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[Model] Add stable diffusion model based on fastdeploy (#297)
* Add stable diffusion model base on fastdeploy * Add sd infer * pipelines->multimodal * add create_ort_runtime * use fp16 input * fix pil * Add optimize unet model * add hf license * Add workspace args * Add profile func * Add schedulers * usrelace torch.Tenosr byp.ndarray * Add readme * Add trt shape setting * add dynamic shape * Add dynamic shape for stable diffusion * fix max shape setting * rename tensorrt file suffix * update dynamic shape setting * Add scheduler output * Add inference_steps and benchmark steps * add diffuser benchmark * Add paddle infer script * Rename 1 * Rename infer.py to torch_onnx_infer.py * Add export torch to onnx model * renmove export model * Add paddle export model for diffusion * Fix export model * mv torch onnx infer to infer * Fix export model * Fix infer * modif create_trt_runtime create_ort_runtime * update export torch * update requirements * add paddle inference backend * Fix unet pp run * remove print * Add paddle model export and infer * Add device id * remove profile to utils * Add -1 device id * Add safety checker args * remove safety checker temporarily * Add export model description * Add predict description * Fix readme * Fix device_id description * add timestep shape * add use fp16 precision * move use gpu * Add EulerAncestralDiscreteScheduler * Use EulerAncestralDiscreteScheduler with v1-5 model * Add export model readme * Add link of exported model * Update scheduler on README * Addd stable-diffusion-v1-5
This commit is contained in:
Jack Zhou
156
examples/multimodal/stable_diffusion/config_utils.py
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156
examples/multimodal/stable_diffusion/config_utils.py
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# Copyright 2022 The HuggingFace Inc. team.
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# Copyright (c) 2022, NVIDIA CORPORATION. All rights reserved.
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# Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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import functools
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import inspect
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from collections import OrderedDict
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from typing import Any, Dict, Tuple, Union
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class ConfigMixin:
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r"""
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Base class for all configuration classes. Stores all configuration parameters under `self.config` Also handles all
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methods for loading/downloading/saving classes inheriting from [`ConfigMixin`] with
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- [`~ConfigMixin.from_config`]
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- [`~ConfigMixin.save_config`]
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Class attributes:
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- **config_name** (`str`) -- A filename under which the config should stored when calling
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[`~ConfigMixin.save_config`] (should be overridden by parent class).
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- **ignore_for_config** (`List[str]`) -- A list of attributes that should not be saved in the config (should be
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overridden by parent class).
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"""
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config_name = None
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ignore_for_config = []
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def register_to_config(self, **kwargs):
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if self.config_name is None:
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raise NotImplementedError(
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f"Make sure that {self.__class__} has defined a class name `config_name`"
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)
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kwargs["_class_name"] = self.__class__.__name__
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# Special case for `kwargs` used in deprecation warning added to schedulers
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# TODO: remove this when we remove the deprecation warning, and the `kwargs` argument,
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# or solve in a more general way.
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kwargs.pop("kwargs", None)
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for key, value in kwargs.items():
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try:
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setattr(self, key, value)
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except AttributeError as err:
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logger.error(f"Can't set {key} with value {value} for {self}")
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raise err
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if not hasattr(self, "_internal_dict"):
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internal_dict = kwargs
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else:
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previous_dict = dict(self._internal_dict)
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internal_dict = { ** self._internal_dict, ** kwargs}
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logger.debug(
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f"Updating config from {previous_dict} to {internal_dict}")
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self._internal_dict = FrozenDict(internal_dict)
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@property
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def config(self) -> Dict[str, Any]:
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return self._internal_dict
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class FrozenDict(OrderedDict):
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def __init__(self, *args, **kwargs):
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super().__init__(*args, **kwargs)
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for key, value in self.items():
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setattr(self, key, value)
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self.__frozen = True
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def __delitem__(self, *args, **kwargs):
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raise Exception(
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f"You cannot use ``__delitem__`` on a {self.__class__.__name__} instance."
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)
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def setdefault(self, *args, **kwargs):
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raise Exception(
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f"You cannot use ``setdefault`` on a {self.__class__.__name__} instance."
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)
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def pop(self, *args, **kwargs):
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raise Exception(
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f"You cannot use ``pop`` on a {self.__class__.__name__} instance.")
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def update(self, *args, **kwargs):
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raise Exception(
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f"You cannot use ``update`` on a {self.__class__.__name__} instance."
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)
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def __setattr__(self, name, value):
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if hasattr(self, "__frozen") and self.__frozen:
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raise Exception(
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f"You cannot use ``__setattr__`` on a {self.__class__.__name__} instance."
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)
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super().__setattr__(name, value)
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def __setitem__(self, name, value):
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if hasattr(self, "__frozen") and self.__frozen:
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raise Exception(
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f"You cannot use ``__setattr__`` on a {self.__class__.__name__} instance."
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)
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super().__setitem__(name, value)
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def register_to_config(init):
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r"""
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Decorator to apply on the init of classes inheriting from [`ConfigMixin`] so that all the arguments are
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automatically sent to `self.register_for_config`. To ignore a specific argument accepted by the init but that
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shouldn't be registered in the config, use the `ignore_for_config` class variable
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Warning: Once decorated, all private arguments (beginning with an underscore) are trashed and not sent to the init!
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"""
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@functools.wraps(init)
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def inner_init(self, *args, **kwargs):
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# Ignore private kwargs in the init.
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init_kwargs = {
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k: v
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for k, v in kwargs.items() if not k.startswith("_")
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}
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init(self, *args, **init_kwargs)
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if not isinstance(self, ConfigMixin):
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raise RuntimeError(
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f"`@register_for_config` was applied to {self.__class__.__name__} init method, but this class does "
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"not inherit from `ConfigMixin`.")
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ignore = getattr(self, "ignore_for_config", [])
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# Get positional arguments aligned with kwargs
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new_kwargs = {}
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signature = inspect.signature(init)
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parameters = {
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name: p.default
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for i, (name, p) in enumerate(signature.parameters.items())
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if i > 0 and name not in ignore
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}
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for arg, name in zip(args, parameters.keys()):
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new_kwargs[name] = arg
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# Then add all kwargs
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new_kwargs.update({
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k: init_kwargs.get(k, default)
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for k, default in parameters.items()
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if k not in ignore and k not in new_kwargs
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})
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getattr(self, "register_to_config")(**new_kwargs)
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return inner_init
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