231 lines
8.7 KiB
Python
231 lines
8.7 KiB
Python
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# Copyright (c) 2022 Pablo Pernías MIT License
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# Copyright 2025 UC Berkeley 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 file is strongly influenced by https://github.com/ermongroup/ddim
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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 torch
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from ..configuration_utils import ConfigMixin, register_to_config
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from ..utils import BaseOutput
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from ..utils.torch_utils import randn_tensor
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from .scheduling_utils import SchedulerMixin
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@dataclass
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class DDPMWuerstchenSchedulerOutput(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.Tensor` 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: torch.Tensor
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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_transform_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 DDPMWuerstchenScheduler(SchedulerMixin, ConfigMixin):
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"""
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Denoising diffusion probabilistic models (DDPMs) explores the connections between denoising score matching and
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Langevin dynamics sampling.
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[`~ConfigMixin`] takes care of storing all config attributes that are passed in the scheduler's `__init__`
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function, such as `num_train_timesteps`. They can be accessed via `scheduler.config.num_train_timesteps`.
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[`SchedulerMixin`] provides general loading and saving functionality via the [`SchedulerMixin.save_pretrained`] and
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[`~SchedulerMixin.from_pretrained`] functions.
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For more details, see the original paper: https://huggingface.co/papers/2006.11239
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Args:
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scaler (`float`): ....
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s (`float`): ....
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"""
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@register_to_config
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def __init__(
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self,
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scaler: float = 1.0,
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s: float = 0.008,
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):
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self.scaler = scaler
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self.s = torch.tensor([s])
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self._init_alpha_cumprod = torch.cos(self.s / (1 + self.s) * torch.pi * 0.5) ** 2
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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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def _alpha_cumprod(self, t, device):
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if self.scaler > 1:
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t = 1 - (1 - t) ** self.scaler
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elif self.scaler < 1:
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t = t**self.scaler
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alpha_cumprod = torch.cos(
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(t + self.s.to(device)) / (1 + self.s.to(device)) * torch.pi * 0.5
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) ** 2 / self._init_alpha_cumprod.to(device)
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return alpha_cumprod.clamp(0.0001, 0.9999)
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def scale_model_input(self, sample: torch.Tensor, timestep: Optional[int] = None) -> 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`): input sample
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timestep (`int`, optional): current timestep
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Returns:
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`torch.Tensor`: scaled input sample
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"""
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return sample
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def set_timesteps(
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self,
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num_inference_steps: int = None,
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timesteps: Optional[List[int]] = None,
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device: Union[str, torch.device] = None,
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):
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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 (`Dict[float, int]`):
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the number of diffusion steps used when generating samples with a pre-trained model. If passed, then
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`timesteps` must be `None`.
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device (`str` or `torch.device`, optional):
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the device to which the timesteps are moved to. {2 / 3: 20, 0.0: 10}
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"""
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if timesteps is None:
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timesteps = torch.linspace(1.0, 0.0, num_inference_steps + 1, device=device)
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if not isinstance(timesteps, torch.Tensor):
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timesteps = torch.Tensor(timesteps).to(device)
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self.timesteps = timesteps
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def step(
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self,
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model_output: torch.Tensor,
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timestep: int,
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sample: torch.Tensor,
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generator=None,
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return_dict: bool = True,
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) -> Union[DDPMWuerstchenSchedulerOutput, Tuple]:
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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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Args:
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model_output (`torch.Tensor`): direct output from learned diffusion model.
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timestep (`int`): current discrete timestep in the diffusion chain.
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sample (`torch.Tensor`):
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current instance of sample being created by diffusion process.
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generator: random number generator.
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return_dict (`bool`): option for returning tuple rather than DDPMWuerstchenSchedulerOutput class
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Returns:
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[`DDPMWuerstchenSchedulerOutput`] or `tuple`: [`DDPMWuerstchenSchedulerOutput`] if `return_dict` is True,
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otherwise a `tuple`. When returning a tuple, the first element is the sample tensor.
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"""
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dtype = model_output.dtype
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device = model_output.device
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t = timestep
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prev_t = self.previous_timestep(t)
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alpha_cumprod = self._alpha_cumprod(t, device).view(t.size(0), *[1 for _ in sample.shape[1:]])
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alpha_cumprod_prev = self._alpha_cumprod(prev_t, device).view(prev_t.size(0), *[1 for _ in sample.shape[1:]])
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alpha = alpha_cumprod / alpha_cumprod_prev
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mu = (1.0 / alpha).sqrt() * (sample - (1 - alpha) * model_output / (1 - alpha_cumprod).sqrt())
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std_noise = randn_tensor(mu.shape, generator=generator, device=model_output.device, dtype=model_output.dtype)
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std = ((1 - alpha) * (1.0 - alpha_cumprod_prev) / (1.0 - alpha_cumprod)).sqrt() * std_noise
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pred = mu + std * (prev_t != 0).float().view(prev_t.size(0), *[1 for _ in sample.shape[1:]])
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if not return_dict:
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return (pred.to(dtype),)
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return DDPMWuerstchenSchedulerOutput(prev_sample=pred.to(dtype))
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def add_noise(
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self,
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original_samples: torch.Tensor,
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noise: torch.Tensor,
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timesteps: torch.Tensor,
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) -> torch.Tensor:
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device = original_samples.device
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dtype = original_samples.dtype
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alpha_cumprod = self._alpha_cumprod(timesteps, device=device).view(
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timesteps.size(0), *[1 for _ in original_samples.shape[1:]]
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)
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noisy_samples = alpha_cumprod.sqrt() * original_samples + (1 - alpha_cumprod).sqrt() * noise
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return noisy_samples.to(dtype=dtype)
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def __len__(self):
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return self.config.num_train_timesteps
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def previous_timestep(self, timestep):
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index = (self.timesteps - timestep[0]).abs().argmin().item()
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prev_t = self.timesteps[index + 1][None].expand(timestep.shape[0])
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return prev_t
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