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# 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.
import json
import os
import re
import sys
import tempfile
import warnings
from pathlib import Path
from uuid import uuid4
from huggingface_hub import (
DDUFEntry,
ModelCard,
ModelCardData,
create_repo,
hf_hub_download,
model_info,
snapshot_download,
upload_folder,
)
from huggingface_hub.constants import HF_HUB_DISABLE_TELEMETRY, HF_HUB_OFFLINE
from huggingface_hub.file_download import REGEX_COMMIT_HASH
from huggingface_hub.utils import (
EntryNotFoundError,
HfHubHTTPError,
RepositoryNotFoundError,
RevisionNotFoundError,
is_jinja_available,
validate_hf_hub_args,
)
from packaging import version
from .. import __version__
from .constants import (
DEPRECATED_REVISION_ARGS,
HUGGINGFACE_CO_RESOLVE_ENDPOINT,
SAFETENSORS_WEIGHTS_NAME,
WEIGHTS_NAME,
)
from .import_utils import (
ENV_VARS_TRUE_VALUES,
_onnxruntime_version,
_torch_version,
is_onnx_available,
is_torch_available,
)
from .logging import get_logger
logger = get_logger(__name__)
MODEL_CARD_TEMPLATE_PATH = Path(__file__).parent / "model_card_template.md"
SESSION_ID = uuid4().hex
def http_user_agent(user_agent: dict | str | None = None) -> str:
"""
Formats a user-agent string with basic info about a request.
"""
ua = f"diffusers/{__version__}; python/{sys.version.split()[0]}; session_id/{SESSION_ID}"
if HF_HUB_DISABLE_TELEMETRY or HF_HUB_OFFLINE:
return ua + "; telemetry/off"
if is_torch_available():
ua += f"; torch/{_torch_version}"
if is_onnx_available():
ua += f"; onnxruntime/{_onnxruntime_version}"
# CI will set this value to True
if os.environ.get("DIFFUSERS_IS_CI", "").upper() in ENV_VARS_TRUE_VALUES:
ua += "; is_ci/true"
if isinstance(user_agent, dict):
ua += "; " + "; ".join(f"{k}/{v}" for k, v in user_agent.items())
elif isinstance(user_agent, str):
ua += "; " + user_agent
return ua
def load_or_create_model_card(
repo_id_or_path: str = None,
token: str | None = None,
is_pipeline: bool = False,
from_training: bool = False,
model_description: str | None = None,
base_model: str = None,
prompt: str | None = None,
license: str | None = None,
widget: list[dict] | None = None,
inference: bool | None = None,
is_modular: bool = False,
update_model_card: bool = False,
) -> ModelCard:
"""
Loads or creates a model card.
Args:
repo_id_or_path (`str`):
The repo id (e.g., "stable-diffusion-v1-5/stable-diffusion-v1-5") or local path where to look for the model
card.
token (`str`, *optional*):
Authentication token. Will default to the stored token. See https://huggingface.co/settings/token for more
details.
is_pipeline (`bool`):
Boolean to indicate if we're adding tag to a [`DiffusionPipeline`].
from_training: (`bool`): Boolean flag to denote if the model card is being created from a training script.
model_description (`str`, *optional*): Model description to add to the model card. Helpful when using
`load_or_create_model_card` from a training script.
base_model (`str`): Base model identifier (e.g., "stabilityai/stable-diffusion-xl-base-1.0"). Useful
for DreamBooth-like training.
prompt (`str`, *optional*): Prompt used for training. Useful for DreamBooth-like training.
license: (`str`, *optional*): License of the output artifact. Helpful when using
`load_or_create_model_card` from a training script.
widget (`list[dict]`, *optional*): Widget to accompany a gallery template.
inference: (`bool`, optional): Whether to turn on inference widget. Helpful when using
`load_or_create_model_card` from a training script.
is_modular: (`bool`, optional): Boolean flag to denote if the model card is for a modular pipeline.
