diff --git a/_ b/_
new file mode 100644
index 000000000000..bbe9245b98c4
--- /dev/null
+++ b/_
@@ -0,0 +1,456 @@
+# coding=utf-8
+# Copyright 2022 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
+from distutils.version import StrictVersion
+from pathlib import Path
+from typing import Dict, Optional, Union
+from urllib import request
+
+from huggingface_hub import HfFolder, cached_download, hf_hub_download, model_info
+
+from . import __version__
+from .utils import DIFFUSERS_DYNAMIC_MODULE_NAME, HF_MODULES_CACHE, logging
+
+
+COMMUNITY_PIPELINES_URL = (
+ "https://raw.githubusercontent.com/huggingface/diffusers/{revision}/examples/community/{pipeline}.py"
+)
+
+
+logger = logging.get_logger(__name__) # pylint: disable=invalid-name
+
+
+def get_diffusers_versions():
+ url = "https://pypi.org/pypi/diffusers/json"
+ releases = json.loads(request.urlopen(url).read())["releases"].keys()
+ return sorted(releases, key=StrictVersion)
+
+
+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: Union[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("^\s*import\s+\.(\S+)\s*$", content, flags=re.MULTILINE)
+ # Imports of the form `from .xxx import yyy`
+ relative_imports += re.findall("^\s*from\s+\.(\S+)\s+import", content, flags=re.MULTILINE)
+ # Unique-ify
+ return list(set(relative_imports))
+
+
+def get_relative_import_files(module_file):
+ """
+ Get the list of all files that are needed for a given module. Note that this function recurses through the relative
+ imports (if a imports b and b imports c, it will return module files for b and c).
+
+ Args:
+ module_file (`str` or `os.PathLike`): The module file to inspect.
+ """
+ no_change = False
+ files_to_check = [module_file]
+ all_relative_imports = []
+
+ # Let's recurse through all relative imports
+ while not no_change:
+ new_imports = []
+ for f in files_to_check:
+ new_imports.extend(get_relative_imports(f))
+
+ module_path = Path(module_file).parent
+ new_import_files = [str(module_path / m) for m in new_imports]
+ new_import_files = [f for f in new_import_files if f not in all_relative_imports]
+ files_to_check = [f"{f}.py" for f in new_import_files]
+
+ no_change = len(new_import_files) == 0
+ all_relative_imports.extend(files_to_check)
+
+ return all_relative_imports
+
+
+def check_imports(filename):
+ """
+ Check if the current Python environment contains all the libraries that are imported in a file.
+ """
+ with open(filename, "r", encoding="utf-8") as f:
+ content = f.read()
+
+ # Imports of the form `import xxx`
+ imports = re.findall("^\s*import\s+(\S+)\s*$", content, flags=re.MULTILINE)
+ # Imports of the form `from xxx import yyy`
+ imports += re.findall("^\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:
+ raise ImportError(
+ "This modeling file requires 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 get_class_in_module(class_name, module_path):
+ """
+ Import a module on the cache directory for modules and extract a class from it.
+ """
+ module_path = module_path.replace(os.path.sep, ".")
+ module = importlib.import_module(module_path)
+
+ 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 .pipeline_utils 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
+
+
+def get_cached_module_file(
+ pretrained_model_name_or_path: Union[str, os.PathLike],
+ module_file: str,
+ cache_dir: Optional[Union[str, os.PathLike]] = None,
+ force_download: bool = False,
+ resume_download: bool = False,
+ proxies: Optional[Dict[str, str]] = None,
+ use_auth_token: Optional[Union[bool, str]] = None,
+ revision: Optional[str] = None,
+ local_files_only: 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.
+ resume_download (`bool`, *optional*, defaults to `False`):
+ Whether or not to delete incompletely received file. Attempts to resume the download if such a file exists.
+ 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.
+ use_auth_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.
+
+
+
+ You may pass a token in `use_auth_token` if you are not logged in (`huggingface-cli long`) 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)
+
+ module_file_or_url = os.path.join(pretrained_model_name_or_path, module_file)
+
+ if os.path.isfile(module_file_or_url):
+ resolved_module_file = module_file_or_url
+ submodule = "local"
+ elif pretrained_model_name_or_path.count("/") == 0:
+ 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 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'])}."
