded to 128x128.
+
+        Args:
+            mask_image (PIL.Image.Image): Mask image.
+            width (int): Width of the image to be processed.
+            height (int): Height of the image to be processed.
+            pad (int, optional): Padding to be added to the crop region. Defaults to 0.
+
+        Returns:
+            tuple: (x1, y1, x2, y2) represent a rectangular region that contains all masked ares in an image and
+            matches the original aspect ratio.
+        """
+
+        mask_image = mask_image.convert("L")
+        mask = np.array(mask_image)
+
+        # 1. find a rectangular region that contains all masked ares in an image
+        h, w = mask.shape
+        crop_left = 0
+        for i in range(w):
+            if not (mask[:, i] == 0).all():
+                break
+            crop_left += 1
+
+        crop_right = 0
+        for i in reversed(range(w)):
+            if not (mask[:, i] == 0).all():
+                break
+            crop_right += 1
+
+        crop_top = 0
+        for i in range(h):
+            if not (mask[i] == 0).all():
+                break
+            crop_top += 1
+
+        crop_bottom = 0
+        for i in reversed(range(h)):
+            if not (mask[i] == 0).all():
+                break
+            crop_bottom += 1
+
+        # 2. add padding to the crop region
+        x1, y1, x2, y2 = (
+            int(max(crop_left - pad, 0)),
+            int(max(crop_top - pad, 0)),
+            int(min(w - crop_right + pad, w)),
+            int(min(h - crop_bottom + pad, h)),
+        )
+
+        # 3. expands crop region to match the aspect ratio of the image to be processed
+        ratio_crop_region = (x2 - x1) / (y2 - y1)
+        ratio_processing = width / height
+
+        if ratio_crop_region > ratio_processing:
+            desired_height = (x2 - x1) / ratio_processing
+            desired_height_diff = int(desired_height - (y2 - y1))
+            y1 -= desired_height_diff // 2
+            y2 += desired_height_diff - desired_height_diff // 2
+            if y2 >= mask_image.height:
+                diff = y2 - mask_image.height
+                y2 -= diff
+                y1 -= diff
+            if y1 < 0:
+                y2 -= y1
+                y1 -= y1
+            if y2 >= mask_image.height:
+                y2 = mask_image.height
+        else:
+            desired_width = (y2 - y1) * ratio_processing
+            desired_width_diff = int(desired_width - (x2 - x1))
+            x1 -= desired_width_diff // 2
+            x2 += desired_width_diff - desired_width_diff // 2
+            if x2 >= mask_image.width:
+                diff = x2 - mask_image.width
+                x2 -= diff
+                x1 -= diff
+            if x1 < 0:
+                x2 -= x1
+                x1 -= x1
+            if x2 >= mask_image.width:
+                x2 = mask_image.width
+
+        return x1, y1, x2, y2
+
+    def _resize_and_fill(
+        self,
+        image: PIL.Image.Image,
+        width: int,
+        height: int,
+    ) -> PIL.Image.Image:
+        """
+        Resize the image to fit within the specified width and height, maintaining the aspect ratio, and then center
+        the image within the dimensions, filling empty with data from image.
+
+        Args:
+            image: The image to resize.
+            width: The width to resize the image to.
+            height: The height to resize the image to.
+        """
+
+        ratio = width / height
+        src_ratio = image.width / image.height
+
+        src_w = width if ratio < src_ratio else image.width * height // image.height
+        src_h = height if ratio >= src_ratio else image.height * width // image.width
+
+        resized = image.resize((src_w, src_h), resample=PIL_INTERPOLATION["lanczos"])
+        res = Image.new("RGB", (width, height))
+        res.paste(resized, box=(width // 2 - src_w // 2, height // 2 - src_h // 2))
+
+        if ratio < src_ratio:
+            fill_height = height // 2 - src_h // 2
+            if fill_height > 0:
+                res.paste(resized.resize((width, fill_height), box=(0, 0, width, 0)), box=(0, 0))
+                res.paste(
+                    resized.resize((width, fill_height), box=(0, resized.height, width, resized.height)),
+                    box=(0, fill_height + src_h),
+                )
+        elif ratio > src_ratio:
+            fill_width = width // 2 - src_w // 2
+            if fill_width > 0:
+                res.paste(resized.resize((fill_width, height), box=(0, 0, 0, height)), box=(0, 0))
+                res.paste(
+                    resized.resize((fill_width, height), box=(resized.width, 0, resized.width, height)),
+                    box=(fill_width + src_w, 0),
+                )
+
+        return res
+
+    def _resize_and_crop(
+        self,
+        image: PIL.Image.Image,
+        width: int,
+        height: int,
+    ) -> PIL.Image.Image:
+        """
+        Resize the image to fit within the specified width and height, maintaining the aspect ratio, and then center
+        the image within the dimensions, cropping the excess.
+
+        Args:
+            image: The image to resize.
+            width: The width to resize the image to.
+            height: The height to resize the image to.
+        """
+        ratio = width / height
+        src_ratio = image.width / image.height
+
+        src_w = width if ratio > src_ratio else image.width * height // image.height
+        src_h = height if ratio <= src_ratio else image.height * width // image.width
+
+        resized = image.resize((src_w, src_h), resample=PIL_INTERPOLATION["lanczos"])
+        res = Image.new("RGB", (width, height))
+        res.paste(resized, box=(width // 2 - src_w // 2, height // 2 - src_h // 2))
+        return res
+
+    def resize(
+        self,
+        image: Union[PIL.Image.Image, np.ndarray, torch.Tensor],
+        height: int,
+        width: int,
+        resize_mode: str = "default",  # "default", "fill", "crop"
+    ) -> Union[PIL.Image.Image, np.ndarray, torch.Tensor]:
+        """
+        Resize image.
+
+        Args:
+            image (`PIL.Image.Image`, `np.ndarray` or `torch.Tensor`):
+                The image input, can be a PIL image, numpy array or pytorch tensor.
+            height (`int`):
+                The height to resize to.
+            width (`int`):
+                The width to resize to.
+            resize_mode (`str`, *optional*, defaults to `default`):
+                The resize mode to use, can be one of `default` or `fill`. If `default`, will resize the image to fit
+                within the specified width and height, and it may not maintaining the original aspect ratio. If `fill`,
+                will resize the image to fit within the specified width and height, maintaining the aspect ratio, and
+                then center the image within the dimensions, filling empty with data from image. If `crop`, will resize
+                the image to fit within the specified width and height, maintaining the aspect ratio, and then center
+                the image within the dimensions, cropping the excess. Note that resize_mode `fill` and `crop` are only
+                supported for PIL image input.
+
+        Returns:
+            `PIL.Image.Image`, `np.ndarray` or `torch.Tensor`:
+                The resized image.
+        """
+        if resize_mode != "default" and not isinstance(image, PIL.Image.Image):
+            raise ValueError(f"Only PIL image input is supported for resize_mode {resize_mode}")
+        if isinstance(image, PIL.Image.Image):
+            if resize_mode == "default":
+                image = image.resize((width, height), resample=PIL_INTERPOLATION[self.config.resample])
+            elif resize_mode == "fill":
+                image = self._resize_and_fill(image, width, height)
+            elif resize_mode == "crop":
+                image = self._resize_and_crop(image, width, height)
+            else:
+                raise ValueError(f"resize_mode {resize_mode} is not supported")
+
+        elif isinstance(image, torch.Tensor):
+            image = torch.nn.functional.interpolate(
+                image,
+                size=(height, width),
+            )
+        elif isinstance(image, np.ndarray):
+            image = self.numpy_to_pt(image)
+            image = torch.nn.functional.interpolate(
+                image,
+                size=(height, width),
+            )
+            image = self.pt_to_numpy(image)
+        return image
+
+    def binarize(self, image: PIL.Image.Image) -> PIL.Image.Image:
+        """
+        Create a mask.
+
+        Args:
+            image (`PIL.Image.Image`):
+                The image input, should be a PIL image.
+
+        Returns:
+            `PIL.Image.Image`:
+                The binarized image. Values less than 0.5 are set to 0, values greater than 0.5 are set to 1.
+        """
+        image[image < 0.5] = 0
+        image[image >= 0.5] = 1
+
+        return image
+
+    def get_default_height_width(
+        self,
+        image: Union[PIL.Image.Image, np.ndarray, torch.Tensor],
+        height: Optional[int] = None,
+        width: Optional[int] = None,
+    ) -> Tuple[int, int]:
+        """
+        This function return the height and width that are downscaled to the next integer multiple of
+        `vae_scale_factor`.
+
+        Args:
+            image(`PIL.Image.Image`, `np.ndarray` or `torch.Tensor`):
+                The image input, can be a PIL image, numpy array or pytorch tensor. if it is a numpy array, should have
+                shape `[batch, height, width]` or `[batch, height, width, channel]` if it is a pytorch tensor, should
+                have shape `[batch, channel, height, width]`.
+            height (`int`, *optional*, defaults to `None`):
+                The height in preprocessed image. If `None`, will use the height of `image` input.
+            width (`int`, *optional*`, defaults to `None`):
+                The width in preprocessed. If `None`, will use the width of the `image` input.
+        """
+
+        if height is None:
+            if isinstance(image, PIL.Image.Image):
+                height = image.height
+            elif isinstance(image, torch.Tensor):
+                height = image.shape[2]
+            else:
+                height = image.shape[1]
+
+        if width is None:
+            if isinstance(image, PIL.Image.Image):
+                width = image.width
+            elif isinstance(image, torch.Tensor):
+                width = image.shape[3]
+            else:
+                width = image.shape[2]
+
+        width, height = (
+            x - x % self.config.vae_scale_factor for x in (width, height)
+        )  # resize to integer multiple of vae_scale_factor
+
+        return height, width
+
+    def preprocess(
+        self,
+        image: PipelineImageInput,
+        height: Optional[int] = None,
+        width: Optional[int] = None,
+        resize_mode: str = "default",  # "default", "fill", "crop"
+        crops_coords: Optional[Tuple[int, int, int, int]] = None,
+    ) -> torch.Tensor:
+        """
+        Preprocess the image input.
+
+        Args:
+            image (`pipeline_image_input`):
+                The image input, accepted formats are PIL images, NumPy arrays, PyTorch tensors; Also accept list of
+                supported formats.
+            height (`int`, *optional*, defaults to `None`):
+                The height in preprocessed image. If `None`, will use the `get_default_height_width()` to get default
+                height.
+            width (`int`, *optional*`, defaults to `None`):
+                The width in preprocessed. If `None`, will use get_default_height_width()` to get the default width.
+            resize_mode (`str`, *optional*, defaults to `default`):
+                The resize mode, can be one of `default` or `fill`. If `default`, will resize the image to fit within
+                the specified width and height, and it may not maintaining the original aspect ratio. If `fill`, will
+                resize the image to fit within the specified width and height, maintaining the aspect ratio, and then
+                center the image within the dimensions, filling empty with data from image. If `crop`, will resize the
+                image to fit within the specified width and height, maintaining the aspect ratio, and then center the
+                image within the dimensions, cropping the excess. Note that resize_mode `fill` and `crop` are only
+                supported for PIL image input.
+            crops_coords (`List[Tuple[int, int, int, int]]`, *optional*, defaults to `None`):
+                The crop coordinates for each image in the batch. If `None`, will not crop the image.
+        """
+        supported_formats = (PIL.Image.Image, np.ndarray, torch.Tensor)
+
+        # Expand the missing dimension for 3-dimensional pytorch tensor or numpy array that represents grayscale image
+        if self.config.do_convert_grayscale and isinstance(image, (torch.Tensor, np.ndarray)) and image.ndim == 3:
+            if isinstance(image, torch.Tensor):
+                # if image is a pytorch tensor could have 2 possible shapes:
+                #    1. batch x height x width: we should insert the channel dimension at position 1
+                #    2. channel x height x width: we should insert batch dimension at position 0,
+                #       however, since both channel and batch dimension has same size 1, it is same to insert at position 1
+                #    for simplicity, we insert a dimension of size 1 at position 1 for both cases
+                image = image.unsqueeze(1)
+            else:
+                # if it is a numpy array, it could have 2 possible shapes:
+                #   1. batch x height x width: insert channel dimension on last position
+                #   2. height x width x channel: insert batch dimension on first position
+                if image.shape[-1] == 1:
+                    image = np.expand_dims(image, axis=0)
+                else:
+                    image = np.expand_dims(image, axis=-1)
+
+        if isinstance(image, supported_formats):
+            image = [image]
+        elif not (isinstance(image, list) and all(isinstance(i, supported_formats) for i in image)):
+            raise ValueError(
+                f"Input is in incorrect format: {[type(i) for i in image]}. Currently, we only support {', '.join(supported_formats)}"
+            )
+
+        if isinstance(image[0], PIL.Image.Image):
+            if crops_coords is not None:
+                image = [i.crop(crops_coords) for i in image]
+            if self.config.do_resize:
+                height, width = self.get_default_height_width(image[0], height, width)
+                image = [self.resize(i, height, width, resize_mode=resize_mode) for i in image]
+            if self.config.do_convert_rgb:
+                image = [self.convert_to_rgb(i) for i in image]
+            elif self.config.do_convert_grayscale:
+                image = [self.convert_to_grayscale(i) for i in image]
+            image = self.pil_to_numpy(image)  # to np
+            image = self.numpy_to_pt(image)  # to pt
+
+        elif isinstance(image[0], np.ndarray):
+            image = np.concatenate(image, axis=0) if image[0].ndim == 4 else np.stack(image, axis=0)
+
+            image = self.numpy_to_pt(image)
+
+            height, width = self.get_default_height_width(image, height, width)
+            if self.config.do_resize:
+                image = self.resize(image, height, width)
+
+        elif isinstance(image[0], torch.Tensor):
+            image = torch.cat(image, axis=0) if image[0].ndim == 4 else torch.stack(image, axis=0)
+
+            if self.config.do_convert_grayscale and image.ndim == 3:
+                image = image.unsqueeze(1)
+
+            channel = image.shape[1]
+            # don't need any preprocess if the image is latents
+            if channel == 4:
+                return image
+
+            height, width = self.get_default_height_width(image, height, width)
+            if self.config.do_resize:
+                image = self.resize(image, height, width)
+
+        # expected range [0,1], normalize to [-1,1]
+        do_normalize = self.config.do_normalize
+        if do_normalize and image.min() < 0:
+            warnings.warn(
+                "Passing `image` as torch tensor with value range in [-1,1] is deprecated. The expected value range for image tensor is [0,1] "
+                f"when passing as pytorch tensor or numpy Array. You passed `image` with value range [{image.min()},{image.max()}]",
+                FutureWarning,
+            )
+            do_normalize = False
+
+        if do_normalize:
+            image = self.normalize(image)
+
+        if self.config.do_binarize:
+            image = self.binarize(image)
+
+        return image
+
+    def postprocess(
+        self,
+        image: torch.FloatTensor,
+        output_type: str = "pil",
+        do_denormalize: Optional[List[bool]] = None,
+    ) -> Union[PIL.Image.Image, np.ndarray, torch.FloatTensor]:
+        """
+        Postprocess the image output from tensor to `output_type`.
+
+        Args:
+            image (`torch.FloatTensor`):
+                The image input, should be a pytorch tensor with shape `B x C x H x W`.
+            output_type (`str`, *optional*, defaults to `pil`):
+                The output type of the image, can be one of `pil`, `np`, `pt`, `latent`.
+            do_denormalize (`List[bool]`, *optional*, defaults to `None`):
+                Whether to denormalize the image to [0,1]. If `None`, will use the value of `do_normalize` in the
+                `VaeImageProcessor` config.
+
+        Returns:
+            `PIL.Image.Image`, `np.ndarray` or `torch.FloatTensor`:
+                The postprocessed image.
+        """
+        if not isinstance(image, torch.Tensor):
+            raise ValueError(
+                f"Input for postprocessing is in incorrect format: {type(image)}. We only support pytorch tensor"
+            )
+        if output_type not in ["latent", "pt", "np", "pil"]:
+            deprecation_message = (
+                f"the output_type {output_type} is outdated and has been set to `np`. Please make sure to set it to one of these instead: "
+                "`pil`, `np`, `pt`, `latent`"
+            )
+            deprecate("Unsupported output_type", "1.0.0", deprecation_message, standard_warn=False)
+            output_type = "np"
+
+        if output_type == "latent":
+            return image
+
+        if do_denormalize is None:
+            do_denormalize = [self.config.do_normalize] * image.shape[0]
+
+        image = torch.stack(
+            [self.denormalize(image[i]) if do_denormalize[i] else image[i] for i in range(image.shape[0])]
+        )
+
+        if output_type == "pt":
+            return image
+
+        image = self.pt_to_numpy(image)
+
+        if output_type == "np":
+            return image
+
+        if output_type == "pil":
+            return self.numpy_to_pil(image)
+
+    def apply_overlay(
+        self,
+        mask: PIL.Image.Image,
+        init_image: PIL.Image.Image,
+        image: PIL.Image.Image,
+        crop_coords: Optional[Tuple[int, int, int, int]] = None,
+    ) -> PIL.Image.Image:
+        """
+        overlay the inpaint output to the original image
+        """
+
+        width, height = image.width, image.height
+
+        init_image = self.resize(init_image, width=width, height=height)
+        mask = self.resize(mask, width=width, height=height)
+
+        init_image_masked = PIL.Image.new("RGBa", (width, height))
+        init_image_masked.paste(init_image.convert("RGBA").convert("RGBa"), mask=ImageOps.invert(mask.convert("L")))
+        init_image_masked = init_image_masked.convert("RGBA")
+
+        if crop_coords is not None:
+            x, y, x2, y2 = crop_coords
+            w = x2 - x
+            h = y2 - y
+            base_image = PIL.Image.new("RGBA", (width, height))
+            image = self.resize(image, height=h, width=w, resize_mode="crop")
+            base_image.paste(image, (x, y))
+            image = base_image.convert("RGB")
+
+        image = image.convert("RGBA")
+        image.alpha_composite(init_image_masked)
+        image = image.convert("RGB")
+
+        return image
+
+
+class VaeImageProcessorLDM3D(VaeImageProcessor):
+    """
+    Image processor for VAE LDM3D.
+
+    Args:
+        do_resize (`bool`, *optional*, defaults to `True`):
+            Whether to downscale the image's (height, width) dimensions to multiples of `vae_scale_factor`.
+        vae_scale_factor (`int`, *optional*, defaults to `8`):
+            VAE scale factor. If `do_resize` is `True`, the image is automatically resized to multiples of this factor.
+        resample (`str`, *optional*, defaults to `lanczos`):
+            Resampling filter to use when resizing the image.
+        do_normalize (`bool`, *optional*, defaults to `True`):
+            Whether to normalize the image to [-1,1].
+    """
+
+    config_name = CONFIG_NAME
+
+    @register_to_config
+    def __init__(
+        self,
+        do_resize: bool = True,
+        vae_scale_factor: int = 8,
+        resample: str = "lanczos",
+        do_normalize: bool = True,
+    ):
+        super().__init__()
+
+    @staticmethod
+    def numpy_to_pil(images: np.ndarray) -> List[PIL.Image.Image]:
+        """
+        Convert a NumPy image or a batch of images to a PIL image.
+        """
+        if images.ndim == 3:
+            images = images[None, ...]
+        images = (images * 255).round().astype("uint8")
+        if images.shape[-1] == 1:
+            # special case for grayscale (single channel) images
+            pil_images = [Image.fromarray(image.squeeze(), mode="L") for image in images]
+        else:
+            pil_images = [Image.fromarray(image[:, :, :3]) for image in images]
+
+        return pil_images
+
+    @staticmethod
+    def depth_pil_to_numpy(images: Union[List[PIL.Image.Image], PIL.Image.Image]) -> np.ndarray:
+        """
+        Convert a PIL image or a list of PIL images to NumPy arrays.
+        """
+        if not isinstance(images, list):
+            images = [images]
+
+        images = [np.array(image).astype(np.float32) / (2**16 - 1) for image in images]
+        images = np.stack(images, axis=0)
+        return images
+
+    @staticmethod
+    def rgblike_to_depthmap(image: Union[np.ndarray, torch.Tensor]) -> Union[np.ndarray, torch.Tensor]:
+        """
+        Args:
+            image: RGB-like depth image
+
+        Returns: depth map
+
+        """
+        return image[:, :, 1] * 2**8 + image[:, :, 2]
+
+    def numpy_to_depth(self, images: np.ndarray) -> List[PIL.Image.Image]:
+        """
+        Convert a NumPy depth image or a batch of images to a PIL image.
+        """
+        if images.ndim == 3:
+            images = images[None, ...]
+        images_depth = images[:, :, :, 3:]
+        if images.shape[-1] == 6:
+            images_depth = (images_depth * 255).round().astype("uint8")
+            pil_images = [
+                Image.fromarray(self.rgblike_to_depthmap(image_depth), mode="I;16") for image_depth in images_depth
+            ]
+        elif images.shape[-1] == 4:
+            images_depth = (images_depth * 65535.0).astype(np.uint16)
+            pil_images = [Image.fromarray(image_depth, mode="I;16") for image_depth in images_depth]
+        else:
+            raise Exception("Not supported")
+
+        return pil_images
+
+    def postprocess(
+        self,
+        image: torch.FloatTensor,
+        output_type: str = "pil",
+        do_denormalize: Optional[List[bool]] = None,
+    ) -> Union[PIL.Image.Image, np.ndarray, torch.FloatTensor]:
+        """
+        Postprocess the image output from tensor to `output_type`.
+
+        Args:
+            image (`torch.FloatTensor`):
+                The image input, should be a pytorch tensor with shape `B x C x H x W`.
+            output_type (`str`, *optional*, defaults to `pil`):
+                The output type of the image, can be one of `pil`, `np`, `pt`, `latent`.
+            do_denormalize (`List[bool]`, *optional*, defaults to `None`):
+                Whether to denormalize the image to [0,1]. If `None`, will use the value of `do_normalize` in the
+                `VaeImageProcessor` config.
+
+        Returns:
+            `PIL.Image.Image`, `np.ndarray` or `torch.FloatTensor`:
+                The postprocessed image.
+        """
+        if not isinstance(image, torch.Tensor):
+            raise ValueError(
+                f"Input for postprocessing is in incorrect format: {type(image)}. We only support pytorch tensor"
+            )
+        if output_type not in ["latent", "pt", "np", "pil"]:
+            deprecation_message = (
+                f"the output_type {output_type} is outdated and has been set to `np`. Please make sure to set it to one of these instead: "
+                "`pil`, `np`, `pt`, `latent`"
+            )
+            deprecate("Unsupported output_type", "1.0.0", deprecation_message, standard_warn=False)
+            output_type = "np"
+
+        if do_denormalize is None:
+            do_denormalize = [self.config.do_normalize] * image.shape[0]
+
+        image = torch.stack(
+            [self.denormalize(image[i]) if do_denormalize[i] else image[i] for i in range(image.shape[0])]
+        )
+
+        image = self.pt_to_numpy(image)
+
+        if output_type == "np":
+            if image.shape[-1] == 6:
+                image_depth = np.stack([self.rgblike_to_depthmap(im[:, :, 3:]) for im in image], axis=0)
+            else:
+                image_depth = image[:, :, :, 3:]
+            return image[:, :, :, :3], image_depth
+
+        if output_type == "pil":
+            return self.numpy_to_pil(image), self.numpy_to_depth(image)
+        else:
+            raise Exception(f"This type {output_type} is not supported")
+
+    def preprocess(
+        self,
+        rgb: Union[torch.FloatTensor, PIL.Image.Image, np.ndarray],
+        depth: Union[torch.FloatTensor, PIL.Image.Image, np.ndarray],
+        height: Optional[int] = None,
+        width: Optional[int] = None,
+        target_res: Optional[int] = None,
+    ) -> torch.Tensor:
+        """
+        Preprocess the image input. Accepted formats are PIL images, NumPy arrays or PyTorch tensors.
+        """
+        supported_formats = (PIL.Image.Image, np.ndarray, torch.Tensor)
+
+        # Expand the missing dimension for 3-dimensional pytorch tensor or numpy array that represents grayscale image
+        if self.config.do_convert_grayscale and isinstance(rgb, (torch.Tensor, np.ndarray)) and rgb.ndim == 3:
+            raise Exception("This is not yet supported")
+
+        if isinstance(rgb, supported_formats):
+            rgb = [rgb]
+            depth = [depth]
+        elif not (isinstance(rgb, list) and all(isinstance(i, supported_formats) for i in rgb)):
+            raise ValueError(
+                f"Input is in incorrect format: {[type(i) for i in rgb]}. Currently, we only support {', '.join(supported_formats)}"
+            )
+
+        if isinstance(rgb[0], PIL.Image.Image):
+            if self.config.do_convert_rgb:
+                raise Exception("This is not yet supported")
+                # rgb = [self.convert_to_rgb(i) for i in rgb]
+                # depth = [self.convert_to_depth(i) for i in depth]  #TODO define convert_to_depth
+            if self.config.do_resize or target_res:
+                height, width = self.get_default_height_width(rgb[0], height, width) if not target_res else target_res
+                rgb = [self.resize(i, height, width) for i in rgb]
+                depth = [self.resize(i, height, width) for i in depth]
+            rgb = self.pil_to_numpy(rgb)  # to np
+            rgb = self.numpy_to_pt(rgb)  # to pt
+
+            depth = self.depth_pil_to_numpy(depth)  # to np
+            depth = self.numpy_to_pt(depth)  # to pt
+
+        elif isinstance(rgb[0], np.ndarray):
+            rgb = np.concatenate(rgb, axis=0) if rgb[0].ndim == 4 else np.stack(rgb, axis=0)
+            rgb = self.numpy_to_pt(rgb)
+            height, width = self.get_default_height_width(rgb, height, width)
+            if self.config.do_resize:
+                rgb = self.resize(rgb, height, width)
+
+            depth = np.concatenate(depth, axis=0) if rgb[0].ndim == 4 else np.stack(depth, axis=0)
+            depth = self.numpy_to_pt(depth)
+            height, width = self.get_default_height_width(depth, height, width)
+            if self.config.do_resize:
+                depth = self.resize(depth, height, width)
+
+        elif isinstance(rgb[0], torch.Tensor):
+            raise Exception("This is not yet supported")
+            # rgb = torch.cat(rgb, axis=0) if rgb[0].ndim == 4 else torch.stack(rgb, axis=0)
+
+            # if self.config.do_convert_grayscale and rgb.ndim == 3:
+            #     rgb = rgb.unsqueeze(1)
+
+            # channel = rgb.shape[1]
+
+            # height, width = self.get_default_height_width(rgb, height, width)
+            # if self.config.do_resize:
+            #     rgb = self.resize(rgb, height, width)
+
+            # depth = torch.cat(depth, axis=0) if depth[0].ndim == 4 else torch.stack(depth, axis=0)
+
+            # if self.config.do_convert_grayscale and depth.ndim == 3:
+            #     depth = depth.unsqueeze(1)
+
+            # channel = depth.shape[1]
+            # # don't need any preprocess if the image is latents
+            # if depth == 4:
+            #     return rgb, depth
+
+            # height, width = self.get_default_height_width(depth, height, width)
+            # if self.config.do_resize:
+            #     depth = self.resize(depth, height, width)
+        # expected range [0,1], normalize to [-1,1]
+        do_normalize = self.config.do_normalize
+        if rgb.min() < 0 and do_normalize:
+            warnings.warn(
+                "Passing `image` as torch tensor with value range in [-1,1] is deprecated. The expected value range for image tensor is [0,1] "
+                f"when passing as pytorch tensor or numpy Array. You passed `image` with value range [{rgb.min()},{rgb.max()}]",
+                FutureWarning,
+            )
+            do_normalize = False
+
+        if do_normalize:
+            rgb = self.normalize(rgb)
+            depth = self.normalize(depth)
+
+        if self.config.do_binarize:
+            rgb = self.binarize(rgb)
+            depth = self.binarize(depth)
+
+        return rgb, depth
+
+
+class IPAdapterMaskProcessor(VaeImageProcessor):
+    """
+    Image processor for IP Adapter image masks.
+
+    Args:
+        do_resize (`bool`, *optional*, defaults to `True`):
+            Whether to downscale the image's (height, width) dimensions to multiples of `vae_scale_factor`.
+        vae_scale_factor (`int`, *optional*, defaults to `8`):
+            VAE scale factor. If `do_resize` is `True`, the image is automatically resized to multiples of this factor.
+        resample (`str`, *optional*, defaults to `lanczos`):
+            Resampling filter to use when resizing the image.
+        do_normalize (`bool`, *optional*, defaults to `False`):
+            Whether to normalize the image to [-1,1].
+        do_binarize (`bool`, *optional*, defaults to `True`):
+            Whether to binarize the image to 0/1.
+        do_convert_grayscale (`bool`, *optional*, defaults to be `True`):
+            Whether to convert the images to grayscale format.
+
+    """
+
+    config_name = CONFIG_NAME
+
+    @register_to_config
+    def __init__(
+        self,
+        do_resize: bool = True,
+        vae_scale_factor: int = 8,
+        resample: str = "lanczos",
+        do_normalize: bool = False,
+        do_binarize: bool = True,
+        do_convert_grayscale: bool = True,
+    ):
+        super().__init__(
+            do_resize=do_resize,
+            vae_scale_factor=vae_scale_factor,
+            resample=resample,
+            do_normalize=do_normalize,
+            do_binarize=do_binarize,
+            do_convert_grayscale=do_convert_grayscale,
+        )
+
+    @staticmethod
+    def downsample(mask: torch.FloatTensor, batch_size: int, num_queries: int, value_embed_dim: int):
+        """
+        Downsamples the provided mask tensor to match the expected dimensions for scaled dot-product attention. If the
+        aspect ratio of the mask does not match the aspect ratio of the output image, a warning is issued.
+
+        Args:
+            mask (`torch.FloatTensor`):
+                The input mask tensor generated with `IPAdapterMaskProcessor.preprocess()`.
+            batch_size (`int`):
+                The batch size.
+            num_queries (`int`):
+                The number of queries.
+            value_embed_dim (`int`):
+                The dimensionality of the value embeddings.
+
+        Returns:
+            `torch.FloatTensor`:
+                The downsampled mask tensor.
+
+        """
+        o_h = mask.shape[1]
+        o_w = mask.shape[2]
+        ratio = o_w / o_h
+        mask_h = int(math.sqrt(num_queries / ratio))
+        mask_h = int(mask_h) + int((num_queries % int(mask_h)) != 0)
+        mask_w = num_queries // mask_h
+
+        mask_downsample = F.interpolate(mask.unsqueeze(0), size=(mask_h, mask_w), mode="bicubic").squeeze(0)
+
+        # Repeat batch_size times
+        if mask_downsample.shape[0] < batch_size:
+            mask_downsample = mask_downsample.repeat(batch_size, 1, 1)
+
+        mask_downsample = mask_downsample.view(mask_downsample.shape[0], -1)
+
+        downsampled_area = mask_h * mask_w
+        # If the output image and the mask do not have the same aspect ratio, tensor shapes will not match
+        # Pad tensor if downsampled_mask.shape[1] is smaller than num_queries
+        if downsampled_area < num_queries:
+            warnings.warn(
+                "The aspect ratio of the mask does not match the aspect ratio of the output image. "
+                "Please update your masks or adjust the output size for optimal performance.",
+                UserWarning,
+            )
+            mask_downsample = F.pad(mask_downsample, (0, num_queries - mask_downsample.shape[1]), value=0.0)
+        # Discard last embeddings if downsampled_mask.shape[1] is bigger than num_queries
+        if downsampled_area > num_queries:
+            warnings.warn(
+                "The aspect ratio of the mask does not match the aspect ratio of the output image. "
+                "Please update your masks or adjust the output size for optimal performance.",
+                UserWarning,
+            )
+            mask_downsample = mask_downsample[:, :num_queries]
+
+        # Repeat last dimension to match SDPA output shape
+        mask_downsample = mask_downsample.view(mask_downsample.shape[0], mask_downsample.shape[1], 1).repeat(
+            1, 1, value_embed_dim
+        )
+
+        return mask_downsample
+
+
+class PixArtImageProcessor(VaeImageProcessor):
+    """
+    Image processor for PixArt image resize and crop.
+
+    Args:
+        do_resize (`bool`, *optional*, defaults to `True`):
+            Whether to downscale the image's (height, width) dimensions to multiples of `vae_scale_factor`. Can accept
+            `height` and `width` arguments from [`image_processor.VaeImageProcessor.preprocess`] method.
+        vae_scale_factor (`int`, *optional*, defaults to `8`):
+            VAE scale factor. If `do_resize` is `True`, the image is automatically resized to multiples of this factor.
+        resample (`str`, *optional*, defaults to `lanczos`):
+            Resampling filter to use when resizing the image.
+        do_normalize (`bool`, *optional*, defaults to `True`):
+            Whether to normalize the image to [-1,1].
+        do_binarize (`bool`, *optional*, defaults to `False`):
+            Whether to binarize the image to 0/1.
+        do_convert_rgb (`bool`, *optional*, defaults to be `False`):
+            Whether to convert the images to RGB format.
+        do_convert_grayscale (`bool`, *optional*, defaults to be `False`):
+            Whether to convert the images to grayscale format.
+    """
+
+    @register_to_config
+    def __init__(
+        self,
+        do_resize: bool = True,
+        vae_scale_factor: int = 8,
+        resample: str = "lanczos",
+        do_normalize: bool = True,
+        do_binarize: bool = False,
+        do_convert_grayscale: bool = False,
+    ):
+        super().__init__(
+            do_resize=do_resize,
+            vae_scale_factor=vae_scale_factor,
+            resample=resample,
+            do_normalize=do_normalize,
+            do_binarize=do_binarize,
+            do_convert_grayscale=do_convert_grayscale,
+        )
+
+    @staticmethod
+    def classify_height_width_bin(height: int, width: int, ratios: dict) -> Tuple[int, int]:
+        """Returns binned height and width."""
+        ar = float(height / width)
+        closest_ratio = min(ratios.keys(), key=lambda ratio: abs(float(ratio) - ar))
+        default_hw = ratios[closest_ratio]
+        return int(default_hw[0]), int(default_hw[1])
+
+    @staticmethod
+    def resize_and_crop_tensor(samples: torch.Tensor, new_width: int, new_height: int) -> torch.Tensor:
+        orig_height, orig_width = samples.shape[2], samples.shape[3]
+
+        # Check if resizing is needed
+        if orig_height != new_height or orig_width != new_width:
+            ratio = max(new_height / orig_height, new_width / orig_width)
+            resized_width = int(orig_width * ratio)
+            resized_height = int(orig_height * ratio)
+
+            # Resize
+            samples = F.interpolate(
+                samples, size=(resized_height, resized_width), mode="bilinear", align_corners=False
+            )
+
+            # Center Crop
+            start_x = (resized_width - new_width) // 2
+            end_x = start_x + new_width
+            start_y = (resized_height - new_height) // 2
+            end_y = start_y + new_height
+            samples = samples[:, :, start_y:end_y, start_x:end_x]
+
+        return samples