When True, uses model_description as-is without additional template formatting.
update_model_card: (`bool`, optional): When True, regenerates the model card content even if one
already exists on the remote repo. Existing card metadata (tags, license, etc.) is preserved. Only
supported for modular pipelines (i.e., `is_modular=True`).
"""
if not is_jinja_available():
raise ValueError(
"Modelcard rendering is based on Jinja templates."
" Please make sure to have `jinja` installed before using `load_or_create_model_card`."
" To install it, please run `pip install Jinja2`."
)
if update_model_card and not is_modular:
raise ValueError("`update_model_card=True` is only supported for modular pipelines (`is_modular=True`).")
try:
# Check if the model card is present on the remote repo
model_card = ModelCard.load(repo_id_or_path, token=token)
# For modular pipelines, regenerate card content when requested (preserve existing metadata)
if update_model_card and is_modular and model_description is not None:
existing_data = model_card.data
model_card = ModelCard(model_description)
model_card.data = existing_data
except (EntryNotFoundError, RepositoryNotFoundError):
# Otherwise create a model card from template
if from_training:
model_card = ModelCard.from_template(
card_data=ModelCardData( # Card metadata object that will be converted to YAML block
license=license,
library_name="diffusers",
inference=inference,
base_model=base_model,
instance_prompt=prompt,
widget=widget,
),
template_path=MODEL_CARD_TEMPLATE_PATH,
model_description=model_description,
)
else:
card_data = ModelCardData()
if is_modular and model_description is not None:
model_card = ModelCard(model_description)
model_card.data = card_data
else:
component = "pipeline" if is_pipeline else "model"
if model_description is None:
model_description = f"This is the model card of a 馃Ж diffusers {component} that has been pushed on the Hub. This model card has been automatically generated."
model_card = ModelCard.from_template(card_data, model_description=model_description)
return model_card
def populate_model_card(model_card: ModelCard, tags: str | list[str] | None = None) -> ModelCard:
"""Populates the `model_card` with library name and optional tags."""
if model_card.data.library_name is None:
model_card.data.library_name = "diffusers"
if tags is not None:
if isinstance(tags, str):
tags = [tags]
if model_card.data.tags is None:
model_card.data.tags = []
for tag in tags:
model_card.data.tags.append(tag)
return model_card
def extract_commit_hash(resolved_file: str | None, commit_hash: str | None = None):
"""
Extracts the commit hash from a resolved filename toward a cache file.
"""
if resolved_file is None or commit_hash is not None:
return commit_hash
resolved_file = str(Path(resolved_file).as_posix())
search = re.search(r"snapshots/([^/]+)/", resolved_file)
if search is None:
return None
commit_hash = search.groups()[0]
return commit_hash if REGEX_COMMIT_HASH.match(commit_hash) else None
def _add_variant(weights_name: str, variant: str | None = None) -> str:
if variant is not None:
splits = weights_name.split(".")
splits = splits[:-1] + [variant] + splits[-1:]
weights_name = ".".join(splits)
return weights_name
@validate_hf_hub_args
def _get_model_file(
pretrained_model_name_or_path: str | Path,
*,
weights_name: str,
subfolder: str | None = None,
cache_dir: str | None = None,
force_download: bool = False,
proxies: dict | None = None,
local_files_only: bool = False,
token: str | None = None,
user_agent: dict | str | None = None,
revision: str | None = None,
commit_hash: str | None = None,
dduf_entries: dict[str, DDUFEntry] | None = None,
):
pretrained_model_name_or_path = str(pretrained_model_name_or_path)
if dduf_entries:
if subfolder is not None:
raise ValueError(
"DDUF file only allow for 1 level of directory (e.g transformer/model1/model.safetentors is not allowed). "
"Please check the DDUF structure"
)
model_file = (
weights_name
if pretrained_model_name_or_path == ""
else "/".join([pretrained_model_name_or_path, weights_name])
)
if model_file in dduf_entries:
return model_file
else:
raise EnvironmentError(f"Error no file named {weights_name} found in archive {dduf_entries.keys()}.")
