mirror of
https://github.com/PaddlePaddle/FastDeploy.git
synced 2025-10-06 00:57:33 +08:00
d4995e5468
* 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
1129 lines
46 KiB
Python
1129 lines
46 KiB
Python
# 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 math
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from typing import Optional, Tuple, Union, Any
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from scipy import integrate
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import numpy as np
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from config_utils import register_to_config, ConfigMixin
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from dataclasses import dataclass
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from collections import OrderedDict
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SCHEDULER_CONFIG_NAME = "scheduler_config.json"
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class BaseOutput(OrderedDict):
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"""
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Base class for all model outputs as dataclass. Has a `__getitem__` that allows indexing by integer or slice (like a
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tuple) or strings (like a dictionary) that will ignore the `None` attributes. Otherwise behaves like a regular
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python dictionary.
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<Tip warning={true}>
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You can't unpack a `BaseOutput` directly. Use the [`~utils.BaseOutput.to_tuple`] method to convert it to a tuple
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before.
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</Tip>
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"""
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def __post_init__(self):
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class_fields = fields(self)
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# Safety and consistency checks
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if not len(class_fields):
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raise ValueError(f"{self.__class__.__name__} has no fields.")
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first_field = getattr(self, class_fields[0].name)
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other_fields_are_none = all(
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getattr(self, field.name) is None for field in class_fields[1:])
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if other_fields_are_none and isinstance(first_field, dict):
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for key, value in first_field.items():
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self[key] = value
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else:
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for field in class_fields:
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v = getattr(self, field.name)
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if v is not None:
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self[field.name] = v
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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 __getitem__(self, k):
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if isinstance(k, str):
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inner_dict = {k: v for (k, v) in self.items()}
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if self.__class__.__name__ in [
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"StableDiffusionPipelineOutput", "ImagePipelineOutput"
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] and k == "sample":
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deprecate("samples", "0.6.0",
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"Please use `.images` or `'images'` instead.")
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return inner_dict["images"]
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return inner_dict[k]
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else:
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return self.to_tuple()[k]
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def __setattr__(self, name, value):
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if name in self.keys() and value is not None:
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# Don't call self.__setitem__ to avoid recursion errors
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super().__setitem__(name, value)
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super().__setattr__(name, value)
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def __setitem__(self, key, value):
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# Will raise a KeyException if needed
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super().__setitem__(key, value)
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# Don't call self.__setattr__ to avoid recursion errors
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super().__setattr__(key, value)
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def to_tuple(self) -> Tuple[Any]:
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"""
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Convert self to a tuple containing all the attributes/keys that are not `None`.
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"""
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return tuple(self[k] for k in self.keys())
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class SchedulerMixin:
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"""
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Mixin containing common functions for the schedulers.
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"""
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config_name = SCHEDULER_CONFIG_NAME
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def set_format(self, tensor_format="pt"):
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return self
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class SchedulerOutput(BaseOutput):
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"""
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Base class for the scheduler's step function output.
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Args:
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prev_sample (`np.ndarray` of shape `(batch_size, num_channels, height, width)` for images):
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Computed sample (x_{t-1}) of previous timestep. `prev_sample` should be used as next model input in the
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denoising loop.
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"""
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prev_sample: np.ndarray
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class DDIMSchedulerOutput(BaseOutput):
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"""
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Output class for the scheduler's step function output.
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Args:
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prev_sample (` np.ndarray` of shape `(batch_size, num_channels, height, width)` for images):
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Computed sample (x_{t-1}) of previous timestep. `prev_sample` should be used as next model input in the
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denoising loop.
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pred_original_sample (` np.ndarray` of shape `(batch_size, num_channels, height, width)` for images):
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The predicted denoised sample (x_{0}) based on the model output from the current timestep.
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`pred_original_sample` can be used to preview progress or for guidance.
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"""
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prev_sample: np.ndarray
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pred_original_sample: Optional[np.ndarray] = None
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class LMSDiscreteSchedulerOutput(BaseOutput):
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"""
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Output class for the scheduler's step function output.
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Args:
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prev_sample (`np.ndarray` of shape `(batch_size, num_channels, height, width)` for images):
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Computed sample (x_{t-1}) of previous timestep. `prev_sample` should be used as next model input in the
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denoising loop.
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pred_original_sample (`np.ndarray` of shape `(batch_size, num_channels, height, width)` for images):
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The predicted denoised sample (x_{0}) based on the model output from the current timestep.
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`pred_original_sample` can be used to preview progress or for guidance.
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"""
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prev_sample: np.ndarray
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pred_original_sample: Optional[np.ndarray] = None
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class EulerAncestralDiscreteSchedulerOutput(BaseOutput):
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"""
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Output class for the scheduler's step function output.
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Args:
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prev_sample (`np.ndarray` of shape `(batch_size, num_channels, height, width)` for images):
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Computed sample (x_{t-1}) of previous timestep. `prev_sample` should be used as next model input in the
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denoising loop.
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pred_original_sample (`np.ndarray` of shape `(batch_size, num_channels, height, width)` for images):
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The predicted denoised sample (x_{0}) based on the model output from the current timestep.
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`pred_original_sample` can be used to preview progress or for guidance.
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"""
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prev_sample: np.ndarray
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pred_original_sample: Optional[np.ndarray] = None
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def betas_for_alpha_bar(num_diffusion_timesteps, max_beta=0.999):
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"""
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Create a beta schedule that discretizes the given alpha_t_bar function, which defines the cumulative product of
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(1-beta) over time from t = [0,1].
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Contains a function alpha_bar that takes an argument t and transforms it to the cumulative product of (1-beta) up
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to that part of the diffusion process.
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Args:
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num_diffusion_timesteps (`int`): the number of betas to produce.
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max_beta (`float`): the maximum beta to use; use values lower than 1 to
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prevent singularities.
