# coding=utf-8

# Copyright 2025 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.

"""Utilities to dynamically load objects from the Hub."""

import importlib

import inspect

import json

import os

import re

import shutil

import sys

import threading

from pathlib import Path

from types import ModuleType

from urllib import request

from huggingface_hub import hf_hub_download, model_info

from huggingface_hub.utils import RevisionNotFoundError, validate_hf_hub_args

from packaging import version

from .. import __version__

from . import DIFFUSERS_DYNAMIC_MODULE_NAME, HF_MODULES_CACHE, logging

from .constants import DIFFUSERS_DISABLE_REMOTE_CODE

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

# See https://huggingface.co/datasets/diffusers/community-pipelines-mirror

COMMUNITY_PIPELINES_MIRROR_ID = "diffusers/community-pipelines-mirror"

TIME_OUT_REMOTE_CODE = int(os.getenv("DIFFUSERS_TIMEOUT_REMOTE_CODE", 15))

_HF_REMOTE_CODE_LOCK = threading.Lock()

def get_diffusers_versions():

url = "https://pypi.org/pypi/diffusers/json"

releases = json.loads(request.urlopen(url).read())["releases"].keys()

return sorted(releases, key=lambda x: version.Version(x))

def init_hf_modules():

"""

Creates the cache directory for modules with an init, and adds it to the Python path.

"""

# This function has already been executed if HF_MODULES_CACHE already is in the Python path.

if HF_MODULES_CACHE in sys.path:

return

sys.path.append(HF_MODULES_CACHE)

os.makedirs(HF_MODULES_CACHE, exist_ok=True)

init_path = Path(HF_MODULES_CACHE) / "__init__.py"

if not init_path.exists():

init_path.touch()

def create_dynamic_module(name: str | os.PathLike):

"""

Creates a dynamic module in the cache directory for modules.

"""

init_hf_modules()

dynamic_module_path = Path(HF_MODULES_CACHE) / name

# If the parent module does not exist yet, recursively create it.

if not dynamic_module_path.parent.exists():

create_dynamic_module(dynamic_module_path.parent)

os.makedirs(dynamic_module_path, exist_ok=True)

init_path = dynamic_module_path / "__init__.py"

if not init_path.exists():

init_path.touch()

def get_relative_imports(module_file):

"""

Get the list of modules that are relatively imported in a module file.

Args:

module_file (`str` or `os.PathLike`): The module file to inspect.

"""

with open(module_file, "r", encoding="utf-8") as f:

content = f.read()

# Imports of the form `import .xxx`

relative_imports = re.findall(r"^\s*import\s+\.(\S+)\s*$", content, flags=re.MULTILINE)

# Imports of the form `from .xxx import yyy`

relative_imports += re.findall(r"^\s*from\s+\.(\S+)\s+import", content, flags=re.MULTILINE)

# Unique-ify

return list(set(relative_imports))

def get_relative_import_files(module_file):

"""

Get the list of all files that are needed for a given module. Note that this function recurses through the relative

imports (if a imports b and b imports c, it will return module files for b and c).

Args:

module_file (`str` or `os.PathLike`): The module file to inspect.

"""

no_change = False

files_to_check = [module_file]

all_relative_imports = []

# Let's recurse through all relative imports

while not no_change:

new_imports = []

for f in files_to_check:

new_imports.extend(get_relative_imports(f))

module_path = Path(module_file).parent

new_import_files = [str(module_path / m) for m in new_imports]

new_import_files = [f for f in new_import_files if f not in all_relative_imports]

files_to_check = [f"{f}.py" for f in new_import_files]

no_change = len(new_import_files) == 0

all_relative_imports.extend(files_to_check)

return all_relative_imports

def check_imports(filename):

"""

Check if the current Python environment contains all the libraries that are imported in a file.

"""

with open(filename, "r", encoding="utf-8") as f:

content = f.read()

# Imports of the form `import xxx`

imports = re.findall(r"^\s*import\s+(\S+)\s*$", content, flags=re.MULTILINE)

# Imports of the form `from xxx import yyy`

imports += re.findall(r"^\s*from\s+(\S+)\s+import", content, flags=re.MULTILINE)

# Only keep the top-level module

imports = [imp.split(".")[0] for imp in imports if not imp.startswith(".")]

