# coding=utf-8 # Copyright 2023 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 fnmatch import importlib import inspect import os import re import sys import warnings from dataclasses import dataclass from pathlib import Path from typing import Any, Callable, Dict, List, Optional, Union import numpy as np import PIL import torch from huggingface_hub import hf_hub_download, model_info, snapshot_download from packaging import version from requests.exceptions import HTTPError from tqdm.auto import tqdm import diffusers from .. import __version__ from ..configuration_utils import ConfigMixin from ..models.modeling_utils import _LOW_CPU_MEM_USAGE_DEFAULT from ..schedulers.scheduling_utils import SCHEDULER_CONFIG_NAME from ..utils import ( CONFIG_NAME, DEPRECATED_REVISION_ARGS, DIFFUSERS_CACHE, HF_HUB_OFFLINE, SAFETENSORS_WEIGHTS_NAME, WEIGHTS_NAME, BaseOutput, deprecate, get_class_from_dynamic_module, is_accelerate_available, is_accelerate_version, is_compiled_module, is_safetensors_available, is_torch_version, is_transformers_available, logging, numpy_to_pil, ) if is_transformers_available(): import transformers from transformers import PreTrainedModel from transformers.utils import FLAX_WEIGHTS_NAME as TRANSFORMERS_FLAX_WEIGHTS_NAME from transformers.utils import SAFE_WEIGHTS_NAME as TRANSFORMERS_SAFE_WEIGHTS_NAME from transformers.utils import WEIGHTS_NAME as TRANSFORMERS_WEIGHTS_NAME from ..utils import FLAX_WEIGHTS_NAME, ONNX_EXTERNAL_WEIGHTS_NAME, ONNX_WEIGHTS_NAME if is_accelerate_available(): import accelerate INDEX_FILE = "diffusion_pytorch_model.bin" CUSTOM_PIPELINE_FILE_NAME = "pipeline.py" DUMMY_MODULES_FOLDER = "diffusers.utils" TRANSFORMERS_DUMMY_MODULES_FOLDER = "transformers.utils" logger = logging.get_logger(__name__) LOADABLE_CLASSES = { "diffusers": { "ModelMixin": ["save_pretrained", "from_pretrained"], "SchedulerMixin": ["save_pretrained", "from_pretrained"], "DiffusionPipeline": ["save_pretrained", "from_pretrained"], "OnnxRuntimeModel": ["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"], "ProcessorMixin": ["save_pretrained", "from_pretrained"], "ImageProcessingMixin": ["save_pretrained", "from_pretrained"], }, "onnxruntime.training": { "ORTModule": ["save_pretrained", "from_pretrained"], }, } ALL_IMPORTABLE_CLASSES = {} for library in LOADABLE_CLASSES: ALL_IMPORTABLE_CLASSES.update(LOADABLE_CLASSES[library]) @dataclass class ImagePipelineOutput(BaseOutput): """ Output class for image pipelines. Args: images (`List[PIL.Image.Image]` or `np.ndarray`) List of denoised PIL images of length `batch_size` or NumPy array of shape `(batch_size, height, width, num_channels)`. """ images: Union[List[PIL.Image.Image], np.ndarray] @dataclass class AudioPipelineOutput(BaseOutput): """ Output class for audio pipelines. Args: audios (`np.ndarray`) List of denoised audio samples of a NumPy array of shape `(batch_size, num_channels, sample_rate)`. """ audios: np.ndarray def is_safetensors_compatible(filenames, variant=None, passed_components=None) -> bool: """ Checking for safetensors compatibility: - By default, all models are saved with the default pytorch serialization, so we use the list of default pytorch files to know which safetensors files are needed. - The model is safetensors compatible only if there is a matching safetensors file for every default pytorch file. Converting default pytorch serialized filenames to safetensors serialized filenames: - For models from the diffusers library, just replace the ".bin" extension with ".safetensors" - For models from the transformers library, the filename changes from "pytorch_model" to "model", and the ".bin" extension is replaced with ".safetensors" """ pt_filenames = [] sf_filenames = set() passed_components = passed_components or [] for filename in filenames: _, extension = os.path.splitext(filename) if len(filename.split("/")) == 2 and filename.split("/")[0] in passed_components: continue if extension == ".bin": pt_filenames.append(filename) elif extension == ".safetensors": sf_filenames.add(filename) for filename in pt_filenames: # filename = 'foo/bar/baz.bam' -> path = 'foo/bar', filename = 'baz', extention = '.bam' path, filename = os.path.split(filename) filename, extension = os.path.splitext(filename) if filename.startswith("pytorch_model"): filename = filename.replace("pytorch_model", "model") else: filename = filename expected_sf_filename = os.path.join(path, filename) expected_sf_filename = f"{expected_sf_filename}.safetensors" if expected_sf_filename not in sf_filenames: logger.warning(f"{expected_sf_filename} not found") return False return True def variant_compatible_siblings(filenames, variant=None) -> Union[List[os.PathLike], str]: weight_names = [ WEIGHTS_NAME, SAFETENSORS_WEIGHTS_NAME, FLAX_WEIGHTS_NAME, ONNX_WEIGHTS_NAME, ONNX_EXTERNAL_WEIGHTS_NAME, ] if is_transformers_available(): weight_names += [TRANSFORMERS_WEIGHTS_NAME, TRANSFORMERS_SAFE_WEIGHTS_NAME, TRANSFORMERS_FLAX_WEIGHTS_NAME] # model_pytorch, diffusion_model_pytorch, ... weight_prefixes = [w.split(".")[0] for w in weight_names] # .bin, .safetensors, ... weight_suffixs = [w.split(".")[-1] for w in weight_names] # -00001-of-00002 transformers_index_format = r"\d{5}-of-\d{5}" if variant is not None: # `diffusion_pytorch_model.fp16.bin` as well as `model.fp16-00001-of-00002.safetensors` variant_file_re = re.compile( rf"({'|'.join(weight_prefixes)})\.({variant}|{variant}-{transformers_index_format})\.({'|'.join(weight_suffixs)})$" ) # `text_encoder/pytorch_model.bin.index.fp16.json` variant_index_re = re.compile( rf"({'|'.join(weight_prefixes)})\.({'|'.join(weight_suffixs)})\.index\.{variant}\.json$" ) # `diffusion_pytorch_model.bin` as well as `model-00001-of-00002.safetensors` non_variant_file_re = re.compile( rf"({'|'.join(weight_prefixes)})(-{transformers_index_format})?\.({'|'.join(weight_suffixs)})$" ) # `text_encoder/pytorch_model.bin.index.json` non_variant_index_re = re.compile(rf"({'|'.join(weight_prefixes)})\.({'|'.join(weight_suffixs)})\.index\.json") if variant is not None: variant_weights = {f for f in filenames if variant_file_re.match(f.split("/")[-1]) is not None} variant_indexes = {f for f in filenames if variant_index_re.match(f.split("/")[-1]) is not None} variant_filenames = variant_weights | variant_indexes else: variant_filenames = set() non_variant_weights = {f for f in filenames if non_variant_file_re.match(f.split("/")[-1]) is not None} non_variant_indexes = {f for f in filenames if non_variant_index_re.match(f.split("/")[-1]) is not None} non_variant_filenames = non_variant_weights | non_variant_indexes # all variant filenames will be used by default usable_filenames = set(variant_filenames) def convert_to_variant(filename): if "index" in filename: variant_filename = filename.replace("index", f"index.{variant}") elif re.compile(f"^(.*?){transformers_index_format}").match(filename) is not None: variant_filename = f"{filename.split('-')[0]}.{variant}-{'-'.join(filename.split('-')[1:])}" else: variant_filename = f"{filename.split('.')