225 lines
8.6 KiB
Python
225 lines
8.6 KiB
Python
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# Copyright 2025 Zhejiang University Team and The HuggingFace Team. 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 List, Optional, Tuple, Union
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import numpy as np
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import torch
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from ..configuration_utils import ConfigMixin, register_to_config
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from .scheduling_utils import SchedulerMixin, SchedulerOutput
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class IPNDMScheduler(SchedulerMixin, ConfigMixin):
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"""
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A fourth-order Improved Pseudo Linear Multistep scheduler.
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This model inherits from [`SchedulerMixin`] and [`ConfigMixin`]. Check the superclass documentation for the generic
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methods the library implements for all schedulers such as loading and saving.
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Args:
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num_train_timesteps (`int`, defaults to 1000):
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The number of diffusion steps to train the model.
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trained_betas (`np.ndarray`, *optional*):
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Pass an array of betas directly to the constructor to bypass `beta_start` and `beta_end`.
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"""
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order = 1
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@register_to_config
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def __init__(
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self, num_train_timesteps: int = 1000, trained_betas: Optional[Union[np.ndarray, List[float]]] = None
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):
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# set `betas`, `alphas`, `timesteps`
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self.set_timesteps(num_train_timesteps)
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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://huggingface.co/papers/2202.09778
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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.ets = []
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self._step_index = None
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self._begin_index = None
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@property
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def step_index(self):
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"""
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The index counter for current timestep. It will increase 1 after each scheduler step.
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"""
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return self._step_index
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@property
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def begin_index(self):
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"""
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The index for the first timestep. It should be set from pipeline with `set_begin_index` method.
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"""
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return self._begin_index
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# Copied from diffusers.schedulers.scheduling_dpmsolver_multistep.DPMSolverMultistepScheduler.set_begin_index
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def set_begin_index(self, begin_index: int = 0):
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"""
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Sets the begin index for the scheduler. This function should be run from pipeline before the inference.
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Args:
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begin_index (`int`):
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The begin index for the scheduler.
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"""
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self._begin_index = begin_index
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def set_timesteps(self, num_inference_steps: int, device: Union[str, torch.device] = None):
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"""
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Sets the discrete timesteps used for the diffusion chain (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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device (`str` or `torch.device`, *optional*):
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The device to which the timesteps should be moved to. If `None`, the timesteps are not moved.
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"""
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self.num_inference_steps = num_inference_steps
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steps = torch.linspace(1, 0, num_inference_steps + 1)[:-1]
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steps = torch.cat([steps, torch.tensor([0.0])])
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if self.config.trained_betas is not None:
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self.betas = torch.tensor(self.config.trained_betas, dtype=torch.float32)
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else:
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self.betas = torch.sin(steps * math.pi / 2) ** 2
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self.alphas = (1.0 - self.betas**2) ** 0.5
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timesteps = (torch.atan2(self.betas, self.alphas) / math.pi * 2)[:-1]
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self.timesteps = timesteps.to(device)
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self.ets = []
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self._step_index = None
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self._begin_index = None
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# Copied from diffusers.schedulers.scheduling_euler_discrete.EulerDiscreteScheduler.index_for_timestep
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def index_for_timestep(self, timestep, schedule_timesteps=None):
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if schedule_timesteps is None:
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schedule_timesteps = self.timesteps
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indices = (schedule_timesteps == timestep).nonzero()
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# The sigma index that is taken for the **very** first `step`
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# is always the second index (or the last index if there is only 1)
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# This way we can ensure we don't accidentally skip a sigma in
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# case we start in the middle of the denoising schedule (e.g. for image-to-image)
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pos = 1 if len(indices) > 1 else 0
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return indices[pos].item()
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# Copied from diffusers.schedulers.scheduling_euler_discrete.EulerDiscreteScheduler._init_step_index
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def _init_step_index(self, timestep):
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if self.begin_index is None:
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if isinstance(timestep, torch.Tensor):
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timestep = timestep.to(self.timesteps.device)
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self._step_index = self.index_for_timestep(timestep)
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else:
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self._step_index = self._begin_index
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def step(
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self,
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model_output: torch.Tensor,
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timestep: Union[int, torch.Tensor],
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sample: torch.Tensor,
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return_dict: bool = True,
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) -> Union[SchedulerOutput, Tuple]:
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"""
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Predict the sample from the previous timestep by reversing the SDE. This function propagates the sample with
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the linear multistep method. It performs one forward pass multiple times to approximate the solution.
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Args:
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model_output (`torch.Tensor`):
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The direct output from learned diffusion model.
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timestep (`int`):
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The current discrete timestep in the diffusion chain.
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sample (`torch.Tensor`):
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A current instance of a sample created by the diffusion process.
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return_dict (`bool`):
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Whether or not to return a [`~schedulers.scheduling_utils.SchedulerOutput`] or tuple.
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Returns:
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[`~schedulers.scheduling_utils.SchedulerOutput`] or `tuple`:
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If return_dict is `True`, [`~schedulers.scheduling_utils.SchedulerOutput`] is returned, otherwise a
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tuple is returned where 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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if self.step_index is None:
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self._init_step_index(timestep)
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timestep_index = self.step_index
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prev_timestep_index = self.step_index + 1
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ets = sample * self.betas[timestep_index] + model_output * self.alphas[timestep_index]
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self.ets.append(ets)
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if len(self.ets) == 1:
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ets = self.ets[-1]
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elif len(self.ets) == 2:
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ets = (3 * self.ets[-1] - self.ets[-2]) / 2
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elif len(self.ets) == 3:
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ets = (23 * self.ets[-1] - 16 * self.ets[-2] + 5 * self.ets[-3]) / 12
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else:
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ets = (1 / 24) * (55 * self.ets[-1] - 59 * self.ets[-2] + 37 * self.ets[-3] - 9 * self.ets[-4])
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prev_sample = self._get_prev_sample(sample, timestep_index, prev_timestep_index, ets)
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# upon completion increase step index by one
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self._step_index += 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 scale_model_input(self, sample: torch.Tensor, *args, **kwargs) -> torch.Tensor:
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"""
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Ensures interchangeability with schedulers that need to scale the denoising model input depending on the
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current timestep.
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Args:
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sample (`torch.Tensor`):
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The input sample.
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Returns:
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`torch.Tensor`:
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A scaled input sample.
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"""
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return sample
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def _get_prev_sample(self, sample, timestep_index, prev_timestep_index, ets):
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alpha = self.alphas[timestep_index]
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sigma = self.betas[timestep_index]
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next_alpha = self.alphas[prev_timestep_index]
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next_sigma = self.betas[prev_timestep_index]
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pred = (sample - sigma * ets) / max(alpha, 1e-8)
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prev_sample = next_alpha * pred + ets * next_sigma
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return prev_sample
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def __len__(self):
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return self.config.num_train_timesteps
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