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Returns:
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betas (`np.ndarray`): the betas used by the scheduler to step the model outputs
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"""
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def alpha_bar(time_step):
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return math.cos((time_step + 0.008) / 1.008 * math.pi / 2)**2
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betas = []
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for i in range(num_diffusion_timesteps):
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t1 = i / num_diffusion_timesteps
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t2 = (i + 1) / num_diffusion_timesteps
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betas.append(min(1 - alpha_bar(t2) / alpha_bar(t1), max_beta))
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return np.concatenate(betas).astype(np.float32)
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class PNDMScheduler(SchedulerMixin, ConfigMixin):
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@register_to_config
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def __init__(
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self,
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num_train_timesteps: int=1000,
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beta_start: float=0.0001,
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beta_end: float=0.02,
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beta_schedule: str="linear",
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trained_betas: Optional[np.ndarray]=None,
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skip_prk_steps: bool=False,
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set_alpha_to_one: bool=False,
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steps_offset: int=0,
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**kwargs, ):
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if trained_betas is not None:
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self.betas = trained_betas
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elif beta_schedule == "linear":
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self.betas = np.linspace(
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beta_start, beta_end, num_train_timesteps, dtype=np.float32)
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elif beta_schedule == "scaled_linear":
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# this schedule is very specific to the latent diffusion model.
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self.betas = (np.linspace(
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beta_start**0.5,
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beta_end**0.5,
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num_train_timesteps,
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dtype=np.float32)**2)
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elif beta_schedule == "squaredcos_cap_v2":
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# Glide cosine schedule
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self.betas = betas_for_alpha_bar(num_train_timesteps)
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else:
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raise NotImplementedError(
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f"{beta_schedule} does is not implemented for {self.__class__}")
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self.alphas = 1.0 - self.betas
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self.alphas_cumprod = np.cumprod(self.alphas, axis=0)
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self.final_alpha_cumprod = 1.0 if set_alpha_to_one else self.alphas_cumprod[
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0]
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# standard deviation of the initial noise distribution
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self.init_noise_sigma = 1.0
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# For now we only support F-PNDM, i.e. the runge-kutta method
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# For more information on the algorithm please take a look at the paper: https://arxiv.org/pdf/2202.09778.pdf
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# mainly at formula (9), (12), (13) and the Algorithm 2.
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self.pndm_order = 4
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# running values
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self.cur_model_output = 0
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self.counter = 0
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self.cur_sample = None
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self.ets = []
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# setable values
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self.num_inference_steps = None
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self._timesteps = np.arange(
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0, num_train_timesteps)[::-1].copy().astype("int64")
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self.prk_timesteps = None
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self.plms_timesteps = None
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self.timesteps = None
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def set_timesteps(self, num_inference_steps: int, **kwargs) -> np.ndarray:
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"""
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Sets the discrete timesteps used for the diffusion chain. Supporting function to be run before inference.
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Args:
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num_inference_steps (`int`):
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the number of diffusion steps used when generating samples with a pre-trained model.
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"""
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offset = self.config.steps_offset
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self.num_inference_steps = num_inference_steps
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step_ratio = self.config.num_train_timesteps // self.num_inference_steps
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# creates integer timesteps by multiplying by ratio
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# casting to int to avoid issues when num_inference_step is power of 3
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self._timesteps = (np.arange(0, num_inference_steps) *
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step_ratio).round()
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self._timesteps += offset
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if self.config.skip_prk_steps:
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# for some models like stable diffusion the prk steps can/should be skipped to
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# produce better results. When using PNDM with `self.config.skip_prk_steps` the implementation
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# is based on crowsonkb's PLMS sampler implementation: https://github.com/CompVis/latent-diffusion/pull/51
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self.prk_timesteps = np.array([])
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self.plms_timesteps = np.concatenate([
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self._timesteps[:-1], self._timesteps[-2:-1],
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self._timesteps[-1:]
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])[::-1].copy()
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else:
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prk_timesteps = np.array(self._timesteps[
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-self.pndm_order:]).repeat(2) + np.tile(
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np.array([
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0, self.config.num_train_timesteps //
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num_inference_steps // 2
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]), self.pndm_order)
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self.prk_timesteps = (
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prk_timesteps[:-1].repeat(2)[1:-1])[::-1].copy()
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self.plms_timesteps = self._timesteps[:-3][::-1].copy()
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self.timesteps = np.concatenate(
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[self.prk_timesteps, self.plms_timesteps]).astype(np.int64)
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self.ets = []
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self.counter = 0
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def step(
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self,
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model_output: np.ndarray,
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timestep: int,
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sample: np.ndarray,
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return_dict: bool=True, ):
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"""
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Predict the sample at the previous timestep by reversing the SDE. Core function to propagate the diffusion
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process from the learned model outputs (most often the predicted noise).
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This function calls `step_prk()` or `step_plms()` depending on the internal variable `counter`.
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Args:
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model_output (`np.ndarray`): direct output from learned diffusion model.
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timestep (`int`): current discrete timestep in the diffusion chain.
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sample (`np.ndarray`):
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current instance of sample being created by diffusion process.
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return_dict (`bool`): option for returning tuple rather than SchedulerOutput class
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Returns:
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[`~schedulers.scheduling_utils.SchedulerOutput`] or `tuple`:
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[`~schedulers.scheduling_utils.SchedulerOutput`] if `return_dict` is True, otherwise a `tuple`. When
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returning a tuple, the first element is the sample tensor.
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"""
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if self.counter < len(
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self.prk_timesteps) and not self.config.skip_prk_steps:
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return self.step_prk(
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model_output=model_output,
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timestep=timestep,
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sample=sample,
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return_dict=return_dict)
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else:
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return self.step_plms(
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model_output=model_output,
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timestep=timestep,
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sample=sample,
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return_dict=return_dict)
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def step_prk(self,
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model_output: np.ndarray,
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timestep: int,
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sample: np.ndarray,
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return_dict: bool=True):
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"""
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Step function propagating the sample with the Runge-Kutta method. RK takes 4 forward passes to approximate the
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solution to the differential equation.
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Args:
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model_output (`np.ndarray`): direct output from learned diffusion model.