[0]}.{variant}.{filename.split('.')[1]}" return variant_filename for f in non_variant_filenames: variant_filename = convert_to_variant(f) if variant_filename not in usable_filenames: usable_filenames.add(f) return usable_filenames, variant_filenames def warn_deprecated_model_variant(pretrained_model_name_or_path, use_auth_token, variant, revision, model_filenames): info = model_info( pretrained_model_name_or_path, use_auth_token=use_auth_token, revision=None, ) filenames = {sibling.rfilename for sibling in info.siblings} comp_model_filenames, _ = variant_compatible_siblings(filenames, variant=revision) comp_model_filenames = [".".join(f.split(".")[:1] + f.split(".")[2:]) for f in comp_model_filenames] if set(comp_model_filenames) == set(model_filenames): warnings.warn( f"You are loading the variant {revision} from {pretrained_model_name_or_path} via `revision='{revision}'` even though you can load it via `variant=`{revision}`. Loading model variants via `revision='{revision}'` is deprecated and will be removed in diffusers v1. Please use `variant='{revision}'` instead.", FutureWarning, ) else: 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 the required variant filenames in the 'main' branch. \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 {revision} files' so that the correct variant file can be added.", FutureWarning, ) def maybe_raise_or_warn( library_name, library, class_name, importable_classes, passed_class_obj, name, is_pipeline_module ): """Simple helper method to raise or warn in case incorrect module has been passed""" if not is_pipeline_module: library = importlib.import_module(library_name) class_obj = getattr(library, class_name) class_candidates = {c: getattr(library, c, None) for c in importable_classes.keys()} expected_class_obj = None for class_name, class_candidate in class_candidates.items(): if class_candidate is not None and issubclass(class_obj, class_candidate): expected_class_obj = class_candidate # Dynamo wraps the original model in a private class. # I didn't find a public API to get the original class. sub_model = passed_class_obj[name] model_cls = sub_model.__class__ if is_compiled_module(sub_model): model_cls = sub_model._orig_mod.__class__ if not issubclass(model_cls, expected_class_obj): raise ValueError( f"{passed_class_obj[name]} is of type: {model_cls}, but should be" f" {expected_class_obj}" ) else: logger.warning( f"You have passed a non-standard module {passed_class_obj[name]}. We cannot verify whether it" " has the correct type" ) def get_class_obj_and_candidates(library_name, class_name, importable_classes, pipelines, is_pipeline_module): """Simple helper method to retrieve class object of module as well as potential parent class objects""" if is_pipeline_module: pipeline_module = getattr(pipelines, library_name) class_obj = getattr(pipeline_module, class_name) 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) class_candidates = {c: getattr(library, c, None) for c in importable_classes.keys()} return class_obj, class_candidates def _get_pipeline_class(class_obj, config, custom_pipeline=None, cache_dir=None, revision=None): if custom_pipeline is not None: if custom_pipeline.endswith(".py"): path = Path(custom_pipeline) # decompose into folder & file file_name = path.name custom_pipeline = path.parent.absolute() else: file_name = CUSTOM_PIPELINE_FILE_NAME return get_class_from_dynamic_module( custom_pipeline, module_file=file_name, cache_dir=cache_dir, revision=revision ) if class_obj != DiffusionPipeline: return class_obj diffusers_module = importlib.import_module(class_obj.__module__.split(".")[0]) return getattr(diffusers_module, config["_class_name"]) def load_sub_model( library_name: str, class_name: str, importable_classes: List[Any], pipelines: Any, is_pipeline_module: bool, pipeline_class: Any, torch_dtype: torch.dtype, provider: Any, sess_options: Any, device_map: Optional[Union[Dict[str, torch.device], str]], max_memory: Optional[Dict[Union[int, str], Union[int, str]]], offload_folder: Optional[Union[str, os.PathLike]], offload_state_dict: bool, model_variants: Dict[str, str], name: str, from_flax: bool, variant: str, low_cpu_mem_usage: bool, cached_folder: Union[str, os.PathLike], ): """Helper method to load the module `name` from `library_name` and `class_name`""" # retrieve class candidates class_obj, class_candidates = get_class_obj_and_candidates( library_name, class_name, importable_classes, pipelines, is_pipeline_module ) load_method_name = None # retrive load method name for class_name, class_candidate in class_candidates.items(): if class_candidate is not None and issubclass(class_obj, class_candidate): load_method_name = importable_classes[class_name][1] # if load method name is None, then we have a dummy module -> raise Error if load_method_name is None: none_module = class_obj.__module__ is_dummy_path = none_module.startswith(DUMMY_MODULES_FOLDER) or none_module.startswith( TRANSFORMERS_DUMMY_MODULES_FOLDER ) if is_dummy_path and "dummy" in none_module: # call class_obj for nice error message of missing requirements class_obj() raise ValueError( f"The component {class_obj} of {pipeline_class} cannot be loaded as it does not seem to have" f" any of the loading methods defined in {ALL_IMPORTABLE_CLASSES}." ) load_method = getattr(class_obj, load_method_name) # add kwargs to loading method loading_kwargs = {} if issubclass(class_obj, torch.nn.Module): loading_kwargs["torch_dtype"] = torch_dtype if issubclass(class_obj, diffusers.OnnxRuntimeModel): loading_kwargs["provider"] = provider loading_kwargs["sess_options"] = sess_options is_diffusers_model = issubclass(class_obj, diffusers.ModelMixin) if is_transformers_available(): transformers_version = version.parse(version.parse(transformers.__version__).base_version) else: transformers_version = "N/A" is_transformers_model = ( is_transformers_available() and issubclass(class_obj, PreTrainedModel) and transformers_version >= version.parse("4.20.0") ) # When loading a transformers model, if the device_map is None, the weights will be initialized as opposed to diffusers. # To make default loading faster we set the `low_cpu_mem_usage=low_cpu_mem_usage` flag which is `True` by default. # This makes sure that the weights won't be initialized which significantly speeds up loading. if is_diffusers_model or is_transformers_model: loading_kwargs["device_map"] = device_map loading_kwargs["max_memory"] = max_memory loading_kwargs["offload_folder"] = offload_folder loading_kwargs["offload_state_dict"] = offload_state_dict loading_kwargs["variant"] = model_variants.pop(name, None) if from_flax: loading_kwargs["from_flax"] = True # the following can be deleted once the minimum required `transformers` version # is higher than 4.27 if ( is_transformers_model and loading_kwargs["variant"] is not None and transformers_version < version.parse("4.27.0") ): raise ImportError( f"When passing `variant='{variant}'`, please make sure to upgrade your `transformers` version to at least 4.27.0.dev0" ) elif