diffusers/src/diffusers/utils/hub_utils.py at main · mkasu/diffusers · GitHub

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# coding=utf-8

# 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 os

import sys

import traceback

from pathlib import Path

from typing import Dict, Optional, Union

from uuid import uuid4

from huggingface_hub import HfFolder, ModelCard, ModelCardData, whoami

from huggingface_hub.utils import is_jinja_available

from .. import __version__

from .constants import DIFFUSERS_CACHE, HUGGINGFACE_CO_RESOLVE_ENDPOINT

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

HF_HUB_OFFLINE = os.getenv("HF_HUB_OFFLINE", "").upper() in ENV_VARS_TRUE_VALUES

DISABLE_TELEMETRY = os.getenv("DISABLE_TELEMETRY", "").upper() in ENV_VARS_TRUE_VALUES

HUGGINGFACE_CO_TELEMETRY = HUGGINGFACE_CO_RESOLVE_ENDPOINT + "/api/telemetry/"

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 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 get_full_repo_name(model_id: str, organization: Optional[str] = None, token: Optional[str] = None):

if token is None:

token = HfFolder.get_token()

if organization is None:

username = whoami(token)["name"]

return f"{username}/{model_id}"

else:

return f"{organization}/{model_id}"

def create_model_card(args, model_name):

if not is_jinja_available():

raise ValueError(

"Modelcard rendering is based on Jinja templates."

" Please make sure to have `jinja` installed before using `create_model_card`."

" To install it, please run `pip install Jinja2`."

)

if hasattr(args, "local_rank") and args.local_rank not in [-1, 0]:

return

hub_token = args.hub_token if hasattr(args, "hub_token") else None

repo_name = get_full_repo_name(model_name, token=hub_token)

model_card = ModelCard.from_template(

card_data=ModelCardData( # Card metadata object that will be converted to YAML block

language="en",

license="apache-2.0",

library_name="diffusers",

tags=[],

datasets=args.dataset_name,

metrics=[],

template_path=MODEL_CARD_TEMPLATE_PATH,

model_name=model_name,

repo_name=repo_name,

dataset_name=args.dataset_name if hasattr(args, "dataset_name") else None,

learning_rate=args.learning_rate,

train_batch_size=args.train_batch_size,

eval_batch_size=args.eval_batch_size,

gradient_accumulation_steps=(

args.gradient_accumulation_steps if hasattr(args, "gradient_accumulation_steps") else None

adam_beta1=args.adam_beta1 if hasattr(args, "adam_beta1") else None,

adam_beta2=args.adam_beta2 if hasattr(args, "adam_beta2") else None,

adam_weight_decay=args.adam_weight_decay if hasattr(args, "adam_weight_decay") else None,

adam_epsilon=args.adam_epsilon if hasattr(args, "adam_epsilon") else None,

lr_scheduler=args.lr_scheduler if hasattr(args, "lr_scheduler") else None,

lr_warmup_steps=args.lr_warmup_steps if hasattr(args, "lr_warmup_steps") else None,

ema_inv_gamma=args.ema_inv_gamma if hasattr(args, "ema_inv_gamma") else None,

ema_power=args.ema_power if hasattr(args, "ema_power") else None,

ema_max_decay=args.ema_max_decay if hasattr(args, "ema_max_decay") else None,

mixed_precision=args.mixed_precision,

)

card_path = os.path.join(args.output_dir, "README.md")

model_card.save(card_path)

# Old default cache path, potentially to be migrated.

# This logic was more or less taken from `transformers`, with the following differences:

# - Diffusers doesn't use custom environment variables to specify the cache path.

# - There is no need to migrate the cache format, just move the files to the new location.

hf_cache_home = os.path.expanduser(

os.getenv("HF_HOME", os.path.join(os.getenv("XDG_CACHE_HOME", "~/.cache"), "huggingface"))

)

old_diffusers_cache = os.path.join(hf_cache_home, "diffusers")

def move_cache(old_cache_dir: Optional[str] = None, new_cache_dir: Optional[str] = None) -> None:

if new_cache_dir is None:

new_cache_dir = DIFFUSERS_CACHE

if old_cache_dir is None:

old_cache_dir = old_diffusers_cache

old_cache_dir = Path(old_cache_dir).expanduser()

new_cache_dir = Path(new_cache_dir).expanduser()

for old_blob_path in old_cache_dir.glob("**/blobs/*"): # move file blob by blob

if old_blob_path.is_file() and not old_blob_path.is_symlink():

new_blob_path = new_cache_dir / old_blob_path.relative_to(old_cache_dir)

new_blob_path.parent.mkdir(parents=True, exist_ok=True)

os.replace(old_blob_path, new_blob_path)

try:

os.symlink(new_blob_path, old_blob_path)

except OSError:

logger.warning(

"Could not create symlink between old cache and new cache. If you use an older version of diffusers again, files will be re-downloaded."

)

# At this point, old_cache_dir contains symlinks to the new cache (it can still be used).

cache_version_file = os.path.join(DIFFUSERS_CACHE, "version_diffusers_cache.txt")

if not os.path.isfile(cache_version_file):

cache_version = 0

else:

with open(cache_version_file) as f:

cache_version = int(f.read())

if cache_version < 1:

old_cache_is_not_empty = os.path.isdir(old_diffusers_cache) and len(os.listdir(old_diffusers_cache)) > 0

if old_cache_is_not_empty:

logger.warning(

"The cache for model files in Diffusers v0.14.0 has moved to a new location. Moving your "

"existing cached models. This is a one-time operation, you can interrupt it or run it "

"later by calling `diffusers.utils.hub_utils.move_cache()`."

)

try:

move_cache()

except Exception as e:

trace = "\n".join(traceback.format_tb(e.__traceback__))

logger.error(

f"There was a problem when trying to move your cache:\n\n{trace}\n{e.__class__.__name__}: {e}\n\nPlease "

"file an issue at https://github.com/huggingface/diffusers/issues/new/choose, copy paste this whole "

"message and we will do our best to help."

)

if cache_version < 1:

try:

os.makedirs(DIFFUSERS_CACHE, exist_ok=True)

with open(cache_version_file, "w") as f:

f.write("1")

except Exception:

logger.warning(

f"There was a problem when trying to write in your cache folder ({DIFFUSERS_CACHE}). Please, ensure "

"the directory exists and can be written to."

)

diffusers/src/diffusers/utils/hub_utils.py at main · mkasu/diffusers · GitHub

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