elif os.path.isfile(pretrained_model_name_or_path):
return 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, weights_name)):
# Load from a PyTorch checkpoint
model_file = os.path.join(pretrained_model_name_or_path, weights_name)
return model_file
elif subfolder is not None and os.path.isfile(
os.path.join(pretrained_model_name_or_path, subfolder, weights_name)
):
model_file = os.path.join(pretrained_model_name_or_path, subfolder, weights_name)
return model_file
else:
raise EnvironmentError(
f"Error no file named {weights_name} found in directory {pretrained_model_name_or_path}."
)
else:
# 1. First check if deprecated way of loading from branches is used
if (
revision in DEPRECATED_REVISION_ARGS
and (weights_name == WEIGHTS_NAME or weights_name == SAFETENSORS_WEIGHTS_NAME)
and version.parse(version.parse(__version__).base_version) >= version.parse("0.22.0")
):
try:
model_file = hf_hub_download(
pretrained_model_name_or_path,
filename=_add_variant(weights_name, revision),
cache_dir=cache_dir,
force_download=force_download,
proxies=proxies,
local_files_only=local_files_only,
token=token,
user_agent=user_agent,
subfolder=subfolder,
revision=revision or commit_hash,
)
warnings.warn(
f"Loading the variant {revision} from {pretrained_model_name_or_path} via `revision='{revision}'` is deprecated. Loading instead from `revision='main'` with `variant={revision}`. Loading model variants via `revision='{revision}'` will be removed in diffusers v1. Please use `variant='{revision}'` instead.",
FutureWarning,
)
return model_file
except: # noqa: E722
warnings.warn(
f"You are loading the variant {revision} from {pretrained_model_name_or_path} via `revision='{revision}'`. This behavior is deprecated and will be removed in diffusers v1. One should use `variant='{revision}'` instead. However, it appears that {pretrained_model_name_or_path} currently does not have a {_add_variant(weights_name, revision)} file in the 'main' branch of {pretrained_model_name_or_path}. \n The Diffusers team and community would be very grateful if you could open an issue: https://github.com/huggingface/diffusers/issues/new with the title '{pretrained_model_name_or_path} is missing {_add_variant(weights_name, revision)}' so that the correct variant file can be added.",
FutureWarning,
)
try:
# 2. Load model file as usual
model_file = hf_hub_download(
pretrained_model_name_or_path,
filename=weights_name,
cache_dir=cache_dir,
force_download=force_download,
proxies=proxies,
local_files_only=local_files_only,
token=token,
user_agent=user_agent,
subfolder=subfolder,
revision=revision or commit_hash,
)
return model_file
except RepositoryNotFoundError as e:
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 `hf auth login`."
) from e
except RevisionNotFoundError as e:
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."
) from e
except EntryNotFoundError as e:
raise EnvironmentError(
f"{pretrained_model_name_or_path} does not appear to have a file named {weights_name}."
) from e
except HfHubHTTPError as e:
raise EnvironmentError(
f"There was a specific connection error when trying to load {pretrained_model_name_or_path}:\n{e}"
) from e
except ValueError as e:
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 file named {weights_name} or"
" \nCheckout your internet connection or see how to run the library in"
" offline mode at 'https://huggingface.co/docs/diffusers/installation#offline-mode'."
) from e
except EnvironmentError as e:
raise EnvironmentError(
f"Can't load the model 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 file named {weights_name}"
) from e
def _get_checkpoint_shard_files(
pretrained_model_name_or_path,
index_filename,
cache_dir=None,
proxies=None,
local_files_only=False,
token=None,
user_agent=None,
revision=None,
subfolder="",
dduf_entries: dict[str, DDUFEntry] | None = None,
):
"""
For a given model:
- download and cache all the shards of a sharded checkpoint if `pretrained_model_name_or_path` is a model ID on the
Hub
- returns the list of paths to all the shards, as well as some metadata.
