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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 typing import Dict, List, Optional, Union
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,
RepositoryNotFoundError,
RevisionNotFoundError,
is_jinja_available,
validate_hf_hub_args,
)
from packaging import version
from requests import HTTPError
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,
_flax_version,
_jax_version,
_onnxruntime_version,
_torch_version,
is_flax_available,
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: Union[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_flax_available():
ua += f"; jax/{_jax_version}"
ua += f"; flax/{_flax_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: Optional[str] = None,
is_pipeline: bool = False,
from_training: bool = False,
model_description: Optional[str] = None,
base_model: str = None,
prompt: Optional[str] = None,
license: Optional[str] = None,
widget: Optional[List[dict]] = None,
inference: Optional[bool] = None,
) -> ModelCard:
"""
Loads or creates a model card.
Args:
repo_id_or_path (`str`):
The repo id (e.g., "runwayml/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.
"""
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`."
)
try:
# Check if the model card is present on the remote repo
model_card = ModelCard.load(repo_id_or_path, token=token)
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()
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: Union[str, List[str]] = 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: Optional[str], commit_hash: Optional[str] = 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: Optional[str] = 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: Union[str, Path],
*,
weights_name: str,
subfolder: Optional[str] = None,
cache_dir: Optional[str] = None,
force_download: bool = False,
proxies: Optional[Dict] = None,
local_files_only: bool = False,
token: Optional[str] = None,
user_agent: Optional[Union[Dict, str]] = None,
revision: Optional[str] = None,
commit_hash: Optional[str] = None,
dduf_entries: Optional[Dict[str, DDUFEntry]] = 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 `huggingface-cli "
"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 HTTPError 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: Optional[Dict[str, DDUFEntry]] = 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()))
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"]
# `model_info` call must guarded with the above condition.
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 HTTPError 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]
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: Union[str, os.PathLike],
repo_id: str,
token: Optional[str] = None,
commit_message: Optional[str] = None,
create_pr: bool = False,
):
"""
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
)
def push_to_hub(
self,
repo_id: str,
commit_message: Optional[str] = None,
private: Optional[bool] = None,
token: Optional[str] = None,
create_pr: bool = False,
safe_serialization: bool = True,
variant: Optional[str] = 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
`huggingface-cli 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
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:
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,
)