+ )
+
+ # community pipeline on GitHub
+ github_url = COMMUNITY_PIPELINES_URL.format(revision=revision, pipeline=pretrained_model_name_or_path)
+ try:
+ resolved_module_file = cached_download(
+ github_url,
+ cache_dir=cache_dir,
+ force_download=True,
+ proxies=proxies,
+ resume_download=resume_download,
+ local_files_only=local_files_only,
+ use_auth_token=False,
+ )
+ submodule = "git"
+ module_file = pretrained_model_name_or_path + ".py"
+ except EnvironmentError:
+ logger.error(f"Could not locate the {module_file} inside {pretrained_model_name_or_path}.")
+ raise
+ else:
+ try:
+ # Load from URL or cache if already cached
+ resolved_module_file = hf_hub_download(
+ pretrained_model_name_or_path,
+ module_file,
+ cache_dir=cache_dir,
+ force_download=force_download,
+ proxies=proxies,
+ resume_download=resume_download,
+ local_files_only=local_files_only,
+ use_auth_token=use_auth_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.copy(resolved_module_file, submodule_path / module_file)
+ for module_needed in modules_needed:
+ module_needed = f"{module_needed}.py"
+ shutil.copy(os.path.join(pretrained_model_name_or_path, module_needed), 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.
+ if isinstance(use_auth_token, str):
+ token = use_auth_token
+ elif use_auth_token is True:
+ token = HfFolder.get_token()
+ else:
+ token = None
+
+ 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():
+ shutil.copy(resolved_module_file, submodule_path / module_file)
+ # Make sure we also have every file with relative
+ for module_needed in modules_needed:
+ if not (submodule_path / module_needed).exists():
+ get_cached_module_file(
+ pretrained_model_name_or_path,
+ f"{module_needed}.py",
+ cache_dir=cache_dir,
+ force_download=force_download,
+ resume_download=resume_download,
+ proxies=proxies,
+ use_auth_token=use_auth_token,
+ revision=revision,
+ local_files_only=local_files_only,
+ )
+ return os.path.join(full_submodule, module_file)
+
+
+def get_class_from_dynamic_module(
+ pretrained_model_name_or_path: Union[str, os.PathLike],
+ module_file: str,
+ class_name: Optional[str] = None,
+ cache_dir: Optional[Union[str, os.PathLike]] = None,
+ force_download: bool = False,
+ resume_download: bool = False,
+ proxies: Optional[Dict[str, str]] = None,
+ use_auth_token: Optional[Union[bool, str]] = None,
+ revision: Optional[str] = None,
+ local_files_only: bool = False,
+ **kwargs,
+):
+ """
+ Extracts a class from a module file, present in the local folder or repository of a model.
+
+
+
+ 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.
+ resume_download (`bool`, *optional*, defaults to `False`):
+ Whether or not to delete incompletely received file. Attempts to resume the download if such a file exists.
+ 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.
+ use_auth_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.
+
+
+
+ You may pass a token in `use_auth_token` if you are not logged in (`huggingface-cli long`) 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,
+ cache_dir=cache_dir,
+ force_download=force_download,
+ resume_download=resume_download,
+ proxies=proxies,
+ use_auth_token=use_auth_token,
+ revision=revision,
+ local_files_only=local_files_only,
+ )
+ return get_class_in_module(class_name, final_module.replace(".py", ""))
diff --git a/src/diffusers/dynamic_modules_utils.py b/src/diffusers/dynamic_modules_utils.py
index 31f3bed2ecf9..693d9811fc83 100644
--- a/src/diffusers/dynamic_modules_utils.py
+++ b/src/diffusers/dynamic_modules_utils.py
@@ -16,26 +16,36 @@
import importlib
import inspect
+import json
import os
import re
import shutil
import sys
+from distutils.version import StrictVersion
from pathlib import Path
from typing import Dict, Optional, Union
+from urllib import request
from huggingface_hub import HfFolder, cached_download, hf_hub_download, model_info
+from . import __version__
from .utils import DIFFUSERS_DYNAMIC_MODULE_NAME, HF_MODULES_CACHE, logging
COMMUNITY_PIPELINES_URL = (
- "https://raw.githubusercontent.com/huggingface/diffusers/main/examples/community/{pipeline}.py"
+ "https://raw.githubusercontent.com/huggingface/diffusers/{revision}/examples/community/{pipeline}.py"
)
logger = logging.get_logger(__name__) # pylint: disable=invalid-name
+def get_diffusers_versions():
+ url = "https://pypi.org/pypi/diffusers/json"
+ releases = json.loads(request.urlopen(url).read())["releases"].keys()
+ return sorted(releases, key=StrictVersion)
+
+
def init_hf_modules():
"""
Creates the cache directory for modules with an init, and adds it to the Python path.