For the description of each arg, see [`PreTrainedModel.from_pretrained`]. `index_filename` is the full path to the
index (downloaded and cached if `pretrained_model_name_or_path` is a model ID on the Hub).
"""
if dduf_entries:
if index_filename not in dduf_entries:
raise ValueError(f"Can't find a checkpoint index ({index_filename}) in {pretrained_model_name_or_path}.")
else:
if not os.path.isfile(index_filename):
raise ValueError(f"Can't find a checkpoint index ({index_filename}) in {pretrained_model_name_or_path}.")
if dduf_entries:
index = json.loads(dduf_entries[index_filename].read_text())
else:
with open(index_filename, "r") as f:
index = json.loads(f.read())
original_shard_filenames = sorted(set(index["weight_map"].values()))
for shard_filename in original_shard_filenames:
if os.path.basename(shard_filename) != shard_filename:
raise ValueError(
f"The shard filename {shard_filename!r} in the checkpoint index contains a path separator or a "
"parent-directory reference, which is not allowed. Shard filenames must be plain filenames located "
"in the model directory."
)
sharded_metadata = index["metadata"]
sharded_metadata["all_checkpoint_keys"] = list(index["weight_map"].keys())
sharded_metadata["weight_map"] = index["weight_map"].copy()
shards_path = os.path.join(pretrained_model_name_or_path, subfolder)
# First, let's deal with local folder.
if os.path.isdir(pretrained_model_name_or_path) or dduf_entries:
shard_filenames = [os.path.join(shards_path, f) for f in original_shard_filenames]
for shard_file in shard_filenames:
if dduf_entries:
if shard_file not in dduf_entries:
raise FileNotFoundError(
f"{shards_path} does not appear to have a file named {shard_file} which is "
"required according to the checkpoint index."
)
else:
if not os.path.exists(shard_file):
raise FileNotFoundError(
f"{shards_path} does not appear to have a file named {shard_file} which is "
"required according to the checkpoint index."
)
return shard_filenames, sharded_metadata
# At this stage pretrained_model_name_or_path is a model identifier on the Hub
allow_patterns = original_shard_filenames
if subfolder is not None:
allow_patterns = [os.path.join(subfolder, p) for p in allow_patterns]
ignore_patterns = ["*.json", "*.md"]
# If the repo doesn't have the required shards, error out early even before downloading anything.
if not local_files_only:
model_files_info = model_info(pretrained_model_name_or_path, revision=revision, token=token)
for shard_file in original_shard_filenames:
shard_file_present = any(shard_file in k.rfilename for k in model_files_info.siblings)
if not shard_file_present:
raise EnvironmentError(
f"{shards_path} does not appear to have a file named {shard_file} which is "
"required according to the checkpoint index."
)
try:
# Load from URL
cached_folder = snapshot_download(
pretrained_model_name_or_path,
cache_dir=cache_dir,
proxies=proxies,
local_files_only=local_files_only,
token=token,
revision=revision,
allow_patterns=allow_patterns,
ignore_patterns=ignore_patterns,
user_agent=user_agent,
)
if subfolder is not None:
cached_folder = os.path.join(cached_folder, subfolder)
# We have already dealt with RepositoryNotFoundError and RevisionNotFoundError when getting the index, so
# we don't have to catch them here. We have also dealt with EntryNotFoundError.
except HfHubHTTPError as e:
raise EnvironmentError(
f"We couldn't connect to '{HUGGINGFACE_CO_RESOLVE_ENDPOINT}' to load {pretrained_model_name_or_path}. You should try"
" again after checking your internet connection."
) from e
cached_filenames = [os.path.join(cached_folder, f) for f in original_shard_filenames]
for cached_file in cached_filenames:
if not os.path.isfile(cached_file):
raise EnvironmentError(
f"{cached_folder} does not have a file named {cached_file} which is required according to the checkpoint index."