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timestep (`int`): current discrete timestep in the diffusion chain.
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sample (`np.ndarray`):
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current instance of sample being created by diffusion process.
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Returns:
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[`~scheduling_utils.SchedulerOutput`] or `tuple`: [`~scheduling_utils.SchedulerOutput`] if `return_dict` is
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True, otherwise a `tuple`. When returning a tuple, the first element is the sample tensor.
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"""
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if self.num_inference_steps is None:
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raise ValueError(
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"Number of inference steps is 'None', you need to run 'set_timesteps' after creating the scheduler"
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)
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diff_to_prev = 0 if self.counter % 2 else self.config.num_train_timesteps // self.num_inference_steps // 2
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prev_timestep = timestep - diff_to_prev
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timestep = self.prk_timesteps[self.counter // 4 * 4]
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if self.counter % 4 == 0:
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self.cur_model_output += 1 / 6 * model_output
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self.ets.append(model_output)
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self.cur_sample = sample
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elif (self.counter - 1) % 4 == 0:
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self.cur_model_output += 1 / 3 * model_output
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elif (self.counter - 2) % 4 == 0:
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self.cur_model_output += 1 / 3 * model_output
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elif (self.counter - 3) % 4 == 0:
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model_output = self.cur_model_output + 1 / 6 * model_output
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self.cur_model_output = 0
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# cur_sample should not be `None`
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cur_sample = self.cur_sample if self.cur_sample is not None else sample
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prev_sample = self._get_prev_sample(cur_sample, timestep,
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prev_timestep, model_output)
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self.counter += 1
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if not return_dict:
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return (prev_sample, )
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return SchedulerOutput(prev_sample=prev_sample)
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def step_plms(self,
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model_output: np.ndarray,
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timestep: int,
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sample: np.ndarray,
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return_dict: bool=True):
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"""
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Step function propagating the sample with the linear multi-step method. This has one forward pass with multiple
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times to approximate the solution.
|
||
|
||
Args:
|
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model_output (`np.ndarray`): direct output from learned diffusion model.
|
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timestep (`int`): current discrete timestep in the diffusion chain.
|
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sample (`np.ndarray`):
|
||
current instance of sample being created by diffusion process.
|
||
return_dict (`bool`): option for returning tuple rather than SchedulerOutput class
|
||
|
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Returns:
|
||
[`~scheduling_utils.SchedulerOutput`] or `tuple`: [`~scheduling_utils.SchedulerOutput`] if `return_dict` is
|
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True, otherwise a `tuple`. When returning a tuple, the first element is the sample tensor.
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"""
|
||
if self.num_inference_steps is None:
|
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raise ValueError(
|
||
"Number of inference steps is 'None', you need to run 'set_timesteps' after creating the scheduler"
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)
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||
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||
if not self.config.skip_prk_steps and len(self.ets) < 3:
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raise ValueError(
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||
f"{self.__class__} can only be run AFTER scheduler has been run "
|
||
"in 'prk' mode for at least 12 iterations "
|
||
"See: https://github.com/huggingface/diffusers/blob/main/src/diffusers/pipelines/pipeline_pndm.py "
|
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"for more information.")
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prev_timestep = timestep - self.config.num_train_timesteps // self.num_inference_steps
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if self.counter != 1:
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self.ets.append(model_output)
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else:
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prev_timestep = timestep
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timestep = timestep + self.config.num_train_timesteps // self.num_inference_steps
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if len(self.ets) == 1 and self.counter == 0:
|
||
model_output = model_output
|
||
self.cur_sample = sample
|
||
elif len(self.ets) == 1 and self.counter == 1:
|
||
model_output = (model_output + self.ets[-1]) / 2
|
||
sample = self.cur_sample
|
||
self.cur_sample = None
|
||
elif len(self.ets) == 2:
|
||
model_output = (3 * self.ets[-1] - self.ets[-2]) / 2
|
||
elif len(self.ets) == 3:
|
||
model_output = (
|
||
23 * self.ets[-1] - 16 * self.ets[-2] + 5 * self.ets[-3]) / 12
|
||
else:
|
||
model_output = (1 / 24) * (55 * self.ets[-1] - 59 * self.ets[-2] +
|
||
37 * self.ets[-3] - 9 * self.ets[-4])
|
||
|
||
prev_sample = self._get_prev_sample(sample, timestep, prev_timestep,
|
||
model_output)
|
||
self.counter += 1
|
||
if not return_dict:
|
||
return (prev_sample, )
|
||
return SchedulerOutput(prev_sample=prev_sample)
|
||
|
||
def scale_model_input(self, sample: np.ndarray, *args,
|
||
**kwargs) -> np.ndarray:
|
||
"""
|
||
Ensures interchangeability with schedulers that need to scale the denoising model input depending on the
|
||
current timestep.