diffusers/loaders/autoencoder.py

@@ -0,0 +1,146 @@
+# Copyright 2024 The HuggingFace Team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+from huggingface_hub.utils import validate_hf_hub_args
+
+from .single_file_utils import (
+    create_diffusers_vae_model_from_ldm,
+    fetch_ldm_config_and_checkpoint,
+)
+
+
+class FromOriginalVAEMixin:
+    """
+    Load pretrained AutoencoderKL weights saved in the `.ckpt` or `.safetensors` format into a [`AutoencoderKL`].
+    """
+
+    @classmethod
+    @validate_hf_hub_args
+    def from_single_file(cls, pretrained_model_link_or_path, **kwargs):
+        r"""
+        Instantiate a [`AutoencoderKL`] from pretrained ControlNet weights saved in the original `.ckpt` or
+        `.safetensors` format. The pipeline is set in evaluation mode (`model.eval()`) by default.
+
+        Parameters:
+            pretrained_model_link_or_path (`str` or `os.PathLike`, *optional*):
+                Can be either:
+                    - A link to the `.ckpt` file (for example
+                      `"https://huggingface.co/<repo_id>/blob/main/<path_to_file>.ckpt"`) on the Hub.
+                    - A path to a *file* containing all pipeline weights.
+            config_file (`str`, *optional*):
+                Filepath to the configuration YAML file associated with the model. If not provided it will default to:
+                https://raw.githubusercontent.com/CompVis/stable-diffusion/main/configs/stable-diffusion/v1-inference.yaml
+            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.
+            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.
+            resume_download:
+                Deprecated and ignored. All downloads are now resumed by default when possible. Will be removed in v1
+                of Diffusers.
+            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.
+            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.
+            image_size (`int`, *optional*, defaults to 512):
+                The image size the model was trained on. Use 512 for all Stable Diffusion v1 models and the Stable
+                Diffusion v2 base model. Use 768 for Stable Diffusion v2.
+            scaling_factor (`float`, *optional*, defaults to 0.18215):
+                The component-wise standard deviation of the trained latent space computed using the first batch of the
+                training set. This is used to scale the latent space to have unit variance when training the diffusion
+                model. The latents are scaled with the formula `z = z * scaling_factor` before being passed to the
+                diffusion model. When decoding, the latents are scaled back to the original scale with the formula: `z
+                = 1 / scaling_factor * z`. For more details, refer to sections 4.3.2 and D.1 of the [High-Resolution
+                Image Synthesis with Latent Diffusion Models](https://arxiv.org/abs/2112.10752) paper.
+            kwargs (remaining dictionary of keyword arguments, *optional*):
+                Can be used to overwrite load and saveable variables (for example the pipeline components of the
+                specific pipeline class). The overwritten components are directly passed to the pipelines `__init__`
+                method. See example below for more information.
+
+        <Tip warning={true}>
+
+            Make sure to pass both `image_size` and `scaling_factor` to `from_single_file()` if you're loading
+            a VAE from SDXL or a Stable Diffusion v2 model or higher.
+
+        </Tip>
+
+        Examples:
+
+        ```py
+        from diffusers import AutoencoderKL
+
+        url = "https://huggingface.co/stabilityai/sd-vae-ft-mse-original/blob/main/vae-ft-mse-840000-ema-pruned.safetensors"  # can also be local file
+        model = AutoencoderKL.from_single_file(url)
+        ```
+        """
+
+        original_config_file = kwargs.pop("original_config_file", None)
+        config_file = kwargs.pop("config_file", None)
+        resume_download = kwargs.pop("resume_download", None)
+        force_download = kwargs.pop("force_download", False)
+        proxies = kwargs.pop("proxies", None)
+        token = kwargs.pop("token", None)
+        cache_dir = kwargs.pop("cache_dir", None)
+        local_files_only = kwargs.pop("local_files_only", None)
+        revision = kwargs.pop("revision", None)
+        torch_dtype = kwargs.pop("torch_dtype", None)
+
+        class_name = cls.__name__
+
+        if (config_file is not None) and (original_config_file is not None):
+            raise ValueError(
+                "You cannot pass both `config_file` and `original_config_file` to `from_single_file`. Please use only one of these arguments."
+            )
+
+        original_config_file = original_config_file or config_file
+        original_config, checkpoint = fetch_ldm_config_and_checkpoint(
+            pretrained_model_link_or_path=pretrained_model_link_or_path,
+            class_name=class_name,
+            original_config_file=original_config_file,
+            resume_download=resume_download,
+            force_download=force_download,
+            proxies=proxies,
+            token=token,
+            revision=revision,
+            local_files_only=local_files_only,
+            cache_dir=cache_dir,
+        )
+
+        image_size = kwargs.pop("image_size", None)
+        scaling_factor = kwargs.pop("scaling_factor", None)
+        component = create_diffusers_vae_model_from_ldm(
+            class_name,
+            original_config,
+            checkpoint,
+            image_size=image_size,
+            scaling_factor=scaling_factor,
+            torch_dtype=torch_dtype,
+        )
+        vae = component["vae"]
+        if torch_dtype is not None:
+            vae = vae.to(torch_dtype)
+
+        return vae

diffusers/loaders/controlnet.py

@@ -0,0 +1,136 @@
+# Copyright 2024 The HuggingFace Team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+from huggingface_hub.utils import validate_hf_hub_args
+
+from .single_file_utils import (
+    create_diffusers_controlnet_model_from_ldm,
+    fetch_ldm_config_and_checkpoint,
+)
+
+
+class FromOriginalControlNetMixin:
+    """
+    Load pretrained ControlNet weights saved in the `.ckpt` or `.safetensors` format into a [`ControlNetModel`].
+    """
+
+    @classmethod
+    @validate_hf_hub_args
+    def from_single_file(cls, pretrained_model_link_or_path, **kwargs):
+        r"""
+        Instantiate a [`ControlNetModel`] from pretrained ControlNet weights saved in the original `.ckpt` or
+        `.safetensors` format. The pipeline is set in evaluation mode (`model.eval()`) by default.
+
+        Parameters:
+            pretrained_model_link_or_path (`str` or `os.PathLike`, *optional*):
+                Can be either:
+                    - A link to the `.ckpt` file (for example
+                      `"https://huggingface.co/<repo_id>/blob/main/<path_to_file>.ckpt"`) on the Hub.
+                    - A path to a *file* containing all pipeline weights.
+            config_file (`str`, *optional*):
+                Filepath to the configuration YAML file associated with the model. If not provided it will default to:
+                https://raw.githubusercontent.com/lllyasviel/ControlNet/main/models/cldm_v15.yaml
+            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.
+            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.
+            resume_download:
+                Deprecated and ignored. All downloads are now resumed by default when possible. Will be removed in v1
+                of Diffusers.
+            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.
+            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.
+            image_size (`int`, *optional*, defaults to 512):
+                The image size the model was trained on. Use 512 for all Stable Diffusion v1 models and the Stable
+                Diffusion v2 base model. Use 768 for Stable Diffusion v2.
+            upcast_attention (`bool`, *optional*, defaults to `None`):
+                Whether the attention computation should always be upcasted.
+            kwargs (remaining dictionary of keyword arguments, *optional*):
+                Can be used to overwrite load and saveable variables (for example the pipeline components of the
+                specific pipeline class). The overwritten components are directly passed to the pipelines `__init__`
+                method. See example below for more information.
+
+        Examples:
+
+        ```py
+        from diffusers import StableDiffusionControlNetPipeline, ControlNetModel
+
+        url = "https://huggingface.co/lllyasviel/ControlNet-v1-1/blob/main/control_v11p_sd15_canny.pth"  # can also be a local path
+        model = ControlNetModel.from_single_file(url)
+
+        url = "https://huggingface.co/runwayml/stable-diffusion-v1-5/blob/main/v1-5-pruned.safetensors"  # can also be a local path
+        pipe = StableDiffusionControlNetPipeline.from_single_file(url, controlnet=controlnet)
+        ```
+        """
+        original_config_file = kwargs.pop("original_config_file", None)
+        config_file = kwargs.pop("config_file", None)
+        resume_download = kwargs.pop("resume_download", None)
+        force_download = kwargs.pop("force_download", False)
+        proxies = kwargs.pop("proxies", None)
+        token = kwargs.pop("token", None)
+        cache_dir = kwargs.pop("cache_dir", None)
+        local_files_only = kwargs.pop("local_files_only", None)
+        revision = kwargs.pop("revision", None)
+        torch_dtype = kwargs.pop("torch_dtype", None)
+
+        class_name = cls.__name__
+        if (config_file is not None) and (original_config_file is not None):
+            raise ValueError(
+                "You cannot pass both `config_file` and `original_config_file` to `from_single_file`. Please use only one of these arguments."
+            )
+
+        original_config_file = config_file or original_config_file
+        original_config, checkpoint = fetch_ldm_config_and_checkpoint(
+            pretrained_model_link_or_path=pretrained_model_link_or_path,
+            class_name=class_name,
+            original_config_file=original_config_file,
+            resume_download=resume_download,
+            force_download=force_download,
+            proxies=proxies,
+            token=token,
+            revision=revision,
+            local_files_only=local_files_only,
+            cache_dir=cache_dir,
+        )
+
+        upcast_attention = kwargs.pop("upcast_attention", False)
+        image_size = kwargs.pop("image_size", None)
+
+        component = create_diffusers_controlnet_model_from_ldm(
+            class_name,
+            original_config,
+            checkpoint,
+            upcast_attention=upcast_attention,
+            image_size=image_size,
+            torch_dtype=torch_dtype,
+        )
+        controlnet = component["controlnet"]
+        if torch_dtype is not None:
+            controlnet = controlnet.to(torch_dtype)
+
+        return controlnet

diffusers/loaders/ip_adapter.py

@@ -0,0 +1,339 @@
+# Copyright 2024 The HuggingFace Team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+from pathlib import Path
+from typing import Dict, List, Optional, Union
+
+import torch
+import torch.nn.functional as F
+from huggingface_hub.utils import validate_hf_hub_args
+from safetensors import safe_open
+
+from ..models.modeling_utils import _LOW_CPU_MEM_USAGE_DEFAULT, load_state_dict
+from ..utils import (
+    USE_PEFT_BACKEND,
+    _get_model_file,
+    is_accelerate_available,
+    is_torch_version,
+    is_transformers_available,
+    logging,
+)
+from .unet_loader_utils import _maybe_expand_lora_scales
+
+
+if is_transformers_available():
+    from transformers import (
+        CLIPImageProcessor,
+        CLIPVisionModelWithProjection,
+    )
+
+    from ..models.attention_processor import (
+        AttnProcessor,
+        AttnProcessor2_0,
+        IPAdapterAttnProcessor,
+        IPAdapterAttnProcessor2_0,
+    )
+
+logger = logging.get_logger(__name__)
+
+
+class IPAdapterMixin:
+    """Mixin for handling IP Adapters."""
+
+    @validate_hf_hub_args
+    def load_ip_adapter(
+        self,
+        pretrained_model_name_or_path_or_dict: Union[str, List[str], Dict[str, torch.Tensor]],
+        subfolder: Union[str, List[str]],
+        weight_name: Union[str, List[str]],
+        image_encoder_folder: Optional[str] = "image_encoder",
+        **kwargs,
+    ):
+        """
+        Parameters:
+            pretrained_model_name_or_path_or_dict (`str` or `List[str]` or `os.PathLike` or `List[os.PathLike]` or `dict` or `List[dict]`):
+                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`].
+                    - A [torch state
+                      dict](https://pytorch.org/tutorials/beginner/saving_loading_models.html#what-is-a-state-dict).
+            subfolder (`str` or `List[str]`):
+                The subfolder location of a model file within a larger model repository on the Hub or locally. If a
+                list is passed, it should have the same length as `weight_name`.
+            weight_name (`str` or `List[str]`):
+                The name of the weight file to load. If a list is passed, it should have the same length as
+                `weight_name`.
+            image_encoder_folder (`str`, *optional*, defaults to `image_encoder`):
+                The subfolder location of the image encoder within a larger model repository on the Hub or locally.
+                Pass `None` to not load the image encoder. If the image encoder is located in a folder inside
+                `subfolder`, you only need to pass the name of the folder that contains image encoder weights, e.g.
+                `image_encoder_folder="image_encoder"`. If the image encoder is located in a folder other than
+                `subfolder`, you should pass the path to the folder that contains image encoder weights, for example,
+                `image_encoder_folder="different_subfolder/image_encoder"`.
+            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.
+            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:
+                Deprecated and ignored. All downloads are now resumed by default when possible. Will be removed in v1
+                of Diffusers.
+            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.
+            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.
+            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.
+        """
+
+        # handle the list inputs for multiple IP Adapters
+        if not isinstance(weight_name, list):
+            weight_name = [weight_name]
+
+        if not isinstance(pretrained_model_name_or_path_or_dict, list):
+            pretrained_model_name_or_path_or_dict = [pretrained_model_name_or_path_or_dict]
+        if len(pretrained_model_name_or_path_or_dict) == 1:
+            pretrained_model_name_or_path_or_dict = pretrained_model_name_or_path_or_dict * len(weight_name)
+
+        if not isinstance(subfolder, list):
+            subfolder = [subfolder]
+        if len(subfolder) == 1:
+            subfolder = subfolder * len(weight_name)
+
+        if len(weight_name) != len(pretrained_model_name_or_path_or_dict):
+            raise ValueError("`weight_name` and `pretrained_model_name_or_path_or_dict` must have the same length.")
+
+        if len(weight_name) != len(subfolder):
+            raise ValueError("`weight_name` and `subfolder` must have the same length.")
+
+        # Load the main state dict first.
+        cache_dir = kwargs.pop("cache_dir", None)
+        force_download = kwargs.pop("force_download", False)
+        resume_download = kwargs.pop("resume_download", None)
+        proxies = kwargs.pop("proxies", None)
+        local_files_only = kwargs.pop("local_files_only", None)
+        token = kwargs.pop("token", None)
+        revision = kwargs.pop("revision", None)
+        low_cpu_mem_usage = kwargs.pop("low_cpu_mem_usage", _LOW_CPU_MEM_USAGE_DEFAULT)
+
+        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 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`."
+            )
+
+        user_agent = {
+            "file_type": "attn_procs_weights",
+            "framework": "pytorch",
+        }
+        state_dicts = []
+        for pretrained_model_name_or_path_or_dict, weight_name, subfolder in zip(
+            pretrained_model_name_or_path_or_dict, weight_name, subfolder
+        ):
+            if not isinstance(pretrained_model_name_or_path_or_dict, dict):
+                model_file = _get_model_file(
+                    pretrained_model_name_or_path_or_dict,
+                    weights_name=weight_name,
+                    cache_dir=cache_dir,
+                    force_download=force_download,
+                    resume_download=resume_download,
+                    proxies=proxies,
+                    local_files_only=local_files_only,
+                    token=token,
+                    revision=revision,
+                    subfolder=subfolder,
+                    user_agent=user_agent,
+                )
+                if weight_name.endswith(".safetensors"):
+                    state_dict = {"image_proj": {}, "ip_adapter": {}}
+                    with safe_open(model_file, framework="pt", device="cpu") as f:
+                        for key in f.keys():
+                            if key.startswith("image_proj."):
+                                state_dict["image_proj"][key.replace("image_proj.", "")] = f.get_tensor(key)
+                            elif key.startswith("ip_adapter."):
+                                state_dict["ip_adapter"][key.replace("ip_adapter.", "")] = f.get_tensor(key)
+                else:
+                    state_dict = load_state_dict(model_file)
+            else:
+                state_dict = pretrained_model_name_or_path_or_dict
+
+            keys = list(state_dict.keys())
+            if keys != ["image_proj", "ip_adapter"]:
+                raise ValueError("Required keys are (`image_proj` and `ip_adapter`) missing from the state dict.")
+
+            state_dicts.append(state_dict)
+
+            # load CLIP image encoder here if it has not been registered to the pipeline yet
+            if hasattr(self, "image_encoder") and getattr(self, "image_encoder", None) is None:
+                if image_encoder_folder is not None:
+                    if not isinstance(pretrained_model_name_or_path_or_dict, dict):
+                        logger.info(f"loading image_encoder from {pretrained_model_name_or_path_or_dict}")
+                        if image_encoder_folder.count("/") == 0:
+                            image_encoder_subfolder = Path(subfolder, image_encoder_folder).as_posix()
+                        else:
+                            image_encoder_subfolder = Path(image_encoder_folder).as_posix()
+
+                        image_encoder = CLIPVisionModelWithProjection.from_pretrained(
+                            pretrained_model_name_or_path_or_dict,
+                            subfolder=image_encoder_subfolder,
+                            low_cpu_mem_usage=low_cpu_mem_usage,
+                        ).to(self.device, dtype=self.dtype)
+                        self.register_modules(image_encoder=image_encoder)
+                    else:
+                        raise ValueError(
+                            "`image_encoder` cannot be loaded because `pretrained_model_name_or_path_or_dict` is a state dict."
+                        )
+                else:
+                    logger.warning(
+                        "image_encoder is not loaded since `image_encoder_folder=None` passed. You will not be able to use `ip_adapter_image` when calling the pipeline with IP-Adapter."
+                        "Use `ip_adapter_image_embeds` to pass pre-generated image embedding instead."
+                    )
+
+            # create feature extractor if it has not been registered to the pipeline yet
+            if hasattr(self, "feature_extractor") and getattr(self, "feature_extractor", None) is None:
+                feature_extractor = CLIPImageProcessor()
+                self.register_modules(feature_extractor=feature_extractor)
+
+        # load ip-adapter into unet
+        unet = getattr(self, self.unet_name) if not hasattr(self, "unet") else self.unet
+        unet._load_ip_adapter_weights(state_dicts, low_cpu_mem_usage=low_cpu_mem_usage)
+
+        extra_loras = unet._load_ip_adapter_loras(state_dicts)
+        if extra_loras != {}:
+            if not USE_PEFT_BACKEND:
+                logger.warning("PEFT backend is required to load these weights.")
+            else:
+                # apply the IP Adapter Face ID LoRA weights
+                peft_config = getattr(unet, "peft_config", {})
+                for k, lora in extra_loras.items():
+                    if f"faceid_{k}" not in peft_config:
+                        self.load_lora_weights(lora, adapter_name=f"faceid_{k}")
+                        self.set_adapters([f"faceid_{k}"], adapter_weights=[1.0])
+
+    def set_ip_adapter_scale(self, scale):
+        """
+        Set IP-Adapter scales per-transformer block. Input `scale` could be a single config or a list of configs for
+        granular control over each IP-Adapter behavior. A config can be a float or a dictionary.
+
+        Example:
+
+        ```py
+        # To use original IP-Adapter
+        scale = 1.0
+        pipeline.set_ip_adapter_scale(scale)
+
+        # To use style block only
+        scale = {
+            "up": {"block_0": [0.0, 1.0, 0.0]},
+        }
+        pipeline.set_ip_adapter_scale(scale)
+
+        # To use style+layout blocks
+        scale = {
+            "down": {"block_2": [0.0, 1.0]},
+            "up": {"block_0": [0.0, 1.0, 0.0]},
+        }
+        pipeline.set_ip_adapter_scale(scale)
+
+        # To use style and layout from 2 reference images
+        scales = [{"down": {"block_2": [0.0, 1.0]}}, {"up": {"block_0": [0.0, 1.0, 0.0]}}]
+        pipeline.set_ip_adapter_scale(scales)
+        ```
+        """
+        unet = getattr(self, self.unet_name) if not hasattr(self, "unet") else self.unet
+        if not isinstance(scale, list):
+            scale = [scale]
+        scale_configs = _maybe_expand_lora_scales(unet, scale, default_scale=0.0)
+
+        for attn_name, attn_processor in unet.attn_processors.items():
+            if isinstance(attn_processor, (IPAdapterAttnProcessor, IPAdapterAttnProcessor2_0)):
+                if len(scale_configs) != len(attn_processor.scale):
+                    raise ValueError(
+                        f"Cannot assign {len(scale_configs)} scale_configs to "
+                        f"{len(attn_processor.scale)} IP-Adapter."
+                    )
+                elif len(scale_configs) == 1:
+                    scale_configs = scale_configs * len(attn_processor.scale)
+                for i, scale_config in enumerate(scale_configs):
+                    if isinstance(scale_config, dict):
+                        for k, s in scale_config.items():
+                            if attn_name.startswith(k):
+                                attn_processor.scale[i] = s
+                    else:
+                        attn_processor.scale[i] = scale_config
+
+    def unload_ip_adapter(self):
+        """
+        Unloads the IP Adapter weights
+
+        Examples:
+
+        ```python
+        >>> # Assuming `pipeline` is already loaded with the IP Adapter weights.
+        >>> pipeline.unload_ip_adapter()
+        >>> ...
+        ```
+        """
+        # remove CLIP image encoder
+        if hasattr(self, "image_encoder") and getattr(self, "image_encoder", None) is not None:
+            self.image_encoder = None
+            self.register_to_config(image_encoder=[None, None])
+
+        # remove feature extractor only when safety_checker is None as safety_checker uses
+        # the feature_extractor later
+        if not hasattr(self, "safety_checker"):
+            if hasattr(self, "feature_extractor") and getattr(self, "feature_extractor", None) is not None:
+                self.feature_extractor = None
+                self.register_to_config(feature_extractor=[None, None])
+
+        # remove hidden encoder
+        self.unet.encoder_hid_proj = None
+        self.config.encoder_hid_dim_type = None
+
+        # restore original Unet attention processors layers
+        attn_procs = {}
+        for name, value in self.unet.attn_processors.items():
+            attn_processor_class = (
+                AttnProcessor2_0() if hasattr(F, "scaled_dot_product_attention") else AttnProcessor()
+            )
+            attn_procs[name] = (
+                attn_processor_class
+                if isinstance(value, (IPAdapterAttnProcessor, IPAdapterAttnProcessor2_0))
+                else value.__class__()
+            )
+        self.unet.set_attn_processor(attn_procs)