)
return cached_filenames, sharded_metadata
def _check_legacy_sharding_variant_format(folder: str = None, filenames: list[str] = None, variant: str = None):
if filenames and folder:
raise ValueError("Both `filenames` and `folder` cannot be provided.")
if not filenames:
filenames = []
for _, _, files in os.walk(folder):
for file in files:
filenames.append(os.path.basename(file))
transformers_index_format = r"\d{5}-of-\d{5}"
variant_file_re = re.compile(rf".*-{transformers_index_format}\.{variant}\.[a-z]+$")
return any(variant_file_re.match(f) is not None for f in filenames)
class PushToHubMixin:
"""
A Mixin to push a model, scheduler, or pipeline to the Hugging Face Hub.
"""
def _upload_folder(
self,
working_dir: str | os.PathLike,
repo_id: str,
token: str | None = None,
commit_message: str | None = None,
create_pr: bool = False,
subfolder: str | None = None,
):
"""
Uploads all files in `working_dir` to `repo_id`.
"""
if commit_message is None:
if "Model" in self.__class__.__name__:
commit_message = "Upload model"
elif "Scheduler" in self.__class__.__name__:
commit_message = "Upload scheduler"
else:
commit_message = f"Upload {self.__class__.__name__}"
logger.info(f"Uploading the files of {working_dir} to {repo_id}.")
return upload_folder(
repo_id=repo_id,
folder_path=working_dir,
token=token,
commit_message=commit_message,
create_pr=create_pr,
path_in_repo=subfolder,
)
def push_to_hub(
self,
repo_id: str,
commit_message: str | None = None,
private: bool | None = None,
token: str | None = None,
create_pr: bool = False,
safe_serialization: bool = True,
variant: str | None = None,
subfolder: str | None = None,
) -> str:
"""
Upload model, scheduler, or pipeline files to the 馃 Hugging Face Hub.
Parameters:
repo_id (`str`):
The name of the repository you want to push your model, scheduler, or pipeline files to. It should
contain your organization name when pushing to an organization. `repo_id` can also be a path to a local
directory.
commit_message (`str`, *optional*):
Message to commit while pushing. Default to `"Upload {object}"`.
private (`bool`, *optional*):
Whether to make the repo private. If `None` (default), the repo will be public unless the
organization's default is private. This value is ignored if the repo already exists.
token (`str`, *optional*):
The token to use as HTTP bearer authorization for remote files. The token generated when running `hf
auth login` (stored in `~/.huggingface`).
create_pr (`bool`, *optional*, defaults to `False`):
Whether or not to create a PR with the uploaded files or directly commit.
safe_serialization (`bool`, *optional*, defaults to `True`):
Whether or not to convert the model weights to the `safetensors` format.
variant (`str`, *optional*):
If specified, weights are saved in the format `pytorch_model.<variant>.bin`.
Examples:
```python
from diffusers import UNet2DConditionModel
unet = UNet2DConditionModel.from_pretrained("stabilityai/stable-diffusion-2", subfolder="unet")
# Push the `unet` to your namespace with the name "my-finetuned-unet".
unet.push_to_hub("my-finetuned-unet")
# Push the `unet` to an organization with the name "my-finetuned-unet".
unet.push_to_hub("your-org/my-finetuned-unet")
```
"""
repo_id = create_repo(repo_id, private=private, token=token, exist_ok=True).repo_id
# Create a new empty model card and eventually tag it
if not subfolder:
model_card = load_or_create_model_card(repo_id, token=token)
model_card = populate_model_card(model_card)
# Save all files.
save_kwargs = {"safe_serialization": safe_serialization}
if "Scheduler" not in self.__class__.__name__:
save_kwargs.update({"variant": variant})
with tempfile.TemporaryDirectory() as tmpdir:
self.save_pretrained(tmpdir, **save_kwargs)
# Update model card if needed:
if not subfolder:
model_card.save(os.path.join(tmpdir, "README.md"))
return self._upload_folder(
tmpdir,
repo_id,
token=token,
commit_message=commit_message,
create_pr=create_pr,
subfolder=subfolder,
)