# Unique-ify and test we got them all

imports = list(set(imports))

missing_packages = []

for imp in imports:

try:

importlib.import_module(imp)

except ImportError:

missing_packages.append(imp)

if len(missing_packages) > 0:

logger.warning(

"This modeling file might require the following packages that were not found in your environment: "

f"{', '.join(missing_packages)}. Run `pip install {' '.join(missing_packages)}`"

)

return get_relative_imports(filename)

def resolve_trust_remote_code(trust_remote_code, model_name, has_remote_code):

trust_remote_code = trust_remote_code and not DIFFUSERS_DISABLE_REMOTE_CODE

if DIFFUSERS_DISABLE_REMOTE_CODE:

logger.warning(

"Downloading remote code is disabled globally via the DIFFUSERS_DISABLE_REMOTE_CODE environment variable. Ignoring `trust_remote_code`."

)

if has_remote_code and not trust_remote_code:

error_msg = f"The repository for {model_name} contains custom code. "

error_msg += (

"Downloading remote code is disabled globally via the DIFFUSERS_DISABLE_REMOTE_CODE environment variable."

if DIFFUSERS_DISABLE_REMOTE_CODE

else "Pass `trust_remote_code=True` to allow loading remote code modules."

)

raise ValueError(error_msg)

elif has_remote_code and trust_remote_code:

logger.warning(

f"`trust_remote_code` is enabled. Downloading code from {model_name}. Please ensure you trust the contents of this repository"

)

return trust_remote_code

def get_class_in_module(class_name, module_path, force_reload=False):

"""

Import a module on the cache directory for modules and extract a class from it.

"""

name = os.path.normpath(module_path)

if name.endswith(".py"):

name = name[:-3]

name = name.replace(os.path.sep, ".")

module_file: Path = Path(HF_MODULES_CACHE) / module_path

with _HF_REMOTE_CODE_LOCK:

if force_reload:

sys.modules.pop(name, None)

importlib.invalidate_caches()

cached_module: ModuleType | None = sys.modules.get(name)

module_spec = importlib.util.spec_from_file_location(name, location=module_file)

module: ModuleType

if cached_module is None:

module = importlib.util.module_from_spec(module_spec)

# insert it into sys.modules before any loading begins

sys.modules[name] = module

else:

module = cached_module

module_spec.loader.exec_module(module)

if class_name is None:

return find_pipeline_class(module)

return getattr(module, class_name)

def find_pipeline_class(loaded_module):

"""

Retrieve pipeline class that inherits from `DiffusionPipeline`. Note that there has to be exactly one class

inheriting from `DiffusionPipeline`.

"""

from ..pipelines import DiffusionPipeline

cls_members = dict(inspect.getmembers(loaded_module, inspect.isclass))

pipeline_class = None

for cls_name, cls in cls_members.items():

if (

cls_name != DiffusionPipeline.__name__

and issubclass(cls, DiffusionPipeline)

and cls.__module__.split(".")[0] != "diffusers"

):

if pipeline_class is not None:

raise ValueError(

f"Multiple classes that inherit from {DiffusionPipeline.__name__} have been found:"

f" {pipeline_class.__name__}, and {cls_name}. Please make sure to define only one in"

f" {loaded_module}."

)

pipeline_class = cls

return pipeline_class

@validate_hf_hub_args

def get_cached_module_file(

pretrained_model_name_or_path: str | os.PathLike,

module_file: str,

subfolder: str | None = None,

cache_dir: str | os.PathLike | None = None,

force_download: bool = False,

proxies: dict[str, str] | None = None,

token: bool | str | None = None,

revision: str | None = None,

local_files_only: bool = False,

local_dir: str | None = None,

trust_remote_code: bool = False,

):

"""

Prepares Downloads a module from a local folder or a distant repo and returns its path inside the cached

Transformers module.

Args:

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

This can be either:

- a string, the *model id* of a pretrained model configuration hosted inside a model repo on

huggingface.co. Valid model ids can be located at the root-level, like `bert-base-uncased`, or namespaced

under a user or organization name, like `dbmdz/bert-base-german-cased`.

- a path to a *directory* containing a configuration file saved using the

[`~PreTrainedTokenizer.save_pretrained`] method, e.g., `./my_model_directory/`.

module_file (`str`):

The name of the module file containing the class to look for.

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

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

cache should not be used.

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

Whether or not to force to (re-)download the configuration files and override the cached versions if they

exist.

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

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

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

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

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

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

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

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

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

identifier allowed by git.

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

If `True`, will only try to load the tokenizer configuration from local files.

