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369 lines
16 KiB
Python
369 lines
16 KiB
Python
# Copyright 2023 Stanford 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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# DISCLAIMER: This code is strongly influenced by https://github.com/pesser/pytorch_diffusion
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# and https://github.com/hojonathanho/diffusion
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import math
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from dataclasses import dataclass
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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 diffusers.configuration_utils import ConfigMixin, register_to_config
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from diffusers.utils import BaseOutput
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from diffusers.schedulers.scheduling_utils import KarrasDiffusionSchedulers, SchedulerMixin
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class Time_Windows():
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def __init__(self, t_initial=1, t_terminal=0, num_windows=4, precision=1./1000) -> None:
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assert t_terminal < t_initial
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time_windows = [ 1.*i/num_windows for i in range(1, num_windows+1)][::-1]
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self.window_starts = time_windows # [1.0, 0.75, 0.5, 0.25]
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self.window_ends = time_windows[1:] + [t_terminal] # [0.75, 0.5, 0.25, 0]
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self.precision = precision
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def get_window(self, tp):
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idx = 0
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# robust to numerical error; e.g, (0.6+1/10000) belongs to [0.6, 0.3)
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while (tp-0.1*self.precision) <= self.window_ends[idx]:
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idx += 1
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return self.window_starts[idx], self.window_ends[idx]
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def lookup_window(self, timepoint):
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if timepoint.dim() == 0:
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t_start, t_end = self.get_window(timepoint)
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t_start = torch.ones_like(timepoint) * t_start
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t_end = torch.ones_like(timepoint) * t_end
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else:
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t_start = torch.zeros_like(timepoint)
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t_end = torch.zeros_like(timepoint)
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bsz = timepoint.shape[0]
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for i in range(bsz):
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tp = timepoint[i]
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ts, te = self.get_window(tp)
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t_start[i] = ts
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t_end[i] = te
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return t_start, t_end
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@dataclass
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class PeRFlowSchedulerOutput(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 (`torch.FloatTensor` 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 (`torch.FloatTensor` 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: torch.FloatTensor
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pred_original_sample: Optional[torch.FloatTensor] = None
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# Copied from diffusers.schedulers.scheduling_ddpm.betas_for_alpha_bar
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def betas_for_alpha_bar(
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num_diffusion_timesteps,
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max_beta=0.999,
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alpha_transform_type="cosine",
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):
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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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alpha_transform_type (`str`, *optional*, default to `cosine`): the type of noise schedule for alpha_bar.
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Choose from `cosine` or `exp`
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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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if alpha_transform_type == "cosine":
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def alpha_bar_fn(t):
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return math.cos((t + 0.008) / 1.008 * math.pi / 2) ** 2
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elif alpha_transform_type == "exp":
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def alpha_bar_fn(t):
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return math.exp(t * -12.0)
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else:
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raise ValueError(f"Unsupported alpha_tranform_type: {alpha_transform_type}")
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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_fn(t2) / alpha_bar_fn(t1), max_beta))
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return torch.tensor(betas, dtype=torch.float32)
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class PeRFlowScheduler(SchedulerMixin, ConfigMixin):
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"""
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`ReFlowScheduler` extends the denoising procedure introduced in denoising diffusion probabilistic models (DDPMs) with
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non-Markovian guidance.
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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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beta_start (`float`, defaults to 0.0001):
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The starting `beta` value of inference.
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beta_end (`float`, defaults to 0.02):
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The final `beta` value.
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beta_schedule (`str`, defaults to `"linear"`):
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The beta schedule, a mapping from a beta range to a sequence of betas for stepping the model. Choose from
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`linear`, `scaled_linear`, or `squaredcos_cap_v2`.
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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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set_alpha_to_one (`bool`, defaults to `True`):
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Each diffusion step uses the alphas product value at that step and at the previous one. For the final step
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there is no previous alpha. When this option is `True` the previous alpha product is fixed to `1`,
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otherwise it uses the alpha value at step 0.