diffusers/loaders/lora.py

@@ -0,0 +1,1458 @@
+# Copyright 2024 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 copy
+import inspect
+import os
+from pathlib import Path
+from typing import Callable, Dict, List, Optional, Union
+
+import safetensors
+import torch
+from huggingface_hub import model_info
+from huggingface_hub.constants import HF_HUB_OFFLINE
+from huggingface_hub.utils import validate_hf_hub_args
+from packaging import version
+from torch import nn
+
+from .. import __version__
+from ..models.modeling_utils import _LOW_CPU_MEM_USAGE_DEFAULT, load_state_dict
+from ..utils import (
+    USE_PEFT_BACKEND,
+    _get_model_file,
+    convert_state_dict_to_diffusers,
+    convert_state_dict_to_peft,
+    convert_unet_state_dict_to_peft,
+    delete_adapter_layers,
+    get_adapter_name,
+    get_peft_kwargs,
+    is_accelerate_available,
+    is_peft_version,
+    is_transformers_available,
+    logging,
+    recurse_remove_peft_layers,
+    scale_lora_layers,
+    set_adapter_layers,
+    set_weights_and_activate_adapters,
+)
+from .lora_conversion_utils import _convert_kohya_lora_to_diffusers, _maybe_map_sgm_blocks_to_diffusers
+
+
+if is_transformers_available():
+    from transformers import PreTrainedModel
+
+    from ..models.lora import text_encoder_attn_modules, text_encoder_mlp_modules
+
+if is_accelerate_available():
+    from accelerate.hooks import AlignDevicesHook, CpuOffload, remove_hook_from_module
+
+logger = logging.get_logger(__name__)
+
+TEXT_ENCODER_NAME = "text_encoder"
+UNET_NAME = "unet"
+TRANSFORMER_NAME = "transformer"
+
+LORA_WEIGHT_NAME = "pytorch_lora_weights.bin"
+LORA_WEIGHT_NAME_SAFE = "pytorch_lora_weights.safetensors"
+
+LORA_DEPRECATION_MESSAGE = "You are using an old version of LoRA backend. This will be deprecated in the next releases in favor of PEFT make sure to install the latest PEFT and transformers packages in the future."
+
+
+class LoraLoaderMixin:
+    r"""
+    Load LoRA layers into [`UNet2DConditionModel`] and
+    [`CLIPTextModel`](https://huggingface.co/docs/transformers/model_doc/clip#transformers.CLIPTextModel).
+    """
+
+    text_encoder_name = TEXT_ENCODER_NAME
+    unet_name = UNET_NAME
+    transformer_name = TRANSFORMER_NAME
+    num_fused_loras = 0
+
+    def load_lora_weights(
+        self, pretrained_model_name_or_path_or_dict: Union[str, Dict[str, torch.Tensor]], adapter_name=None, **kwargs
+    ):
+        """
+        Load LoRA weights specified in `pretrained_model_name_or_path_or_dict` into `self.unet` and
+        `self.text_encoder`.
+
+        All kwargs are forwarded to `self.lora_state_dict`.
+
+        See [`~loaders.LoraLoaderMixin.lora_state_dict`] for more details on how the state dict is loaded.
+
+        See [`~loaders.LoraLoaderMixin.load_lora_into_unet`] for more details on how the state dict is loaded into
+        `self.unet`.
+
+        See [`~loaders.LoraLoaderMixin.load_lora_into_text_encoder`] for more details on how the state dict is loaded
+        into `self.text_encoder`.
+
+        Parameters:
+            pretrained_model_name_or_path_or_dict (`str` or `os.PathLike` or `dict`):
+                See [`~loaders.LoraLoaderMixin.lora_state_dict`].
+            kwargs (`dict`, *optional*):
+                See [`~loaders.LoraLoaderMixin.lora_state_dict`].
+            adapter_name (`str`, *optional*):
+                Adapter name to be used for referencing the loaded adapter model. If not specified, it will use
+                `default_{i}` where i is the total number of adapters being loaded.
+        """
+        if not USE_PEFT_BACKEND:
+            raise ValueError("PEFT backend is required for this method.")
+
+        # if a dict is passed, copy it instead of modifying it inplace
+        if isinstance(pretrained_model_name_or_path_or_dict, dict):
+            pretrained_model_name_or_path_or_dict = pretrained_model_name_or_path_or_dict.copy()
+
+        # First, ensure that the checkpoint is a compatible one and can be successfully loaded.
+        state_dict, network_alphas = self.lora_state_dict(pretrained_model_name_or_path_or_dict, **kwargs)
+
+        is_correct_format = all("lora" in key or "dora_scale" in key for key in state_dict.keys())
+        if not is_correct_format:
+            raise ValueError("Invalid LoRA checkpoint.")
+
+        low_cpu_mem_usage = kwargs.pop("low_cpu_mem_usage", _LOW_CPU_MEM_USAGE_DEFAULT)
+
+        self.load_lora_into_unet(
+            state_dict,
+            network_alphas=network_alphas,
+            unet=getattr(self, self.unet_name) if not hasattr(self, "unet") else self.unet,
+            low_cpu_mem_usage=low_cpu_mem_usage,
+            adapter_name=adapter_name,
+            _pipeline=self,
+        )
+        self.load_lora_into_text_encoder(
+            state_dict,
+            network_alphas=network_alphas,
+            text_encoder=getattr(self, self.text_encoder_name)
+            if not hasattr(self, "text_encoder")
+            else self.text_encoder,
+            lora_scale=self.lora_scale,
+            low_cpu_mem_usage=low_cpu_mem_usage,
+            adapter_name=adapter_name,
+            _pipeline=self,
+        )
+
+    @classmethod
+    @validate_hf_hub_args
+    def lora_state_dict(
+        cls,
+        pretrained_model_name_or_path_or_dict: Union[str, Dict[str, torch.Tensor]],
+        **kwargs,
+    ):
+        r"""
+        Return state dict for lora weights and the network alphas.
+
+        <Tip warning={true}>
+
+        We support loading A1111 formatted LoRA checkpoints in a limited capacity.
+
+        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* (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`].
+                    - 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 where a downloaded pretrained model configuration is cached if the standard cache
+                is not 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:
+                Deprecated and ignored. All downloads are now resumed by default when possible. Will be removed in v1
+                of Diffusers.
+            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.
+            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.
+            subfolder (`str`, *optional*, defaults to `""`):
+                The subfolder location of a model file within a larger model repository on the Hub or locally.
+            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.
+            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.
+
+        """
+        # 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", None)
+        force_download = kwargs.pop("force_download", False)
+        resume_download = kwargs.pop("resume_download", None)
+        proxies = kwargs.pop("proxies", None)
+        local_files_only = kwargs.pop("local_files_only", None)
+        token = kwargs.pop("token", None)
+        revision = kwargs.pop("revision", None)
+        subfolder = kwargs.pop("subfolder", None)
+        weight_name = kwargs.pop("weight_name", None)
+        unet_config = kwargs.pop("unet_config", None)
+        use_safetensors = kwargs.pop("use_safetensors", None)
+
+        allow_pickle = False
+        if use_safetensors is None:
+            use_safetensors = True
+            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:
+                    # Here we're relaxing the loading check to enable more Inference API
+                    # friendliness where sometimes, it's not at all possible to automatically
+                    # determine `weight_name`.
+                    if weight_name is None:
+                        weight_name = cls._best_guess_weight_name(
+                            pretrained_model_name_or_path_or_dict,
+                            file_extension=".safetensors",
+                            local_files_only=local_files_only,
+                        )
+                    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,
+                        token=token,
+                        revision=revision,
+                        subfolder=subfolder,
+                        user_agent=user_agent,
+                    )
+                    state_dict = safetensors.torch.load_file(model_file, device="cpu")
+                except (IOError, safetensors.SafetensorError) as e:
+                    if not allow_pickle:
+                        raise e
+                    # try loading non-safetensors weights
+                    model_file = None
+                    pass
+
+            if model_file is None:
+                if weight_name is None:
+                    weight_name = cls._best_guess_weight_name(
+                        pretrained_model_name_or_path_or_dict, file_extension=".bin", local_files_only=local_files_only
+                    )
+                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,
+                    token=token,
+                    revision=revision,
+                    subfolder=subfolder,
+                    user_agent=user_agent,
+                )
+                state_dict = load_state_dict(model_file)
+        else:
+            state_dict = pretrained_model_name_or_path_or_dict
+
+        network_alphas = None
+        # TODO: replace it with a method from `state_dict_utils`
+        if all(
+            (
+                k.startswith("lora_te_")
+                or k.startswith("lora_unet_")
+                or k.startswith("lora_te1_")
+                or k.startswith("lora_te2_")
+            )
+            for k in state_dict.keys()
+        ):
+            # Map SDXL blocks correctly.
+            if unet_config is not None:
+                # use unet config to remap block numbers
+                state_dict = _maybe_map_sgm_blocks_to_diffusers(state_dict, unet_config)
+            state_dict, network_alphas = _convert_kohya_lora_to_diffusers(state_dict)
+
+        return state_dict, network_alphas
+
+    @classmethod
+    def _best_guess_weight_name(
+        cls, pretrained_model_name_or_path_or_dict, file_extension=".safetensors", local_files_only=False
+    ):
+        if local_files_only or HF_HUB_OFFLINE:
+            raise ValueError("When using the offline mode, you must specify a `weight_name`.")
+
+        targeted_files = []
+
+        if os.path.isfile(pretrained_model_name_or_path_or_dict):
+            return
+        elif os.path.isdir(pretrained_model_name_or_path_or_dict):
+            targeted_files = [
+                f for f in os.listdir(pretrained_model_name_or_path_or_dict) if f.endswith(file_extension)
+            ]
+        else:
+            files_in_repo = model_info(pretrained_model_name_or_path_or_dict).siblings
+            targeted_files = [f.rfilename for f in files_in_repo if f.rfilename.endswith(file_extension)]
+        if len(targeted_files) == 0:
+            return
+
+        # "scheduler" does not correspond to a LoRA checkpoint.
+        # "optimizer" does not correspond to a LoRA checkpoint
+        # only top-level checkpoints are considered and not the other ones, hence "checkpoint".
+        unallowed_substrings = {"scheduler", "optimizer", "checkpoint"}
+        targeted_files = list(
+            filter(lambda x: all(substring not in x for substring in unallowed_substrings), targeted_files)
+        )
+
+        if any(f.endswith(LORA_WEIGHT_NAME) for f in targeted_files):
+            targeted_files = list(filter(lambda x: x.endswith(LORA_WEIGHT_NAME), targeted_files))
+        elif any(f.endswith(LORA_WEIGHT_NAME_SAFE) for f in targeted_files):
+            targeted_files = list(filter(lambda x: x.endswith(LORA_WEIGHT_NAME_SAFE), targeted_files))
+
+        if len(targeted_files) > 1:
+            raise ValueError(
+                f"Provided path contains more than one weights file in the {file_extension} format. Either specify `weight_name` in `load_lora_weights` or make sure there's only one  `.safetensors` or `.bin` file in  {pretrained_model_name_or_path_or_dict}."
+            )
+        weight_name = targeted_files[0]
+        return weight_name
+
+    @classmethod
+    def _optionally_disable_offloading(cls, _pipeline):
+        """
+        Optionally removes offloading in case the pipeline has been already sequentially offloaded to CPU.
+
+        Args:
+            _pipeline (`DiffusionPipeline`):
+                The pipeline to disable offloading for.
+
+        Returns:
+            tuple:
+                A tuple indicating if `is_model_cpu_offload` or `is_sequential_cpu_offload` is True.
+        """
+        is_model_cpu_offload = False
+        is_sequential_cpu_offload = False
+
+        if _pipeline is not None:
+            for _, component in _pipeline.components.items():
+                if isinstance(component, nn.Module) and hasattr(component, "_hf_hook"):
+                    if not is_model_cpu_offload:
+                        is_model_cpu_offload = isinstance(component._hf_hook, CpuOffload)
+                    if not is_sequential_cpu_offload:
+                        is_sequential_cpu_offload = (
+                            isinstance(component._hf_hook, AlignDevicesHook)
+                            or hasattr(component._hf_hook, "hooks")
+                            and isinstance(component._hf_hook.hooks[0], AlignDevicesHook)
+                        )
+
+                    logger.info(
+                        "Accelerate hooks detected. Since you have called `load_lora_weights()`, the previous hooks will be first removed. Then the LoRA parameters will be loaded and the hooks will be applied again."
+                    )
+                    remove_hook_from_module(component, recurse=is_sequential_cpu_offload)
+
+        return (is_model_cpu_offload, is_sequential_cpu_offload)
+
+    @classmethod
+    def load_lora_into_unet(
+        cls, state_dict, network_alphas, unet, low_cpu_mem_usage=None, adapter_name=None, _pipeline=None
+    ):
+        """
+        This will load the LoRA layers specified in `state_dict` into `unet`.
+
+        Parameters:
+            state_dict (`dict`):
+                A standard state dict containing the lora layer parameters. The keys can either be indexed directly
+                into the unet or prefixed with an additional `unet` which can be used to distinguish between text
+                encoder lora layers.
+            network_alphas (`Dict[str, float]`):
+                See `LoRALinearLayer` for more details.
+            unet (`UNet2DConditionModel`):
+                The UNet model to load the LoRA layers into.
+            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.
+            adapter_name (`str`, *optional*):
+                Adapter name to be used for referencing the loaded adapter model. If not specified, it will use
+                `default_{i}` where i is the total number of adapters being loaded.
+        """
+        if not USE_PEFT_BACKEND:
+            raise ValueError("PEFT backend is required for this method.")
+
+        from peft import LoraConfig, inject_adapter_in_model, set_peft_model_state_dict
+
+        low_cpu_mem_usage = low_cpu_mem_usage if low_cpu_mem_usage is not None else _LOW_CPU_MEM_USAGE_DEFAULT
+        # If the serialization format is new (introduced in https://github.com/huggingface/diffusers/pull/2918),
+        # then the `state_dict` keys should have `cls.unet_name` and/or `cls.text_encoder_name` as
+        # their prefixes.
+        keys = list(state_dict.keys())
+
+        if all(key.startswith(cls.unet_name) or key.startswith(cls.text_encoder_name) for key in keys):
+            # Load the layers corresponding to UNet.
+            logger.info(f"Loading {cls.unet_name}.")
+
+            unet_keys = [k for k in keys if k.startswith(cls.unet_name)]
+            state_dict = {k.replace(f"{cls.unet_name}.", ""): v for k, v in state_dict.items() if k in unet_keys}
+
+            if network_alphas is not None:
+                alpha_keys = [k for k in network_alphas.keys() if k.startswith(cls.unet_name)]
+                network_alphas = {
+                    k.replace(f"{cls.unet_name}.", ""): v for k, v in network_alphas.items() if k in alpha_keys
+                }
+
+        else:
+            # 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.
+            if not USE_PEFT_BACKEND:
+                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()}`."
+                logger.warning(warn_message)
+
+        if len(state_dict.keys()) > 0:
+            if adapter_name in getattr(unet, "peft_config", {}):
+                raise ValueError(
+                    f"Adapter name {adapter_name} already in use in the Unet - please select a new adapter name."
+                )
+
+            state_dict = convert_unet_state_dict_to_peft(state_dict)
+
+            if network_alphas is not None:
+                # The alphas state dict have the same structure as Unet, thus we convert it to peft format using
+                # `convert_unet_state_dict_to_peft` method.
+                network_alphas = convert_unet_state_dict_to_peft(network_alphas)
+
+            rank = {}
+            for key, val in state_dict.items():
+                if "lora_B" in key:
+                    rank[key] = val.shape[1]
+
+            lora_config_kwargs = get_peft_kwargs(rank, network_alphas, state_dict, is_unet=True)
+            if "use_dora" in lora_config_kwargs:
+                if lora_config_kwargs["use_dora"]:
+                    if is_peft_version("<", "0.9.0"):
+                        raise ValueError(
+                            "You need `peft` 0.9.0 at least to use DoRA-enabled LoRAs. Please upgrade your installation of `peft`."
+                        )
+                else:
+                    if is_peft_version("<", "0.9.0"):
+                        lora_config_kwargs.pop("use_dora")
+            lora_config = LoraConfig(**lora_config_kwargs)
+
+            # adapter_name
+            if adapter_name is None:
+                adapter_name = get_adapter_name(unet)
+
+            # In case the pipeline has been already offloaded to CPU - temporarily remove the hooks
+            # otherwise loading LoRA weights will lead to an error
+            is_model_cpu_offload, is_sequential_cpu_offload = cls._optionally_disable_offloading(_pipeline)
+
+            inject_adapter_in_model(lora_config, unet, adapter_name=adapter_name)
+            incompatible_keys = set_peft_model_state_dict(unet, state_dict, adapter_name)
+
+            if incompatible_keys is not None:
+                # check only for unexpected keys
+                unexpected_keys = getattr(incompatible_keys, "unexpected_keys", None)
+                if unexpected_keys:
+                    logger.warning(
+                        f"Loading adapter weights from state_dict led to unexpected keys not found in the model: "
+                        f" {unexpected_keys}. "
+                    )
+
+            # Offload back.
+            if is_model_cpu_offload:
+                _pipeline.enable_model_cpu_offload()
+            elif is_sequential_cpu_offload:
+                _pipeline.enable_sequential_cpu_offload()
+            # Unsafe code />
+
+        unet.load_attn_procs(
+            state_dict, network_alphas=network_alphas, low_cpu_mem_usage=low_cpu_mem_usage, _pipeline=_pipeline
+        )
+
+    @classmethod
+    def load_lora_into_text_encoder(
+        cls,
+        state_dict,
+        network_alphas,
+        text_encoder,
+        prefix=None,
+        lora_scale=1.0,
+        low_cpu_mem_usage=None,
+        adapter_name=None,
+        _pipeline=None,
+    ):
+        """
+        This will load the LoRA layers specified in `state_dict` into `text_encoder`
+
+        Parameters:
+            state_dict (`dict`):
+                A standard state dict containing the lora layer parameters. The key should be prefixed with an
+                additional `text_encoder` to distinguish between unet lora layers.
+            network_alphas (`Dict[str, float]`):
+                See `LoRALinearLayer` for more details.
+            text_encoder (`CLIPTextModel`):
+                The text encoder model to load the LoRA layers into.
+            prefix (`str`):
+                Expected prefix of the `text_encoder` in the `state_dict`.
+            lora_scale (`float`):
+                How much to scale the output of the lora linear layer before it is added with the output of the regular
+                lora layer.
+            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.
+            adapter_name (`str`, *optional*):
+                Adapter name to be used for referencing the loaded adapter model. If not specified, it will use
+                `default_{i}` where i is the total number of adapters being loaded.
+        """
+        if not USE_PEFT_BACKEND:
+            raise ValueError("PEFT backend is required for this method.")
+
+        from peft import LoraConfig
+
+        low_cpu_mem_usage = low_cpu_mem_usage if low_cpu_mem_usage is not None else _LOW_CPU_MEM_USAGE_DEFAULT
+
+        # 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())
+        prefix = cls.text_encoder_name if prefix is None else prefix
+
+        # Safe prefix to check with.
+        if any(cls.text_encoder_name in key for key in keys):
+            # Load the layers corresponding to text encoder and make necessary adjustments.
+            text_encoder_keys = [k for k in keys if k.startswith(prefix) and k.split(".")[0] == prefix]
+            text_encoder_lora_state_dict = {
+                k.replace(f"{prefix}.", ""): v for k, v in state_dict.items() if k in text_encoder_keys
+            }
+
+            if len(text_encoder_lora_state_dict) > 0:
+                logger.info(f"Loading {prefix}.")
+                rank = {}
+                text_encoder_lora_state_dict = convert_state_dict_to_diffusers(text_encoder_lora_state_dict)
+
+                # convert state dict
+                text_encoder_lora_state_dict = convert_state_dict_to_peft(text_encoder_lora_state_dict)
+
+                for name, _ in text_encoder_attn_modules(text_encoder):
+                    rank_key = f"{name}.out_proj.lora_B.weight"
+                    rank[rank_key] = text_encoder_lora_state_dict[rank_key].shape[1]
+
+                patch_mlp = any(".mlp." in key for key in text_encoder_lora_state_dict.keys())
+                if patch_mlp:
+                    for name, _ in text_encoder_mlp_modules(text_encoder):
+                        rank_key_fc1 = f"{name}.fc1.lora_B.weight"
+                        rank_key_fc2 = f"{name}.fc2.lora_B.weight"
+
+                        rank[rank_key_fc1] = text_encoder_lora_state_dict[rank_key_fc1].shape[1]
+                        rank[rank_key_fc2] = text_encoder_lora_state_dict[rank_key_fc2].shape[1]
+
+                if network_alphas is not None:
+                    alpha_keys = [
+                        k for k in network_alphas.keys() if k.startswith(prefix) and k.split(".")[0] == prefix
+                    ]
+                    network_alphas = {
+                        k.replace(f"{prefix}.", ""): v for k, v in network_alphas.items() if k in alpha_keys
+                    }
+
+                lora_config_kwargs = get_peft_kwargs(rank, network_alphas, text_encoder_lora_state_dict, is_unet=False)
+                if "use_dora" in lora_config_kwargs:
+                    if lora_config_kwargs["use_dora"]:
+                        if is_peft_version("<", "0.9.0"):
+                            raise ValueError(
+                                "You need `peft` 0.9.0 at least to use DoRA-enabled LoRAs. Please upgrade your installation of `peft`."
+                            )
+                    else:
+                        if is_peft_version("<", "0.9.0"):
+                            lora_config_kwargs.pop("use_dora")
+                lora_config = LoraConfig(**lora_config_kwargs)
+
+                # adapter_name
+                if adapter_name is None:
+                    adapter_name = get_adapter_name(text_encoder)
+
+                is_model_cpu_offload, is_sequential_cpu_offload = cls._optionally_disable_offloading(_pipeline)
+
+                # inject LoRA layers and load the state dict
+                # in transformers we automatically check whether the adapter name is already in use or not
+                text_encoder.load_adapter(
+                    adapter_name=adapter_name,
+                    adapter_state_dict=text_encoder_lora_state_dict,
+                    peft_config=lora_config,
+                )
+
+                # scale LoRA layers with `lora_scale`
+                scale_lora_layers(text_encoder, weight=lora_scale)
+
+                text_encoder.to(device=text_encoder.device, dtype=text_encoder.dtype)
+
+                # Offload back.
+                if is_model_cpu_offload:
+                    _pipeline.enable_model_cpu_offload()
+                elif is_sequential_cpu_offload:
+                    _pipeline.enable_sequential_cpu_offload()
+                # Unsafe code />
+
+    @classmethod
+    def load_lora_into_transformer(
+        cls, state_dict, network_alphas, transformer, low_cpu_mem_usage=None, adapter_name=None, _pipeline=None
+    ):
+        """
+        This will load the LoRA layers specified in `state_dict` into `transformer`.
+
+        Parameters:
+            state_dict (`dict`):
+                A standard state dict containing the lora layer parameters. The keys can either be indexed directly
+                into the unet or prefixed with an additional `unet` which can be used to distinguish between text
+                encoder lora layers.
+            network_alphas (`Dict[str, float]`):
+                See `LoRALinearLayer` for more details.
+            unet (`UNet2DConditionModel`):
+                The UNet model to load the LoRA layers into.
+            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.
+            adapter_name (`str`, *optional*):
+                Adapter name to be used for referencing the loaded adapter model. If not specified, it will use
+                `default_{i}` where i is the total number of adapters being loaded.
+        """
+        from peft import LoraConfig, inject_adapter_in_model, set_peft_model_state_dict
+
+        low_cpu_mem_usage = low_cpu_mem_usage if low_cpu_mem_usage is not None else _LOW_CPU_MEM_USAGE_DEFAULT
+
+        keys = list(state_dict.keys())
+
+        transformer_keys = [k for k in keys if k.startswith(cls.transformer_name)]
+        state_dict = {
+            k.replace(f"{cls.transformer_name}.", ""): v for k, v in state_dict.items() if k in transformer_keys
+        }
+
+        if network_alphas is not None:
+            alpha_keys = [k for k in network_alphas.keys() if k.startswith(cls.transformer_name)]
+            network_alphas = {
+                k.replace(f"{cls.transformer_name}.", ""): v for k, v in network_alphas.items() if k in alpha_keys
+            }
+
+        if len(state_dict.keys()) > 0:
+            if adapter_name in getattr(transformer, "peft_config", {}):
+                raise ValueError(
+                    f"Adapter name {adapter_name} already in use in the transformer - please select a new adapter name."
+                )
+
+            rank = {}
+            for key, val in state_dict.items():
+                if "lora_B" in key:
+                    rank[key] = val.shape[1]
+
+            lora_config_kwargs = get_peft_kwargs(rank, network_alphas, state_dict)
+            if "use_dora" in lora_config_kwargs:
+                if lora_config_kwargs["use_dora"] and is_peft_version("<", "0.9.0"):
+                    raise ValueError(
+                        "You need `peft` 0.9.0 at least to use DoRA-enabled LoRAs. Please upgrade your installation of `peft`."
+                    )
+                else:
+                    lora_config_kwargs.pop("use_dora")
+            lora_config = LoraConfig(**lora_config_kwargs)
+
+            # adapter_name
+            if adapter_name is None:
+                adapter_name = get_adapter_name(transformer)
+
+            # In case the pipeline has been already offloaded to CPU - temporarily remove the hooks
+            # otherwise loading LoRA weights will lead to an error
+            is_model_cpu_offload, is_sequential_cpu_offload = cls._optionally_disable_offloading(_pipeline)
+
+            inject_adapter_in_model(lora_config, transformer, adapter_name=adapter_name)
+            incompatible_keys = set_peft_model_state_dict(transformer, state_dict, adapter_name)
+
+            if incompatible_keys is not None:
+                # check only for unexpected keys
+                unexpected_keys = getattr(incompatible_keys, "unexpected_keys", None)
+                if unexpected_keys:
+                    logger.warning(
+                        f"Loading adapter weights from state_dict led to unexpected keys not found in the model: "
+                        f" {unexpected_keys}. "
+                    )
+
+            # Offload back.
+            if is_model_cpu_offload:
+                _pipeline.enable_model_cpu_offload()
+            elif is_sequential_cpu_offload:
+                _pipeline.enable_sequential_cpu_offload()
+            # Unsafe code />
+
+    @property
+    def lora_scale(self) -> float:
+        # property function that returns the lora scale which can be set at run time by the pipeline.
+        # if _lora_scale has not been set, return 1
+        return self._lora_scale if hasattr(self, "_lora_scale") else 1.0
+
+    def _remove_text_encoder_monkey_patch(self):
+        remove_method = recurse_remove_peft_layers
+        if hasattr(self, "text_encoder"):
+            remove_method(self.text_encoder)
+            # In case text encoder have no Lora attached
+            if getattr(self.text_encoder, "peft_config", None) is not None:
+                del self.text_encoder.peft_config
+                self.text_encoder._hf_peft_config_loaded = None
+
+        if hasattr(self, "text_encoder_2"):
+            remove_method(self.text_encoder_2)
+            if getattr(self.text_encoder_2, "peft_config", None) is not None:
+                del self.text_encoder_2.peft_config
+                self.text_encoder_2._hf_peft_config_loaded = None
+
+    @classmethod
+    def save_lora_weights(
+        cls,
+        save_directory: Union[str, os.PathLike],
+        unet_lora_layers: Dict[str, Union[torch.nn.Module, torch.Tensor]] = None,
+        text_encoder_lora_layers: Dict[str, torch.nn.Module] = None,
+        transformer_lora_layers: Dict[str, torch.nn.Module] = None,
+        is_main_process: bool = True,
+        weight_name: str = None,
+        save_function: Callable = None,
+        safe_serialization: bool = True,
+    ):
+        r"""
+        Save the LoRA parameters corresponding to the UNet and text encoder.
+
+        Arguments:
+            save_directory (`str` or `os.PathLike`):
+                Directory to save LoRA parameters to. Will be created if it doesn't exist.
+            unet_lora_layers (`Dict[str, torch.nn.Module]` or `Dict[str, torch.Tensor]`):
+                State dict of the LoRA layers corresponding to the `unet`.
+            text_encoder_lora_layers (`Dict[str, torch.nn.Module]` or `Dict[str, torch.Tensor]`):
+                State dict of the LoRA layers corresponding to the `text_encoder`. Must explicitly pass the text
+                encoder LoRA state dict because it comes from 🤗 Transformers.
+            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`.
+        """
+        state_dict = {}
+
+        def pack_weights(layers, prefix):
+            layers_weights = layers.state_dict() if isinstance(layers, torch.nn.Module) else layers
+            layers_state_dict = {f"{prefix}.{module_name}": param for module_name, param in layers_weights.items()}
+            return layers_state_dict
+
+        if not (unet_lora_layers or text_encoder_lora_layers or transformer_lora_layers):
+            raise ValueError(
+                "You must pass at least one of `unet_lora_layers`, `text_encoder_lora_layers`, or `transformer_lora_layers`."
+            )
+
+        if unet_lora_layers:
+            state_dict.update(pack_weights(unet_lora_layers, cls.unet_name))
+
+        if text_encoder_lora_layers:
+            state_dict.update(pack_weights(text_encoder_lora_layers, cls.text_encoder_name))
+
+        if transformer_lora_layers:
+            state_dict.update(pack_weights(transformer_lora_layers, "transformer"))
+
+        # Save the model
+        cls.write_lora_layers(
+            state_dict=state_dict,
+            save_directory=save_directory,
+            is_main_process=is_main_process,
+            weight_name=weight_name,
+            save_function=save_function,
+            safe_serialization=safe_serialization,
+        )
+
+    @staticmethod
+    def write_lora_layers(
+        state_dict: Dict[str, torch.Tensor],
+        save_directory: str,
+        is_main_process: bool,
+        weight_name: str,
+        save_function: Callable,
+        safe_serialization: bool,
+    ):
+        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)
+
+        if weight_name is None:
+            if safe_serialization:
+                weight_name = LORA_WEIGHT_NAME_SAFE
+            else:
+                weight_name = LORA_WEIGHT_NAME
+
+        save_path = Path(save_directory, weight_name).as_posix()
+        save_function(state_dict, save_path)
+        logger.info(f"Model weights saved in {save_path}")
+
+    def unload_lora_weights(self):
+        """
+        Unloads the LoRA parameters.
+
+        Examples:
+
+        ```python
+        >>> # Assuming `pipeline` is already loaded with the LoRA parameters.
+        >>> pipeline.unload_lora_weights()
+        >>> ...
+        ```
+        """
+        unet = getattr(self, self.unet_name) if not hasattr(self, "unet") else self.unet
+
+        if not USE_PEFT_BACKEND:
+            if version.parse(__version__) > version.parse("0.23"):
+                logger.warning(
+                    "You are using `unload_lora_weights` to disable and unload lora weights. If you want to iteratively enable and disable adapter weights,"
+                    "you can use `pipe.enable_lora()` or `pipe.disable_lora()`. After installing the latest version of PEFT."
+                )
+
+            for _, module in unet.named_modules():
+                if hasattr(module, "set_lora_layer"):
+                    module.set_lora_layer(None)
+        else:
+            recurse_remove_peft_layers(unet)
+            if hasattr(unet, "peft_config"):
+                del unet.peft_config
+
+        # Safe to call the following regardless of LoRA.
+        self._remove_text_encoder_monkey_patch()
+
+    def fuse_lora(
+        self,
+        fuse_unet: bool = True,
+        fuse_text_encoder: bool = True,
+        lora_scale: float = 1.0,
+        safe_fusing: bool = False,
+        adapter_names: Optional[List[str]] = None,
+    ):
+        r"""
+        Fuses the LoRA parameters into the original parameters of the corresponding blocks.
+
+        <Tip warning={true}>
+
+        This is an experimental API.
+
+        </Tip>
+
+        Args:
+            fuse_unet (`bool`, defaults to `True`): Whether to fuse the UNet LoRA parameters.
+            fuse_text_encoder (`bool`, defaults to `True`):
+                Whether to fuse the text encoder LoRA parameters. If the text encoder wasn't monkey-patched with the
+                LoRA parameters then it won't have any effect.
+            lora_scale (`float`, defaults to 1.0):
+                Controls how much to influence the outputs with the LoRA parameters.
+            safe_fusing (`bool`, defaults to `False`):
+                Whether to check fused weights for NaN values before fusing and if values are NaN not fusing them.
+            adapter_names (`List[str]`, *optional*):
+                Adapter names to be used for fusing. If nothing is passed, all active adapters will be fused.
+
+        Example:
+
+        ```py
+        from diffusers import DiffusionPipeline
+        import torch
+
+        pipeline = DiffusionPipeline.from_pretrained(
+            "stabilityai/stable-diffusion-xl-base-1.0", torch_dtype=torch.float16
+        ).to("cuda")
+        pipeline.load_lora_weights("nerijs/pixel-art-xl", weight_name="pixel-art-xl.safetensors", adapter_name="pixel")
+        pipeline.fuse_lora(lora_scale=0.7)
+        ```
+        """
+        from peft.tuners.tuners_utils import BaseTunerLayer
+
+        if fuse_unet or fuse_text_encoder:
+            self.num_fused_loras += 1
+            if self.num_fused_loras > 1:
+                logger.warning(
+                    "The current API is supported for operating with a single LoRA file. You are trying to load and fuse more than one LoRA which is not well-supported.",
+                )
+
+        if fuse_unet:
+            unet = getattr(self, self.unet_name) if not hasattr(self, "unet") else self.unet
+            unet.fuse_lora(lora_scale, safe_fusing=safe_fusing, adapter_names=adapter_names)
+
+        def fuse_text_encoder_lora(text_encoder, lora_scale=1.0, safe_fusing=False, adapter_names=None):
+            merge_kwargs = {"safe_merge": safe_fusing}
+
+            for module in text_encoder.modules():
+                if isinstance(module, BaseTunerLayer):
+                    if lora_scale != 1.0:
+                        module.scale_layer(lora_scale)
+
+                    # For BC with previous PEFT versions, we need to check the signature
+                    # of the `merge` method to see if it supports the `adapter_names` argument.
+                    supported_merge_kwargs = list(inspect.signature(module.merge).parameters)
+                    if "adapter_names" in supported_merge_kwargs:
+                        merge_kwargs["adapter_names"] = adapter_names
+                    elif "adapter_names" not in supported_merge_kwargs and adapter_names is not None:
+                        raise ValueError(
+                            "The `adapter_names` argument is not supported with your PEFT version. "
+                            "Please upgrade to the latest version of PEFT. `pip install -U peft`"
+                        )
+
+                    module.merge(**merge_kwargs)
+
+        if fuse_text_encoder:
+            if hasattr(self, "text_encoder"):
+                fuse_text_encoder_lora(self.text_encoder, lora_scale, safe_fusing, adapter_names=adapter_names)
+            if hasattr(self, "text_encoder_2"):
+                fuse_text_encoder_lora(self.text_encoder_2, lora_scale, safe_fusing, adapter_names=adapter_names)
+
+    def unfuse_lora(self, unfuse_unet: bool = True, unfuse_text_encoder: bool = True):
+        r"""
+        Reverses the effect of
+        [`pipe.fuse_lora()`](https://huggingface.co/docs/diffusers/main/en/api/loaders#diffusers.loaders.LoraLoaderMixin.fuse_lora).
+
+        <Tip warning={true}>
+
+        This is an experimental API.
+
+        </Tip>
+
+        Args:
+            unfuse_unet (`bool`, defaults to `True`): Whether to unfuse the UNet LoRA parameters.
+            unfuse_text_encoder (`bool`, defaults to `True`):
+                Whether to unfuse the text encoder LoRA parameters. If the text encoder wasn't monkey-patched with the
+                LoRA parameters then it won't have any effect.
+        """
+        from peft.tuners.tuners_utils import BaseTunerLayer
+
+        unet = getattr(self, self.unet_name) if not hasattr(self, "unet") else self.unet
+        if unfuse_unet:
+            for module in unet.modules():
+                if isinstance(module, BaseTunerLayer):
+                    module.unmerge()
+
+        def unfuse_text_encoder_lora(text_encoder):
+            for module in text_encoder.modules():
+                if isinstance(module, BaseTunerLayer):
+                    module.unmerge()
+
+        if unfuse_text_encoder:
+            if hasattr(self, "text_encoder"):
+                unfuse_text_encoder_lora(self.text_encoder)
+            if hasattr(self, "text_encoder_2"):
+                unfuse_text_encoder_lora(self.text_encoder_2)
+
+        self.num_fused_loras -= 1
+
+    def set_adapters_for_text_encoder(
+        self,
+        adapter_names: Union[List[str], str],
+        text_encoder: Optional["PreTrainedModel"] = None,  # noqa: F821
+        text_encoder_weights: Optional[Union[float, List[float], List[None]]] = None,
+    ):
+        """
+        Sets the adapter layers for the text encoder.
+
+        Args:
+            adapter_names (`List[str]` or `str`):
+                The names of the adapters to use.
+            text_encoder (`torch.nn.Module`, *optional*):
+                The text encoder module to set the adapter layers for. If `None`, it will try to get the `text_encoder`
+                attribute.
+            text_encoder_weights (`List[float]`, *optional*):
+                The weights to use for the text encoder. If `None`, the weights are set to `1.0` for all the adapters.
+        """
+        if not USE_PEFT_BACKEND:
+            raise ValueError("PEFT backend is required for this method.")
+
+        def process_weights(adapter_names, weights):
+            # Expand weights into a list, one entry per adapter
+            # e.g. for 2 adapters:  7 -> [7,7] ; [3, None] -> [3, None]
+            if not isinstance(weights, list):
+                weights = [weights] * len(adapter_names)
+
+            if len(adapter_names) != len(weights):
+                raise ValueError(
+                    f"Length of adapter names {len(adapter_names)} is not equal to the length of the weights {len(weights)}"
+                )
+
+            # Set None values to default of 1.0
+            # e.g. [7,7] -> [7,7] ; [3, None] -> [3,1]
+            weights = [w if w is not None else 1.0 for w in weights]
+
+            return weights
+
+        adapter_names = [adapter_names] if isinstance(adapter_names, str) else adapter_names
+        text_encoder_weights = process_weights(adapter_names, text_encoder_weights)
+        text_encoder = text_encoder or getattr(self, "text_encoder", None)
+        if text_encoder is None:
+            raise ValueError(
+                "The pipeline does not have a default `pipe.text_encoder` class. Please make sure to pass a `text_encoder` instead."
+            )
+        set_weights_and_activate_adapters(text_encoder, adapter_names, text_encoder_weights)
+
+    def disable_lora_for_text_encoder(self, text_encoder: Optional["PreTrainedModel"] = None):
+        """
+        Disables the LoRA layers for the text encoder.
+
+        Args:
+            text_encoder (`torch.nn.Module`, *optional*):
+                The text encoder module to disable the LoRA layers for. If `None`, it will try to get the
+                `text_encoder` attribute.
+        """
+        if not USE_PEFT_BACKEND:
+            raise ValueError("PEFT backend is required for this method.")
+
+        text_encoder = text_encoder or getattr(self, "text_encoder", None)
+        if text_encoder is None:
+            raise ValueError("Text Encoder not found.")
+        set_adapter_layers(text_encoder, enabled=False)
+
+    def enable_lora_for_text_encoder(self, text_encoder: Optional["PreTrainedModel"] = None):
+        """
+        Enables the LoRA layers for the text encoder.
+
+        Args:
+            text_encoder (`torch.nn.Module`, *optional*):
+                The text encoder module to enable the LoRA layers for. If `None`, it will try to get the `text_encoder`
+                attribute.
+        """
+        if not USE_PEFT_BACKEND:
+            raise ValueError("PEFT backend is required for this method.")
+        text_encoder = text_encoder or getattr(self, "text_encoder", None)
+        if text_encoder is None:
+            raise ValueError("Text Encoder not found.")
+        set_adapter_layers(self.text_encoder, enabled=True)
+
+    def set_adapters(
+        self,
+        adapter_names: Union[List[str], str],
+        adapter_weights: Optional[Union[float, Dict, List[float], List[Dict]]] = None,
+    ):
+        adapter_names = [adapter_names] if isinstance(adapter_names, str) else adapter_names
+
+        adapter_weights = copy.deepcopy(adapter_weights)
+
+        # Expand weights into a list, one entry per adapter
+        if not isinstance(adapter_weights, list):
+            adapter_weights = [adapter_weights] * len(adapter_names)
+
+        if len(adapter_names) != len(adapter_weights):
+            raise ValueError(
+                f"Length of adapter names {len(adapter_names)} is not equal to the length of the weights {len(adapter_weights)}"
+            )
+
+        # Decompose weights into weights for unet, text_encoder and text_encoder_2
+        unet_lora_weights, text_encoder_lora_weights, text_encoder_2_lora_weights = [], [], []
+
+        list_adapters = self.get_list_adapters()  # eg {"unet": ["adapter1", "adapter2"], "text_encoder": ["adapter2"]}
+        all_adapters = {
+            adapter for adapters in list_adapters.values() for adapter in adapters
+        }  # eg ["adapter1", "adapter2"]
+        invert_list_adapters = {
+            adapter: [part for part, adapters in list_adapters.items() if adapter in adapters]
+            for adapter in all_adapters
+        }  # eg {"adapter1": ["unet"], "adapter2": ["unet", "text_encoder"]}
+
+        for adapter_name, weights in zip(adapter_names, adapter_weights):
+            if isinstance(weights, dict):
+                unet_lora_weight = weights.pop("unet", None)
+                text_encoder_lora_weight = weights.pop("text_encoder", None)
+                text_encoder_2_lora_weight = weights.pop("text_encoder_2", None)
+
+                if len(weights) > 0:
+                    raise ValueError(
+                        f"Got invalid key '{weights.keys()}' in lora weight dict for adapter {adapter_name}."
+                    )
+
+                if text_encoder_2_lora_weight is not None and not hasattr(self, "text_encoder_2"):
+                    logger.warning(
+                        "Lora weight dict contains text_encoder_2 weights but will be ignored because pipeline does not have text_encoder_2."
+                    )
+
+                # warn if adapter doesn't have parts specified by adapter_weights
+                for part_weight, part_name in zip(
+                    [unet_lora_weight, text_encoder_lora_weight, text_encoder_2_lora_weight],
+                    ["unet", "text_encoder", "text_encoder_2"],
+                ):
+                    if part_weight is not None and part_name not in invert_list_adapters[adapter_name]:
+                        logger.warning(
+                            f"Lora weight dict for adapter '{adapter_name}' contains {part_name}, but this will be ignored because {adapter_name} does not contain weights for {part_name}. Valid parts for {adapter_name} are: {invert_list_adapters[adapter_name]}."
+                        )
+
+            else:
+                unet_lora_weight = weights
+                text_encoder_lora_weight = weights
+                text_encoder_2_lora_weight = weights
+
+            unet_lora_weights.append(unet_lora_weight)
+            text_encoder_lora_weights.append(text_encoder_lora_weight)
+            text_encoder_2_lora_weights.append(text_encoder_2_lora_weight)
+
+        unet = getattr(self, self.unet_name) if not hasattr(self, "unet") else self.unet
+        # Handle the UNET
+        unet.set_adapters(adapter_names, unet_lora_weights)
+
+        # Handle the Text Encoder
+        if hasattr(self, "text_encoder"):
+            self.set_adapters_for_text_encoder(adapter_names, self.text_encoder, text_encoder_lora_weights)
+        if hasattr(self, "text_encoder_2"):
+            self.set_adapters_for_text_encoder(adapter_names, self.text_encoder_2, text_encoder_2_lora_weights)
+
+    def disable_lora(self):
+        if not USE_PEFT_BACKEND:
+            raise ValueError("PEFT backend is required for this method.")
+
+        # Disable unet adapters
+        unet = getattr(self, self.unet_name) if not hasattr(self, "unet") else self.unet
+        unet.disable_lora()
+
+        # Disable text encoder adapters
+        if hasattr(self, "text_encoder"):
+            self.disable_lora_for_text_encoder(self.text_encoder)
+        if hasattr(self, "text_encoder_2"):
+            self.disable_lora_for_text_encoder(self.text_encoder_2)
+
+    def enable_lora(self):
+        if not USE_PEFT_BACKEND:
+            raise ValueError("PEFT backend is required for this method.")
+
+        # Enable unet adapters
+        unet = getattr(self, self.unet_name) if not hasattr(self, "unet") else self.unet
+        unet.enable_lora()
+
+        # Enable text encoder adapters
+        if hasattr(self, "text_encoder"):
+            self.enable_lora_for_text_encoder(self.text_encoder)
+        if hasattr(self, "text_encoder_2"):
+            self.enable_lora_for_text_encoder(self.text_encoder_2)
+
+    def delete_adapters(self, adapter_names: Union[List[str], str]):
+        """
+        Args:
+        Deletes the LoRA layers of `adapter_name` for the unet and text-encoder(s).
+            adapter_names (`Union[List[str], str]`):
+                The names of the adapter to delete. Can be a single string or a list of strings
+        """
+        if not USE_PEFT_BACKEND:
+            raise ValueError("PEFT backend is required for this method.")
+
+        if isinstance(adapter_names, str):
+            adapter_names = [adapter_names]
+
+        # Delete unet adapters
+        unet = getattr(self, self.unet_name) if not hasattr(self, "unet") else self.unet
+        unet.delete_adapters(adapter_names)
+
+        for adapter_name in adapter_names:
+            # Delete text encoder adapters
+            if hasattr(self, "text_encoder"):
+                delete_adapter_layers(self.text_encoder, adapter_name)
+            if hasattr(self, "text_encoder_2"):
+                delete_adapter_layers(self.text_encoder_2, adapter_name)
+
+    def get_active_adapters(self) -> List[str]:
+        """
+        Gets the list of the current active adapters.
+
+        Example:
+
+        ```python
+        from diffusers import DiffusionPipeline
+
+        pipeline = DiffusionPipeline.from_pretrained(
+            "stabilityai/stable-diffusion-xl-base-1.0",
+        ).to("cuda")
+        pipeline.load_lora_weights("CiroN2022/toy-face", weight_name="toy_face_sdxl.safetensors", adapter_name="toy")
+        pipeline.get_active_adapters()
+        ```
+        """
+        if not USE_PEFT_BACKEND:
+            raise ValueError(
+                "PEFT backend is required for this method. Please install the latest version of PEFT `pip install -U peft`"
+            )
+
+        from peft.tuners.tuners_utils import BaseTunerLayer
+
+        active_adapters = []
+        unet = getattr(self, self.unet_name) if not hasattr(self, "unet") else self.unet
+        for module in unet.modules():
+            if isinstance(module, BaseTunerLayer):
+                active_adapters = module.active_adapters
+                break
+
+        return active_adapters
+
+    def get_list_adapters(self) -> Dict[str, List[str]]:
+        """
+        Gets the current list of all available adapters in the pipeline.
+        """
+        if not USE_PEFT_BACKEND:
+            raise ValueError(
+                "PEFT backend is required for this method. Please install the latest version of PEFT `pip install -U peft`"
+            )
+
+        set_adapters = {}
+
+        if hasattr(self, "text_encoder") and hasattr(self.text_encoder, "peft_config"):
+            set_adapters["text_encoder"] = list(self.text_encoder.peft_config.keys())
+
+        if hasattr(self, "text_encoder_2") and hasattr(self.text_encoder_2, "peft_config"):
+            set_adapters["text_encoder_2"] = list(self.text_encoder_2.peft_config.keys())
+
+        unet = getattr(self, self.unet_name) if not hasattr(self, "unet") else self.unet
+        if hasattr(self, self.unet_name) and hasattr(unet, "peft_config"):
+            set_adapters[self.unet_name] = list(self.unet.peft_config.keys())
+
+        return set_adapters
+
+    def set_lora_device(self, adapter_names: List[str], device: Union[torch.device, str, int]) -> None:
+        """
+        Moves the LoRAs listed in `adapter_names` to a target device. Useful for offloading the LoRA to the CPU in case
+        you want to load multiple adapters and free some GPU memory.
+
+        Args:
+            adapter_names (`List[str]`):
+                List of adapters to send device to.
+            device (`Union[torch.device, str, int]`):
+                Device to send the adapters to. Can be either a torch device, a str or an integer.
+        """
+        if not USE_PEFT_BACKEND:
+            raise ValueError("PEFT backend is required for this method.")
+
+        from peft.tuners.tuners_utils import BaseTunerLayer
+
+        # Handle the UNET
+        unet = getattr(self, self.unet_name) if not hasattr(self, "unet") else self.unet
+        for unet_module in unet.modules():
+            if isinstance(unet_module, BaseTunerLayer):
+                for adapter_name in adapter_names:
+                    unet_module.lora_A[adapter_name].to(device)
+                    unet_module.lora_B[adapter_name].to(device)
+                    # this is a param, not a module, so device placement is not in-place -> re-assign
+                    if hasattr(unet_module, "lora_magnitude_vector") and unet_module.lora_magnitude_vector is not None:
+                        unet_module.lora_magnitude_vector[adapter_name] = unet_module.lora_magnitude_vector[
+                            adapter_name
+                        ].to(device)
+
+        # Handle the text encoder
+        modules_to_process = []
+        if hasattr(self, "text_encoder"):
+            modules_to_process.append(self.text_encoder)
+
+        if hasattr(self, "text_encoder_2"):
+            modules_to_process.append(self.text_encoder_2)
+
+        for text_encoder in modules_to_process:
+            # loop over submodules
+            for text_encoder_module in text_encoder.modules():
+                if isinstance(text_encoder_module, BaseTunerLayer):
+                    for adapter_name in adapter_names:
+                        text_encoder_module.lora_A[adapter_name].to(device)
+                        text_encoder_module.lora_B[adapter_name].to(device)
+                        # this is a param, not a module, so device placement is not in-place -> re-assign
+                        if (
+                            hasattr(text_encoder, "lora_magnitude_vector")
+                            and text_encoder_module.lora_magnitude_vector is not None
+                        ):
+                            text_encoder_module.lora_magnitude_vector[
+                                adapter_name
+                            ] = text_encoder_module.lora_magnitude_vector[adapter_name].to(device)
+
+
+class StableDiffusionXLLoraLoaderMixin(LoraLoaderMixin):
+    """This class overrides `LoraLoaderMixin` with LoRA loading/saving code that's specific to SDXL"""
+
+    # Override to properly handle the loading and unloading of the additional text encoder.
+    def load_lora_weights(
+        self,
+        pretrained_model_name_or_path_or_dict: Union[str, Dict[str, torch.Tensor]],
+        adapter_name: Optional[str] = None,
+        **kwargs,
+    ):
+        """
+        Load LoRA weights specified in `pretrained_model_name_or_path_or_dict` into `self.unet` and
+        `self.text_encoder`.
+
+        All kwargs are forwarded to `self.lora_state_dict`.
+
+        See [`~loaders.LoraLoaderMixin.lora_state_dict`] for more details on how the state dict is loaded.
+
+        See [`~loaders.LoraLoaderMixin.load_lora_into_unet`] for more details on how the state dict is loaded into
+        `self.unet`.
+
+        See [`~loaders.LoraLoaderMixin.load_lora_into_text_encoder`] for more details on how the state dict is loaded
+        into `self.text_encoder`.
+
+        Parameters:
+            pretrained_model_name_or_path_or_dict (`str` or `os.PathLike` or `dict`):
+                See [`~loaders.LoraLoaderMixin.lora_state_dict`].
+            adapter_name (`str`, *optional*):
+                Adapter name to be used for referencing the loaded adapter model. If not specified, it will use
+                `default_{i}` where i is the total number of adapters being loaded.
+            kwargs (`dict`, *optional*):
+                See [`~loaders.LoraLoaderMixin.lora_state_dict`].
+        """
+        if not USE_PEFT_BACKEND:
+            raise ValueError("PEFT backend is required for this method.")
+
+        # We could have accessed the unet config from `lora_state_dict()` too. We pass
+        # it here explicitly to be able to tell that it's coming from an SDXL
+        # pipeline.
+
+        # if a dict is passed, copy it instead of modifying it inplace
+        if isinstance(pretrained_model_name_or_path_or_dict, dict):
+            pretrained_model_name_or_path_or_dict = pretrained_model_name_or_path_or_dict.copy()
+
+        # First, ensure that the checkpoint is a compatible one and can be successfully loaded.
+        state_dict, network_alphas = self.lora_state_dict(
+            pretrained_model_name_or_path_or_dict,
+            unet_config=self.unet.config,
+            **kwargs,
+        )
+        is_correct_format = all("lora" in key or "dora_scale" in key for key in state_dict.keys())
+        if not is_correct_format:
+            raise ValueError("Invalid LoRA checkpoint.")
+
+        self.load_lora_into_unet(
+            state_dict, network_alphas=network_alphas, unet=self.unet, adapter_name=adapter_name, _pipeline=self
+        )
+        text_encoder_state_dict = {k: v for k, v in state_dict.items() if "text_encoder." in k}
+        if len(text_encoder_state_dict) > 0:
+            self.load_lora_into_text_encoder(
+                text_encoder_state_dict,
+                network_alphas=network_alphas,
+                text_encoder=self.text_encoder,
+                prefix="text_encoder",
+                lora_scale=self.lora_scale,
+                adapter_name=adapter_name,
+                _pipeline=self,
+            )
+
+        text_encoder_2_state_dict = {k: v for k, v in state_dict.items() if "text_encoder_2." in k}
+        if len(text_encoder_2_state_dict) > 0:
+            self.load_lora_into_text_encoder(
+                text_encoder_2_state_dict,
+                network_alphas=network_alphas,
+                text_encoder=self.text_encoder_2,
+                prefix="text_encoder_2",
+                lora_scale=self.lora_scale,
+                adapter_name=adapter_name,
+                _pipeline=self,
+            )
+
+    @classmethod
+    def save_lora_weights(
+        cls,
+        save_directory: Union[str, os.PathLike],
+        unet_lora_layers: Dict[str, Union[torch.nn.Module, torch.Tensor]] = None,
+        text_encoder_lora_layers: Dict[str, Union[torch.nn.Module, torch.Tensor]] = None,
+        text_encoder_2_lora_layers: Dict[str, Union[torch.nn.Module, torch.Tensor]] = None,
+        is_main_process: bool = True,
+        weight_name: str = None,
+        save_function: Callable = None,
+        safe_serialization: bool = True,
+    ):
+        r"""
+        Save the LoRA parameters corresponding to the UNet and text encoder.
+
+        Arguments:
+            save_directory (`str` or `os.PathLike`):
+                Directory to save LoRA parameters to. Will be created if it doesn't exist.
+            unet_lora_layers (`Dict[str, torch.nn.Module]` or `Dict[str, torch.Tensor]`):
+                State dict of the LoRA layers corresponding to the `unet`.
+            text_encoder_lora_layers (`Dict[str, torch.nn.Module]` or `Dict[str, torch.Tensor]`):
+                State dict of the LoRA layers corresponding to the `text_encoder`. Must explicitly pass the text
+                encoder LoRA state dict because it comes from 🤗 Transformers.
+            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`.
+        """
+        state_dict = {}
+
+        def pack_weights(layers, prefix):
+            layers_weights = layers.state_dict() if isinstance(layers, torch.nn.Module) else layers
+            layers_state_dict = {f"{prefix}.{module_name}": param for module_name, param in layers_weights.items()}
+            return layers_state_dict
+
+        if not (unet_lora_layers or text_encoder_lora_layers or text_encoder_2_lora_layers):
+            raise ValueError(
+                "You must pass at least one of `unet_lora_layers`, `text_encoder_lora_layers` or `text_encoder_2_lora_layers`."
+            )
+
+        if unet_lora_layers:
+            state_dict.update(pack_weights(unet_lora_layers, "unet"))
+
+        if text_encoder_lora_layers and text_encoder_2_lora_layers:
+            state_dict.update(pack_weights(text_encoder_lora_layers, "text_encoder"))
+            state_dict.update(pack_weights(text_encoder_2_lora_layers, "text_encoder_2"))
+
+        cls.write_lora_layers(
+            state_dict=state_dict,
+            save_directory=save_directory,
+            is_main_process=is_main_process,
+            weight_name=weight_name,
+            save_function=save_function,
+            safe_serialization=safe_serialization,
+        )
+
+    def _remove_text_encoder_monkey_patch(self):
+        recurse_remove_peft_layers(self.text_encoder)
+        # TODO: @younesbelkada handle this in transformers side
+        if getattr(self.text_encoder, "peft_config", None) is not None:
+            del self.text_encoder.peft_config
+            self.text_encoder._hf_peft_config_loaded = None
+
+        recurse_remove_peft_layers(self.text_encoder_2)
+        if getattr(self.text_encoder_2, "peft_config", None) is not None:
+            del self.text_encoder_2.peft_config
+            self.text_encoder_2._hf_peft_config_loaded = None