|
||
Args:
|
||
sample (`np.ndarray`): input sample
|
||
Returns:
|
||
`np.ndarray`: scaled input sample
|
||
"""
|
||
return sample
|
||
|
||
def _get_prev_sample(self, sample, timestep, prev_timestep, model_output):
|
||
alpha_prod_t = self.alphas_cumprod[timestep]
|
||
alpha_prod_t_prev = self.alphas_cumprod[
|
||
prev_timestep] if prev_timestep >= 0 else self.final_alpha_cumprod
|
||
beta_prod_t = 1 - alpha_prod_t
|
||
beta_prod_t_prev = 1 - alpha_prod_t_prev
|
||
|
||
sample_coeff = (alpha_prod_t_prev / alpha_prod_t)**(0.5)
|
||
|
||
model_output_denom_coeff = alpha_prod_t * beta_prod_t_prev**(0.5) + (
|
||
alpha_prod_t * beta_prod_t * alpha_prod_t_prev)**(0.5)
|
||
|
||
prev_sample = (sample_coeff * sample -
|
||
(alpha_prod_t_prev - alpha_prod_t
|
||
) * model_output / model_output_denom_coeff)
|
||
|
||
return prev_sample
|
||
|
||
def add_noise(
|
||
self,
|
||
original_samples: np.ndarray,
|
||
noise: np.ndarray,
|
||
timesteps: np.ndarray, ) -> np.ndarray:
|
||
|
||
sqrt_alpha_prod = self.alphas_cumprod[timesteps]**0.5
|
||
sqrt_alpha_prod = sqrt_alpha_prod.flatten()
|
||
while len(sqrt_alpha_prod.shape) < len(original_samples.shape):
|
||
sqrt_alpha_prod = sqrt_alpha_prod.unsqueeze(-1)
|
||
|
||
sqrt_one_minus_alpha_prod = (1 - self.alphas_cumprod[timesteps])**0.5
|
||
sqrt_one_minus_alpha_prod = sqrt_one_minus_alpha_prod.flatten()
|
||
while len(sqrt_one_minus_alpha_prod.shape) < len(
|
||
original_samples.shape):
|
||
sqrt_one_minus_alpha_prod = sqrt_one_minus_alpha_prod.unsqueeze(-1)
|
||
|
||
noisy_samples = sqrt_alpha_prod * original_samples + sqrt_one_minus_alpha_prod * noise
|
||
return noisy_samples
|
||
|
||
def __len__(self):
|
||
return self.config.num_train_timesteps
|
||
|
||
|
||
class DDIMScheduler(SchedulerMixin, ConfigMixin):
|
||
"""
|
||
Denoising diffusion implicit models is a scheduler that extends the denoising procedure introduced in denoising
|
||
diffusion probabilistic models (DDPMs) with non-Markovian guidance.
|
||
|
||
[`~ConfigMixin`] takes care of storing all config attributes that are passed in the scheduler's `__init__`
|
||
function, such as `num_train_timesteps`. They can be accessed via `scheduler.config.num_train_timesteps`.
|
||
[`~ConfigMixin`] also provides general loading and saving functionality via the [`~ConfigMixin.save_config`] and
|
||
[`~ConfigMixin.from_config`] functions.
|
||
|
||
For more details, see the original paper: https://arxiv.org/abs/2010.02502
|
||
|
||
Args:
|
||
num_train_timesteps (`int`): number of diffusion steps used to train the model.
|
||
beta_start (`float`): the starting `beta` value of inference.
|
||
beta_end (`float`): the final `beta` value.
|
||
beta_schedule (`str`):
|
||
the beta schedule, a mapping from a beta range to a sequence of betas for stepping the model. Choose from
|
||
`linear`, `scaled_linear`, or `squaredcos_cap_v2`.
|
||
trained_betas (`np.ndarray`, optional):
|
||
option to pass an array of betas directly to the constructor to bypass `beta_start`, `beta_end` etc.
|
||
clip_sample (`bool`, default `True`):
|
||
option to clip predicted sample between -1 and 1 for numerical stability.
|
||
set_alpha_to_one (`bool`, default `True`):
|
||
each diffusion step uses the value of alphas product at that step and at the previous one. For the final
|
||
step there is no previous alpha. When this option is `True` the previous alpha product is fixed to `1`,
|
||
otherwise it uses the value of alpha at step 0.
|
||
steps_offset (`int`, default `0`):
|
||
an offset added to the inference steps. You can use a combination of `offset=1` and
|
||
`set_alpha_to_one=False`, to make the last step use step 0 for the previous alpha product, as done in
|
||
stable diffusion.
|
||
|
||
"""
|
||
|
||
@register_to_config
|
||
def __init__(
|
||
self,
|
||
num_train_timesteps: int=1000,
|
||
beta_start: float=0.0001,
|
||
beta_end: float=0.02,
|
||
beta_schedule: str="linear",
|
||
trained_betas: Optional[np.ndarray]=None,
|
||
clip_sample: bool=True,
|
||
set_alpha_to_one: bool=True,
|
||
steps_offset: int=0,
|
||
**kwargs, ):
|
||
if trained_betas is not None:
|
||
self.betas = trained_betas
|
||
elif beta_schedule == "linear":
|
||
self.betas = np.linspace(
|
||
beta_start, beta_end, num_train_timesteps, dtype=np.float32)
|
||
elif beta_schedule == "scaled_linear":
|
||
# this schedule is very specific to the latent diffusion model.
|
||
self.betas = (np.linspace(
|
||
beta_start**0.5,
|
||
beta_end**0.5,
|
||
num_train_timesteps,
|
||
dtype=np.float32)**2)
|
||
elif beta_schedule == "squaredcos_cap_v2":
|
||
# Glide cosine schedule
|
||
self.betas = betas_for_alpha_bar(num_train_timesteps)
|
||
else:
|
||
raise NotImplementedError(
|
||
f"{beta_schedule} does is not implemented for {self.__class__}")
|
||
|
||
self.alphas = 1.0 - self.betas
|
||
self.alphas_cumprod = np.cumprod(self.alphas, axis=0)
|
||
|
||
# At every step in ddim, we are looking into the previous alphas_cumprod
|
||
# For the final step, there is no previous alphas_cumprod because we are already at 0
|
||
# `set_alpha_to_one` decides whether we set this parameter simply to one or
|
||
# whether we use the final alpha of the "non-previous" one.
|
||
self.final_alpha_cumprod = 1.0 if set_alpha_to_one else self.alphas_cumprod[
|
||
0]
|
||
|
||
# standard deviation of the initial noise distribution
|
||
self.init_noise_sigma = 1.0
|
||
|
||
# setable values
|
||
self.num_inference_steps = None
|
||
self.timesteps = np.arange(0, num_train_timesteps)[::-1]
|
||
|
||
def scale_model_input(self,
|
||
sample: np.ndarray,
|
||
timestep: Optional[int]=None) -> np.ndarray:
|
||
"""
|
||
Ensures interchangeability with schedulers that need to scale the denoising model input depending on the
|
||
current timestep.