@@ -251,8 +261,26 @@ def get_cached_module_file(
resolved_module_file = module_file_or_url
submodule = "local"
elif pretrained_model_name_or_path.count("/") == 0:
+ 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 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'])}."
+ )
+
# community pipeline on GitHub
- github_url = COMMUNITY_PIPELINES_URL.format(pipeline=pretrained_model_name_or_path)
+ github_url = COMMUNITY_PIPELINES_URL.format(revision=revision, pipeline=pretrained_model_name_or_path)
try:
resolved_module_file = cached_download(
github_url,
diff --git a/src/diffusers/pipeline_utils.py b/src/diffusers/pipeline_utils.py
index f76a4da149d4..d1052234b58c 100644
--- a/src/diffusers/pipeline_utils.py
+++ b/src/diffusers/pipeline_utils.py
@@ -375,6 +375,10 @@ def from_pretrained(cls, pretrained_model_name_or_path: Optional[Union[str, os.P
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.
+ custom_revision (`str`, *optional*, defaults to `"main"` when loading from the Hub and to local version of `diffusers` when loading from GitHub):
+ The specific model version to use. It can be a branch name, a tag name, or a commit id similar to
+ `revision` when loading a custom pipeline from the Hub. It can be a diffusers version when loading a
+ custom pipeline from GitHub.
mirror (`str`, *optional*):
Mirror source to accelerate downloads in China. If you are from China and have an accessibility
problem, you can set this option to resolve it. Note that we do not guarantee the timeliness or safety.
@@ -442,6 +446,7 @@ def from_pretrained(cls, pretrained_model_name_or_path: Optional[Union[str, os.P
revision = kwargs.pop("revision", None)
torch_dtype = kwargs.pop("torch_dtype", None)
custom_pipeline = kwargs.pop("custom_pipeline", None)
+ custom_revision = kwargs.pop("custom_revision", None)
provider = kwargs.pop("provider", None)
sess_options = kwargs.pop("sess_options", None)
device_map = kwargs.pop("device_map", None)
@@ -528,7 +533,9 @@ def from_pretrained(cls, pretrained_model_name_or_path: Optional[Union[str, os.P
else:
file_name = CUSTOM_PIPELINE_FILE_NAME
- pipeline_class = get_class_from_dynamic_module(custom_pipeline, module_file=file_name, cache_dir=cache_dir)
+ pipeline_class = get_class_from_dynamic_module(
+ custom_pipeline, module_file=file_name, cache_dir=cache_dir, revision=custom_revision
+ )
elif cls != DiffusionPipeline:
pipeline_class = cls
else:
diff --git a/tests/test_pipelines.py b/tests/test_pipelines.py
index 3a6d5139e95b..5b2f86ef7187 100644
--- a/tests/test_pipelines.py
+++ b/tests/test_pipelines.py
@@ -18,6 +18,7 @@
import os
import random
import shutil
+import sys
import tempfile
import unittest
@@ -207,6 +208,31 @@ def test_load_custom_pipeline(self):
# under https://huggingface.co/hf-internal-testing/diffusers-dummy-pipeline/blob/main/pipeline.py#L24
assert pipeline.__class__.__name__ == "CustomPipeline"
+ def test_load_custom_github(self):
+ pipeline = DiffusionPipeline.from_pretrained(
+ "google/ddpm-cifar10-32", custom_pipeline="one_step_unet", custom_revision="main"
+ )
+
+ # make sure that on "main" pipeline gives only ones because of: https://github.com/huggingface/diffusers/pull/1690
+ with torch.no_grad():
+ output = pipeline()
+
+ assert output.numel() == output.sum()
+
+ # hack since Python doesn't like overwriting modules: https://stackoverflow.com/questions/3105801/unload-a-module-in-python
+ # Could in the future work with hashes instead.
+ del sys.modules["diffusers_modules.git.one_step_unet"]
+
+ pipeline = DiffusionPipeline.from_pretrained(
+ "google/ddpm-cifar10-32", custom_pipeline="one_step_unet", custom_revision="0.10.2"
+ )
+ with torch.no_grad():
+ output = pipeline()
+
+ assert output.numel() != output.sum()
+
+ assert pipeline.__class__.__name__ == "UnetSchedulerOneForwardPipeline"
+
def test_run_custom_pipeline(self):
pipeline = DiffusionPipeline.from_pretrained(
"google/ddpm-cifar10-32", custom_pipeline="hf-internal-testing/diffusers-dummy-pipeline"