diffusers/loaders/lora_conversion_utils.py

@@ -0,0 +1,287 @@
+# Copyright 2024 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 re
+
+from ..utils import is_peft_version, logging
+
+
+logger = logging.get_logger(__name__)
+
+
+def _maybe_map_sgm_blocks_to_diffusers(state_dict, unet_config, delimiter="_", block_slice_pos=5):
+    # 1. get all state_dict_keys
+    all_keys = list(state_dict.keys())
+    sgm_patterns = ["input_blocks", "middle_block", "output_blocks"]
+
+    # 2. check if needs remapping, if not return original dict
+    is_in_sgm_format = False
+    for key in all_keys:
+        if any(p in key for p in sgm_patterns):
+            is_in_sgm_format = True
+            break
+
+    if not is_in_sgm_format:
+        return state_dict
+
+    # 3. Else remap from SGM patterns
+    new_state_dict = {}
+    inner_block_map = ["resnets", "attentions", "upsamplers"]
+
+    # Retrieves # of down, mid and up blocks
+    input_block_ids, middle_block_ids, output_block_ids = set(), set(), set()
+
+    for layer in all_keys:
+        if "text" in layer:
+            new_state_dict[layer] = state_dict.pop(layer)
+        else:
+            layer_id = int(layer.split(delimiter)[:block_slice_pos][-1])
+            if sgm_patterns[0] in layer:
+                input_block_ids.add(layer_id)
+            elif sgm_patterns[1] in layer:
+                middle_block_ids.add(layer_id)
+            elif sgm_patterns[2] in layer:
+                output_block_ids.add(layer_id)
+            else:
+                raise ValueError(f"Checkpoint not supported because layer {layer} not supported.")
+
+    input_blocks = {
+        layer_id: [key for key in state_dict if f"input_blocks{delimiter}{layer_id}" in key]
+        for layer_id in input_block_ids
+    }
+    middle_blocks = {
+        layer_id: [key for key in state_dict if f"middle_block{delimiter}{layer_id}" in key]
+        for layer_id in middle_block_ids
+    }
+    output_blocks = {
+        layer_id: [key for key in state_dict if f"output_blocks{delimiter}{layer_id}" in key]
+        for layer_id in output_block_ids
+    }
+
+    # Rename keys accordingly
+    for i in input_block_ids:
+        block_id = (i - 1) // (unet_config.layers_per_block + 1)
+        layer_in_block_id = (i - 1) % (unet_config.layers_per_block + 1)
+
+        for key in input_blocks[i]:
+            inner_block_id = int(key.split(delimiter)[block_slice_pos])
+            inner_block_key = inner_block_map[inner_block_id] if "op" not in key else "downsamplers"
+            inner_layers_in_block = str(layer_in_block_id) if "op" not in key else "0"
+            new_key = delimiter.join(
+                key.split(delimiter)[: block_slice_pos - 1]
+                + [str(block_id), inner_block_key, inner_layers_in_block]
+                + key.split(delimiter)[block_slice_pos + 1 :]
+            )
+            new_state_dict[new_key] = state_dict.pop(key)
+
+    for i in middle_block_ids:
+        key_part = None
+        if i == 0:
+            key_part = [inner_block_map[0], "0"]
+        elif i == 1:
+            key_part = [inner_block_map[1], "0"]
+        elif i == 2:
+            key_part = [inner_block_map[0], "1"]
+        else:
+            raise ValueError(f"Invalid middle block id {i}.")
+
+        for key in middle_blocks[i]:
+            new_key = delimiter.join(
+                key.split(delimiter)[: block_slice_pos - 1] + key_part + key.split(delimiter)[block_slice_pos:]
+            )
+            new_state_dict[new_key] = state_dict.pop(key)
+
+    for i in output_block_ids:
+        block_id = i // (unet_config.layers_per_block + 1)
+        layer_in_block_id = i % (unet_config.layers_per_block + 1)
+
+        for key in output_blocks[i]:
+            inner_block_id = int(key.split(delimiter)[block_slice_pos])
+            inner_block_key = inner_block_map[inner_block_id]
+            inner_layers_in_block = str(layer_in_block_id) if inner_block_id < 2 else "0"
+            new_key = delimiter.join(
+                key.split(delimiter)[: block_slice_pos - 1]
+                + [str(block_id), inner_block_key, inner_layers_in_block]
+                + key.split(delimiter)[block_slice_pos + 1 :]
+            )
+            new_state_dict[new_key] = state_dict.pop(key)
+
+    if len(state_dict) > 0:
+        raise ValueError("At this point all state dict entries have to be converted.")
+
+    return new_state_dict
+
+
+def _convert_kohya_lora_to_diffusers(state_dict, unet_name="unet", text_encoder_name="text_encoder"):
+    unet_state_dict = {}
+    te_state_dict = {}
+    te2_state_dict = {}
+    network_alphas = {}
+    is_unet_dora_lora = any("dora_scale" in k and "lora_unet_" in k for k in state_dict)
+    is_te_dora_lora = any("dora_scale" in k and ("lora_te_" in k or "lora_te1_" in k) for k in state_dict)
+    is_te2_dora_lora = any("dora_scale" in k and "lora_te2_" in k for k in state_dict)
+
+    if is_unet_dora_lora or is_te_dora_lora or is_te2_dora_lora:
+        if is_peft_version("<", "0.9.0"):
+            raise ValueError(
+                "You need `peft` 0.9.0 at least to use DoRA-enabled LoRAs. Please upgrade your installation of `peft`."
+            )
+
+    # every down weight has a corresponding up weight and potentially an alpha weight
+    lora_keys = [k for k in state_dict.keys() if k.endswith("lora_down.weight")]
+    for key in lora_keys:
+        lora_name = key.split(".")[0]
+        lora_name_up = lora_name + ".lora_up.weight"
+        lora_name_alpha = lora_name + ".alpha"
+
+        if lora_name.startswith("lora_unet_"):
+            diffusers_name = key.replace("lora_unet_", "").replace("_", ".")
+
+            if "input.blocks" in diffusers_name:
+                diffusers_name = diffusers_name.replace("input.blocks", "down_blocks")
+            else:
+                diffusers_name = diffusers_name.replace("down.blocks", "down_blocks")
+
+            if "middle.block" in diffusers_name:
+                diffusers_name = diffusers_name.replace("middle.block", "mid_block")
+            else:
+                diffusers_name = diffusers_name.replace("mid.block", "mid_block")
+            if "output.blocks" in diffusers_name:
+                diffusers_name = diffusers_name.replace("output.blocks", "up_blocks")
+            else:
+                diffusers_name = diffusers_name.replace("up.blocks", "up_blocks")
+
+            diffusers_name = diffusers_name.replace("transformer.blocks", "transformer_blocks")
+            diffusers_name = diffusers_name.replace("to.q.lora", "to_q_lora")
+            diffusers_name = diffusers_name.replace("to.k.lora", "to_k_lora")
+            diffusers_name = diffusers_name.replace("to.v.lora", "to_v_lora")
+            diffusers_name = diffusers_name.replace("to.out.0.lora", "to_out_lora")
+            diffusers_name = diffusers_name.replace("proj.in", "proj_in")
+            diffusers_name = diffusers_name.replace("proj.out", "proj_out")
+            diffusers_name = diffusers_name.replace("emb.layers", "time_emb_proj")
+
+            # SDXL specificity.
+            if "emb" in diffusers_name and "time.emb.proj" not in diffusers_name:
+                pattern = r"\.\d+(?=\D*$)"
+                diffusers_name = re.sub(pattern, "", diffusers_name, count=1)
+            if ".in." in diffusers_name:
+                diffusers_name = diffusers_name.replace("in.layers.2", "conv1")
+            if ".out." in diffusers_name:
+                diffusers_name = diffusers_name.replace("out.layers.3", "conv2")
+            if "downsamplers" in diffusers_name or "upsamplers" in diffusers_name:
+                diffusers_name = diffusers_name.replace("op", "conv")
+            if "skip" in diffusers_name:
+                diffusers_name = diffusers_name.replace("skip.connection", "conv_shortcut")
+
+            # LyCORIS specificity.
+            if "time.emb.proj" in diffusers_name:
+                diffusers_name = diffusers_name.replace("time.emb.proj", "time_emb_proj")
+            if "conv.shortcut" in diffusers_name:
+                diffusers_name = diffusers_name.replace("conv.shortcut", "conv_shortcut")
+
+            # General coverage.
+            if "transformer_blocks" in diffusers_name:
+                if "attn1" in diffusers_name or "attn2" in diffusers_name:
+                    diffusers_name = diffusers_name.replace("attn1", "attn1.processor")
+                    diffusers_name = diffusers_name.replace("attn2", "attn2.processor")
+                    unet_state_dict[diffusers_name] = state_dict.pop(key)
+                    unet_state_dict[diffusers_name.replace(".down.", ".up.")] = state_dict.pop(lora_name_up)
+                elif "ff" in diffusers_name:
+                    unet_state_dict[diffusers_name] = state_dict.pop(key)
+                    unet_state_dict[diffusers_name.replace(".down.", ".up.")] = state_dict.pop(lora_name_up)
+            elif any(key in diffusers_name for key in ("proj_in", "proj_out")):
+                unet_state_dict[diffusers_name] = state_dict.pop(key)
+                unet_state_dict[diffusers_name.replace(".down.", ".up.")] = state_dict.pop(lora_name_up)
+            else:
+                unet_state_dict[diffusers_name] = state_dict.pop(key)
+                unet_state_dict[diffusers_name.replace(".down.", ".up.")] = state_dict.pop(lora_name_up)
+
+            if is_unet_dora_lora:
+                dora_scale_key_to_replace = "_lora.down." if "_lora.down." in diffusers_name else ".lora.down."
+                unet_state_dict[
+                    diffusers_name.replace(dora_scale_key_to_replace, ".lora_magnitude_vector.")
+                ] = state_dict.pop(key.replace("lora_down.weight", "dora_scale"))
+
+        elif lora_name.startswith(("lora_te_", "lora_te1_", "lora_te2_")):
+            if lora_name.startswith(("lora_te_", "lora_te1_")):
+                key_to_replace = "lora_te_" if lora_name.startswith("lora_te_") else "lora_te1_"
+            else:
+                key_to_replace = "lora_te2_"
+
+            diffusers_name = key.replace(key_to_replace, "").replace("_", ".")
+            diffusers_name = diffusers_name.replace("text.model", "text_model")
+            diffusers_name = diffusers_name.replace("self.attn", "self_attn")
+            diffusers_name = diffusers_name.replace("q.proj.lora", "to_q_lora")
+            diffusers_name = diffusers_name.replace("k.proj.lora", "to_k_lora")
+            diffusers_name = diffusers_name.replace("v.proj.lora", "to_v_lora")
+            diffusers_name = diffusers_name.replace("out.proj.lora", "to_out_lora")
+            if "self_attn" in diffusers_name:
+                if lora_name.startswith(("lora_te_", "lora_te1_")):
+                    te_state_dict[diffusers_name] = state_dict.pop(key)
+                    te_state_dict[diffusers_name.replace(".down.", ".up.")] = state_dict.pop(lora_name_up)
+                else:
+                    te2_state_dict[diffusers_name] = state_dict.pop(key)
+                    te2_state_dict[diffusers_name.replace(".down.", ".up.")] = state_dict.pop(lora_name_up)
+            elif "mlp" in diffusers_name:
+                # Be aware that this is the new diffusers convention and the rest of the code might
+                # not utilize it yet.
+                diffusers_name = diffusers_name.replace(".lora.", ".lora_linear_layer.")
+                if lora_name.startswith(("lora_te_", "lora_te1_")):
+                    te_state_dict[diffusers_name] = state_dict.pop(key)
+                    te_state_dict[diffusers_name.replace(".down.", ".up.")] = state_dict.pop(lora_name_up)
+                else:
+                    te2_state_dict[diffusers_name] = state_dict.pop(key)
+                    te2_state_dict[diffusers_name.replace(".down.", ".up.")] = state_dict.pop(lora_name_up)
+
+            if (is_te_dora_lora or is_te2_dora_lora) and lora_name.startswith(("lora_te_", "lora_te1_", "lora_te2_")):
+                dora_scale_key_to_replace_te = (
+                    "_lora.down." if "_lora.down." in diffusers_name else ".lora_linear_layer."
+                )
+                if lora_name.startswith(("lora_te_", "lora_te1_")):
+                    te_state_dict[
+                        diffusers_name.replace(dora_scale_key_to_replace_te, ".lora_magnitude_vector.")
+                    ] = state_dict.pop(key.replace("lora_down.weight", "dora_scale"))
+                elif lora_name.startswith("lora_te2_"):
+                    te2_state_dict[
+                        diffusers_name.replace(dora_scale_key_to_replace_te, ".lora_magnitude_vector.")
+                    ] = state_dict.pop(key.replace("lora_down.weight", "dora_scale"))
+
+        # Rename the alphas so that they can be mapped appropriately.
+        if lora_name_alpha in state_dict:
+            alpha = state_dict.pop(lora_name_alpha).item()
+            if lora_name_alpha.startswith("lora_unet_"):
+                prefix = "unet."
+            elif lora_name_alpha.startswith(("lora_te_", "lora_te1_")):
+                prefix = "text_encoder."
+            else:
+                prefix = "text_encoder_2."
+            new_name = prefix + diffusers_name.split(".lora.")[0] + ".alpha"
+            network_alphas.update({new_name: alpha})
+
+    if len(state_dict) > 0:
+        raise ValueError(f"The following keys have not been correctly be renamed: \n\n {', '.join(state_dict.keys())}")
+
+    logger.info("Kohya-style checkpoint detected.")
+    unet_state_dict = {f"{unet_name}.{module_name}": params for module_name, params in unet_state_dict.items()}
+    te_state_dict = {f"{text_encoder_name}.{module_name}": params for module_name, params in te_state_dict.items()}
+    te2_state_dict = (
+        {f"text_encoder_2.{module_name}": params for module_name, params in te2_state_dict.items()}
+        if len(te2_state_dict) > 0
+        else None
+    )
+    if te2_state_dict is not None:
+        te_state_dict.update(te2_state_dict)
+
+    new_state_dict = {**unet_state_dict, **te_state_dict}
+    return new_state_dict, network_alphas

diffusers/loaders/peft.py

@@ -0,0 +1,187 @@
+# coding=utf-8
+# Copyright 2024 The HuggingFace Inc. team.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+from typing import List, Union
+
+from ..utils import MIN_PEFT_VERSION, check_peft_version, is_peft_available
+
+
+class PeftAdapterMixin:
+    """
+    A class containing all functions for loading and using adapters weights that are supported in PEFT library. For
+    more details about adapters and injecting them in a transformer-based model, check out the PEFT
+    [documentation](https://huggingface.co/docs/peft/index).
+
+    Install the latest version of PEFT, and use this mixin to:
+
+    - Attach new adapters in the model.
+    - Attach multiple adapters and iteratively activate/deactivate them.
+    - Activate/deactivate all adapters from the model.
+    - Get a list of the active adapters.
+    """
+
+    _hf_peft_config_loaded = False
+
+    def add_adapter(self, adapter_config, adapter_name: str = "default") -> None:
+        r"""
+        Adds a new adapter to the current model for training. If no adapter name is passed, a default name is assigned
+        to the adapter to follow the convention of the PEFT library.
+
+        If you are not familiar with adapters and PEFT methods, we invite you to read more about them in the PEFT
+        [documentation](https://huggingface.co/docs/peft).
+
+        Args:
+            adapter_config (`[~peft.PeftConfig]`):
+                The configuration of the adapter to add; supported adapters are non-prefix tuning and adaption prompt
+                methods.
+            adapter_name (`str`, *optional*, defaults to `"default"`):
+                The name of the adapter to add. If no name is passed, a default name is assigned to the adapter.
+        """
+        check_peft_version(min_version=MIN_PEFT_VERSION)
+
+        if not is_peft_available():
+            raise ImportError("PEFT is not available. Please install PEFT to use this function: `pip install peft`.")
+
+        from peft import PeftConfig, inject_adapter_in_model
+
+        if not self._hf_peft_config_loaded:
+            self._hf_peft_config_loaded = True
+        elif adapter_name in self.peft_config:
+            raise ValueError(f"Adapter with name {adapter_name} already exists. Please use a different name.")
+
+        if not isinstance(adapter_config, PeftConfig):
+            raise ValueError(
+                f"adapter_config should be an instance of PeftConfig. Got {type(adapter_config)} instead."
+            )
+
+        # Unlike transformers, here we don't need to retrieve the name_or_path of the unet as the loading logic is
+        # handled by the `load_lora_layers` or `LoraLoaderMixin`. Therefore we set it to `None` here.
+        adapter_config.base_model_name_or_path = None
+        inject_adapter_in_model(adapter_config, self, adapter_name)
+        self.set_adapter(adapter_name)
+
+    def set_adapter(self, adapter_name: Union[str, List[str]]) -> None:
+        """
+        Sets a specific adapter by forcing the model to only use that adapter and disables the other adapters.
+
+        If you are not familiar with adapters and PEFT methods, we invite you to read more about them on the PEFT
+        [documentation](https://huggingface.co/docs/peft).
+
+        Args:
+            adapter_name (Union[str, List[str]])):
+                The list of adapters to set or the adapter name in the case of a single adapter.
+        """
+        check_peft_version(min_version=MIN_PEFT_VERSION)
+
+        if not self._hf_peft_config_loaded:
+            raise ValueError("No adapter loaded. Please load an adapter first.")
+
+        if isinstance(adapter_name, str):
+            adapter_name = [adapter_name]
+
+        missing = set(adapter_name) - set(self.peft_config)
+        if len(missing) > 0:
+            raise ValueError(
+                f"Following adapter(s) could not be found: {', '.join(missing)}. Make sure you are passing the correct adapter name(s)."
+                f" current loaded adapters are: {list(self.peft_config.keys())}"
+            )
+
+        from peft.tuners.tuners_utils import BaseTunerLayer
+
+        _adapters_has_been_set = False
+
+        for _, module in self.named_modules():
+            if isinstance(module, BaseTunerLayer):
+                if hasattr(module, "set_adapter"):
+                    module.set_adapter(adapter_name)
+                # Previous versions of PEFT does not support multi-adapter inference
+                elif not hasattr(module, "set_adapter") and len(adapter_name) != 1:
+                    raise ValueError(
+                        "You are trying to set multiple adapters and you have a PEFT version that does not support multi-adapter inference. Please upgrade to the latest version of PEFT."
+                        " `pip install -U peft` or `pip install -U git+https://github.com/huggingface/peft.git`"
+                    )
+                else:
+                    module.active_adapter = adapter_name
+                _adapters_has_been_set = True
+
+        if not _adapters_has_been_set:
+            raise ValueError(
+                "Did not succeeded in setting the adapter. Please make sure you are using a model that supports adapters."
+            )
+
+    def disable_adapters(self) -> None:
+        r"""
+        Disable all adapters attached to the model and fallback to inference with the base model only.
+
+        If you are not familiar with adapters and PEFT methods, we invite you to read more about them on the PEFT
+        [documentation](https://huggingface.co/docs/peft).
+        """
+        check_peft_version(min_version=MIN_PEFT_VERSION)
+
+        if not self._hf_peft_config_loaded:
+            raise ValueError("No adapter loaded. Please load an adapter first.")
+
+        from peft.tuners.tuners_utils import BaseTunerLayer
+
+        for _, module in self.named_modules():
+            if isinstance(module, BaseTunerLayer):
+                if hasattr(module, "enable_adapters"):
+                    module.enable_adapters(enabled=False)
+                else:
+                    # support for older PEFT versions
+                    module.disable_adapters = True
+
+    def enable_adapters(self) -> None:
+        """
+        Enable adapters that are attached to the model. The model uses `self.active_adapters()` to retrieve the list of
+        adapters to enable.
+
+        If you are not familiar with adapters and PEFT methods, we invite you to read more about them on the PEFT
+        [documentation](https://huggingface.co/docs/peft).
+        """
+        check_peft_version(min_version=MIN_PEFT_VERSION)
+
+        if not self._hf_peft_config_loaded:
+            raise ValueError("No adapter loaded. Please load an adapter first.")
+
+        from peft.tuners.tuners_utils import BaseTunerLayer
+
+        for _, module in self.named_modules():
+            if isinstance(module, BaseTunerLayer):
+                if hasattr(module, "enable_adapters"):
+                    module.enable_adapters(enabled=True)
+                else:
+                    # support for older PEFT versions
+                    module.disable_adapters = False
+
+    def active_adapters(self) -> List[str]:
+        """
+        Gets the current list of active adapters of the model.
+
+        If you are not familiar with adapters and PEFT methods, we invite you to read more about them on the PEFT
+        [documentation](https://huggingface.co/docs/peft).
+        """
+        check_peft_version(min_version=MIN_PEFT_VERSION)
+
+        if not is_peft_available():
+            raise ImportError("PEFT is not available. Please install PEFT to use this function: `pip install peft`.")
+
+        if not self._hf_peft_config_loaded:
+            raise ValueError("No adapter loaded. Please load an adapter first.")
+
+        from peft.tuners.tuners_utils import BaseTunerLayer
+
+        for _, module in self.named_modules():
+            if isinstance(module, BaseTunerLayer):
+                return module.active_adapter