|
||
Args:
|
||
sample (`np.ndarray`): input sample
|
||
timestep (`int`, optional): current timestep
|
||
Returns:
|
||
`np.ndarray`: scaled input sample
|
||
"""
|
||
return sample
|
||
|
||
def _get_variance(self, timestep, prev_timestep):
|
||
alpha_prod_t = self.alphas_cumprod[timestep]
|
||
alpha_prod_t_prev = self.alphas_cumprod[
|
||
prev_timestep] if prev_timestep >= 0 else self.final_alpha_cumprod
|
||
beta_prod_t = 1 - alpha_prod_t
|
||
beta_prod_t_prev = 1 - alpha_prod_t_prev
|
||
|
||
variance = (beta_prod_t_prev / beta_prod_t) * (
|
||
1 - alpha_prod_t / alpha_prod_t_prev)
|
||
|
||
return variance
|
||
|
||
def set_timesteps(self, num_inference_steps: int, **kwargs):
|
||
"""
|
||
Sets the discrete timesteps used for the diffusion chain. Supporting function to be run before inference.
|
||
|
||
Args:
|
||
num_inference_steps (`int`):
|
||
the number of diffusion steps used when generating samples with a pre-trained model.
|
||
"""
|
||
offset = self.config.steps_offset
|
||
|
||
self.num_inference_steps = num_inference_steps
|
||
step_ratio = self.config.num_train_timesteps // self.num_inference_steps
|
||
# creates integer timesteps by multiplying by ratio
|
||
# casting to int to avoid issues when num_inference_step is power of 3
|
||
self.timesteps = (np.arange(0, num_inference_steps) *
|
||
step_ratio).round()[::-1]
|
||
self.timesteps += offset
|
||
|
||
def step(
|
||
self,
|
||
model_output: np.ndarray,
|
||
timestep: int,
|
||
sample: np.ndarray,
|
||
eta: float=0.0,
|
||
use_clipped_model_output: bool=False,
|
||
generator=None,
|
||
return_dict: bool=True, ):
|
||
"""
|
||
Predict the sample at the previous timestep by reversing the SDE. Core function to propagate the diffusion
|
||
process from the learned model outputs (most often the predicted noise).
|
||
|
||
Args:
|
||
model_output (`np.ndarray`): direct output from learned diffusion model.
|
||
timestep (`int`): current discrete timestep in the diffusion chain.
|
||
sample (`np.ndarray`):
|
||
current instance of sample being created by diffusion process.
|
||
eta (`float`): weight of noise for added noise in diffusion step.
|
||
use_clipped_model_output (`bool`): TODO
|
||
generator: random number generator.
|
||
return_dict (`bool`): option for returning tuple rather than DDIMSchedulerOutput class
|
||
|
||
Returns:
|
||
[`~scheduling_utils.DDIMSchedulerOutput`] or `tuple`:
|
||
[`~scheduling_utils.DDIMSchedulerOutput`] if `return_dict` is True, otherwise a `tuple`. When
|
||
returning a tuple, the first element is the sample tensor.
|
||
|
||
"""
|
||
if self.num_inference_steps is None:
|
||
raise ValueError(
|
||
"Number of inference steps is 'None', you need to run 'set_timesteps' after creating the scheduler"
|
||
)
|
||
|
||
# See formulas (12) and (16) of DDIM paper https://arxiv.org/pdf/2010.02502.pdf
|
||
# Ideally, read DDIM paper in-detail understanding
|
||
|
||
# Notation (<variable name> -> <name in paper>
|
||
# - pred_noise_t -> e_theta(x_t, t)
|
||
# - pred_original_sample -> f_theta(x_t, t) or x_0
|
||
# - std_dev_t -> sigma_t
|
||
# - eta -> η
|
||
# - pred_sample_direction -> "direction pointing to x_t"
|
||
# - pred_prev_sample -> "x_t-1"
|
||
|
||
# 1. get previous step value (=t-1)
|
||
prev_timestep = timestep - self.config.num_train_timesteps // self.num_inference_steps
|
||
|
||
# 2. compute alphas, betas
|
||
alpha_prod_t = self.alphas_cumprod[timestep]
|
||
alpha_prod_t_prev = self.alphas_cumprod[
|
||
prev_timestep] if prev_timestep >= 0 else self.final_alpha_cumprod
|
||
|
||
beta_prod_t = 1 - alpha_prod_t
|
||
|
||
# 3. compute predicted original sample from predicted noise also called
|
||
# "predicted x_0" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
|
||
pred_original_sample = (sample - beta_prod_t**
|
||
(0.5) * model_output) / alpha_prod_t**(0.5)
|
||
|
||
# 4. Clip "predicted x_0"
|
||
if self.config.clip_sample:
|
||
pred_original_sample = np.clip(pred_original_sample, -1, 1)
|
||
|
||
# 5. compute variance: "sigma_t(η)" -> see formula (16)
|
||
# σ_t = sqrt((1 − α_t−1)/(1 − α_t)) * sqrt(1 − α_t/α_t−1)
|
||
variance = self._get_variance(timestep, prev_timestep)
|
||
std_dev_t = eta * variance**(0.5)
|
||
|
||
if use_clipped_model_output:
|
||
# the model_output is always re-derived from the clipped x_0 in Glide
|
||
model_output = (sample - alpha_prod_t**
|
||
(0.5) * pred_original_sample) / beta_prod_t**(0.5)
|
||
|
||
# 6. compute "direction pointing to x_t" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
|
||
pred_sample_direction = (1 - alpha_prod_t_prev - std_dev_t**2)**(
|
||
0.5) * model_output
|
||
|
||
# 7. compute x_t without "random noise" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
|
||
prev_sample = alpha_prod_t_prev**(
|
||
0.5) * pred_original_sample + pred_sample_direction
|
||
|
||
if eta > 0:
|
||
noise = np.random.randn(*model_output.shape)
|
||
variance = self._get_variance(timestep, prev_timestep)**(
|
||
0.5) * eta * noise
|
||
|
||
prev_sample = prev_sample + variance
|
||
if not return_dict:
|
||
return (prev_sample, )
|
||
return DDIMSchedulerOutput(
|
||
prev_sample=prev_sample, pred_original_sample=pred_original_sample)
|
||
|
||
def __len__(self):
|
||
return self.config.num_train_timesteps
|
||
|
||
|
||
class LMSDiscreteScheduler(SchedulerMixin, ConfigMixin):
|
||
"""
|
||
Linear Multistep Scheduler for discrete beta schedules. Based on the original k-diffusion implementation by
|
||
Katherine Crowson:
|
||
https://github.com/crowsonkb/k-diffusion/blob/481677d114f6ea445aa009cf5bd7a9cdee909e47/k_diffusion/sampling.py#L181
|
||
|
||
[`~ConfigMixin`] takes care of storing all config attributes that are passed in the scheduler's `__init__`
|
||
function, such as `num_train_timesteps`. They can be accessed via `scheduler.config.num_train_timesteps`.
