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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 inspect
import os
from typing import Optional, Union
import torch
from huggingface_hub import snapshot_download
from PIL import Image
from .configuration_utils import ConfigMixin
from .utils import DIFFUSERS_CACHE, logging
INDEX_FILE = "diffusion_pytorch_model.bin"
logger = logging.get_logger(__name__)
LOADABLE_CLASSES = {
"diffusers": {
"ModelMixin": ["save_pretrained", "from_pretrained"],
"SchedulerMixin": ["save_config", "from_config"],
"DiffusionPipeline": ["save_pretrained", "from_pretrained"],
},
"transformers": {
"PreTrainedTokenizer": ["save_pretrained", "from_pretrained"],
"PreTrainedTokenizerFast": ["save_pretrained", "from_pretrained"],
"PreTrainedModel": ["save_pretrained", "from_pretrained"],
"FeatureExtractionMixin": ["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():
# retrive library
library = module.__module__.split(".")[0]
# check if the module is a pipeline module
pipeline_dir = module.__module__.split(".")[-2]
path = module.__module__.split(".")
is_pipeline_module = pipeline_dir in path and hasattr(pipelines, pipeline_dir)
# 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
# folder so we set the library to module name.
if library not in LOADABLE_CLASSES or is_pipeline_module:
library = pipeline_dir
# 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)
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", None)
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))
def to(self, torch_device: Optional[Union[str, torch.device]] = None):
if torch_device is None:
return self
module_names, _ = self.extract_init_dict(dict(self.config))
for name in module_names.keys():
module = getattr(self, name)
if isinstance(module, torch.nn.Module):
module.to(torch_device)
return self
@property
def device(self) -> torch.device:
module_names, _ = self.extract_init_dict(dict(self.config))
for name in module_names.keys():
module = getattr(self, name)
if isinstance(module, torch.nn.Module):
return module.device
return torch.device("cpu")
@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)
revision = kwargs.pop("revision", None)
torch_dtype = kwargs.pop("torch_dtype", 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,
revision=revision,
)
else:
cached_folder = pretrained_model_name_or_path
config_dict = cls.get_config_dict(cached_folder)
# 2. 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"])
# some modules can be passed directly to the init
# in this case they are already instantiated in `kwargs`
# extract them here
expected_modules = set(inspect.signature(pipeline_class.__init__).parameters.keys())
passed_class_obj = {k: kwargs.pop(k) for k in expected_modules if k in kwargs}
init_dict, _ = pipeline_class.extract_init_dict(config_dict, **kwargs)
init_kwargs = {}
# import it here to avoid circular import
from diffusers import pipelines
# 3. Load each module in the pipeline
for name, (library_name, class_name) in init_dict.items():
is_pipeline_module = hasattr(pipelines, library_name)
loaded_sub_model = None
# if the model is in a pipeline module, then we load it from the pipeline
if name in passed_class_obj:
# 1. check that passed_class_obj has correct parent class
if not is_pipeline_module:
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()}
expected_class_obj = None
for class_name, class_candidate in class_candidates.items():
if issubclass(class_obj, class_candidate):
expected_class_obj = class_candidate
if not issubclass(passed_class_obj[name].__class__, expected_class_obj):
raise ValueError(
f"{passed_class_obj[name]} is of type: {type(passed_class_obj[name])}, but should be"
f" {expected_class_obj}"
)
else:
logger.warn(
f"You have passed a non-standard module {passed_class_obj[name]}. We cannot verify whether it"
" has the correct type"
)
# set passed class object
loaded_sub_model = passed_class_obj[name]
elif 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 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()}
if loaded_sub_model is None:
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)
loading_kwargs = {}
if issubclass(class_obj, torch.nn.Module):
loading_kwargs["torch_dtype"] = torch_dtype
# 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), **loading_kwargs)
else:
# else load from the root directory
loaded_sub_model = load_method(cached_folder, **loading_kwargs)
init_kwargs[name] = loaded_sub_model # UNet(...), # DiffusionSchedule(...)
# 4. Instantiate the pipeline
model = pipeline_class(**init_kwargs)
return model
@staticmethod
def numpy_to_pil(images):
"""
Convert a numpy image or a batch of images to a PIL image.
"""
if images.ndim == 3:
images = images[None, ...]
images = (images * 255).round().astype("uint8")
pil_images = [Image.fromarray(image) for image in images]
return pil_images
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