is_transformers_model and loading_kwargs["variant"] is None: loading_kwargs.pop("variant") # if `from_flax` and model is transformer model, can currently not load with `low_cpu_mem_usage` if not (from_flax and is_transformers_model): loading_kwargs["low_cpu_mem_usage"] = low_cpu_mem_usage else: loading_kwargs["low_cpu_mem_usage"] = False # 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) return loaded_sub_model class DiffusionPipeline(ConfigMixin): r""" Base class for all pipelines. [`DiffusionPipeline`] stores all components (models, schedulers, and processors) for diffusion pipelines and provides methods for loading, downloading and saving models. It also includes methods to: - move all PyTorch modules to the device of your choice - enabling/disabling the progress bar for the denoising iteration Class attributes: - **config_name** (`str`) -- The configuration filename that stores the class and module names of all the diffusion pipeline's components. - **_optional_components** (List[`str`]) -- List of all optional components that don't have to be passed to the pipeline to function (should be overridden by subclasses). """ config_name = "model_index.json" _optional_components = [] def register_modules(self, **kwargs): # import it here to avoid circular import from diffusers import pipelines for name, module in kwargs.items(): # retrieve library if module is None: register_dict = {name: (None, None)} else: # register the config from the original module, not the dynamo compiled one if is_compiled_module(module): not_compiled_module = module._orig_mod else: not_compiled_module = module library = not_compiled_module.__module__.split(".")[0] # check if the module is a pipeline module module_path_items = not_compiled_module.__module__.split(".") pipeline_dir = module_path_items[-2] if len(module_path_items) > 2 else None path = not_compiled_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 is_pipeline_module: library = pipeline_dir elif library not in LOADABLE_CLASSES: library = not_compiled_module.__module__ # retrieve class_name class_name = not_compiled_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 __setattr__(self, name: str, value: Any): if name in self.__dict__ and hasattr(self.config, name): # We need to overwrite the config if name exists in config if isinstance(getattr(self.config, name), (tuple, list)): if value is not None and self.config[name][0] is not None: class_library_tuple = (value.__module__.split(".")[0], value.__class__.__name__) else: class_library_tuple = (None, None) self.register_to_config(**{name: class_library_tuple}) else: self.register_to_config(**{name: value}) super().__setattr__(name, value) def save_pretrained( self, save_directory: Union[str, os.PathLike], safe_serialization: bool = False, variant: Optional[str] = None, ): """ Save all saveable variables of the pipeline to a directory. A pipeline variable can be saved and loaded if its class implements both a save and loading method. The pipeline is easily reloaded using the [`~DiffusionPipeline.from_pretrained`] class method. Arguments: save_directory (`str` or `os.PathLike`): Directory to save a pipeline to. Will be created if it doesn't exist. safe_serialization (`bool`, *optional*, defaults to `False`): Whether to save the model using `safetensors` or the traditional PyTorch way with `pickle`. variant (`str`, *optional*): If specified, weights are saved in the format `pytorch_model..bin`. """ model_index_dict = dict(self.config) model_index_dict.pop("_class_name", None) model_index_dict.pop("_diffusers_version", None) model_index_dict.pop("_module", None) expected_modules, optional_kwargs = self._get_signature_keys(self) def is_saveable_module(name, value): if name not in expected_modules: return False if name in self._optional_components and value[0] is None: return False return True model_index_dict = {k: v for k, v in model_index_dict.items() if is_saveable_module(k, v)} for pipeline_component_name in model_index_dict.keys(): sub_model = getattr(self, pipeline_component_name) model_cls = sub_model.__class__ # Dynamo wraps the original model in a private class. # I didn't find a public API to get the original class. if is_compiled_module(sub_model): sub_model = sub_model._orig_mod 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(): if library_name in sys.modules: library = importlib.import_module(library_name) else: logger.info( f"{library_name} is not installed. Cannot save {pipeline_component_name} as {library_classes} from {library_name}" ) for base_class, save_load_methods in library_classes.items(): class_candidate = getattr(library, base_class, None) if class_candidate is not None and 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 if save_method_name is None: logger.warn(f"self.{pipeline_component_name}={sub_model} of type {type(sub_model)} cannot be saved.") # make sure that unsaveable components are not tried to be loaded afterward self.register_to_config(**{pipeline_component_name: (None, None)}) continue save_method = getattr(sub_model, save_method_name) # Call the save method with the argument safe_serialization only if it's supported save_method_signature = inspect.signature(save_method) save_method_accept_safe = "safe_serialization" in save_method_signature.parameters save_method_accept_variant = "variant" in save_method_signature.parameters save_kwargs = {} if save_method_accept_safe: save_kwargs["safe_serialization"] = safe_serialization if save_method_accept_variant: save_kwargs["variant"] = variant save_method(os.path.join(save_directory, pipeline_component_name), **save_kwargs) # finally save the config self.save_config(save_directory) def to( self, torch_device: Optional[Union[str, torch.device]] = None, torch_dtype: Optional[torch.dtype] = None, silence_dtype_warnings: bool = False, ): if torch_device is None and torch_dtype is None: return self # throw warning if pipeline is in "offloaded"-mode but user tries to manually set to GPU. def module_is_sequentially_offloaded(module): if not is_accelerate_available() or is_accelerate_version("<", "0.14.0"): return False return hasattr(module, "_hf_hook") and not isinstance( module._hf_hook, (accelerate.hooks.CpuOffload, accelerate.hooks.AlignDevicesHook) ) def module_is_offloaded(module): if not is_accelerate_available() or is_accelerate_version("<", "0.17.0.dev0"): return False return hasattr(module, "_hf_hook") and isinstance(module._hf_hook, accelerate.hooks.CpuOffload) # .to("cuda") would raise an error if the pipeline is sequentially offloaded, so we raise our own to make it clearer pipeline_is_sequentially_offloaded = any( module_is_sequentially_offloaded(module) for _, module in self.components.items() ) if pipeline_is_sequentially_offloaded and torch.device(torch_device).type == "cuda": raise ValueError( "It seems like you have activated sequential model offloading by calling `enable_sequential_cpu_offload`, but are now attempting to move the pipeline to GPU. This is not compatible with offloading. Please, move your