|
||
[`~ConfigMixin`] also provides general loading and saving functionality via the [`~ConfigMixin.save_config`] and
|
||
[`~ConfigMixin.from_config`] functions.
|
||
|
||
Args:
|
||
num_train_timesteps (`int`): number of diffusion steps used to train the model.
|
||
beta_start (`float`): the starting `beta` value of inference.
|
||
beta_end (`float`): the final `beta` value.
|
||
beta_schedule (`str`):
|
||
the beta schedule, a mapping from a beta range to a sequence of betas for stepping the model. Choose from
|
||
`linear` or `scaled_linear`.
|
||
trained_betas (`np.ndarray`, optional):
|
||
option to pass an array of betas directly to the constructor to bypass `beta_start`, `beta_end` etc.
|
||
|
||
"""
|
||
|
||
@register_to_config
|
||
def __init__(
|
||
self,
|
||
num_train_timesteps: int=1000,
|
||
beta_start: float=0.0001,
|
||
beta_end: float=0.02,
|
||
beta_schedule: str="linear",
|
||
trained_betas: Optional[np.ndarray]=None,
|
||
**kwargs, ):
|
||
if trained_betas is not None:
|
||
self.betas = trained_betas
|
||
elif beta_schedule == "linear":
|
||
self.betas = np.linspace(
|
||
beta_start, beta_end, num_train_timesteps, dtype=np.float32)
|
||
elif beta_schedule == "scaled_linear":
|
||
# this schedule is very specific to the latent diffusion model.
|
||
self.betas = (np.linspace(
|
||
beta_start**0.5,
|
||
beta_end**0.5,
|
||
num_train_timesteps,
|
||
dtype=np.float32)**2)
|
||
else:
|
||
raise NotImplementedError(
|
||
f"{beta_schedule} does is not implemented for {self.__class__}")
|
||
|
||
self.alphas = 1.0 - self.betas
|
||
self.alphas_cumprod = np.cumprod(self.alphas, axis=0)
|
||
|
||
sigmas = np.array(((1 - self.alphas_cumprod) / self.alphas_cumprod)**
|
||
0.5)
|
||
self.sigmas = np.concatenate([sigmas[::-1], [0.0]]).astype(np.float32)
|
||
|
||
# standard deviation of the initial noise distribution
|
||
self.init_noise_sigma = self.sigmas.max()
|
||
|
||
# setable values
|
||
self.num_inference_steps = None
|
||
self.timesteps = np.linspace(
|
||
0, num_train_timesteps - 1, num_train_timesteps,
|
||
dtype=float)[::-1].copy()
|
||
self.derivatives = []
|
||
|
||
def scale_model_input(self,
|
||
sample: np.ndarray,
|
||
timestep: Union[float, np.ndarray]) -> np.ndarray:
|
||
"""
|
||
Scales the denoising model input by `(sigma**2 + 1) ** 0.5` to match the K-LMS algorithm.
|
||
Args:
|
||
sample (`np.ndarray`): input sample
|
||
timestep (`float` or `np.ndarray`): the current timestep in the diffusion chain
|
||
Returns:
|
||
`np.ndarray`: scaled input sample
|
||
"""
|
||
step_index = (self.timesteps == timestep).nonzero()[0]
|
||
sigma = self.sigmas[step_index]
|
||
sample = sample / ((sigma**2 + 1)**0.5)
|
||
self.is_scale_input_called = True
|
||
return sample
|
||
|
||
def get_lms_coefficient(self, order, t, current_order):
|
||
"""
|
||
Compute a linear multistep coefficient.
|
||
|
||
Args:
|
||
order (TODO):
|
||
t (TODO):
|
||
current_order (TODO):
|
||
"""
|
||
|
||
def lms_derivative(tau):
|
||
prod = 1.0
|
||
for k in range(order):
|
||
if current_order == k:
|
||
continue
|
||
prod *= (tau - self.sigmas[t - k]) / (
|
||
self.sigmas[t - current_order] - self.sigmas[t - k])
|
||
return prod
|
||
|
||
integrated_coeff = integrate.quad(
|
||
lms_derivative, self.sigmas[t], self.sigmas[t + 1], epsrel=1e-4)[0]
|
||
|
||
return integrated_coeff
|
||
|
||
def set_timesteps(self, num_inference_steps: int):
|
||
"""
|
||
Sets the timesteps used for the diffusion chain. Supporting function to be run before inference.
|
||
|
||
Args:
|
||
num_inference_steps (`int`):
|
||
the number of diffusion steps used when generating samples with a pre-trained model.
|
||
"""
|
||
self.num_inference_steps = num_inference_steps
|
||
|
||
timesteps = np.linspace(
|
||
0,
|
||
self.config.num_train_timesteps - 1,
|
||
num_inference_steps,
|
||
dtype=float)[::-1].copy()
|
||
sigmas = np.array(((1 - self.alphas_cumprod) / self.alphas_cumprod)**
|
||
0.5)
|
||
sigmas = np.interp(timesteps, np.arange(0, len(sigmas)), sigmas)
|
||
sigmas = np.concatenate([sigmas, [0.0]]).astype(np.float32)
|
||
self.sigmas = sigmas
|
||
self.timesteps = timesteps
|
||
|
||
self.derivatives = []
|
||
|
||
def step(
|
||
self,
|
||
model_output: np.ndarray,
|
||
timestep: int,
|
||
sample: np.ndarray,
|
||
order: int=4,
|
||
return_dict: bool=True, ):
|
||
"""
|
||
Predict the sample at the previous timestep by reversing the SDE. Core function to propagate the diffusion
|
||
process from the learned model outputs (most often the predicted noise).