diffusers/loaders/single_file.py

@@ -0,0 +1,323 @@
+# Copyright 2024 The HuggingFace Team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+from huggingface_hub.utils import validate_hf_hub_args
+
+from ..utils import is_transformers_available, logging
+from .single_file_utils import (
+    create_diffusers_unet_model_from_ldm,
+    create_diffusers_vae_model_from_ldm,
+    create_scheduler_from_ldm,
+    create_text_encoders_and_tokenizers_from_ldm,
+    fetch_ldm_config_and_checkpoint,
+    infer_model_type,
+)
+
+
+logger = logging.get_logger(__name__)
+
+# Pipelines that support the SDXL Refiner checkpoint
+REFINER_PIPELINES = [
+    "StableDiffusionXLImg2ImgPipeline",
+    "StableDiffusionXLInpaintPipeline",
+    "StableDiffusionXLControlNetImg2ImgPipeline",
+]
+
+if is_transformers_available():
+    from transformers import AutoFeatureExtractor
+
+
+def build_sub_model_components(
+    pipeline_components,
+    pipeline_class_name,
+    component_name,
+    original_config,
+    checkpoint,
+    local_files_only=False,
+    load_safety_checker=False,
+    model_type=None,
+    image_size=None,
+    torch_dtype=None,
+    **kwargs,
+):
+    if component_name in pipeline_components:
+        return {}
+
+    if component_name == "unet":
+        num_in_channels = kwargs.pop("num_in_channels", None)
+        upcast_attention = kwargs.pop("upcast_attention", None)
+
+        unet_components = create_diffusers_unet_model_from_ldm(
+            pipeline_class_name,
+            original_config,
+            checkpoint,
+            num_in_channels=num_in_channels,
+            image_size=image_size,
+            torch_dtype=torch_dtype,
+            model_type=model_type,
+            upcast_attention=upcast_attention,
+        )
+        return unet_components
+
+    if component_name == "vae":
+        scaling_factor = kwargs.get("scaling_factor", None)
+        vae_components = create_diffusers_vae_model_from_ldm(
+            pipeline_class_name,
+            original_config,
+            checkpoint,
+            image_size,
+            scaling_factor,
+            torch_dtype,
+            model_type=model_type,
+        )
+        return vae_components
+
+    if component_name == "scheduler":
+        scheduler_type = kwargs.get("scheduler_type", "ddim")
+        prediction_type = kwargs.get("prediction_type", None)
+
+        scheduler_components = create_scheduler_from_ldm(
+            pipeline_class_name,
+            original_config,
+            checkpoint,
+            scheduler_type=scheduler_type,
+            prediction_type=prediction_type,
+            model_type=model_type,
+        )
+
+        return scheduler_components
+
+    if component_name in ["text_encoder", "text_encoder_2", "tokenizer", "tokenizer_2"]:
+        text_encoder_components = create_text_encoders_and_tokenizers_from_ldm(
+            original_config,
+            checkpoint,
+            model_type=model_type,
+            local_files_only=local_files_only,
+            torch_dtype=torch_dtype,
+        )
+        return text_encoder_components
+
+    if component_name == "safety_checker":
+        if load_safety_checker:
+            from ..pipelines.stable_diffusion.safety_checker import StableDiffusionSafetyChecker
+
+            safety_checker = StableDiffusionSafetyChecker.from_pretrained(
+                "CompVis/stable-diffusion-safety-checker", local_files_only=local_files_only, torch_dtype=torch_dtype
+            )
+        else:
+            safety_checker = None
+        return {"safety_checker": safety_checker}
+
+    if component_name == "feature_extractor":
+        if load_safety_checker:
+            feature_extractor = AutoFeatureExtractor.from_pretrained(
+                "CompVis/stable-diffusion-safety-checker", local_files_only=local_files_only
+            )
+        else:
+            feature_extractor = None
+        return {"feature_extractor": feature_extractor}
+
+    return
+
+
+def set_additional_components(
+    pipeline_class_name,
+    original_config,
+    checkpoint=None,
+    model_type=None,
+):
+    components = {}
+    if pipeline_class_name in REFINER_PIPELINES:
+        model_type = infer_model_type(original_config, checkpoint=checkpoint, model_type=model_type)
+        is_refiner = model_type == "SDXL-Refiner"
+        components.update(
+            {
+                "requires_aesthetics_score": is_refiner,
+                "force_zeros_for_empty_prompt": False if is_refiner else True,
+            }
+        )
+
+    return components
+
+
+class FromSingleFileMixin:
+    """
+    Load model weights saved in the `.ckpt` format into a [`DiffusionPipeline`].
+    """
+
+    @classmethod
+    @validate_hf_hub_args
+    def from_single_file(cls, pretrained_model_link_or_path, **kwargs):
+        r"""
+        Instantiate a [`DiffusionPipeline`] from pretrained pipeline weights saved in the `.ckpt` or `.safetensors`
+        format. The pipeline is set in evaluation mode (`model.eval()`) by default.
+
+        Parameters:
+            pretrained_model_link_or_path (`str` or `os.PathLike`, *optional*):
+                Can be either:
+                    - A link to the `.ckpt` file (for example
+                      `"https://huggingface.co/<repo_id>/blob/main/<path_to_file>.ckpt"`) on the Hub.
+                    - A path to a *file* containing all pipeline weights.
+            torch_dtype (`str` or `torch.dtype`, *optional*):
+                Override the default `torch.dtype` and load the model with another dtype.
+            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.
+            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.
+            resume_download:
+                Deprecated and ignored. All downloads are now resumed by default when possible. Will be removed in v1
+                of Diffusers.
+            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.
+            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.
+            original_config_file (`str`, *optional*):
+                The path to the original config file that was used to train the model. If not provided, the config file
+                will be inferred from the checkpoint file.
+            model_type (`str`, *optional*):
+                The type of model to load. If not provided, the model type will be inferred from the checkpoint file.
+            image_size (`int`, *optional*):
+                The size of the image output. It's used to configure the `sample_size` parameter of the UNet and VAE
+                model.
+            load_safety_checker (`bool`, *optional*, defaults to `False`):
+                Whether to load the safety checker model or not. By default, the safety checker is not loaded unless a
+                `safety_checker` component is passed to the `kwargs`.
+            num_in_channels (`int`, *optional*):
+                Specify the number of input channels for the UNet model. Read more about how to configure UNet model
+                with this parameter
+                [here](https://huggingface.co/docs/diffusers/training/adapt_a_model#configure-unet2dconditionmodel-parameters).
+            scaling_factor (`float`, *optional*):
+                The scaling factor to use for the VAE model. If not provided, it is inferred from the config file
+                first. If the scaling factor is not found in the config file, the default value 0.18215 is used.
+            scheduler_type (`str`, *optional*):
+                The type of scheduler to load. If not provided, the scheduler type will be inferred from the checkpoint
+                file.
+            prediction_type (`str`, *optional*):
+                The type of prediction to load. If not provided, the prediction type will be inferred from the
+                checkpoint file.
+            kwargs (remaining dictionary of keyword arguments, *optional*):
+                Can be used to overwrite load and saveable variables (the pipeline components of the specific pipeline
+                class). The overwritten components are passed directly to the pipelines `__init__` method. See example
+                below for more information.
+
+        Examples:
+
+        ```py
+        >>> from diffusers import StableDiffusionPipeline
+
+        >>> # Download pipeline from huggingface.co and cache.
+        >>> pipeline = StableDiffusionPipeline.from_single_file(
+        ...     "https://huggingface.co/WarriorMama777/OrangeMixs/blob/main/Models/AbyssOrangeMix/AbyssOrangeMix.safetensors"
+        ... )
+
+        >>> # Download pipeline from local file
+        >>> # file is downloaded under ./v1-5-pruned-emaonly.ckpt
+        >>> pipeline = StableDiffusionPipeline.from_single_file("./v1-5-pruned-emaonly")
+
+        >>> # Enable float16 and move to GPU
+        >>> pipeline = StableDiffusionPipeline.from_single_file(
+        ...     "https://huggingface.co/runwayml/stable-diffusion-v1-5/blob/main/v1-5-pruned-emaonly.ckpt",
+        ...     torch_dtype=torch.float16,
+        ... )
+        >>> pipeline.to("cuda")
+        ```
+        """
+        original_config_file = kwargs.pop("original_config_file", None)
+        resume_download = kwargs.pop("resume_download", None)
+        force_download = kwargs.pop("force_download", False)
+        proxies = kwargs.pop("proxies", None)
+        token = kwargs.pop("token", None)
+        cache_dir = kwargs.pop("cache_dir", None)
+        local_files_only = kwargs.pop("local_files_only", False)
+        revision = kwargs.pop("revision", None)
+        torch_dtype = kwargs.pop("torch_dtype", None)
+
+        class_name = cls.__name__
+
+        original_config, checkpoint = fetch_ldm_config_and_checkpoint(
+            pretrained_model_link_or_path=pretrained_model_link_or_path,
+            class_name=class_name,
+            original_config_file=original_config_file,
+            resume_download=resume_download,
+            force_download=force_download,
+            proxies=proxies,
+            token=token,
+            revision=revision,
+            local_files_only=local_files_only,
+            cache_dir=cache_dir,
+        )
+
+        from ..pipelines.pipeline_utils import _get_pipeline_class
+
+        pipeline_class = _get_pipeline_class(
+            cls,
+            config=None,
+            cache_dir=cache_dir,
+        )
+
+        expected_modules, optional_kwargs = cls._get_signature_keys(pipeline_class)
+        passed_class_obj = {k: kwargs.pop(k) for k in expected_modules if k in kwargs}
+        passed_pipe_kwargs = {k: kwargs.pop(k) for k in optional_kwargs if k in kwargs}
+
+        model_type = kwargs.pop("model_type", None)
+        image_size = kwargs.pop("image_size", None)
+        load_safety_checker = (kwargs.pop("load_safety_checker", False)) or (
+            passed_class_obj.get("safety_checker", None) is not None
+        )
+
+        init_kwargs = {}
+        for name in expected_modules:
+            if name in passed_class_obj:
+                init_kwargs[name] = passed_class_obj[name]
+            else:
+                components = build_sub_model_components(
+                    init_kwargs,
+                    class_name,
+                    name,
+                    original_config,
+                    checkpoint,
+                    model_type=model_type,
+                    image_size=image_size,
+                    load_safety_checker=load_safety_checker,
+                    local_files_only=local_files_only,
+                    torch_dtype=torch_dtype,
+                    **kwargs,
+                )
+                if not components:
+                    continue
+                init_kwargs.update(components)
+
+        additional_components = set_additional_components(
+            class_name, original_config, checkpoint=checkpoint, model_type=model_type
+        )
+        if additional_components:
+            init_kwargs.update(additional_components)
+
+        init_kwargs.update(passed_pipe_kwargs)
+        pipe = pipeline_class(**init_kwargs)
+
+        if torch_dtype is not None:
+            pipe.to(dtype=torch_dtype)
+
+        return pipe