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

Whether or not to allow for custom pipelines and components defined on the Hub in their own files. This

option should only be set to `True` for repositories you trust and in which you have read the code, as it

will execute code present on the Hub on your local machine.

> [!TIP] > You may pass a token in `token` if you are not logged in (`hf auth login`) and want to use private or

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

Returns:

`str`: The path to the module inside the cache.

"""

# Download and cache module_file from the repo `pretrained_model_name_or_path` of grab it if it's a local file.

pretrained_model_name_or_path = str(pretrained_model_name_or_path)

if DIFFUSERS_DISABLE_REMOTE_CODE:

raise ValueError(

"Downloading remote code is disabled globally via the DIFFUSERS_DISABLE_REMOTE_CODE environment variable."

)

if subfolder is not None:

module_file_or_url = os.path.join(pretrained_model_name_or_path, subfolder, module_file)

else:

module_file_or_url = os.path.join(pretrained_model_name_or_path, module_file)

is_local_file = os.path.isfile(module_file_or_url)

is_community_pipeline = not is_local_file and pretrained_model_name_or_path.count("/") == 0

if is_local_file:

resolved_module_file = module_file_or_url

submodule = "local"

if not trust_remote_code:

raise ValueError(

f"The directory {pretrained_model_name_or_path} contains custom code in {module_file} which must be executed to correctly "

f"load the model. You can inspect the file content at {module_file_or_url}.\n"

f"Pass `trust_remote_code=True` to allow loading remote code modules."

)

elif is_community_pipeline:

available_versions = get_diffusers_versions()

# cut ".dev0"

latest_version = "v" + ".".join(__version__.split(".")[:3])

# retrieve github version that matches

if revision is None:

revision = latest_version if latest_version[1:] in available_versions else "main"

logger.info(f"Defaulting to latest_version: {revision}.")

elif revision in available_versions:

revision = f"v{revision}"

elif revision == "main":

revision = revision

else:

raise ValueError(

f"`custom_revision`: {revision} does not exist. Please make sure to choose one of"

f" {', '.join(available_versions + ['main'])}."

)

try:

resolved_module_file = hf_hub_download(

repo_id=COMMUNITY_PIPELINES_MIRROR_ID,

repo_type="dataset",

filename=f"{revision}/{pretrained_model_name_or_path}.py",

cache_dir=cache_dir,

force_download=force_download,

proxies=proxies,

local_files_only=local_files_only,

local_dir=local_dir,

)

submodule = "git"

module_file = pretrained_model_name_or_path + ".py"

except RevisionNotFoundError as e:

raise EnvironmentError(

f"Revision '{revision}' not found in the community pipelines mirror. Check available revisions on"

" https://huggingface.co/datasets/diffusers/community-pipelines-mirror/tree/main."

" If you don't find the revision you are looking for, please open an issue on https://github.com/huggingface/diffusers/issues."

) from e

except EnvironmentError:

logger.error(f"Could not locate the {module_file} inside {pretrained_model_name_or_path}.")

raise

else:

if not trust_remote_code:

raise ValueError(

f"The repository for {pretrained_model_name_or_path} contains custom code in {module_file} which must be executed to correctly "

f"load the model. You can inspect the repository content at https://hf.co/{pretrained_model_name_or_path}/blob/main/{module_file}.\n"

f"Pass `trust_remote_code=True` to allow loading remote code modules."

)

try:

# Load from URL or cache if already cached

resolved_module_file = hf_hub_download(

pretrained_model_name_or_path,

module_file,

subfolder=subfolder,

cache_dir=cache_dir,

force_download=force_download,

proxies=proxies,

local_files_only=local_files_only,

local_dir=local_dir,

revision=revision,

token=token,

)

submodule = os.path.join("local", "--".join(pretrained_model_name_or_path.split("/")))

except EnvironmentError:

logger.error(f"Could not locate the {module_file} inside {pretrained_model_name_or_path}.")

raise

# Check we have all the requirements in our environment

modules_needed = check_imports(resolved_module_file)

# Now we move the module inside our cached dynamic modules.

full_submodule = DIFFUSERS_DYNAMIC_MODULE_NAME + os.path.sep + submodule

create_dynamic_module(full_submodule)

submodule_path = Path(HF_MODULES_CACHE) / full_submodule

if submodule == "local" or submodule == "git":

# We always copy local files (we could hash the file to see if there was a change, and give them the name of

# that hash, to only copy when there is a modification but it seems overkill for now).

# The only reason we do the copy is to avoid putting too many folders in sys.path.

shutil.copyfile(resolved_module_file, submodule_path / module_file)

for module_needed in modules_needed:

if len(module_needed.split(".")) == 2:

module_needed = "/".join(module_needed.split("."))