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prediction_type (`str`, defaults to `epsilon`, *optional*)
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"""
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_compatibles = [e.name for e in KarrasDiffusionSchedulers]
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order = 1
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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 = 0.00085,
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beta_end: float = 0.012,
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beta_schedule: str = "scaled_linear",
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trained_betas: Optional[Union[np.ndarray, List[float]]] = None,
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set_alpha_to_one: bool = False,
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prediction_type: str = "ddim_eps",
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t_noise: float = 1,
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t_clean: float = 0,
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num_time_windows = 4,
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):
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if trained_betas is not None:
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self.betas = torch.tensor(trained_betas, dtype=torch.float32)
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elif beta_schedule == "linear":
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self.betas = torch.linspace(beta_start, beta_end, num_train_timesteps, dtype=torch.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 = torch.linspace(beta_start**0.5, beta_end**0.5, num_train_timesteps, dtype=torch.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(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 = torch.cumprod(self.alphas, dim=0)
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# At every step in ddim, we are looking into the previous alphas_cumprod
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# For the final step, there is no previous alphas_cumprod because we are already at 0
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# `set_alpha_to_one` decides whether we set this parameter simply to one or
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# whether we use the final alpha of the "non-previous" one.
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self.final_alpha_cumprod = torch.tensor(1.0) if set_alpha_to_one else self.alphas_cumprod[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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self.time_windows = Time_Windows(t_initial=t_noise, t_terminal=t_clean,
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num_windows=num_time_windows,
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precision=1./num_train_timesteps)
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assert prediction_type in ["ddim_eps", "diff_eps", "velocity"]
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def scale_model_input(self, sample: torch.FloatTensor, timestep: Optional[int] = None) -> torch.FloatTensor: # pylint: disable=unused-argument
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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.FloatTensor`):
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The input sample.
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timestep (`int`, *optional*):
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The current timestep in the diffusion chain.
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Returns:
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`torch.FloatTensor`:
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A scaled input sample.
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"""
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return sample
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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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"""
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if num_inference_steps < self.config.num_time_windows: # pylint: disable=no-member
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num_inference_steps = self.config.num_time_windows # pylint: disable=no-member
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print(f"### We recommend a num_inference_steps not less than num_time_windows. It's set as {self.config.num_time_windows}.") # pylint: disable=no-member
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timesteps = []
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for i in range(self.config.num_time_windows): # pylint: disable=no-member
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if i < num_inference_steps%self.config.num_time_windows: # pylint: disable=no-member
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num_steps_cur_win = num_inference_steps//self.config.num_time_windows+1 # pylint: disable=no-member
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else:
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num_steps_cur_win = num_inference_steps//self.config.num_time_windows # pylint: disable=no-member
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t_s = self.time_windows.window_starts[i]
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t_e = self.time_windows.window_ends[i]
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timesteps_cur_win = np.linspace(t_s, t_e, num=num_steps_cur_win, endpoint=False)
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timesteps.append(timesteps_cur_win)
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timesteps = np.concatenate(timesteps)
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self.timesteps = torch.from_numpy( # pylint: disable=attribute-defined-outside-init
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(timesteps*self.config.num_train_timesteps).astype(np.int64) # pylint: disable=no-member,
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).to(device)
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def get_window_alpha(self, timepoints):
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time_windows = self.time_windows
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num_train_timesteps = self.config.num_train_timesteps # pylint: disable=no-member
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t_win_start, t_win_end = time_windows.lookup_window(timepoints)
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t_win_len = t_win_end - t_win_start
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t_interval = timepoints - t_win_start # NOTE: negative value
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idx_start = (t_win_start*num_train_timesteps - 1 ).long()
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alphas_cumprod_start = self.alphas_cumprod[idx_start]
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idx_end = torch.clamp( (t_win_end*num_train_timesteps - 1 ).long(), min=0)
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alphas_cumprod_end = self.alphas_cumprod[idx_end]
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alpha_cumprod_s_e = alphas_cumprod_start / alphas_cumprod_end
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gamma_s_e = alpha_cumprod_s_e ** 0.5
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return t_win_start, t_win_end, t_win_len, t_interval, gamma_s_e, alphas_cumprod_start, alphas_cumprod_end
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def step(
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self,
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model_output: torch.FloatTensor,
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timestep: int,
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sample: torch.FloatTensor,
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return_dict: bool = True,
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) -> Union[PeRFlowSchedulerOutput, Tuple]:
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"""
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Predict the sample from the previous timestep by reversing the SDE. This function propagates the diffusion
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process from the learned model outputs (most often the predicted noise).
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Args:
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model_output (`torch.FloatTensor`):
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The direct output from learned diffusion model.
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timestep (`float`):
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The current discrete timestep in the diffusion chain.
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sample (`torch.FloatTensor`):
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A current instance of a sample created by the diffusion process.
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return_dict (`bool`, *optional*, defaults to `True`):
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Whether or not to return a [`~schedulers.scheduling_ddim.PeRFlowSchedulerOutput`] or `tuple`.