diffusers/loaders/single_file_utils.py

@@ -0,0 +1,1609 @@
+# coding=utf-8
+# Copyright 2024 The HuggingFace Inc. team.
+#
+# 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.
+"""Conversion script for the Stable Diffusion checkpoints."""
+
+import os
+import re
+from contextlib import nullcontext
+from io import BytesIO
+from urllib.parse import urlparse
+
+import requests
+import yaml
+
+from ..models.modeling_utils import load_state_dict
+from ..schedulers import (
+    DDIMScheduler,
+    DDPMScheduler,
+    DPMSolverMultistepScheduler,
+    EDMDPMSolverMultistepScheduler,
+    EulerAncestralDiscreteScheduler,
+    EulerDiscreteScheduler,
+    HeunDiscreteScheduler,
+    LMSDiscreteScheduler,
+    PNDMScheduler,
+)
+from ..utils import is_accelerate_available, is_transformers_available, logging
+from ..utils.hub_utils import _get_model_file
+
+
+if is_transformers_available():
+    from transformers import (
+        CLIPTextConfig,
+        CLIPTextModel,
+        CLIPTextModelWithProjection,
+        CLIPTokenizer,
+    )
+
+if is_accelerate_available():
+    from accelerate import init_empty_weights
+
+    from ..models.modeling_utils import load_model_dict_into_meta
+
+logger = logging.get_logger(__name__)  # pylint: disable=invalid-name
+
+CONFIG_URLS = {
+    "v1": "https://raw.githubusercontent.com/CompVis/stable-diffusion/main/configs/stable-diffusion/v1-inference.yaml",
+    "v2": "https://raw.githubusercontent.com/Stability-AI/stablediffusion/main/configs/stable-diffusion/v2-inference-v.yaml",
+    "xl": "https://raw.githubusercontent.com/Stability-AI/generative-models/main/configs/inference/sd_xl_base.yaml",
+    "xl_refiner": "https://raw.githubusercontent.com/Stability-AI/generative-models/main/configs/inference/sd_xl_refiner.yaml",
+    "upscale": "https://raw.githubusercontent.com/Stability-AI/stablediffusion/main/configs/stable-diffusion/x4-upscaling.yaml",
+    "controlnet": "https://raw.githubusercontent.com/lllyasviel/ControlNet/main/models/cldm_v15.yaml",
+}
+
+CHECKPOINT_KEY_NAMES = {
+    "v2": "model.diffusion_model.input_blocks.2.1.transformer_blocks.0.attn2.to_k.weight",
+    "xl_base": "conditioner.embedders.1.model.transformer.resblocks.9.mlp.c_proj.bias",
+    "xl_refiner": "conditioner.embedders.0.model.transformer.resblocks.9.mlp.c_proj.bias",
+}
+
+SCHEDULER_DEFAULT_CONFIG = {
+    "beta_schedule": "scaled_linear",
+    "beta_start": 0.00085,
+    "beta_end": 0.012,
+    "interpolation_type": "linear",
+    "num_train_timesteps": 1000,
+    "prediction_type": "epsilon",
+    "sample_max_value": 1.0,
+    "set_alpha_to_one": False,
+    "skip_prk_steps": True,
+    "steps_offset": 1,
+    "timestep_spacing": "leading",
+}
+
+
+STABLE_CASCADE_DEFAULT_CONFIGS = {
+    "stage_c": {"pretrained_model_name_or_path": "diffusers/stable-cascade-configs", "subfolder": "prior"},
+    "stage_c_lite": {"pretrained_model_name_or_path": "diffusers/stable-cascade-configs", "subfolder": "prior_lite"},
+    "stage_b": {"pretrained_model_name_or_path": "diffusers/stable-cascade-configs", "subfolder": "decoder"},
+    "stage_b_lite": {"pretrained_model_name_or_path": "diffusers/stable-cascade-configs", "subfolder": "decoder_lite"},
+}
+
+
+def convert_stable_cascade_unet_single_file_to_diffusers(original_state_dict):
+    is_stage_c = "clip_txt_mapper.weight" in original_state_dict
+
+    if is_stage_c:
+        state_dict = {}
+        for key in original_state_dict.keys():
+            if key.endswith("in_proj_weight"):
+                weights = original_state_dict[key].chunk(3, 0)
+                state_dict[key.replace("attn.in_proj_weight", "to_q.weight")] = weights[0]
+                state_dict[key.replace("attn.in_proj_weight", "to_k.weight")] = weights[1]
+                state_dict[key.replace("attn.in_proj_weight", "to_v.weight")] = weights[2]
+            elif key.endswith("in_proj_bias"):
+                weights = original_state_dict[key].chunk(3, 0)
+                state_dict[key.replace("attn.in_proj_bias", "to_q.bias")] = weights[0]
+                state_dict[key.replace("attn.in_proj_bias", "to_k.bias")] = weights[1]
+                state_dict[key.replace("attn.in_proj_bias", "to_v.bias")] = weights[2]
+            elif key.endswith("out_proj.weight"):
+                weights = original_state_dict[key]
+                state_dict[key.replace("attn.out_proj.weight", "to_out.0.weight")] = weights
+            elif key.endswith("out_proj.bias"):
+                weights = original_state_dict[key]
+                state_dict[key.replace("attn.out_proj.bias", "to_out.0.bias")] = weights
+            else:
+                state_dict[key] = original_state_dict[key]
+    else:
+        state_dict = {}
+        for key in original_state_dict.keys():
+            if key.endswith("in_proj_weight"):
+                weights = original_state_dict[key].chunk(3, 0)
+                state_dict[key.replace("attn.in_proj_weight", "to_q.weight")] = weights[0]
+                state_dict[key.replace("attn.in_proj_weight", "to_k.weight")] = weights[1]
+                state_dict[key.replace("attn.in_proj_weight", "to_v.weight")] = weights[2]
+            elif key.endswith("in_proj_bias"):
+                weights = original_state_dict[key].chunk(3, 0)
+                state_dict[key.replace("attn.in_proj_bias", "to_q.bias")] = weights[0]
+                state_dict[key.replace("attn.in_proj_bias", "to_k.bias")] = weights[1]
+                state_dict[key.replace("attn.in_proj_bias", "to_v.bias")] = weights[2]
+            elif key.endswith("out_proj.weight"):
+                weights = original_state_dict[key]
+                state_dict[key.replace("attn.out_proj.weight", "to_out.0.weight")] = weights
+            elif key.endswith("out_proj.bias"):
+                weights = original_state_dict[key]
+                state_dict[key.replace("attn.out_proj.bias", "to_out.0.bias")] = weights
+            # rename clip_mapper to clip_txt_pooled_mapper
+            elif key.endswith("clip_mapper.weight"):
+                weights = original_state_dict[key]
+                state_dict[key.replace("clip_mapper.weight", "clip_txt_pooled_mapper.weight")] = weights
+            elif key.endswith("clip_mapper.bias"):
+                weights = original_state_dict[key]
+                state_dict[key.replace("clip_mapper.bias", "clip_txt_pooled_mapper.bias")] = weights
+            else:
+                state_dict[key] = original_state_dict[key]
+
+    return state_dict
+
+
+def infer_stable_cascade_single_file_config(checkpoint):
+    is_stage_c = "clip_txt_mapper.weight" in checkpoint
+    is_stage_b = "down_blocks.1.0.channelwise.0.weight" in checkpoint
+
+    if is_stage_c and (checkpoint["clip_txt_mapper.weight"].shape[0] == 1536):
+        config_type = "stage_c_lite"
+    elif is_stage_c and (checkpoint["clip_txt_mapper.weight"].shape[0] == 2048):
+        config_type = "stage_c"
+    elif is_stage_b and checkpoint["down_blocks.1.0.channelwise.0.weight"].shape[-1] == 576:
+        config_type = "stage_b_lite"
+    elif is_stage_b and checkpoint["down_blocks.1.0.channelwise.0.weight"].shape[-1] == 640:
+        config_type = "stage_b"
+
+    return STABLE_CASCADE_DEFAULT_CONFIGS[config_type]
+
+
+DIFFUSERS_TO_LDM_MAPPING = {
+    "unet": {
+        "layers": {
+            "time_embedding.linear_1.weight": "time_embed.0.weight",
+            "time_embedding.linear_1.bias": "time_embed.0.bias",
+            "time_embedding.linear_2.weight": "time_embed.2.weight",
+            "time_embedding.linear_2.bias": "time_embed.2.bias",
+            "conv_in.weight": "input_blocks.0.0.weight",
+            "conv_in.bias": "input_blocks.0.0.bias",
+            "conv_norm_out.weight": "out.0.weight",
+            "conv_norm_out.bias": "out.0.bias",
+            "conv_out.weight": "out.2.weight",
+            "conv_out.bias": "out.2.bias",
+        },
+        "class_embed_type": {
+            "class_embedding.linear_1.weight": "label_emb.0.0.weight",
+            "class_embedding.linear_1.bias": "label_emb.0.0.bias",
+            "class_embedding.linear_2.weight": "label_emb.0.2.weight",
+            "class_embedding.linear_2.bias": "label_emb.0.2.bias",
+        },
+        "addition_embed_type": {
+            "add_embedding.linear_1.weight": "label_emb.0.0.weight",
+            "add_embedding.linear_1.bias": "label_emb.0.0.bias",
+            "add_embedding.linear_2.weight": "label_emb.0.2.weight",
+            "add_embedding.linear_2.bias": "label_emb.0.2.bias",
+        },
+    },
+    "controlnet": {
+        "layers": {
+            "time_embedding.linear_1.weight": "time_embed.0.weight",
+            "time_embedding.linear_1.bias": "time_embed.0.bias",
+            "time_embedding.linear_2.weight": "time_embed.2.weight",
+            "time_embedding.linear_2.bias": "time_embed.2.bias",
+            "conv_in.weight": "input_blocks.0.0.weight",
+            "conv_in.bias": "input_blocks.0.0.bias",
+            "controlnet_cond_embedding.conv_in.weight": "input_hint_block.0.weight",
+            "controlnet_cond_embedding.conv_in.bias": "input_hint_block.0.bias",
+            "controlnet_cond_embedding.conv_out.weight": "input_hint_block.14.weight",
+            "controlnet_cond_embedding.conv_out.bias": "input_hint_block.14.bias",
+        },
+        "class_embed_type": {
+            "class_embedding.linear_1.weight": "label_emb.0.0.weight",
+            "class_embedding.linear_1.bias": "label_emb.0.0.bias",
+            "class_embedding.linear_2.weight": "label_emb.0.2.weight",
+            "class_embedding.linear_2.bias": "label_emb.0.2.bias",
+        },
+        "addition_embed_type": {
+            "add_embedding.linear_1.weight": "label_emb.0.0.weight",
+            "add_embedding.linear_1.bias": "label_emb.0.0.bias",
+            "add_embedding.linear_2.weight": "label_emb.0.2.weight",
+            "add_embedding.linear_2.bias": "label_emb.0.2.bias",
+        },
+    },
+    "vae": {
+        "encoder.conv_in.weight": "encoder.conv_in.weight",
+        "encoder.conv_in.bias": "encoder.conv_in.bias",
+        "encoder.conv_out.weight": "encoder.conv_out.weight",
+        "encoder.conv_out.bias": "encoder.conv_out.bias",
+        "encoder.conv_norm_out.weight": "encoder.norm_out.weight",
+        "encoder.conv_norm_out.bias": "encoder.norm_out.bias",
+        "decoder.conv_in.weight": "decoder.conv_in.weight",
+        "decoder.conv_in.bias": "decoder.conv_in.bias",
+        "decoder.conv_out.weight": "decoder.conv_out.weight",
+        "decoder.conv_out.bias": "decoder.conv_out.bias",
+        "decoder.conv_norm_out.weight": "decoder.norm_out.weight",
+        "decoder.conv_norm_out.bias": "decoder.norm_out.bias",
+        "quant_conv.weight": "quant_conv.weight",
+        "quant_conv.bias": "quant_conv.bias",
+        "post_quant_conv.weight": "post_quant_conv.weight",
+        "post_quant_conv.bias": "post_quant_conv.bias",
+    },
+    "openclip": {
+        "layers": {
+            "text_model.embeddings.position_embedding.weight": "positional_embedding",
+            "text_model.embeddings.token_embedding.weight": "token_embedding.weight",
+            "text_model.final_layer_norm.weight": "ln_final.weight",
+            "text_model.final_layer_norm.bias": "ln_final.bias",
+            "text_projection.weight": "text_projection",
+        },
+        "transformer": {
+            "text_model.encoder.layers.": "resblocks.",
+            "layer_norm1": "ln_1",
+            "layer_norm2": "ln_2",
+            ".fc1.": ".c_fc.",
+            ".fc2.": ".c_proj.",
+            ".self_attn": ".attn",
+            "transformer.text_model.final_layer_norm.": "ln_final.",
+            "transformer.text_model.embeddings.token_embedding.weight": "token_embedding.weight",
+            "transformer.text_model.embeddings.position_embedding.weight": "positional_embedding",
+        },
+    },
+}
+
+LDM_VAE_KEY = "first_stage_model."
+LDM_VAE_DEFAULT_SCALING_FACTOR = 0.18215
+PLAYGROUND_VAE_SCALING_FACTOR = 0.5
+LDM_UNET_KEY = "model.diffusion_model."
+LDM_CONTROLNET_KEY = "control_model."
+LDM_CLIP_PREFIX_TO_REMOVE = ["cond_stage_model.transformer.", "conditioner.embedders.0.transformer."]
+LDM_OPEN_CLIP_TEXT_PROJECTION_DIM = 1024
+
+SD_2_TEXT_ENCODER_KEYS_TO_IGNORE = [
+    "cond_stage_model.model.transformer.resblocks.23.attn.in_proj_bias",
+    "cond_stage_model.model.transformer.resblocks.23.attn.in_proj_weight",
+    "cond_stage_model.model.transformer.resblocks.23.attn.out_proj.bias",
+    "cond_stage_model.model.transformer.resblocks.23.attn.out_proj.weight",
+    "cond_stage_model.model.transformer.resblocks.23.ln_1.bias",
+    "cond_stage_model.model.transformer.resblocks.23.ln_1.weight",
+    "cond_stage_model.model.transformer.resblocks.23.ln_2.bias",
+    "cond_stage_model.model.transformer.resblocks.23.ln_2.weight",
+    "cond_stage_model.model.transformer.resblocks.23.mlp.c_fc.bias",
+    "cond_stage_model.model.transformer.resblocks.23.mlp.c_fc.weight",
+    "cond_stage_model.model.transformer.resblocks.23.mlp.c_proj.bias",
+    "cond_stage_model.model.transformer.resblocks.23.mlp.c_proj.weight",
+    "cond_stage_model.model.text_projection",
+]
+
+
+VALID_URL_PREFIXES = ["https://huggingface.co/", "huggingface.co/", "hf.co/", "https://hf.co/"]
+
+
+def _extract_repo_id_and_weights_name(pretrained_model_name_or_path):
+    pattern = r"([^/]+)/([^/]+)/(?:blob/main/)?(.+)"
+    weights_name = None
+    repo_id = (None,)
+    for prefix in VALID_URL_PREFIXES:
+        pretrained_model_name_or_path = pretrained_model_name_or_path.replace(prefix, "")
+    match = re.match(pattern, pretrained_model_name_or_path)
+    if not match:
+        return repo_id, weights_name
+
+    repo_id = f"{match.group(1)}/{match.group(2)}"
+    weights_name = match.group(3)
+
+    return repo_id, weights_name
+
+
+def fetch_ldm_config_and_checkpoint(
+    pretrained_model_link_or_path,
+    class_name,
+    original_config_file=None,
+    resume_download=None,
+    force_download=False,
+    proxies=None,
+    token=None,
+    cache_dir=None,
+    local_files_only=None,
+    revision=None,
+):
+    checkpoint = load_single_file_model_checkpoint(
+        pretrained_model_link_or_path,
+        resume_download=resume_download,
+        force_download=force_download,
+        proxies=proxies,
+        token=token,
+        cache_dir=cache_dir,
+        local_files_only=local_files_only,
+        revision=revision,
+    )
+    original_config = fetch_original_config(class_name, checkpoint, original_config_file)
+
+    return original_config, checkpoint
+
+
+def load_single_file_model_checkpoint(
+    pretrained_model_link_or_path,
+    resume_download=False,
+    force_download=False,
+    proxies=None,
+    token=None,
+    cache_dir=None,
+    local_files_only=None,
+    revision=None,
+):
+    if os.path.isfile(pretrained_model_link_or_path):
+        checkpoint = load_state_dict(pretrained_model_link_or_path)
+    else:
+        repo_id, weights_name = _extract_repo_id_and_weights_name(pretrained_model_link_or_path)
+        checkpoint_path = _get_model_file(
+            repo_id,
+            weights_name=weights_name,
+            force_download=force_download,
+            cache_dir=cache_dir,
+            resume_download=resume_download,
+            proxies=proxies,
+            local_files_only=local_files_only,
+            token=token,
+            revision=revision,
+        )
+        checkpoint = load_state_dict(checkpoint_path)
+
+    # some checkpoints contain the model state dict under a "state_dict" key
+    while "state_dict" in checkpoint:
+        checkpoint = checkpoint["state_dict"]
+
+    return checkpoint
+
+
+def infer_original_config_file(class_name, checkpoint):
+    if CHECKPOINT_KEY_NAMES["v2"] in checkpoint and checkpoint[CHECKPOINT_KEY_NAMES["v2"]].shape[-1] == 1024:
+        config_url = CONFIG_URLS["v2"]
+
+    elif CHECKPOINT_KEY_NAMES["xl_base"] in checkpoint:
+        config_url = CONFIG_URLS["xl"]
+
+    elif CHECKPOINT_KEY_NAMES["xl_refiner"] in checkpoint:
+        config_url = CONFIG_URLS["xl_refiner"]
+
+    elif class_name == "StableDiffusionUpscalePipeline":
+        config_url = CONFIG_URLS["upscale"]
+
+    elif class_name == "ControlNetModel":
+        config_url = CONFIG_URLS["controlnet"]
+
+    else:
+        config_url = CONFIG_URLS["v1"]
+
+    original_config_file = BytesIO(requests.get(config_url).content)
+
+    return original_config_file
+
+
+def fetch_original_config(pipeline_class_name, checkpoint, original_config_file=None):
+    def is_valid_url(url):
+        result = urlparse(url)
+        if result.scheme and result.netloc:
+            return True
+
+        return False
+
+    if original_config_file is None:
+        original_config_file = infer_original_config_file(pipeline_class_name, checkpoint)
+
+    elif os.path.isfile(original_config_file):
+        with open(original_config_file, "r") as fp:
+            original_config_file = fp.read()
+
+    elif is_valid_url(original_config_file):
+        original_config_file = BytesIO(requests.get(original_config_file).content)
+
+    else:
+        raise ValueError("Invalid `original_config_file` provided. Please set it to a valid file path or URL.")
+
+    original_config = yaml.safe_load(original_config_file)
+
+    return original_config
+
+
+def infer_model_type(original_config, checkpoint, model_type=None):
+    if model_type is not None:
+        return model_type
+
+    has_cond_stage_config = (
+        "cond_stage_config" in original_config["model"]["params"]
+        and original_config["model"]["params"]["cond_stage_config"] is not None
+    )
+    has_network_config = (
+        "network_config" in original_config["model"]["params"]
+        and original_config["model"]["params"]["network_config"] is not None
+    )
+
+    if has_cond_stage_config:
+        model_type = original_config["model"]["params"]["cond_stage_config"]["target"].split(".")[-1]
+
+    elif has_network_config:
+        context_dim = original_config["model"]["params"]["network_config"]["params"]["context_dim"]
+        if "edm_mean" in checkpoint and "edm_std" in checkpoint:
+            model_type = "Playground"
+        elif context_dim == 2048:
+            model_type = "SDXL"
+        else:
+            model_type = "SDXL-Refiner"
+    else:
+        raise ValueError("Unable to infer model type from config")
+
+    logger.debug(f"No `model_type` given, `model_type` inferred as: {model_type}")
+
+    return model_type
+
+
+def get_default_scheduler_config():
+    return SCHEDULER_DEFAULT_CONFIG
+
+
+def set_image_size(pipeline_class_name, original_config, checkpoint, image_size=None, model_type=None):
+    if image_size:
+        return image_size
+
+    global_step = checkpoint["global_step"] if "global_step" in checkpoint else None
+    model_type = infer_model_type(original_config, checkpoint, model_type)
+
+    if pipeline_class_name == "StableDiffusionUpscalePipeline":
+        image_size = original_config["model"]["params"]["unet_config"]["params"]["image_size"]
+        return image_size
+
+    elif model_type in ["SDXL", "SDXL-Refiner", "Playground"]:
+        image_size = 1024
+        return image_size
+
+    elif (
+        "parameterization" in original_config["model"]["params"]
+        and original_config["model"]["params"]["parameterization"] == "v"
+    ):
+        # NOTE: For stable diffusion 2 base one has to pass `image_size==512`
+        # as it relies on a brittle global step parameter here
+        image_size = 512 if global_step == 875000 else 768
+        return image_size
+
+    else:
+        image_size = 512
+        return image_size
+
+
+# Copied from diffusers.pipelines.stable_diffusion.convert_from_ckpt.conv_attn_to_linear
+def conv_attn_to_linear(checkpoint):
+    keys = list(checkpoint.keys())
+    attn_keys = ["query.weight", "key.weight", "value.weight"]
+    for key in keys:
+        if ".".join(key.split(".")[-2:]) in attn_keys:
+            if checkpoint[key].ndim > 2:
+                checkpoint[key] = checkpoint[key][:, :, 0, 0]
+        elif "proj_attn.weight" in key:
+            if checkpoint[key].ndim > 2:
+                checkpoint[key] = checkpoint[key][:, :, 0]
+
+
+def create_unet_diffusers_config(original_config, image_size: int):
+    """
+    Creates a config for the diffusers based on the config of the LDM model.
+    """
+    if (
+        "unet_config" in original_config["model"]["params"]
+        and original_config["model"]["params"]["unet_config"] is not None
+    ):
+        unet_params = original_config["model"]["params"]["unet_config"]["params"]
+    else:
+        unet_params = original_config["model"]["params"]["network_config"]["params"]
+
+    vae_params = original_config["model"]["params"]["first_stage_config"]["params"]["ddconfig"]
+    block_out_channels = [unet_params["model_channels"] * mult for mult in unet_params["channel_mult"]]
+
+    down_block_types = []
+    resolution = 1
+    for i in range(len(block_out_channels)):
+        block_type = "CrossAttnDownBlock2D" if resolution in unet_params["attention_resolutions"] else "DownBlock2D"
+        down_block_types.append(block_type)
+        if i != len(block_out_channels) - 1:
+            resolution *= 2
+
+    up_block_types = []
+    for i in range(len(block_out_channels)):
+        block_type = "CrossAttnUpBlock2D" if resolution in unet_params["attention_resolutions"] else "UpBlock2D"
+        up_block_types.append(block_type)
+        resolution //= 2
+
+    if unet_params["transformer_depth"] is not None:
+        transformer_layers_per_block = (
+            unet_params["transformer_depth"]
+            if isinstance(unet_params["transformer_depth"], int)
+            else list(unet_params["transformer_depth"])
+        )
+    else:
+        transformer_layers_per_block = 1
+
+    vae_scale_factor = 2 ** (len(vae_params["ch_mult"]) - 1)
+
+    head_dim = unet_params["num_heads"] if "num_heads" in unet_params else None
+    use_linear_projection = (
+        unet_params["use_linear_in_transformer"] if "use_linear_in_transformer" in unet_params else False
+    )
+    if use_linear_projection:
+        # stable diffusion 2-base-512 and 2-768
+        if head_dim is None:
+            head_dim_mult = unet_params["model_channels"] // unet_params["num_head_channels"]
+            head_dim = [head_dim_mult * c for c in list(unet_params["channel_mult"])]
+
+    class_embed_type = None
+    addition_embed_type = None
+    addition_time_embed_dim = None
+    projection_class_embeddings_input_dim = None
+    context_dim = None
+
+    if unet_params["context_dim"] is not None:
+        context_dim = (
+            unet_params["context_dim"]
+            if isinstance(unet_params["context_dim"], int)
+            else unet_params["context_dim"][0]
+        )
+
+    if "num_classes" in unet_params:
+        if unet_params["num_classes"] == "sequential":
+            if context_dim in [2048, 1280]:
+                # SDXL
+                addition_embed_type = "text_time"
+                addition_time_embed_dim = 256
+            else:
+                class_embed_type = "projection"
+            assert "adm_in_channels" in unet_params
+            projection_class_embeddings_input_dim = unet_params["adm_in_channels"]
+
+    config = {
+        "sample_size": image_size // vae_scale_factor,
+        "in_channels": unet_params["in_channels"],
+        "down_block_types": down_block_types,
+        "block_out_channels": block_out_channels,
+        "layers_per_block": unet_params["num_res_blocks"],
+        "cross_attention_dim": context_dim,
+        "attention_head_dim": head_dim,
+        "use_linear_projection": use_linear_projection,
+        "class_embed_type": class_embed_type,
+        "addition_embed_type": addition_embed_type,
+        "addition_time_embed_dim": addition_time_embed_dim,
+        "projection_class_embeddings_input_dim": projection_class_embeddings_input_dim,
+        "transformer_layers_per_block": transformer_layers_per_block,
+    }
+
+    if "disable_self_attentions" in unet_params:
+        config["only_cross_attention"] = unet_params["disable_self_attentions"]
+
+    if "num_classes" in unet_params and isinstance(unet_params["num_classes"], int):
+        config["num_class_embeds"] = unet_params["num_classes"]
+
+    config["out_channels"] = unet_params["out_channels"]
+    config["up_block_types"] = up_block_types
+
+    return config
+
+
+def create_controlnet_diffusers_config(original_config, image_size: int):
+    unet_params = original_config["model"]["params"]["control_stage_config"]["params"]
+    diffusers_unet_config = create_unet_diffusers_config(original_config, image_size=image_size)
+
+    controlnet_config = {
+        "conditioning_channels": unet_params["hint_channels"],
+        "in_channels": diffusers_unet_config["in_channels"],
+        "down_block_types": diffusers_unet_config["down_block_types"],
+        "block_out_channels": diffusers_unet_config["block_out_channels"],
+        "layers_per_block": diffusers_unet_config["layers_per_block"],
+        "cross_attention_dim": diffusers_unet_config["cross_attention_dim"],
+        "attention_head_dim": diffusers_unet_config["attention_head_dim"],
+        "use_linear_projection": diffusers_unet_config["use_linear_projection"],
+        "class_embed_type": diffusers_unet_config["class_embed_type"],
+        "addition_embed_type": diffusers_unet_config["addition_embed_type"],
+        "addition_time_embed_dim": diffusers_unet_config["addition_time_embed_dim"],
+        "projection_class_embeddings_input_dim": diffusers_unet_config["projection_class_embeddings_input_dim"],
+        "transformer_layers_per_block": diffusers_unet_config["transformer_layers_per_block"],
+    }
+
+    return controlnet_config
+
+
+def create_vae_diffusers_config(original_config, image_size, scaling_factor=None, latents_mean=None, latents_std=None):
+    """
+    Creates a config for the diffusers based on the config of the LDM model.
+    """
+    vae_params = original_config["model"]["params"]["first_stage_config"]["params"]["ddconfig"]
+    if (scaling_factor is None) and (latents_mean is not None) and (latents_std is not None):
+        scaling_factor = PLAYGROUND_VAE_SCALING_FACTOR
+    elif (scaling_factor is None) and ("scale_factor" in original_config["model"]["params"]):
+        scaling_factor = original_config["model"]["params"]["scale_factor"]
+    elif scaling_factor is None:
+        scaling_factor = LDM_VAE_DEFAULT_SCALING_FACTOR
+
+    block_out_channels = [vae_params["ch"] * mult for mult in vae_params["ch_mult"]]
+    down_block_types = ["DownEncoderBlock2D"] * len(block_out_channels)
+    up_block_types = ["UpDecoderBlock2D"] * len(block_out_channels)
+
+    config = {
+        "sample_size": image_size,
+        "in_channels": vae_params["in_channels"],
+        "out_channels": vae_params["out_ch"],
+        "down_block_types": down_block_types,
+        "up_block_types": up_block_types,
+        "block_out_channels": block_out_channels,
+        "latent_channels": vae_params["z_channels"],
+        "layers_per_block": vae_params["num_res_blocks"],
+        "scaling_factor": scaling_factor,
+    }
+    if latents_mean is not None and latents_std is not None:
+        config.update({"latents_mean": latents_mean, "latents_std": latents_std})
+
+    return config
+
+
+def update_unet_resnet_ldm_to_diffusers(ldm_keys, new_checkpoint, checkpoint, mapping=None):
+    for ldm_key in ldm_keys:
+        diffusers_key = (
+            ldm_key.replace("in_layers.0", "norm1")
+            .replace("in_layers.2", "conv1")
+            .replace("out_layers.0", "norm2")
+            .replace("out_layers.3", "conv2")
+            .replace("emb_layers.1", "time_emb_proj")
+            .replace("skip_connection", "conv_shortcut")
+        )
+        if mapping:
+            diffusers_key = diffusers_key.replace(mapping["old"], mapping["new"])
+        new_checkpoint[diffusers_key] = checkpoint.pop(ldm_key)
+
+
+def update_unet_attention_ldm_to_diffusers(ldm_keys, new_checkpoint, checkpoint, mapping):
+    for ldm_key in ldm_keys:
+        diffusers_key = ldm_key.replace(mapping["old"], mapping["new"])
+        new_checkpoint[diffusers_key] = checkpoint.pop(ldm_key)
+
+
+def convert_ldm_unet_checkpoint(checkpoint, config, extract_ema=False):
+    """
+    Takes a state dict and a config, and returns a converted checkpoint.
+    """
+    # extract state_dict for UNet
+    unet_state_dict = {}
+    keys = list(checkpoint.keys())
+    unet_key = LDM_UNET_KEY
+
+    # at least a 100 parameters have to start with `model_ema` in order for the checkpoint to be EMA
+    if sum(k.startswith("model_ema") for k in keys) > 100 and extract_ema:
+        logger.warning("Checkpoint has both EMA and non-EMA weights.")
+        logger.warning(
+            "In this conversion only the EMA weights are extracted. If you want to instead extract the non-EMA"
+            " weights (useful to continue fine-tuning), please make sure to remove the `--extract_ema` flag."
+        )
+        for key in keys:
+            if key.startswith("model.diffusion_model"):
+                flat_ema_key = "model_ema." + "".join(key.split(".")[1:])
+                unet_state_dict[key.replace(unet_key, "")] = checkpoint.pop(flat_ema_key)
+    else:
+        if sum(k.startswith("model_ema") for k in keys) > 100:
+            logger.warning(
+                "In this conversion only the non-EMA weights are extracted. If you want to instead extract the EMA"
+                " weights (usually better for inference), please make sure to add the `--extract_ema` flag."
+            )
+        for key in keys:
+            if key.startswith(unet_key):
+                unet_state_dict[key.replace(unet_key, "")] = checkpoint.pop(key)
+
+    new_checkpoint = {}
+    ldm_unet_keys = DIFFUSERS_TO_LDM_MAPPING["unet"]["layers"]
+    for diffusers_key, ldm_key in ldm_unet_keys.items():
+        if ldm_key not in unet_state_dict:
+            continue
+        new_checkpoint[diffusers_key] = unet_state_dict[ldm_key]
+
+    if ("class_embed_type" in config) and (config["class_embed_type"] in ["timestep", "projection"]):
+        class_embed_keys = DIFFUSERS_TO_LDM_MAPPING["unet"]["class_embed_type"]
+        for diffusers_key, ldm_key in class_embed_keys.items():
+            new_checkpoint[diffusers_key] = unet_state_dict[ldm_key]
+
+    if ("addition_embed_type" in config) and (config["addition_embed_type"] == "text_time"):
+        addition_embed_keys = DIFFUSERS_TO_LDM_MAPPING["unet"]["addition_embed_type"]
+        for diffusers_key, ldm_key in addition_embed_keys.items():
+            new_checkpoint[diffusers_key] = unet_state_dict[ldm_key]
+
+    # Relevant to StableDiffusionUpscalePipeline
+    if "num_class_embeds" in config:
+        if (config["num_class_embeds"] is not None) and ("label_emb.weight" in unet_state_dict):
+            new_checkpoint["class_embedding.weight"] = unet_state_dict["label_emb.weight"]
+
+    # Retrieves the keys for the input blocks only
+    num_input_blocks = len({".".join(layer.split(".")[:2]) for layer in unet_state_dict if "input_blocks" in layer})
+    input_blocks = {
+        layer_id: [key for key in unet_state_dict if f"input_blocks.{layer_id}" in key]
+        for layer_id in range(num_input_blocks)
+    }
+
+    # Retrieves the keys for the middle blocks only
+    num_middle_blocks = len({".".join(layer.split(".")[:2]) for layer in unet_state_dict if "middle_block" in layer})
+    middle_blocks = {
+        layer_id: [key for key in unet_state_dict if f"middle_block.{layer_id}" in key]
+        for layer_id in range(num_middle_blocks)
+    }
+
+    # Retrieves the keys for the output blocks only
+    num_output_blocks = len({".".join(layer.split(".")[:2]) for layer in unet_state_dict if "output_blocks" in layer})
+    output_blocks = {
+        layer_id: [key for key in unet_state_dict if f"output_blocks.{layer_id}" in key]
+        for layer_id in range(num_output_blocks)
+    }
+
+    # Down blocks
+    for i in range(1, num_input_blocks):
+        block_id = (i - 1) // (config["layers_per_block"] + 1)
+        layer_in_block_id = (i - 1) % (config["layers_per_block"] + 1)
+
+        resnets = [
+            key for key in input_blocks[i] if f"input_blocks.{i}.0" in key and f"input_blocks.{i}.0.op" not in key
+        ]
+        update_unet_resnet_ldm_to_diffusers(
+            resnets,
+            new_checkpoint,
+            unet_state_dict,
+            {"old": f"input_blocks.{i}.0", "new": f"down_blocks.{block_id}.resnets.{layer_in_block_id}"},
+        )
+
+        if f"input_blocks.{i}.0.op.weight" in unet_state_dict:
+            new_checkpoint[f"down_blocks.{block_id}.downsamplers.0.conv.weight"] = unet_state_dict.pop(
+                f"input_blocks.{i}.0.op.weight"
+            )
+            new_checkpoint[f"down_blocks.{block_id}.downsamplers.0.conv.bias"] = unet_state_dict.pop(
+                f"input_blocks.{i}.0.op.bias"
+            )
+
+        attentions = [key for key in input_blocks[i] if f"input_blocks.{i}.1" in key]
+        if attentions:
+            update_unet_attention_ldm_to_diffusers(
+                attentions,
+                new_checkpoint,
+                unet_state_dict,
+                {"old": f"input_blocks.{i}.1", "new": f"down_blocks.{block_id}.attentions.{layer_in_block_id}"},