|
||
|
||
Args:
|
||
model_output (`np.ndarray`): direct output from learned diffusion model.
|
||
timestep (`int`): current discrete timestep in the diffusion chain.
|
||
sample (`np.ndarray`):
|
||
current instance of sample being created by diffusion process.
|
||
order: coefficient for multi-step inference.
|
||
return_dict (`bool`): option for returning tuple rather than LMSDiscreteSchedulerOutput class
|
||
|
||
Returns:
|
||
[`~scheduling_utils.LMSDiscreteSchedulerOutput`] or `tuple`:
|
||
[`~scheduling_utils.LMSDiscreteSchedulerOutput`] if `return_dict` is True, otherwise a `tuple`.
|
||
When returning a tuple, the first element is the sample tensor.
|
||
|
||
"""
|
||
sigma = self.sigmas[int(timestep)]
|
||
|
||
# 1. compute predicted original sample (x_0) from sigma-scaled predicted noise
|
||
pred_original_sample = sample - sigma * model_output
|
||
|
||
# 2. Convert to an ODE derivative
|
||
derivative = (sample - pred_original_sample) / sigma
|
||
self.derivatives.append(derivative)
|
||
if len(self.derivatives) > order:
|
||
self.derivatives.pop(0)
|
||
|
||
# 3. Compute linear multistep coefficients
|
||
order = min(timestep + 1, order)
|
||
lms_coeffs = [
|
||
self.get_lms_coefficient(order, timestep, curr_order)
|
||
for curr_order in range(order)
|
||
]
|
||
|
||
# 4. Compute previous sample based on the derivatives path
|
||
prev_sample = sample + sum(coeff * derivative
|
||
for coeff, derivative in zip(
|
||
lms_coeffs, reversed(self.derivatives)))
|
||
|
||
if not return_dict:
|
||
return (prev_sample, )
|
||
|
||
return LMSDiscreteSchedulerOutput(
|
||
prev_sample=prev_sample, pred_original_sample=pred_original_sample)
|
||
|
||
def add_noise(
|
||
self,
|
||
original_samples: np.ndarray,
|
||
noise: np.ndarray,
|
||
timesteps: np.ndarray, ) -> np.ndarray:
|
||
sigmas = self.sigmas
|
||
|
||
sigma = sigmas[timesteps].flatten()
|
||
while len(sigma.shape) < len(original_samples.shape):
|
||
sigma = sigma.unsqueeze(-1)
|
||
|
||
noisy_samples = original_samples + noise * sigma
|
||
return noisy_samples
|
||
|
||
def __len__(self):
|
||
return self.config.num_train_timesteps
|
||
|
||
|
||
class EulerAncestralDiscreteScheduler(SchedulerMixin, ConfigMixin):
|
||
"""
|
||
Ancestral sampling with Euler method steps. Based on the original k-diffusion implementation by Katherine Crowson:
|
||
https://github.com/crowsonkb/k-diffusion/blob/481677d114f6ea445aa009cf5bd7a9cdee909e47/k_diffusion/sampling.py#L72
|
||
[`~ConfigMixin`] takes care of storing all config attributes that are passed in the scheduler's `__init__`
|
||
function, such as `num_train_timesteps`. They can be accessed via `scheduler.config.num_train_timesteps`.
|
||
[`~ConfigMixin`] also provides general loading and saving functionality via the [`~ConfigMixin.save_config`] and
|
||
[`~ConfigMixin.from_config`] functions.
|
||
Args:
|
||
num_train_timesteps (`int`): number of diffusion steps used to train the model.
|
||
beta_start (`float`): the starting `beta` value of inference.
|
||
beta_end (`float`): the final `beta` value.
|
||
beta_schedule (`str`):
|
||
the beta schedule, a mapping from a beta range to a sequence of betas for stepping the model. Choose from
|
||
`linear` or `scaled_linear`.
|
||
trained_betas (`np.ndarray`, optional):
|
||
option to pass an array of betas directly to the constructor to bypass `beta_start`, `beta_end` etc.
|
||
"""
|
||
|
||
_compatible_classes = [
|
||
"DDIMScheduler",
|
||
"DDPMScheduler",
|
||
"LMSDiscreteScheduler",
|
||
"PNDMScheduler",
|
||
"EulerDiscreteScheduler",
|
||
"DPMSolverMultistepScheduler",
|
||
]
|
||
|
||
@register_to_config
|
||
def __init__(
|
||
self,
|
||
num_train_timesteps: int=1000,
|
||
beta_start: float=0.0001,
|
||
beta_end: float=0.02,
|
||
beta_schedule: str="linear",
|
||
trained_betas: Optional[np.ndarray]=None, ):
|
||
if trained_betas is not None:
|
||
self.betas = np.array(trained_betas)
|
||
elif beta_schedule == "linear":
|
||
self.betas = np.linspace(
|
||
beta_start, beta_end, num_train_timesteps, dtype=np.float32)
|
||
elif beta_schedule == "scaled_linear":
