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# coding=utf-8
# Copyright 2022 The HuggingFace Inc. team.
# Copyright (c) 2022, NVIDIA CORPORATION. All rights reserved.
#
# 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 importlib
import os
from typing import Optional, Union
from huggingface_hub import snapshot_download
from .configuration_utils import ConfigMixin
from .dynamic_modules_utils import get_class_from_dynamic_module
from .utils import DIFFUSERS_CACHE, logging
INDEX_FILE = "diffusion_model.pt"
logger = logging.get_logger(__name__)
LOADABLE_CLASSES = {
"diffusers": {
"ModelMixin": ["save_pretrained", "from_pretrained"],
"SchedulerMixin": ["save_config", "from_config"],
"DiffusionPipeline": ["save_pretrained", "from_pretrained"],
"ClassifierFreeGuidanceScheduler": ["save_config", "from_config"],
},
"transformers": {
"PreTrainedTokenizer": ["save_pretrained", "from_pretrained"],
"PreTrainedTokenizerFast": ["save_pretrained", "from_pretrained"],
"PreTrainedModel": ["save_pretrained", "from_pretrained"],
},
}
ALL_IMPORTABLE_CLASSES = {}
for library in LOADABLE_CLASSES:
ALL_IMPORTABLE_CLASSES.update(LOADABLE_CLASSES[library])
class DiffusionPipeline(ConfigMixin):
config_name = "model_index.json"
def register_modules(self, **kwargs):
# import it here to avoid circular import
from diffusers import pipelines
for name, module in kwargs.items():
# check if the module is a pipeline module
is_pipeline_module = hasattr(pipelines, module.__module__.split(".")[-1])
# retrive library
library = module.__module__.split(".")[0]
# if library is not in LOADABLE_CLASSES, then it is a custom module.
# Or if it's a pipeline module, then the module is inside the pipeline
# so we set the library to module name.
if library not in LOADABLE_CLASSES or is_pipeline_module:
library = module.__module__.split(".")[-1]
# retrive class_name
class_name = module.__class__.__name__
register_dict = {name: (library, class_name)}
# save model index config
self.register_to_config(**register_dict)
# set models
setattr(self, name, module)
register_dict = {"_module": self.__module__.split(".")[-1]}
self.register_to_config(**register_dict)
def save_pretrained(self, save_directory: Union[str, os.PathLike]):
self.save_config(save_directory)
model_index_dict = dict(self.config)
model_index_dict.pop("_class_name")
model_index_dict.pop("_diffusers_version")
model_index_dict.pop("_module")
for pipeline_component_name in model_index_dict.keys():
sub_model = getattr(self, pipeline_component_name)
model_cls = sub_model.__class__
save_method_name = None
# search for the model's base class in LOADABLE_CLASSES
for library_name, library_classes in LOADABLE_CLASSES.items():
library = importlib.import_module(library_name)
for base_class, save_load_methods in library_classes.items():
class_candidate = getattr(library, base_class)
if issubclass(model_cls, class_candidate):
# if we found a suitable base class in LOADABLE_CLASSES then grab its save method
save_method_name = save_load_methods[0]
break
if save_method_name is not None:
break
save_method = getattr(sub_model, save_method_name)
save_method(os.path.join(save_directory, pipeline_component_name))
@classmethod
def from_pretrained(cls, pretrained_model_name_or_path: Optional[Union[str, os.PathLike]], **kwargs):
r"""
Add docstrings
"""
cache_dir = kwargs.pop("cache_dir", DIFFUSERS_CACHE)
resume_download = kwargs.pop("resume_download", False)
proxies = kwargs.pop("proxies", None)
local_files_only = kwargs.pop("local_files_only", False)
use_auth_token = kwargs.pop("use_auth_token", None)
# 1. Download the checkpoints and configs
# use snapshot download here to get it working from from_pretrained
if not os.path.isdir(pretrained_model_name_or_path):
cached_folder = snapshot_download(
pretrained_model_name_or_path,
cache_dir=cache_dir,
resume_download=resume_download,
proxies=proxies,
local_files_only=local_files_only,
use_auth_token=use_auth_token,
)
else:
cached_folder = pretrained_model_name_or_path
config_dict = cls.get_config_dict(cached_folder)
# 2. Get class name and module candidates to load custom models
module_candidate_name = config_dict["_module"]
module_candidate = module_candidate_name + ".py"
# 3. Load the pipeline class, if using custom module then load it from the hub
# if we load from explicit class, let's use it
if cls != DiffusionPipeline:
pipeline_class = cls
else:
diffusers_module = importlib.import_module(cls.__module__.split(".")[0])
pipeline_class = getattr(diffusers_module, config_dict["_class_name"])
# (TODO - we should allow to load custom pipelines
# else we need to load the correct module from the Hub
# module = module_candidate
# pipeline_class = get_class_from_dynamic_module(cached_folder, module, class_name_, cached_folder)
init_dict, _ = pipeline_class.extract_init_dict(config_dict, **kwargs)
init_kwargs = {}
# import it here to avoid circular import
from diffusers import pipelines
# 4. Load each module in the pipeline
for name, (library_name, class_name) in init_dict.items():
is_pipeline_module = hasattr(pipelines, library_name)
# if the model is in a pipeline module, then we load it from the pipeline
if is_pipeline_module:
pipeline_module = getattr(pipelines, library_name)
class_obj = getattr(pipeline_module, class_name)
importable_classes = ALL_IMPORTABLE_CLASSES
class_candidates = {c: class_obj for c in ALL_IMPORTABLE_CLASSES.keys()}
elif library_name == module_candidate_name:
# if the model is not in diffusers or transformers, we need to load it from the hub
# assumes that it's a subclass of ModelMixin
class_obj = get_class_from_dynamic_module(cached_folder, module_candidate, class_name, cached_folder)
# since it's not from a library, we need to check class candidates for all importable classes
importable_classes = ALL_IMPORTABLE_CLASSES
class_candidates = {c: class_obj for c in ALL_IMPORTABLE_CLASSES.keys()}
else:
# else we just import it from the library.
library = importlib.import_module(library_name)
class_obj = getattr(library, class_name)
importable_classes = LOADABLE_CLASSES[library_name]
class_candidates = {c: getattr(library, c) for c in importable_classes.keys()}
load_method_name = None
for class_name, class_candidate in class_candidates.items():
if issubclass(class_obj, class_candidate):
load_method_name = importable_classes[class_name][1]
load_method = getattr(class_obj, load_method_name)
# check if the module is in a subdirectory
if os.path.isdir(os.path.join(cached_folder, name)):
loaded_sub_model = load_method(os.path.join(cached_folder, name))
else:
# else load from the root directory
loaded_sub_model = load_method(cached_folder)
init_kwargs[name] = loaded_sub_model # UNet(...), # DiffusionSchedule(...)
# 5. Instantiate the pipeline
model = pipeline_class(**init_kwargs)
return model