+            )
+
+    # Mid blocks
+    resnet_0 = middle_blocks[0]
+    attentions = middle_blocks[1]
+    resnet_1 = middle_blocks[2]
+
+    update_unet_resnet_ldm_to_diffusers(
+        resnet_0, new_checkpoint, unet_state_dict, mapping={"old": "middle_block.0", "new": "mid_block.resnets.0"}
+    )
+    update_unet_resnet_ldm_to_diffusers(
+        resnet_1, new_checkpoint, unet_state_dict, mapping={"old": "middle_block.2", "new": "mid_block.resnets.1"}
+    )
+    update_unet_attention_ldm_to_diffusers(
+        attentions, new_checkpoint, unet_state_dict, mapping={"old": "middle_block.1", "new": "mid_block.attentions.0"}
+    )
+
+    # Up Blocks
+    for i in range(num_output_blocks):
+        block_id = i // (config["layers_per_block"] + 1)
+        layer_in_block_id = i % (config["layers_per_block"] + 1)
+
+        resnets = [
+            key for key in output_blocks[i] if f"output_blocks.{i}.0" in key and f"output_blocks.{i}.0.op" not in key
+        ]
+        update_unet_resnet_ldm_to_diffusers(
+            resnets,
+            new_checkpoint,
+            unet_state_dict,
+            {"old": f"output_blocks.{i}.0", "new": f"up_blocks.{block_id}.resnets.{layer_in_block_id}"},
+        )
+
+        attentions = [
+            key for key in output_blocks[i] if f"output_blocks.{i}.1" in key and f"output_blocks.{i}.1.conv" not in key
+        ]
+        if attentions:
+            update_unet_attention_ldm_to_diffusers(
+                attentions,
+                new_checkpoint,
+                unet_state_dict,
+                {"old": f"output_blocks.{i}.1", "new": f"up_blocks.{block_id}.attentions.{layer_in_block_id}"},
+            )
+
+        if f"output_blocks.{i}.1.conv.weight" in unet_state_dict:
+            new_checkpoint[f"up_blocks.{block_id}.upsamplers.0.conv.weight"] = unet_state_dict[
+                f"output_blocks.{i}.1.conv.weight"
+            ]
+            new_checkpoint[f"up_blocks.{block_id}.upsamplers.0.conv.bias"] = unet_state_dict[
+                f"output_blocks.{i}.1.conv.bias"
+            ]
+        if f"output_blocks.{i}.2.conv.weight" in unet_state_dict:
+            new_checkpoint[f"up_blocks.{block_id}.upsamplers.0.conv.weight"] = unet_state_dict[
+                f"output_blocks.{i}.2.conv.weight"
+            ]
+            new_checkpoint[f"up_blocks.{block_id}.upsamplers.0.conv.bias"] = unet_state_dict[
+                f"output_blocks.{i}.2.conv.bias"
+            ]
+
+    return new_checkpoint
+
+
+def convert_controlnet_checkpoint(
+    checkpoint,
+    config,
+):
+    # Some controlnet ckpt files are distributed independently from the rest of the
+    # model components i.e. https://huggingface.co/thibaud/controlnet-sd21/
+    if "time_embed.0.weight" in checkpoint:
+        controlnet_state_dict = checkpoint
+
+    else:
+        controlnet_state_dict = {}
+        keys = list(checkpoint.keys())
+        controlnet_key = LDM_CONTROLNET_KEY
+        for key in keys:
+            if key.startswith(controlnet_key):
+                controlnet_state_dict[key.replace(controlnet_key, "")] = checkpoint.pop(key)
+
+    new_checkpoint = {}
+    ldm_controlnet_keys = DIFFUSERS_TO_LDM_MAPPING["controlnet"]["layers"]
+    for diffusers_key, ldm_key in ldm_controlnet_keys.items():
+        if ldm_key not in controlnet_state_dict:
+            continue
+        new_checkpoint[diffusers_key] = controlnet_state_dict[ldm_key]
+
+    # Retrieves the keys for the input blocks only
+    num_input_blocks = len(
+        {".".join(layer.split(".")[:2]) for layer in controlnet_state_dict if "input_blocks" in layer}
+    )
+    input_blocks = {
+        layer_id: [key for key in controlnet_state_dict if f"input_blocks.{layer_id}" in key]
+        for layer_id in range(num_input_blocks)
+    }
+
+    # Down blocks
+    for i in range(1, num_input_blocks):
+        block_id = (i - 1) // (config["layers_per_block"] + 1)
+        layer_in_block_id = (i - 1) % (config["layers_per_block"] + 1)
+
+        resnets = [
+            key for key in input_blocks[i] if f"input_blocks.{i}.0" in key and f"input_blocks.{i}.0.op" not in key
+        ]
+        update_unet_resnet_ldm_to_diffusers(
+            resnets,
+            new_checkpoint,
+            controlnet_state_dict,
+            {"old": f"input_blocks.{i}.0", "new": f"down_blocks.{block_id}.resnets.{layer_in_block_id}"},
+        )
+
+        if f"input_blocks.{i}.0.op.weight" in controlnet_state_dict:
+            new_checkpoint[f"down_blocks.{block_id}.downsamplers.0.conv.weight"] = controlnet_state_dict.pop(
+                f"input_blocks.{i}.0.op.weight"
+            )
+            new_checkpoint[f"down_blocks.{block_id}.downsamplers.0.conv.bias"] = controlnet_state_dict.pop(
+                f"input_blocks.{i}.0.op.bias"
+            )
+
+        attentions = [key for key in input_blocks[i] if f"input_blocks.{i}.1" in key]
+        if attentions:
+            update_unet_attention_ldm_to_diffusers(
+                attentions,
+                new_checkpoint,
+                controlnet_state_dict,
+                {"old": f"input_blocks.{i}.1", "new": f"down_blocks.{block_id}.attentions.{layer_in_block_id}"},
+            )
+
+    # controlnet down blocks
+    for i in range(num_input_blocks):
+        new_checkpoint[f"controlnet_down_blocks.{i}.weight"] = controlnet_state_dict.pop(f"zero_convs.{i}.0.weight")
+        new_checkpoint[f"controlnet_down_blocks.{i}.bias"] = controlnet_state_dict.pop(f"zero_convs.{i}.0.bias")
+
+    # Retrieves the keys for the middle blocks only
+    num_middle_blocks = len(
+        {".".join(layer.split(".")[:2]) for layer in controlnet_state_dict if "middle_block" in layer}
+    )
+    middle_blocks = {
+        layer_id: [key for key in controlnet_state_dict if f"middle_block.{layer_id}" in key]
+        for layer_id in range(num_middle_blocks)
+    }
+    if middle_blocks:
+        resnet_0 = middle_blocks[0]
+        attentions = middle_blocks[1]
+        resnet_1 = middle_blocks[2]
+
+        update_unet_resnet_ldm_to_diffusers(
+            resnet_0,
+            new_checkpoint,
+            controlnet_state_dict,
+            mapping={"old": "middle_block.0", "new": "mid_block.resnets.0"},
+        )
+        update_unet_resnet_ldm_to_diffusers(
+            resnet_1,
+            new_checkpoint,
+            controlnet_state_dict,
+            mapping={"old": "middle_block.2", "new": "mid_block.resnets.1"},
+        )
+        update_unet_attention_ldm_to_diffusers(
+            attentions,
+            new_checkpoint,
+            controlnet_state_dict,
+            mapping={"old": "middle_block.1", "new": "mid_block.attentions.0"},
+        )
+
+    # mid block
+    new_checkpoint["controlnet_mid_block.weight"] = controlnet_state_dict.pop("middle_block_out.0.weight")
+    new_checkpoint["controlnet_mid_block.bias"] = controlnet_state_dict.pop("middle_block_out.0.bias")
+
+    # controlnet cond embedding blocks
+    cond_embedding_blocks = {
+        ".".join(layer.split(".")[:2])
+        for layer in controlnet_state_dict
+        if "input_hint_block" in layer and ("input_hint_block.0" not in layer) and ("input_hint_block.14" not in layer)
+    }
+    num_cond_embedding_blocks = len(cond_embedding_blocks)
+
+    for idx in range(1, num_cond_embedding_blocks + 1):
+        diffusers_idx = idx - 1
+        cond_block_id = 2 * idx
+
+        new_checkpoint[f"controlnet_cond_embedding.blocks.{diffusers_idx}.weight"] = controlnet_state_dict.pop(
+            f"input_hint_block.{cond_block_id}.weight"
+        )
+        new_checkpoint[f"controlnet_cond_embedding.blocks.{diffusers_idx}.bias"] = controlnet_state_dict.pop(
+            f"input_hint_block.{cond_block_id}.bias"
+        )
+
+    return new_checkpoint
+
+
+def create_diffusers_controlnet_model_from_ldm(
+    pipeline_class_name, original_config, checkpoint, upcast_attention=False, image_size=None, torch_dtype=None
+):
+    # import here to avoid circular imports
+    from ..models import ControlNetModel
+
+    image_size = set_image_size(pipeline_class_name, original_config, checkpoint, image_size=image_size)
+
+    diffusers_config = create_controlnet_diffusers_config(original_config, image_size=image_size)
+    diffusers_config["upcast_attention"] = upcast_attention
+
+    diffusers_format_controlnet_checkpoint = convert_controlnet_checkpoint(checkpoint, diffusers_config)
+
+    ctx = init_empty_weights if is_accelerate_available() else nullcontext
+    with ctx():
+        controlnet = ControlNetModel(**diffusers_config)
+
+    if is_accelerate_available():
+        unexpected_keys = load_model_dict_into_meta(
+            controlnet, diffusers_format_controlnet_checkpoint, dtype=torch_dtype
+        )
+        if controlnet._keys_to_ignore_on_load_unexpected is not None:
+            for pat in controlnet._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 {controlnet.__name__}: \n {[', '.join(unexpected_keys)]}"
+            )
+    else:
+        controlnet.load_state_dict(diffusers_format_controlnet_checkpoint)
+
+    if torch_dtype is not None:
+        controlnet = controlnet.to(torch_dtype)
+
+    return {"controlnet": controlnet}
+
+
+def update_vae_resnet_ldm_to_diffusers(keys, new_checkpoint, checkpoint, mapping):
+    for ldm_key in keys:
+        diffusers_key = ldm_key.replace(mapping["old"], mapping["new"]).replace("nin_shortcut", "conv_shortcut")
+        new_checkpoint[diffusers_key] = checkpoint.pop(ldm_key)
+
+
+def update_vae_attentions_ldm_to_diffusers(keys, new_checkpoint, checkpoint, mapping):
+    for ldm_key in keys:
+        diffusers_key = (
+            ldm_key.replace(mapping["old"], mapping["new"])
+            .replace("norm.weight", "group_norm.weight")
+            .replace("norm.bias", "group_norm.bias")
+            .replace("q.weight", "to_q.weight")
+            .replace("q.bias", "to_q.bias")
+            .replace("k.weight", "to_k.weight")
+            .replace("k.bias", "to_k.bias")
+            .replace("v.weight", "to_v.weight")
+            .replace("v.bias", "to_v.bias")
+            .replace("proj_out.weight", "to_out.0.weight")
+            .replace("proj_out.bias", "to_out.0.bias")
+        )
+        new_checkpoint[diffusers_key] = checkpoint.pop(ldm_key)
+
+        # proj_attn.weight has to be converted from conv 1D to linear
+        shape = new_checkpoint[diffusers_key].shape
+
+        if len(shape) == 3:
+            new_checkpoint[diffusers_key] = new_checkpoint[diffusers_key][:, :, 0]
+        elif len(shape) == 4:
+            new_checkpoint[diffusers_key] = new_checkpoint[diffusers_key][:, :, 0, 0]
+
+
+def convert_ldm_vae_checkpoint(checkpoint, config):
+    # extract state dict for VAE
+    # remove the LDM_VAE_KEY prefix from the ldm checkpoint keys so that it is easier to map them to diffusers keys
+    vae_state_dict = {}
+    keys = list(checkpoint.keys())
+    vae_key = LDM_VAE_KEY if any(k.startswith(LDM_VAE_KEY) for k in keys) else ""
+    for key in keys:
+        if key.startswith(vae_key):
+            vae_state_dict[key.replace(vae_key, "")] = checkpoint.get(key)
+
+    new_checkpoint = {}
+    vae_diffusers_ldm_map = DIFFUSERS_TO_LDM_MAPPING["vae"]
+    for diffusers_key, ldm_key in vae_diffusers_ldm_map.items():
+        if ldm_key not in vae_state_dict:
+            continue
+        new_checkpoint[diffusers_key] = vae_state_dict[ldm_key]
+
+    # Retrieves the keys for the encoder down blocks only
+    num_down_blocks = len(config["down_block_types"])
+    down_blocks = {
+        layer_id: [key for key in vae_state_dict if f"down.{layer_id}" in key] for layer_id in range(num_down_blocks)
+    }
+
+    for i in range(num_down_blocks):
+        resnets = [key for key in down_blocks[i] if f"down.{i}" in key and f"down.{i}.downsample" not in key]
+        update_vae_resnet_ldm_to_diffusers(
+            resnets,
+            new_checkpoint,
+            vae_state_dict,
+            mapping={"old": f"down.{i}.block", "new": f"down_blocks.{i}.resnets"},
+        )
+        if f"encoder.down.{i}.downsample.conv.weight" in vae_state_dict:
+            new_checkpoint[f"encoder.down_blocks.{i}.downsamplers.0.conv.weight"] = vae_state_dict.pop(
+                f"encoder.down.{i}.downsample.conv.weight"
+            )
+            new_checkpoint[f"encoder.down_blocks.{i}.downsamplers.0.conv.bias"] = vae_state_dict.pop(
+                f"encoder.down.{i}.downsample.conv.bias"
+            )
+
+    mid_resnets = [key for key in vae_state_dict if "encoder.mid.block" in key]
+    num_mid_res_blocks = 2
+    for i in range(1, num_mid_res_blocks + 1):
+        resnets = [key for key in mid_resnets if f"encoder.mid.block_{i}" in key]
+        update_vae_resnet_ldm_to_diffusers(
+            resnets,
+            new_checkpoint,
+            vae_state_dict,
+            mapping={"old": f"mid.block_{i}", "new": f"mid_block.resnets.{i - 1}"},
+        )
+
+    mid_attentions = [key for key in vae_state_dict if "encoder.mid.attn" in key]
+    update_vae_attentions_ldm_to_diffusers(
+        mid_attentions, new_checkpoint, vae_state_dict, mapping={"old": "mid.attn_1", "new": "mid_block.attentions.0"}
+    )
+
+    # Retrieves the keys for the decoder up blocks only
+    num_up_blocks = len(config["up_block_types"])
+    up_blocks = {
+        layer_id: [key for key in vae_state_dict if f"up.{layer_id}" in key] for layer_id in range(num_up_blocks)
+    }
+
+    for i in range(num_up_blocks):
+        block_id = num_up_blocks - 1 - i
+        resnets = [
+            key for key in up_blocks[block_id] if f"up.{block_id}" in key and f"up.{block_id}.upsample" not in key
+        ]
+        update_vae_resnet_ldm_to_diffusers(
+            resnets,
+            new_checkpoint,
+            vae_state_dict,
+            mapping={"old": f"up.{block_id}.block", "new": f"up_blocks.{i}.resnets"},
+        )
+        if f"decoder.up.{block_id}.upsample.conv.weight" in vae_state_dict:
+            new_checkpoint[f"decoder.up_blocks.{i}.upsamplers.0.conv.weight"] = vae_state_dict[
+                f"decoder.up.{block_id}.upsample.conv.weight"
+            ]
+            new_checkpoint[f"decoder.up_blocks.{i}.upsamplers.0.conv.bias"] = vae_state_dict[
+                f"decoder.up.{block_id}.upsample.conv.bias"
+            ]
+
+    mid_resnets = [key for key in vae_state_dict if "decoder.mid.block" in key]
+    num_mid_res_blocks = 2
+    for i in range(1, num_mid_res_blocks + 1):
+        resnets = [key for key in mid_resnets if f"decoder.mid.block_{i}" in key]
+        update_vae_resnet_ldm_to_diffusers(
+            resnets,
+            new_checkpoint,
+            vae_state_dict,
+            mapping={"old": f"mid.block_{i}", "new": f"mid_block.resnets.{i - 1}"},
+        )
+
+    mid_attentions = [key for key in vae_state_dict if "decoder.mid.attn" in key]
+    update_vae_attentions_ldm_to_diffusers(
+        mid_attentions, new_checkpoint, vae_state_dict, mapping={"old": "mid.attn_1", "new": "mid_block.attentions.0"}
+    )
+    conv_attn_to_linear(new_checkpoint)
+
+    return new_checkpoint
+
+
+def create_text_encoder_from_ldm_clip_checkpoint(config_name, checkpoint, local_files_only=False, torch_dtype=None):
+    try:
+        config = CLIPTextConfig.from_pretrained(config_name, local_files_only=local_files_only)
+    except Exception:
+        raise ValueError(
+            f"With local_files_only set to {local_files_only}, you must first locally save the configuration in the following path: 'openai/clip-vit-large-patch14'."
+        )
+
+    ctx = init_empty_weights if is_accelerate_available() else nullcontext
+    with ctx():
+        text_model = CLIPTextModel(config)
+
+    keys = list(checkpoint.keys())
+    text_model_dict = {}
+
+    remove_prefixes = LDM_CLIP_PREFIX_TO_REMOVE
+
+    for key in keys:
+        for prefix in remove_prefixes:
+            if key.startswith(prefix):
+                diffusers_key = key.replace(prefix, "")
+                text_model_dict[diffusers_key] = checkpoint[key]
+
+    if is_accelerate_available():
+        unexpected_keys = load_model_dict_into_meta(text_model, text_model_dict, dtype=torch_dtype)
+        if text_model._keys_to_ignore_on_load_unexpected is not None:
+            for pat in text_model._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 {text_model.__class__.__name__}: \n {[', '.join(unexpected_keys)]}"
+            )
+    else:
+        if not (hasattr(text_model, "embeddings") and hasattr(text_model.embeddings.position_ids)):
+            text_model_dict.pop("text_model.embeddings.position_ids", None)
+
+        text_model.load_state_dict(text_model_dict)
+
+    if torch_dtype is not None:
+        text_model = text_model.to(torch_dtype)
+
+    return text_model
+
+
+def create_text_encoder_from_open_clip_checkpoint(
+    config_name,
+    checkpoint,
+    prefix="cond_stage_model.model.",
+    has_projection=False,
+    local_files_only=False,
+    torch_dtype=None,
+    **config_kwargs,
+):
+    try:
+        config = CLIPTextConfig.from_pretrained(config_name, **config_kwargs, local_files_only=local_files_only)
+    except Exception:
+        raise ValueError(
+            f"With local_files_only set to {local_files_only}, you must first locally save the configuration in the following path: '{config_name}'."
+        )
+
+    ctx = init_empty_weights if is_accelerate_available() else nullcontext
+    with ctx():
+        text_model = CLIPTextModelWithProjection(config) if has_projection else CLIPTextModel(config)
+
+    text_model_dict = {}
+    text_proj_key = prefix + "text_projection"
+    text_proj_dim = (
+        int(checkpoint[text_proj_key].shape[0]) if text_proj_key in checkpoint else LDM_OPEN_CLIP_TEXT_PROJECTION_DIM
+    )
+    text_model_dict["text_model.embeddings.position_ids"] = text_model.text_model.embeddings.get_buffer("position_ids")
+
+    keys = list(checkpoint.keys())
+    keys_to_ignore = SD_2_TEXT_ENCODER_KEYS_TO_IGNORE
+
+    openclip_diffusers_ldm_map = DIFFUSERS_TO_LDM_MAPPING["openclip"]["layers"]
+    for diffusers_key, ldm_key in openclip_diffusers_ldm_map.items():
+        ldm_key = prefix + ldm_key
+        if ldm_key not in checkpoint:
+            continue
+        if ldm_key in keys_to_ignore:
+            continue
+        if ldm_key.endswith("text_projection"):
+            text_model_dict[diffusers_key] = checkpoint[ldm_key].T.contiguous()
+        else:
+            text_model_dict[diffusers_key] = checkpoint[ldm_key]
+
+    for key in keys:
+        if key in keys_to_ignore:
+            continue
+
+        if not key.startswith(prefix + "transformer."):
+            continue
+
+        diffusers_key = key.replace(prefix + "transformer.", "")
+        transformer_diffusers_to_ldm_map = DIFFUSERS_TO_LDM_MAPPING["openclip"]["transformer"]
+        for new_key, old_key in transformer_diffusers_to_ldm_map.items():
+            diffusers_key = (
+                diffusers_key.replace(old_key, new_key).replace(".in_proj_weight", "").replace(".in_proj_bias", "")
+            )
+
+        if key.endswith(".in_proj_weight"):
+            weight_value = checkpoint[key]
+
+            text_model_dict[diffusers_key + ".q_proj.weight"] = weight_value[:text_proj_dim, :]
+            text_model_dict[diffusers_key + ".k_proj.weight"] = weight_value[text_proj_dim : text_proj_dim * 2, :]
+            text_model_dict[diffusers_key + ".v_proj.weight"] = weight_value[text_proj_dim * 2 :, :]
+
+        elif key.endswith(".in_proj_bias"):
+            weight_value = checkpoint[key]
+            text_model_dict[diffusers_key + ".q_proj.bias"] = weight_value[:text_proj_dim]
+            text_model_dict[diffusers_key + ".k_proj.bias"] = weight_value[text_proj_dim : text_proj_dim * 2]
+            text_model_dict[diffusers_key + ".v_proj.bias"] = weight_value[text_proj_dim * 2 :]
+        else:
+            text_model_dict[diffusers_key] = checkpoint[key]
+
+    if is_accelerate_available():
+        unexpected_keys = load_model_dict_into_meta(text_model, text_model_dict, dtype=torch_dtype)
+        if text_model._keys_to_ignore_on_load_unexpected is not None:
+            for pat in text_model._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 {text_model.__class__.__name__}: \n {[', '.join(unexpected_keys)]}"
+            )
+
+    else:
+        if not (hasattr(text_model, "embeddings") and hasattr(text_model.embeddings.position_ids)):
+            text_model_dict.pop("text_model.embeddings.position_ids", None)
+
+        text_model.load_state_dict(text_model_dict)
+
+    if torch_dtype is not None:
+        text_model = text_model.to(torch_dtype)
+
+    return text_model
+
+
+def create_diffusers_unet_model_from_ldm(
+    pipeline_class_name,
+    original_config,
+    checkpoint,
+    num_in_channels=None,
+    upcast_attention=None,
+    extract_ema=False,
+    image_size=None,
+    torch_dtype=None,
+    model_type=None,
+):
+    from ..models import UNet2DConditionModel
+
+    if num_in_channels is None:
+        if pipeline_class_name in [
+            "StableDiffusionInpaintPipeline",
+            "StableDiffusionControlNetInpaintPipeline",
+            "StableDiffusionXLInpaintPipeline",
+            "StableDiffusionXLControlNetInpaintPipeline",
+        ]:
+            num_in_channels = 9
+
+        elif pipeline_class_name == "StableDiffusionUpscalePipeline":
+            num_in_channels = 7
+
+        else:
+            num_in_channels = 4
+
+    image_size = set_image_size(
+        pipeline_class_name, original_config, checkpoint, image_size=image_size, model_type=model_type
+    )
+    unet_config = create_unet_diffusers_config(original_config, image_size=image_size)
+    unet_config["in_channels"] = num_in_channels
+    if upcast_attention is not None:
+        unet_config["upcast_attention"] = upcast_attention
+
+    diffusers_format_unet_checkpoint = convert_ldm_unet_checkpoint(checkpoint, unet_config, extract_ema=extract_ema)
+    ctx = init_empty_weights if is_accelerate_available() else nullcontext
+
+    with ctx():
+        unet = UNet2DConditionModel(**unet_config)
+
+    if is_accelerate_available():
+        unexpected_keys = load_model_dict_into_meta(unet, diffusers_format_unet_checkpoint, dtype=torch_dtype)
+        if unet._keys_to_ignore_on_load_unexpected is not None:
+            for pat in unet._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 {unet.__name__}: \n {[', '.join(unexpected_keys)]}"
+            )
+    else:
+        unet.load_state_dict(diffusers_format_unet_checkpoint)
+
+    if torch_dtype is not None:
+        unet = unet.to(torch_dtype)
+
+    return {"unet": unet}
+
+
+def create_diffusers_vae_model_from_ldm(
+    pipeline_class_name,
+    original_config,
+    checkpoint,
+    image_size=None,
+    scaling_factor=None,
+    torch_dtype=None,
+    model_type=None,
+):
+    # import here to avoid circular imports
+    from ..models import AutoencoderKL
+
+    image_size = set_image_size(
+        pipeline_class_name, original_config, checkpoint, image_size=image_size, model_type=model_type
+    )
+    model_type = infer_model_type(original_config, checkpoint, model_type)
+
+    if model_type == "Playground":
+        edm_mean = (
+            checkpoint["edm_mean"].to(dtype=torch_dtype).tolist() if torch_dtype else checkpoint["edm_mean"].tolist()
+        )
+        edm_std = (
+            checkpoint["edm_std"].to(dtype=torch_dtype).tolist() if torch_dtype else checkpoint["edm_std"].tolist()
+        )
+    else:
+        edm_mean = None
+        edm_std = None
+
+    vae_config = create_vae_diffusers_config(
+        original_config,
+        image_size=image_size,
+        scaling_factor=scaling_factor,
+        latents_mean=edm_mean,
+        latents_std=edm_std,
+    )
+    diffusers_format_vae_checkpoint = convert_ldm_vae_checkpoint(checkpoint, vae_config)
+    ctx = init_empty_weights if is_accelerate_available() else nullcontext
+
+    with ctx():
+        vae = AutoencoderKL(**vae_config)
+
+    if is_accelerate_available():
+        unexpected_keys = load_model_dict_into_meta(vae, diffusers_format_vae_checkpoint, dtype=torch_dtype)
+        if vae._keys_to_ignore_on_load_unexpected is not None:
+            for pat in vae._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 {vae.__name__}: \n {[', '.join(unexpected_keys)]}"
+            )
+    else:
+        vae.load_state_dict(diffusers_format_vae_checkpoint)
+
+    if torch_dtype is not None:
+        vae = vae.to(torch_dtype)
+
+    return {"vae": vae}
+
+
+def create_text_encoders_and_tokenizers_from_ldm(
+    original_config,
+    checkpoint,
+    model_type=None,
+    local_files_only=False,
+    torch_dtype=None,
+):
+    model_type = infer_model_type(original_config, checkpoint=checkpoint, model_type=model_type)
+
+    if model_type == "FrozenOpenCLIPEmbedder":
+        config_name = "stabilityai/stable-diffusion-2"
+        config_kwargs = {"subfolder": "text_encoder"}
+
+        try:
+            text_encoder = create_text_encoder_from_open_clip_checkpoint(
+                config_name, checkpoint, local_files_only=local_files_only, torch_dtype=torch_dtype, **config_kwargs
+            )
+            tokenizer = CLIPTokenizer.from_pretrained(
+                config_name, subfolder="tokenizer", local_files_only=local_files_only
+            )
+        except Exception:
+            raise ValueError(
+                f"With local_files_only set to {local_files_only}, you must first locally save the text_encoder in the following path: '{config_name}'."
+            )
+        else:
+            return {"text_encoder": text_encoder, "tokenizer": tokenizer}
+
+    elif model_type == "FrozenCLIPEmbedder":
+        try:
+            config_name = "openai/clip-vit-large-patch14"
+            text_encoder = create_text_encoder_from_ldm_clip_checkpoint(
+                config_name,
+                checkpoint,
+                local_files_only=local_files_only,
+                torch_dtype=torch_dtype,
+            )
+            tokenizer = CLIPTokenizer.from_pretrained(config_name, local_files_only=local_files_only)
+
+        except Exception:
+            raise ValueError(
+                f"With local_files_only set to {local_files_only}, you must first locally save the tokenizer in the following path: '{config_name}'."
+            )
+        else:
+            return {"text_encoder": text_encoder, "tokenizer": tokenizer}
+
+    elif model_type == "SDXL-Refiner":
+        config_name = "laion/CLIP-ViT-bigG-14-laion2B-39B-b160k"
+        config_kwargs = {"projection_dim": 1280}
+        prefix = "conditioner.embedders.0.model."
+
+        try:
+            tokenizer_2 = CLIPTokenizer.from_pretrained(config_name, pad_token="!", local_files_only=local_files_only)
+            text_encoder_2 = create_text_encoder_from_open_clip_checkpoint(
+                config_name,
+                checkpoint,
+                prefix=prefix,
+                has_projection=True,
+                local_files_only=local_files_only,
+                torch_dtype=torch_dtype,
+                **config_kwargs,
+            )
+        except Exception:
+            raise ValueError(
+                f"With local_files_only set to {local_files_only}, you must first locally save the text_encoder_2 and tokenizer_2 in the following path: {config_name} with `pad_token` set to '!'."
+            )
+
+        else:
+            return {
+                "text_encoder": None,
+                "tokenizer": None,
+                "tokenizer_2": tokenizer_2,
+                "text_encoder_2": text_encoder_2,
+            }
+
+    elif model_type in ["SDXL", "Playground"]:
+        try:
+            config_name = "openai/clip-vit-large-patch14"
+            tokenizer = CLIPTokenizer.from_pretrained(config_name, local_files_only=local_files_only)
+            text_encoder = create_text_encoder_from_ldm_clip_checkpoint(
+                config_name, checkpoint, local_files_only=local_files_only, torch_dtype=torch_dtype
+            )
+
+        except Exception:
+            raise ValueError(
+                f"With local_files_only set to {local_files_only}, you must first locally save the text_encoder and tokenizer in the following path: 'openai/clip-vit-large-patch14'."
+            )
+
+        try:
+            config_name = "laion/CLIP-ViT-bigG-14-laion2B-39B-b160k"
+            config_kwargs = {"projection_dim": 1280}
+            prefix = "conditioner.embedders.1.model."
+            tokenizer_2 = CLIPTokenizer.from_pretrained(config_name, pad_token="!", local_files_only=local_files_only)
+            text_encoder_2 = create_text_encoder_from_open_clip_checkpoint(
+                config_name,
+                checkpoint,
+                prefix=prefix,
+                has_projection=True,
+                local_files_only=local_files_only,
+                torch_dtype=torch_dtype,
+                **config_kwargs,
+            )
+        except Exception:
+            raise ValueError(
+                f"With local_files_only set to {local_files_only}, you must first locally save the text_encoder_2 and tokenizer_2 in the following path: {config_name} with `pad_token` set to '!'."
+            )
+
+        return {
+            "tokenizer": tokenizer,
+            "text_encoder": text_encoder,
+            "tokenizer_2": tokenizer_2,
+            "text_encoder_2": text_encoder_2,
+        }
+
+    return
+
+
+def create_scheduler_from_ldm(
+    pipeline_class_name,
+    original_config,
+    checkpoint,
+    prediction_type=None,
+    scheduler_type="ddim",
+    model_type=None,
+):
+    scheduler_config = get_default_scheduler_config()
+    model_type = infer_model_type(original_config, checkpoint=checkpoint, model_type=model_type)
+
+    global_step = checkpoint["global_step"] if "global_step" in checkpoint else None
+
+    num_train_timesteps = getattr(original_config["model"]["params"], "timesteps", None) or 1000
+    scheduler_config["num_train_timesteps"] = num_train_timesteps
+
+    if (
+        "parameterization" in original_config["model"]["params"]
+        and original_config["model"]["params"]["parameterization"] == "v"
+    ):
+        if prediction_type is None:
+            # NOTE: For stable diffusion 2 base it is recommended to pass `prediction_type=="epsilon"`
+            # as it relies on a brittle global step parameter here
+            prediction_type = "epsilon" if global_step == 875000 else "v_prediction"
+
+    else:
+        prediction_type = prediction_type or "epsilon"
+
+    scheduler_config["prediction_type"] = prediction_type
+
+    if model_type in ["SDXL", "SDXL-Refiner"]:
+        scheduler_type = "euler"
+    elif model_type == "Playground":
+        scheduler_type = "edm_dpm_solver_multistep"
+    else:
+        beta_start = original_config["model"]["params"].get("linear_start", 0.02)
+        beta_end = original_config["model"]["params"].get("linear_end", 0.085)
+        scheduler_config["beta_start"] = beta_start
+        scheduler_config["beta_end"] = beta_end
+        scheduler_config["beta_schedule"] = "scaled_linear"
+        scheduler_config["clip_sample"] = False
+        scheduler_config["set_alpha_to_one"] = False
+
+    if scheduler_type == "pndm":
+        scheduler_config["skip_prk_steps"] = True
+        scheduler = PNDMScheduler.from_config(scheduler_config)
+
+    elif scheduler_type == "lms":
+        scheduler = LMSDiscreteScheduler.from_config(scheduler_config)
+
+    elif scheduler_type == "heun":
+        scheduler = HeunDiscreteScheduler.from_config(scheduler_config)
+
+    elif scheduler_type == "euler":
+        scheduler = EulerDiscreteScheduler.from_config(scheduler_config)
+
+    elif scheduler_type == "euler-ancestral":
+        scheduler = EulerAncestralDiscreteScheduler.from_config(scheduler_config)
+
+    elif scheduler_type == "dpm":
+        scheduler = DPMSolverMultistepScheduler.from_config(scheduler_config)
+
+    elif scheduler_type == "ddim":
+        scheduler = DDIMScheduler.from_config(scheduler_config)
+
+    elif scheduler_type == "edm_dpm_solver_multistep":
+        scheduler_config = {
+            "algorithm_type": "dpmsolver++",
+            "dynamic_thresholding_ratio": 0.995,
+            "euler_at_final": False,
+            "final_sigmas_type": "zero",
+            "lower_order_final": True,
+            "num_train_timesteps": 1000,
+            "prediction_type": "epsilon",
+            "rho": 7.0,
+            "sample_max_value": 1.0,
+            "sigma_data": 0.5,
+            "sigma_max": 80.0,
+            "sigma_min": 0.002,
+            "solver_order": 2,
+            "solver_type": "midpoint",
+            "thresholding": False,
+        }
+        scheduler = EDMDPMSolverMultistepScheduler(**scheduler_config)
+
+    else:
+        raise ValueError(f"Scheduler of type {scheduler_type} doesn't exist!")
+
+    if pipeline_class_name == "StableDiffusionUpscalePipeline":
+        scheduler = DDIMScheduler.from_pretrained("stabilityai/stable-diffusion-x4-upscaler", subfolder="scheduler")
+        low_res_scheduler = DDPMScheduler.from_pretrained(
+            "stabilityai/stable-diffusion-x4-upscaler", subfolder="low_res_scheduler"
+        )
+
+        return {
+            "scheduler": scheduler,
+            "low_res_scheduler": low_res_scheduler,
+        }
+
+    return {"scheduler": scheduler}