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Returns:
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[`~schedulers.scheduling_utils.PeRFlowSchedulerOutput`] or `tuple`:
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If return_dict is `True`, [`~schedulers.scheduling_ddim.PeRFlowSchedulerOutput`] 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.config.prediction_type == "ddim_eps": # pylint: disable=no-member
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pred_epsilon = model_output
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t_c = timestep / self.config.num_train_timesteps # pylint: disable=no-member
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t_s, t_e, _, c_to_s, _, alphas_cumprod_start, alphas_cumprod_end = self.get_window_alpha(t_c)
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lambda_s = (alphas_cumprod_end / alphas_cumprod_start)**0.5
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eta_s = (1-alphas_cumprod_end)**0.5 - ( alphas_cumprod_end / alphas_cumprod_start * (1-alphas_cumprod_start) )**0.5
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lambda_t = ( lambda_s * (t_e - t_s) ) / ( lambda_s *(t_c - t_s) + (t_e - t_c) )
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eta_t = ( eta_s * (t_e - t_c) ) / ( lambda_s *(t_c - t_s) + (t_e - t_c) )
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pred_win_end = lambda_t * sample + eta_t * pred_epsilon
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pred_velocity = (pred_win_end - sample) / (t_e - (t_s + c_to_s))
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elif self.config.prediction_type == "diff_eps": # pylint: disable=no-member
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pred_epsilon = model_output
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t_c = timestep / self.config.num_train_timesteps # pylint: disable=no-member
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t_s, t_e, _, c_to_s, gamma_s_e, _, _ = self.get_window_alpha(t_c)
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lambda_s = 1 / gamma_s_e
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eta_s = -1 * ( 1- gamma_s_e**2)**0.5 / gamma_s_e
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lambda_t = ( lambda_s * (t_e - t_s) ) / ( lambda_s *(t_c - t_s) + (t_e - t_c) )
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eta_t = ( eta_s * (t_e - t_c) ) / ( lambda_s *(t_c - t_s) + (t_e - t_c) )
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pred_win_end = lambda_t * sample + eta_t * pred_epsilon
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pred_velocity = (pred_win_end - sample) / (t_e - (t_s + c_to_s))
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elif self.config.prediction_type == "velocity": # pylint: disable=no-member
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pred_velocity = model_output
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else:
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raise ValueError(
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f"prediction_type given as {self.config.prediction_type} must be one of `epsilon` or `velocity`." # pylint: disable=no-member
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)
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# get dt
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idx = torch.argwhere(torch.where(self.timesteps==timestep, 1,0))
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prev_step = self.timesteps[idx+1] if (idx+1)<len(self.timesteps) else 0
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dt = (prev_step - timestep) / self.config.num_train_timesteps # pylint: disable=no-member
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dt = dt.to(sample.device, sample.dtype)
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prev_sample = sample + dt * pred_velocity
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if not return_dict:
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return (prev_sample,)
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return PeRFlowSchedulerOutput(prev_sample=prev_sample, pred_original_sample=None)
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# Copied from diffusers.schedulers.scheduling_ddpm.DDPMScheduler.add_noise
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def add_noise(
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self,
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original_samples: torch.FloatTensor,
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noise: torch.FloatTensor,
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timesteps: torch.IntTensor,
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) -> torch.FloatTensor:
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# Make sure alphas_cumprod and timestep have same device and dtype as original_samples
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alphas_cumprod = self.alphas_cumprod.to(device=original_samples.device, dtype=original_samples.dtype)
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timesteps = timesteps.to(original_samples.device) - 1 # indexing from 0
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sqrt_alpha_prod = alphas_cumprod[timesteps] ** 0.5
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sqrt_alpha_prod = sqrt_alpha_prod.flatten()
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while len(sqrt_alpha_prod.shape) < len(original_samples.shape):
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sqrt_alpha_prod = sqrt_alpha_prod.unsqueeze(-1)
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sqrt_one_minus_alpha_prod = (1 - alphas_cumprod[timesteps]) ** 0.5
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sqrt_one_minus_alpha_prod = sqrt_one_minus_alpha_prod.flatten()
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while len(sqrt_one_minus_alpha_prod.shape) < len(original_samples.shape):
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sqrt_one_minus_alpha_prod = sqrt_one_minus_alpha_prod.unsqueeze(-1)
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noisy_samples = sqrt_alpha_prod * original_samples + sqrt_one_minus_alpha_prod * noise
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return noisy_samples
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def __len__(self):
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return self.config.num_train_timesteps # pylint: disable=no-member
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