|
||
# this schedule is very specific to the latent diffusion model.
|
||
self.betas = (np.linspace(
|
||
beta_start**0.5,
|
||
beta_end**0.5,
|
||
num_train_timesteps,
|
||
dtype="float32")**2)
|
||
else:
|
||
raise NotImplementedError(
|
||
f"{beta_schedule} does is not implemented for {self.__class__}")
|
||
|
||
self.alphas = 1.0 - self.betas
|
||
self.alphas_cumprod = np.cumprod(self.alphas, axis=0)
|
||
|
||
sigmas = np.array(((1 - self.alphas_cumprod) / self.alphas_cumprod)**
|
||
0.5)
|
||
self.sigmas = np.concatenate([sigmas[::-1], [0.0]]).astype(np.float32)
|
||
|
||
# standard deviation of the initial noise distribution
|
||
self.init_noise_sigma = self.sigmas.max()
|
||
|
||
# setable values
|
||
self.num_inference_steps = None
|
||
timesteps = np.linspace(
|
||
0, num_train_timesteps - 1, num_train_timesteps,
|
||
dtype=float)[::-1].copy()
|
||
self.timesteps = timesteps
|
||
self.is_scale_input_called = False
|
||
|
||
def scale_model_input(self,
|
||
sample: np.ndarray,
|
||
timestep: Union[float, np.ndarray]) -> np.ndarray:
|
||
"""
|
||
Scales the denoising model input by `(sigma**2 + 1) ** 0.5` to match the Euler algorithm.
|
||
Args:
|
||
sample (`np.ndarray`): input sample
|
||
timestep (`float` or `np.ndarray`): the current timestep in the diffusion chain
|
||
Returns:
|
||
`np.ndarray`: scaled input sample
|
||
"""
|
||
step_index = (self.timesteps == timestep).nonzero()[0]
|
||
sigma = self.sigmas[step_index]
|
||
sample = sample / ((sigma**2 + 1)**0.5)
|
||
self.is_scale_input_called = True
|
||
return sample
|
||
|
||
def set_timesteps(self, num_inference_steps: int):
|
||
"""
|
||
Sets the timesteps used for the diffusion chain. Supporting function to be run before inference.
|
||
Args:
|
||
num_inference_steps (`int`):
|
||
the number of diffusion steps used when generating samples with a pre-trained model.
|
||
"""
|
||
self.num_inference_steps = num_inference_steps
|
||
|
||
timesteps = np.linspace(
|
||
0,
|
||
self.config.num_train_timesteps - 1,
|
||
num_inference_steps,
|
||
dtype=float)[::-1].copy()
|
||
sigmas = np.array(((1 - self.alphas_cumprod) / self.alphas_cumprod)**
|
||
0.5)
|
||
sigmas = np.interp(timesteps, np.arange(0, len(sigmas)), sigmas)
|
||
sigmas = np.concatenate([sigmas, [0.0]]).astype(np.float32)
|
||
self.sigmas = sigmas
|
||
self.timesteps = timesteps
|
||
|
||
def step(
|
||
self,
|
||
model_output: np.ndarray,
|
||
timestep: Union[float, np.ndarray],
|
||
sample: np.ndarray,
|
||
return_dict: bool=True, ) -> Union[
|
||
EulerAncestralDiscreteSchedulerOutput, Tuple]:
|
||
"""
|
||
Predict the sample at the previous timestep by reversing the SDE. Core function to propagate the diffusion
|
||
process from the learned model outputs (most often the predicted noise).
|
||
Args:
|
||
model_output (`np.ndarray`): direct output from learned diffusion model.
|
||
timestep (`float`): current timestep in the diffusion chain.
|
||
sample (`np.ndarray`):
|
||
current instance of sample being created by diffusion process.
|
||
return_dict (`bool`): option for returning tuple rather than EulerAncestralDiscreteSchedulerOutput class
|
||
Returns:
|
||
[`~scheduling_utils.EulerAncestralDiscreteSchedulerOutput`] or `tuple`:
|
||
[`~scheduling_utils.EulerAncestralDiscreteSchedulerOutput`] if `return_dict` is True, otherwise
|
||
a `tuple`. When returning a tuple, the first element is the sample tensor.
|
||
"""
|
||
if not self.is_scale_input_called:
|
||
logger.warn(
|
||
"The `scale_model_input` function should be called before `step` to ensure correct denoising. "
|
||
"See `StableDiffusionPipeline` for a usage example.")
|
||
step_index = (self.timesteps == timestep).nonzero()[0]
|
||
sigma = self.sigmas[step_index]
|
||
|
||
# 1. compute predicted original sample (x_0) from sigma-scaled predicted noise
|
||
pred_original_sample = sample - sigma * model_output
|
||
sigma_from = self.sigmas[step_index]
|
||
sigma_to = self.sigmas[step_index + 1]
|
||
sigma_up = (sigma_to**2 * (sigma_from**2 - sigma_to**2) / sigma_from
|
||
**2)**0.5
|
||
sigma_down = (sigma_to**2 - sigma_up**2)**0.5
|
||
|
||
# 2. Convert to an ODE derivative
|
||
derivative = (sample - pred_original_sample) / sigma
|
||
|
||
dt = sigma_down - sigma
|
||
|
||
prev_sample = sample + derivative * dt
|
||
noise = np.random.randn(*model_output.shape).astype(model_output.dtype)
|
||
|
||
prev_sample = prev_sample + noise * sigma_up
|
||
|
||
if not return_dict:
|
||
return (prev_sample, )
|
||
|
||
return EulerAncestralDiscreteSchedulerOutput(
|
||
prev_sample=prev_sample, pred_original_sample=pred_original_sample)
|
||
|
||
def add_noise(
|
||
self,
|
||
original_samples: np.ndarray,
|
||
noise: np.ndarray,
|
||
timesteps: np.ndarray, ) -> np.ndarray:
|
||
# Make sure sigmas and timesteps have the same device and dtype as original_samples
|
||
self.sigmas = self.sigmas.astype(original_samples.dtype)
|
||
|
||
schedule_timesteps = self.timesteps
|
||
step_indices = [(schedule_timesteps == t).nonzero() for t in timesteps]
|
||
|
||
sigma = self.sigmas[step_indices].flatten()
|
||
while len(sigma.shape) < len(original_samples.shape):
|
||
sigma = sigma.unsqueeze(-1)
|
||
|
||
noisy_samples = original_samples + noise * sigma
|
||
return noisy_samples
|
||
|
||
def __len__(self):
|
||
return self.config.num_train_timesteps
|