diffusers/loaders/textual_inversion.py

@@ -0,0 +1,582 @@
+# Copyright 2024 The HuggingFace Team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+from typing import Dict, List, Optional, Union
+
+import safetensors
+import torch
+from huggingface_hub.utils import validate_hf_hub_args
+from torch import nn
+
+from ..models.modeling_utils import load_state_dict
+from ..utils import _get_model_file, is_accelerate_available, is_transformers_available, logging
+
+
+if is_transformers_available():
+    from transformers import PreTrainedModel, PreTrainedTokenizer
+
+if is_accelerate_available():
+    from accelerate.hooks import AlignDevicesHook, CpuOffload, remove_hook_from_module
+
+logger = logging.get_logger(__name__)
+
+TEXT_INVERSION_NAME = "learned_embeds.bin"
+TEXT_INVERSION_NAME_SAFE = "learned_embeds.safetensors"
+
+
+@validate_hf_hub_args
+def load_textual_inversion_state_dicts(pretrained_model_name_or_paths, **kwargs):
+    cache_dir = kwargs.pop("cache_dir", None)
+    force_download = kwargs.pop("force_download", False)
+    resume_download = kwargs.pop("resume_download", None)
+    proxies = kwargs.pop("proxies", None)
+    local_files_only = kwargs.pop("local_files_only", None)
+    token = kwargs.pop("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)
+
+    allow_pickle = False
+    if use_safetensors is None:
+        use_safetensors = True
+        allow_pickle = True
+
+    user_agent = {
+        "file_type": "text_inversion",
+        "framework": "pytorch",
+    }
+    state_dicts = []
+    for pretrained_model_name_or_path in pretrained_model_name_or_paths:
+        if not isinstance(pretrained_model_name_or_path, (dict, torch.Tensor)):
+            # 3.1. 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,
+                        token=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,
+                    token=token,
+                    revision=revision,
+                    subfolder=subfolder,
+                    user_agent=user_agent,
+                )
+                state_dict = load_state_dict(model_file)
+        else:
+            state_dict = pretrained_model_name_or_path
+
+        state_dicts.append(state_dict)
+
+    return state_dicts
+
+
+class TextualInversionLoaderMixin:
+    r"""
+    Load Textual Inversion tokens and embeddings to the tokenizer and text encoder.
+    """
+
+    def maybe_convert_prompt(self, prompt: Union[str, List[str]], tokenizer: "PreTrainedTokenizer"):  # noqa: F821
+        r"""
+        Processes prompts that include a special token corresponding to a multi-vector textual inversion embedding to
+        be replaced with multiple special tokens each corresponding to one of the vectors. If the prompt has no textual
+        inversion token or if the textual inversion token is a single vector, the input prompt is 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"):  # noqa: F821
+        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)
+        unique_tokens = set(tokens)
+        for token in unique_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 _check_text_inv_inputs(self, tokenizer, text_encoder, pretrained_model_name_or_paths, tokens):
+        if tokenizer is None:
+            raise ValueError(
+                f"{self.__class__.__name__} requires `self.tokenizer` or passing a `tokenizer` of type `PreTrainedTokenizer` for calling"
+                f" `{self.load_textual_inversion.__name__}`"
+            )
+
+        if text_encoder is None:
+            raise ValueError(
+                f"{self.__class__.__name__} requires `self.text_encoder` or passing a `text_encoder` of type `PreTrainedModel` for calling"
+                f" `{self.load_textual_inversion.__name__}`"
+            )
+
+        if len(pretrained_model_name_or_paths) > 1 and 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."
+            )
+