pipeline `.to('cpu')` or consider removing the move altogether if you use sequential offloading." ) # Display a warning in this case (the operation succeeds but the benefits are lost) pipeline_is_offloaded = any(module_is_offloaded(module) for _, module in self.components.items()) if pipeline_is_offloaded and torch.device(torch_device).type == "cuda": logger.warning( f"It seems like you have activated model offloading by calling `enable_model_cpu_offload`, but are now manually moving the pipeline to GPU. It is strongly recommended against doing so as memory gains from offloading are likely to be lost. Offloading automatically takes care of moving the individual components {', '.join(self.components.keys())} to GPU when needed. To make sure offloading works as expected, you should consider moving the pipeline back to CPU: `pipeline.to('cpu')` or removing the move altogether if you use offloading." ) module_names, _ = self._get_signature_keys(self) modules = [getattr(self, n, None) for n in module_names] modules = [m for m in modules if isinstance(m, torch.nn.Module)] is_offloaded = pipeline_is_offloaded or pipeline_is_sequentially_offloaded for module in modules: is_loaded_in_8bit = hasattr(module, "is_loaded_in_8bit") and module.is_loaded_in_8bit if is_loaded_in_8bit and torch_dtype is not None: logger.warning( f"The module '{module.__class__.__name__}' has been loaded in 8bit and conversion to {torch_dtype} is not yet supported. Module is still in 8bit precision." ) if is_loaded_in_8bit and torch_device is not None: logger.warning( f"The module '{module.__class__.__name__}' has been loaded in 8bit and moving it to {torch_dtype} via `.to()` is not yet supported. Module is still on {module.device}." ) else: module.to(torch_device, torch_dtype) if ( module.dtype == torch.float16 and str(torch_device) in ["cpu"] and not silence_dtype_warnings and not is_offloaded ): logger.warning( "Pipelines loaded with `torch_dtype=torch.float16` cannot run with `cpu` device. It" " is not recommended to move them to `cpu` as running them will fail. Please make" " sure to use an accelerator to run the pipeline in inference, due to the lack of" " support for`float16` operations on this device in PyTorch. Please, remove the" " `torch_dtype=torch.float16` argument, or use another device for inference." ) return self @property def device(self) -> torch.device: r""" Returns: `torch.device`: The torch device on which the pipeline is located. """ module_names, _ = self._get_signature_keys(self) modules = [getattr(self, n, None) for n in module_names] modules = [m for m in modules if isinstance(m, torch.nn.Module)] for module in modules: return module.device return torch.device("cpu") @classmethod def from_pretrained(cls, pretrained_model_name_or_path: Optional[Union[str, os.PathLike]], **kwargs): r""" Instantiate a PyTorch diffusion pipeline from pretrained pipeline weights. The pipeline is set in evaluation mode (`model.eval()`) by default. If you get the error message below, you need to finetune the weights for your downstream task: ``` Some weights of UNet2DConditionModel were not initialized from the model checkpoint at runwayml/stable-diffusion-v1-5 and are newly initialized because the shapes did not match: - conv_in.weight: found shape torch.Size([320, 4, 3, 3]) in the checkpoint and torch.Size([320, 9, 3, 3]) in the model instantiated You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference. ``` Parameters: pretrained_model_name_or_path (`str` or `os.PathLike`, *optional*): Can be either: - A string, the *repo id* (for example `CompVis/ldm-text2im-large-256`) of a pretrained pipeline hosted on the Hub. - A path to a *directory* (for example `./my_pipeline_directory/`) containing pipeline weights saved using [`~DiffusionPipeline.save_pretrained`]. torch_dtype (`str` or `torch.dtype`, *optional*): Override the default `torch.dtype` and load the model with another dtype. If "auto" is passed, the dtype is automatically derived from the model's weights. custom_pipeline (`str`, *optional*): 🧪 This is an experimental feature and may change in the future. Can be either: - A string, the *repo id* (for example `hf-internal-testing/diffusers-dummy-pipeline`) of a custom pipeline hosted on the Hub. The repository must contain a file called pipeline.py that defines the custom pipeline. - A string, the *file name* of a community pipeline hosted on GitHub under [Community](https://github.com/huggingface/diffusers/tree/main/examples/community). Valid file names must match the file name and not the pipeline script (`clip_guided_stable_diffusion` instead of `clip_guided_stable_diffusion.py`). Community pipelines are always loaded from the current main branch of GitHub. - A path to a directory (`./my_pipeline_directory/`) containing a custom pipeline. The directory must contain a file called `pipeline.py` that defines the custom pipeline. For more information on how to load and create custom pipelines, please have a look at [Loading and Adding Custom Pipelines](https://huggingface.co/docs/diffusers/using-diffusers/custom_pipeline_overview) force_download (`bool`, *optional*, defaults to `False`): Whether or not to force the (re-)download of the model weights and configuration files, overriding the cached versions if they exist. cache_dir (`Union[str, os.PathLike]`, *optional*): Path to a directory where a downloaded pretrained model configuration is cached if the standard cache is not used. resume_download (`bool`, *optional*, defaults to `False`): Whether or not to resume downloading the model weights and configuration files. If set to `False`, any incompletely downloaded files are deleted. proxies (`Dict[str, str]`, *optional*): A dictionary of proxy servers to use by protocol or endpoint, for example, `{'http': 'foo.bar:3128', 'http://hostname': 'foo.bar:4012'}`. The proxies are used on each request. output_loading_info(`bool`, *optional*, defaults to `False`): Whether or not to also return a dictionary containing missing keys, unexpected keys and error messages. local_files_only (`bool`, *optional*, defaults to `False`): Whether to only load local model weights and configuration files or not. If set to `True`, the model won't be downloaded from the Hub. use_auth_token (`str` or *bool*, *optional*): The token to use as HTTP bearer authorization for remote files. If `True`, the token generated from `diffusers-cli login` (stored in `~/.huggingface`) is used. revision (`str`, *optional*, defaults to `"main"`): The specific model version to use. It can be a branch name, a tag name, a commit id, or any identifier allowed by Git. custom_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 similar to `revision` when loading a custom pipeline from the Hub. It can be a 🤗 Diffusers version when loading a custom pipeline from GitHub, otherwise it defaults to `"main"` when loading from the Hub. mirror (`str`, *optional*): Mirror source to resolve accessibility issues if you’re downloading a model in China. We do not guarantee the timeliness