module_folder = module_needed.split("/")[0]

if not os.path.exists(submodule_path / module_folder):

os.makedirs(submodule_path / module_folder)

module_needed = f"{module_needed}.py"

if subfolder is not None:

source_path = os.path.join(pretrained_model_name_or_path, subfolder, module_needed)

else:

source_path = os.path.join(pretrained_model_name_or_path, module_needed)

shutil.copyfile(source_path, submodule_path / module_needed)

else:

# Get the commit hash

# TODO: we will get this info in the etag soon, so retrieve it from there and not here.

commit_hash = model_info(pretrained_model_name_or_path, revision=revision, token=token).sha

# The module file will end up being placed in a subfolder with the git hash of the repo. This way we get the

# benefit of versioning.

submodule_path = submodule_path / commit_hash

full_submodule = full_submodule + os.path.sep + commit_hash

create_dynamic_module(full_submodule)

if not (submodule_path / module_file).exists():

if len(module_file.split("/")) == 2:

module_folder = module_file.split("/")[0]

if not os.path.exists(submodule_path / module_folder):

os.makedirs(submodule_path / module_folder)

shutil.copyfile(resolved_module_file, submodule_path / module_file)

# Make sure we also have every file with relative

for module_needed in modules_needed:

if len(module_needed.split(".")) == 2:

module_needed = "/".join(module_needed.split("."))

if not (submodule_path / module_needed).exists():

get_cached_module_file(

pretrained_model_name_or_path,

f"{module_needed}.py",

subfolder=subfolder,

cache_dir=cache_dir,

force_download=force_download,

proxies=proxies,

token=token,

revision=revision,

local_files_only=local_files_only,

local_dir=local_dir,

trust_remote_code=trust_remote_code,

)

return os.path.join(full_submodule, module_file)

@validate_hf_hub_args

def get_class_from_dynamic_module(

pretrained_model_name_or_path: str | os.PathLike,

module_file: str,

subfolder: str | None = None,

class_name: str | None = None,

cache_dir: str | os.PathLike | None = None,

force_download: bool = False,

proxies: dict[str, str] | None = None,

token: bool | str | None = None,

revision: str | None = None,

local_files_only: bool = False,

local_dir: str | None = None,

trust_remote_code: bool = False,

):

"""

Extracts a class from a module file, present in the local folder or repository of a model.

> [!WARNING] > Calling this function will execute the code in the module file found locally or downloaded from the

Hub. It should > therefore only be called on trusted repos.

Args:

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

This can be either:

- a string, the *model id* of a pretrained model configuration hosted inside a model repo on

huggingface.co. Valid model ids can be located at the root-level, like `bert-base-uncased`, or namespaced

under a user or organization name, like `dbmdz/bert-base-german-cased`.

- a path to a *directory* containing a configuration file saved using the

[`~PreTrainedTokenizer.save_pretrained`] method, e.g., `./my_model_directory/`.

module_file (`str`):

The name of the module file containing the class to look for.

class_name (`str`):

The name of the class to import in the module.

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

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

cache should not be used.

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

Whether or not to force to (re-)download the configuration files and override the cached versions if they

exist.

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

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

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

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

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

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

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

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

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

identifier allowed by git.

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

If `True`, will only try to load the tokenizer configuration from local files.

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

Whether or not to allow for custom pipelines and components defined on the Hub in their own files. This

option should only be set to `True` for repositories you trust and in which you have read the code, as it

will execute code present on the Hub on your local machine.

> [!TIP] > You may pass a token in `token` if you are not logged in (`hf auth login`) and want to use private or

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

Returns:

`type`: The class, dynamically imported from the module.

Examples:

```python

# Download module `modeling.py` from huggingface.co and cache then extract the class `MyBertModel` from this

# module.

cls = get_class_from_dynamic_module("sgugger/my-bert-model", "modeling.py", "MyBertModel")

```"""

# And lastly we get the class inside our newly created module

final_module = get_cached_module_file(

pretrained_model_name_or_path,

module_file,

subfolder=subfolder,

cache_dir=cache_dir,

force_download=force_download,

proxies=proxies,

token=token,

revision=revision,

local_files_only=local_files_only,

local_dir=local_dir,

trust_remote_code=trust_remote_code,

)

return get_class_in_module(class_name, final_module)