or safety of the source, and you should refer to the mirror site for more information. device_map (`str` or `Dict[str, Union[int, str, torch.device]]`, *optional*): A map that specifies where each submodule should go. It doesn’t need to be defined for each parameter/buffer name; once a given module name is inside, every submodule of it will be sent to the same device. Set `device_map="auto"` to have 🤗 Accelerate automatically compute the most optimized `device_map`. For more information about each option see [designing a device map](https://hf.co/docs/accelerate/main/en/usage_guides/big_modeling#designing-a-device-map). max_memory (`Dict`, *optional*): A dictionary device identifier for the maximum memory. Will default to the maximum memory available for each GPU and the available CPU RAM if unset. offload_folder (`str` or `os.PathLike`, *optional*): The path to offload weights if device_map contains the value `"disk"`. offload_state_dict (`bool`, *optional*): If `True`, temporarily offloads the CPU state dict to the hard drive to avoid running out of CPU RAM if the weight of the CPU state dict + the biggest shard of the checkpoint does not fit. Defaults to `True` when there is some disk offload. low_cpu_mem_usage (`bool`, *optional*, defaults to `True` if torch version >= 1.9.0 else `False`): Speed up model loading only loading the pretrained weights and not initializing the weights. This also tries to not use more than 1x model size in CPU memory (including peak memory) while loading the model. Only supported for PyTorch >= 1.9.0. If you are using an older version of PyTorch, setting this argument to `True` will raise an error. use_safetensors (`bool`, *optional*, defaults to `None`): If set to `None`, the safetensors weights are downloaded if they're available **and** if the safetensors library is installed. If set to `True`, the model is forcibly loaded from safetensors weights. If set to `False`, safetensors weights are not loaded. kwargs (remaining dictionary of keyword arguments, *optional*): Can be used to overwrite load and saveable variables (the pipeline components of the specific pipeline class). The overwritten components are passed directly to the pipelines `__init__` method. See example below for more information. variant (`str`, *optional*): Load weights from a specified variant filename such as `"fp16"` or `"ema"`. This is ignored when loading `from_flax`. To use private or [gated](https://huggingface.co/docs/hub/models-gated#gated-models) models, log-in with `huggingface-cli login`. Examples: ```py >>> from diffusers import DiffusionPipeline >>> # Download pipeline from huggingface.co and cache. >>> pipeline = DiffusionPipeline.from_pretrained("CompVis/ldm-text2im-large-256") >>> # Download pipeline that requires an authorization token >>> # For more information on access tokens, please refer to this section >>> # of the documentation](https://huggingface.co/docs/hub/security-tokens) >>> pipeline = DiffusionPipeline.from_pretrained("runwayml/stable-diffusion-v1-5") >>> # Use a different scheduler >>> from diffusers import LMSDiscreteScheduler >>> scheduler = LMSDiscreteScheduler.from_config(pipeline.scheduler.config) >>> pipeline.scheduler = scheduler ``` """ cache_dir = kwargs.pop("cache_dir", DIFFUSERS_CACHE) resume_download = kwargs.pop("resume_download", False) force_download = kwargs.pop("force_download", False) proxies = kwargs.pop("proxies", None) local_files_only = kwargs.pop("local_files_only", HF_HUB_OFFLINE) use_auth_token = kwargs.pop("use_auth_token", None) revision = kwargs.pop("revision", None) from_flax = kwargs.pop("from_flax", False) 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) max_memory = kwargs.pop("max_memory", None) offload_folder = kwargs.pop("offload_folder", None) offload_state_dict = kwargs.pop("offload_state_dict", False) low_cpu_mem_usage = kwargs.pop("low_cpu_mem_usage", _LOW_CPU_MEM_USAGE_DEFAULT) variant = kwargs.pop("variant", None) use_safetensors = kwargs.pop("use_safetensors", None if is_safetensors_available() else False) # 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 = cls.download( pretrained_model_name_or_path, cache_dir=cache_dir, resume_download=resume_download, force_download=force_download, proxies=proxies, local_files_only=local_files_only, use_auth_token=use_auth_token, revision=revision, from_flax=from_flax, use_safetensors=use_safetensors, custom_pipeline=custom_pipeline, custom_revision=custom_revision, variant=variant, **kwargs, ) else: cached_folder = pretrained_model_name_or_path config_dict = cls.load_config(cached_folder) # pop out "_ignore_files" as it is only needed for download config_dict.pop("_ignore_files", None) # 2. Define which model components should load variants # We retrieve the information by matching whether variant # model checkpoints exist in the subfolders model_variants = {} if variant is not None: for folder in os.listdir(cached_folder): folder_path = os.path.join(cached_folder, folder) is_folder = os.path.isdir(folder_path) and folder in config_dict variant_exists = is_folder and any( p.split(".")[1].startswith(variant) for p in os.listdir(folder_path) ) if variant_exists: model_variants[folder] = variant # 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 pipeline_class = _get_pipeline_class( cls, config_dict, custom_pipeline=custom_pipeline, cache_dir=cache_dir, revision=custom_revision ) # DEPRECATED: To be removed in 1.0.0 if pipeline_class.__name__ == "StableDiffusionInpaintPipeline" and version.parse( version.parse(config_dict["_diffusers_version"]).base_version ) <= version.parse("0.5.1"): from diffusers import StableDiffusionInpaintPipeline, StableDiffusionInpaintPipelineLegacy pipeline_class = StableDiffusionInpaintPipelineLegacy deprecation_message = ( "You are using a legacy checkpoint for inpainting with Stable Diffusion, therefore we are loading the" f" {StableDiffusionInpaintPipelineLegacy} class instead of {StableDiffusionInpaintPipeline}. For" " better inpainting results, we strongly suggest using Stable Diffusion's official inpainting" " checkpoint: https://huggingface.co/runwayml/stable-diffusion-inpainting instead or adapting your" f" checkpoint {pretrained_model_name_or_path} to the format of" " https://huggingface.co/runwayml/stable-diffusion-inpainting. Note that we do not actively maintain" " the {StableDiffusionInpaintPipelineLegacy} class and will likely remove it in version 1.0.0." ) deprecate("StableDiffusionInpaintPipelineLegacy", "1.0.0", deprecation_message, standard_warn=False) # 4. Define expected modules given pipeline signature # and define non-None initialized modules (=`init_kwargs`) # some modules can be passed directly to the init # in this case they are already instantiated in `kwargs` # extract them here expected_modules, optional_kwargs = cls._get_signature_keys(pipeline_class) passed_class_obj = {k: kwargs.pop(k) for k in expected_modules if k in kwargs} passed_pipe_kwargs = {k: kwargs.pop(k) for k in optional_kwargs if k in kwargs} init_dict, unused_kwargs, _ = pipeline_class.extract_init_dict(config_dict, **kwargs) # define init kwargs init_kwargs = {k: init_dict.pop(k) for k in optional_kwargs if k in init_dict} init_kwargs = {**init_kwargs, **passed_pipe_kwargs} # remove `null` components def load_module(name, value): if value[0] is None: return False if name in passed_class_obj and passed_class_obj[name] is None: return False return True init_dict = {k: v for k, v in init_dict.items() if load_module(k, v)} # Special case: safety_checker must be loaded separately when using `from_flax` if from_flax and "safety_checker" in init_dict and "safety_checker" not in passed_class_obj: raise NotImplementedError( "The safety checker cannot be automatically loaded when loading weights `from_flax`." " Please, pass `safety_checker=None` to `from_pretrained`, and load the safety checker" " separately if you need it." ) # 5. Throw nice warnings / errors for fast accelerate loading if len(unused_kwargs) > 0: logger.warning( f"Keyword arguments {unused_kwargs} are not expected by {pipeline_class.__name__} and will be ignored." ) if low_cpu_mem_usage and not is_accelerate_available(): low_cpu_mem_usage = False logger.warning( "Cannot initialize model with low cpu memory usage because `accelerate` was not found in the" " environment. Defaulting to `low_cpu_mem_usage=False`. It is strongly recommended to install" " `accelerate` for faster and less memory-intense model loading. You can do so with: \n```\npip" " install accelerate\n```\n." ) if device_map is not None and not is_torch_version(">=", "1.9.0"): raise NotImplementedError( "Loading and dispatching requires torch >= 1.9.0. Please either update your PyTorch version or set" " `device_map=None`." ) if low_cpu_mem_usage is True and not is_torch_version(">=", "1.9.0"): raise NotImplementedError( "Low memory initialization requires torch >= 1.9.0. Please either update your PyTorch version or set" " `low_cpu_mem_usage=False`." ) if low_cpu_mem_usage is False and device_map is not None: raise ValueError( f"You cannot set `low_cpu_mem_usage` to False while using device_map={device_map} for loading and" " dispatching. Please make sure to set `low_cpu_mem_usage=True`." ) # import it here to avoid circular import from diffusers import pipelines # 6. Load each module in the pipeline for name, (library_name, class_name) in init_dict.items(): # 6.1 - now that JAX/Flax is an official framework of the library, we might load from Flax names if class_name.startswith("Flax"): class_name = class_name[4:] # 6.2 Define all importable classes is_pipeline_module = hasattr(pipelines, library_name) importable_classes = ALL_IMPORTABLE_CLASSES loaded_sub_model = None # 6.3 Use passed sub model or load class_name from library_name if name in passed_class_obj: # if the model is in a pipeline module, then we load it from the pipeline # check that passed_class_obj has correct parent class maybe_raise_or_warn( library_name, library, class_name, importable_classes, passed_class_obj, name, is_pipeline_module ) loaded_sub_model = passed_class_obj[name] else: # load sub model loaded_sub_model = load_sub_model( library_name=library_name, class_name=class_name, importable_classes=importable_classes, pipelines=pipelines, is_pipeline_module=is_pipeline_module, pipeline_class=pipeline_class, torch_dtype=torch_dtype, provider=provider, sess_options=sess_options, device_map=device_map, max_memory=max_memory, offload_folder=offload_folder, offload_state_dict=offload_state_dict, model_variants=model_variants, name=name, from_flax=from_flax, variant=variant, low_cpu_mem_usage=low_cpu_mem_usage, cached_folder=cached_folder, ) init_kwargs[name] = loaded_sub_model # UNet(...), # DiffusionSchedule(...) # 7. Potentially add passed objects if expected missing_modules = set(expected_modules) - set(init_kwargs.keys()) passed_modules = list(passed_class_obj.keys()) optional_modules = pipeline_class._optional_components if len(missing_modules) > 0 and missing_modules <= set(passed_modules + optional_modules): for module in missing_modules: init_kwargs[module] = passed_class_obj.get(module, None) elif len(missing_modules) > 0: passed_modules = set(list(init_kwargs.keys()) + list(passed_class_obj.keys())) - optional_kwargs raise ValueError( f"Pipeline {pipeline_class} expected {expected_modules}, but only {passed_modules} were passed." ) # 8. Instantiate the pipeline model = pipeline_class(**init_kwargs) return model @classmethod def download(cls, pretrained_model_name, **kwargs) -> Union[str, os.PathLike]: r""" Download and cache a PyTorch diffusion pipeline from pretrained pipeline weights. Parameters: pretrained_model_name (`str` or `os.PathLike`, *optional*): A string, the *repository id* (for example `CompVis/ldm-text2im-large-256`) of a pretrained pipeline hosted on the Hub. custom_pipeline (`str`, *optional*): Can be either: - A string, the *repository id* (for example `CompVis/ldm-text2im-large-256`) of a pretrained pipeline hosted on the Hub. The repository must contain a file called `pipeline.py` that defines the custom pipeline. - A string, the *file name* of a community pipeline hosted on GitHub under [Community](https://github.com/huggingface/diffusers/tree/main/examples/community). Valid file names must match the file name and not the pipeline script (`clip_guided_stable_diffusion` instead of `clip_guided_stable_diffusion.py`). Community pipelines are always loaded from the current `main` branch of GitHub. - A path to a *directory* (`./my_pipeline_directory/`) containing a custom pipeline. The directory must contain a file called `pipeline.py` that defines the custom pipeline. 🧪 This is an experimental feature and may change in the future. For more information on how to load and create custom pipelines, take a look at [How to contribute a community pipeline](https://huggingface.co/docs/diffusers/main/en/using-diffusers/contribute_pipeline). force_download (`bool`, *optional*, defaults to `False`): Whether or not to force the (re-)download of the model weights and configuration files, overriding the cached versions if they exist. resume_download (`bool`, *optional*, defaults to `False`): Whether or not to resume downloading the model weights and configuration files. If set to `False`, any incompletely downloaded files are deleted. proxies (`Dict[str, str]`, *optional*): A dictionary of proxy servers to use by protocol or endpoint, for example, `{'http': 'foo.bar:3128', 'http://hostname': 'foo.bar:4012'}`. The proxies are used on each request. output_loading_info(`bool`, *optional*, defaults to `False`): Whether or not to also return a dictionary containing missing keys, unexpected keys and error messages. local_files_only (`bool`, *optional*, defaults to `False`): Whether to only load local model weights and configuration files or not. If set to `True`, the model won't be downloaded from the Hub. use_auth_token (`str` or *bool*, *optional*): The token to use as HTTP bearer authorization for remote files. If `True`, the token generated from `diffusers-cli login` (stored in `~/.huggingface`) is used. revision (`str`, *optional*, defaults to `"main"`): The specific model version to use. It can be a branch name, a tag name, a commit id, or any identifier allowed by Git. custom_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 similar to `revision` when loading a custom pipeline from the Hub. It can be a 🤗 Diffusers version when loading a custom pipeline from GitHub, otherwise it defaults to `"main"` when loading from the Hub. mirror (`str`, *optional*): Mirror source to resolve accessibility issues if you're downloading a model in China. We do not guarantee the timeliness or safety of the source, and you should refer to the mirror site for more information. variant (`str`, *optional*): Load weights from a specified variant filename such as `"fp16"` or `"ema"`. This is ignored when loading `from_flax`. Returns: `os.PathLike`: A path to the downloaded pipeline. To use private or [gated models](https://huggingface.co/docs/hub/models-gated#gated-models), log-in with `huggingface-cli login`. """ cache_dir = kwargs.pop("cache_dir", DIFFUSERS_CACHE) resume_download = kwargs.pop("resume_download", False) force_download = kwargs.pop("force_download", False) proxies = kwargs.pop("proxies", None) local_files_only = kwargs.pop("local_files_only", HF_HUB_OFFLINE) use_auth_token = kwargs.pop("use_auth_token", None) revision = kwargs.pop("revision", None) from_flax = kwargs.pop("from_flax", False) custom_pipeline = kwargs.pop("custom_pipeline", None) custom_revision = kwargs.pop("custom_revision", None) variant = kwargs.pop("variant", None) use_safetensors = kwargs.pop("use_safetensors", None) if use_safetensors and not is_safetensors_available(): raise ValueError( "`use_safetensors`=True but safetensors is not installed. Please install safetensors with `pip install safetensors" ) allow_pickle = False if use_safetensors is None: use_safetensors = is_safetensors_available() allow_pickle = True pipeline_is_cached = False allow_patterns = None ignore_patterns = None if not local_files_only: try: info = model_info( pretrained_model_name, use_auth_token=use_auth_token, revision=revision, ) except HTTPError as e: logger.warn(f"Couldn't connect to the Hub: {e}.\nWill try to load from local cache.") local_files_only = True if not local_files_only: config_file = hf_hub_download( pretrained_model_name, cls.config_name, cache_dir=cache_dir, revision=revision, proxies=proxies, force_download=force_download, resume_download=resume_download, use_auth_token=use_auth_token, ) config_dict = cls._dict_from_json_file(config_file) ignore_filenames = config_dict.pop("_ignore_files", []) # retrieve all folder_names that contain relevant files folder_names = [k for k, v in config_dict.items() if isinstance(v, list)] filenames = {sibling.rfilename for sibling in info.siblings} model_filenames, variant_filenames = variant_compatible_siblings(filenames, variant=variant) if len(variant_filenames) == 0 and variant is not None: deprecation_message = ( f"You are trying to load the model files of the `variant={variant}`, but no such modeling files are available." f"The default model files: {model_filenames} will be loaded instead. Make sure to not load from `variant={variant}`" "if such variant modeling files are not available. Doing so will lead to an error in v0.22.0 as defaulting to non-variant" "modeling files is deprecated." ) deprecate("no variant default", "0.22.0", deprecation_message, standard_warn=False) # remove ignored filenames model_filenames = set(model_filenames) - set(ignore_filenames) variant_filenames = set(variant_filenames) - set(ignore_filenames) # if the whole pipeline is cached we don't have to ping the Hub if revision in DEPRECATED_REVISION_ARGS and version.parse( version.parse(__version__).base_version ) >= version.parse("0.20.0"): warn_deprecated_model_variant( pretrained_model_name, use_auth_token, variant, revision, model_filenames ) model_folder_names = {os.path.split(f)[0] for f in model_filenames} # all filenames compatible with variant will be added allow_patterns = list(model_filenames) # allow all patterns from non-model folders # this enables downloading schedulers, tokenizers, ... allow_patterns += [f"{k}/*" for k in folder_names if k not in model_folder_names] # also allow downloading config.json files with the model allow_patterns += [os.path.join(k, "config.json") for k in model_folder_names] allow_patterns += [ SCHEDULER_CONFIG_NAME, CONFIG_NAME, cls.config_name, CUSTOM_PIPELINE_FILE_NAME, ] # retrieve passed components that should not be downloaded pipeline_class = _get_pipeline_class( cls, config_dict, custom_pipeline=custom_pipeline, cache_dir=cache_dir, revision=custom_revision ) expected_components, _ = cls._get_signature_keys(pipeline_class) passed_components = [k for k in expected_components if k in kwargs] if ( use_safetensors and not allow_pickle and not is_safetensors_compatible( model_filenames, variant=variant, passed_components=passed_components ) ): raise EnvironmentError( f"Could not found the necessary `safetensors` weights in {model_filenames} (variant={variant})" ) if from_flax: ignore_patterns = ["*.bin", "*.safetensors", "*.onnx", "*.pb"] elif use_safetensors and is_safetensors_compatible( model_filenames, variant=variant, passed_components=passed_components ): ignore_patterns = ["*.bin", "*.msgpack"] safetensors_variant_filenames = {f for f in variant_filenames if f.endswith(".safetensors")} safetensors_model_filenames = {f for f in model_filenames if f.endswith(".safetensors")} if ( len(safetensors_variant_filenames) > 0 and safetensors_model_filenames != safetensors_variant_filenames ): logger.warn( f"\nA mixture of {variant} and non-{variant} filenames will be loaded.\nLoaded {variant} filenames:\n[{', '.join(safetensors_variant_filenames)}]\nLoaded non-{variant} filenames:\n[{', '.join(safetensors_model_filenames - safetensors_variant_filenames)}\nIf this behavior is not expected, please check your folder structure." ) else: ignore_patterns = ["*.safetensors", "*.msgpack"] bin_variant_filenames = {f for f in variant_filenames if f.endswith(".bin")} bin_model_filenames = {f for f in model_filenames if f.endswith(".bin")} if len(bin_variant_filenames) > 0 and bin_model_filenames != bin_variant_filenames: logger.warn( f"\nA mixture of {variant} and non-{variant} filenames will be loaded.\nLoaded {variant} filenames:\n[{', '.join(bin_variant_filenames)}]\nLoaded non-{variant} filenames:\n[{', '.join(bin_model_filenames - bin_variant_filenames)}\nIf this behavior is not expected, please check your folder structure." ) # Don't download any objects that are passed allow_patterns = [ p for p in allow_patterns if not (len(p.split("/")) == 2 and p.split("/")[0] in passed_components) ] # Don't download index files of forbidden patterns either ignore_patterns = ignore_patterns + [f"{i}.index.*json" for i in ignore_patterns] re_ignore_pattern = [re.compile(fnmatch.translate(p)) for p in ignore_patterns] re_allow_pattern = [re.compile(fnmatch.translate(p)) for p in allow_patterns] expected_files = [f for f in filenames if not any(p.match(f) for p in re_ignore_pattern)] expected_files = [f for f in expected_files if any(p.match(f) for p in re_allow_pattern)] snapshot_folder = Path(config_file).parent pipeline_is_cached = all((snapshot_folder / f).is_file() for f in expected_files) if pipeline_is_cached and not force_download: # if the pipeline is cached, we can directly return it # else call snapshot_download return snapshot_folder user_agent = {"pipeline_class": cls.__name__} if custom_pipeline is not None and not custom_pipeline.endswith(".py"): user_agent["custom_pipeline"] = custom_pipeline # download all allow_patterns - ignore_patterns cached_folder = snapshot_download( pretrained_model_name, cache_dir=cache_dir, resume_download=resume_download, proxies=proxies, local_files_only=local_files_only, use_auth_token=use_auth_token, revision=revision, allow_patterns=allow_patterns, ignore_patterns=ignore_patterns, user_agent=user_agent, ) return cached_folder @staticmethod def _get_signature_keys(obj): parameters = inspect.signature(obj.__init__).parameters required_parameters = {k: v for k, v in parameters.items() if v.default == inspect._empty} optional_parameters = set({k for k, v in parameters.items() if v.default != inspect._empty}) expected_modules = set(required_parameters.keys()) - {"self"} return expected_modules, optional_parameters @property def components(self) -> Dict[str, Any]: r""" The `self.components` property can be useful to run different pipelines with the same weights and configurations without reallocating additional memory. Returns (`dict`): A dictionary containing all the modules needed to initialize the pipeline. Examples: ```py >>> from diffusers import ( ... StableDiffusionPipeline, ... StableDiffusionImg2ImgPipeline, ... StableDiffusionInpaintPipeline, ... ) >>> text2img = StableDiffusionPipeline.from_pretrained("runwayml/stable-diffusion-v1-5") >>> img2img = StableDiffusionImg2ImgPipeline(**text2img.components) >>> inpaint = StableDiffusionInpaintPipeline(**text2img.components) ``` """ expected_modules, optional_parameters = self._get_signature_keys(self) components = { k: getattr(self, k) for k in self.config.keys() if not k.startswith("_") and k not in optional_parameters } if set(components.keys()) != expected_modules: raise ValueError( f"{self} has been incorrectly initialized or {self.__class__} is incorrectly implemented. Expected" f" {expected_modules} to be defined, but {components.keys()} are defined." ) return components @staticmethod def numpy_to_pil(images): """ Convert a NumPy image or a batch of images to a PIL image. """ return numpy_to_pil(images) def progress_bar(self, iterable=None, total=None): if not hasattr(self, "_progress_bar_config"): self._progress_bar_config = {} elif not isinstance(self._progress_bar_config, dict): raise ValueError( f"`self._progress_bar_config` should be of type `dict`, but is {type(self._progress_bar_config)}." ) if iterable is not None: return tqdm(iterable, **self._progress_bar_config) elif total is not None: return tqdm(total=total, **self._progress_bar_config) else: raise ValueError("Either `total` or `iterable` has to be defined.") def set_progress_bar_config(self, **kwargs): self._progress_bar_config = kwargs def enable_xformers_memory_efficient_attention(self, attention_op: Optional[Callable] = None): r""" Enable memory efficient attention from [xFormers](https://facebookresearch.github.io/xformers/). When this option is enabled, you should observe lower GPU memory usage and a potential speed up during inference. Speed up during training is not guaranteed. ⚠️ When memory efficient attention and sliced attention are both enabled, memory efficient attention takes precedent. Parameters: attention_op (`Callable`, *optional*): Override the default `None` operator for use as `op` argument to the [`memory_efficient_attention()`](https://facebookresearch.github.io/xformers/components/ops.html#xformers.ops.memory_efficient_attention) function of xFormers. Examples: ```py >>> import torch >>> from diffusers import DiffusionPipeline >>> from xformers.ops import MemoryEfficientAttentionFlashAttentionOp >>> pipe = DiffusionPipeline.from_pretrained("stabilityai/stable-diffusion-2-1", torch_dtype=torch.float16) >>> pipe = pipe.to("cuda") >>> pipe.enable_xformers_memory_efficient_attention(attention_op=MemoryEfficientAttentionFlashAttentionOp) >>> # Workaround for not accepting attention shape using VAE for Flash Attention >>> pipe.vae.enable_xformers_memory_efficient_attention(attention_op=None) ``` """ self.set_use_memory_efficient_attention_xformers(True, attention_op) def disable_xformers_memory_efficient_attention(self): r""" Disable memory efficient attention from [xFormers](https://facebookresearch.github.io/xformers/). """ self.set_use_memory_efficient_attention_xformers(False) def set_use_memory_efficient_attention_xformers( self, valid: bool, attention_op: Optional[Callable] = None ) -> None: # Recursively walk through all the children. # Any children which exposes the set_use_memory_efficient_attention_xformers method # gets the message def fn_recursive_set_mem_eff(module: torch.nn.Module): if hasattr(module, "set_use_memory_efficient_attention_xformers"): module.set_use_memory_efficient_attention_xformers(valid, attention_op) for child in module.children(): fn_recursive_set_mem_eff(child) module_names, _ = self._get_signature_keys(self) modules = [getattr(self, n, None) for n in module_names] modules = [m for m in modules if isinstance(m, torch.nn.Module)] for module in modules: fn_recursive_set_mem_eff(module) def enable_attention_slicing(self, slice_size: Optional[Union[str, int]] = "auto"): r""" Enable sliced attention computation. When this option is enabled, the attention module splits the input tensor in slices to compute attention in several steps. This is useful to save some memory in exchange for a small speed decrease. Args: slice_size (`str` or `int`, *optional*, defaults to `"auto"`): When `"auto"`, halves the input to the attention heads, so attention will be computed in two steps. If `"max"`, maximum amount of memory will be saved by running only one slice at a time. If a number is provided, uses as many slices as `attention_head_dim // slice_size`. In this case, `attention_head_dim` must be a multiple of `slice_size`. """ self.set_attention_slice(slice_size) def disable_attention_slicing(self): r""" Disable sliced attention computation. If `enable_attention_slicing` was previously called, attention is computed in one step. """ # set slice_size = `None` to disable `attention slicing` self.enable_attention_slicing(None) def set_attention_slice(self, slice_size: Optional[int]): module_names, _ = self._get_signature_keys(self) modules = [getattr(self, n, None) for n in module_names] modules = [m for m in modules if isinstance(m, torch.nn.Module) and hasattr(m, "set_attention_slice")] for module in modules: module.set_attention_slice(slice_size)