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| import math |
| from dataclasses import dataclass |
| from typing import List, Optional, Tuple, Union |
|
|
| import torch |
|
|
| from ..configuration_utils import ConfigMixin, register_to_config |
| from ..utils import BaseOutput |
| from ..utils.torch_utils import randn_tensor |
| from .scheduling_utils import SchedulerMixin |
|
|
|
|
| @dataclass |
| class DDPMWuerstchenSchedulerOutput(BaseOutput): |
| """ |
| Output class for the scheduler's step function output. |
| |
| Args: |
| prev_sample (`torch.Tensor` of shape `(batch_size, num_channels, height, width)` for images): |
| Computed sample (x_{t-1}) of previous timestep. `prev_sample` should be used as next model input in the |
| denoising loop. |
| """ |
|
|
| prev_sample: torch.Tensor |
|
|
|
|
| def betas_for_alpha_bar( |
| num_diffusion_timesteps, |
| max_beta=0.999, |
| alpha_transform_type="cosine", |
| ): |
| """ |
| Create a beta schedule that discretizes the given alpha_t_bar function, which defines the cumulative product of |
| (1-beta) over time from t = [0,1]. |
| |
| Contains a function alpha_bar that takes an argument t and transforms it to the cumulative product of (1-beta) up |
| to that part of the diffusion process. |
| |
| |
| Args: |
| num_diffusion_timesteps (`int`): the number of betas to produce. |
| max_beta (`float`): the maximum beta to use; use values lower than 1 to |
| prevent singularities. |
| alpha_transform_type (`str`, *optional*, default to `cosine`): the type of noise schedule for alpha_bar. |
| Choose from `cosine` or `exp` |
| |
| Returns: |
| betas (`np.ndarray`): the betas used by the scheduler to step the model outputs |
| """ |
| if alpha_transform_type == "cosine": |
|
|
| def alpha_bar_fn(t): |
| return math.cos((t + 0.008) / 1.008 * math.pi / 2) ** 2 |
|
|
| elif alpha_transform_type == "exp": |
|
|
| def alpha_bar_fn(t): |
| return math.exp(t * -12.0) |
|
|
| else: |
| raise ValueError(f"Unsupported alpha_transform_type: {alpha_transform_type}") |
|
|
| betas = [] |
| for i in range(num_diffusion_timesteps): |
| t1 = i / num_diffusion_timesteps |
| t2 = (i + 1) / num_diffusion_timesteps |
| betas.append(min(1 - alpha_bar_fn(t2) / alpha_bar_fn(t1), max_beta)) |
| return torch.tensor(betas, dtype=torch.float32) |
|
|
|
|
| class DDPMWuerstchenScheduler(SchedulerMixin, ConfigMixin): |
| """ |
| Denoising diffusion probabilistic models (DDPMs) explores the connections between denoising score matching and |
| Langevin dynamics sampling. |
| |
| [`~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`. |
| [`SchedulerMixin`] provides general loading and saving functionality via the [`SchedulerMixin.save_pretrained`] and |
| [`~SchedulerMixin.from_pretrained`] functions. |
| |
| For more details, see the original paper: https://arxiv.org/abs/2006.11239 |
| |
| Args: |
| scaler (`float`): .... |
| s (`float`): .... |
| """ |
|
|
| @register_to_config |
| def __init__( |
| self, |
| scaler: float = 1.0, |
| s: float = 0.008, |
| ): |
| self.scaler = scaler |
| self.s = torch.tensor([s]) |
| self._init_alpha_cumprod = torch.cos(self.s / (1 + self.s) * torch.pi * 0.5) ** 2 |
|
|
| |
| self.init_noise_sigma = 1.0 |
|
|
| def _alpha_cumprod(self, t, device): |
| if self.scaler > 1: |
| t = 1 - (1 - t) ** self.scaler |
| elif self.scaler < 1: |
| t = t**self.scaler |
| alpha_cumprod = torch.cos( |
| (t + self.s.to(device)) / (1 + self.s.to(device)) * torch.pi * 0.5 |
| ) ** 2 / self._init_alpha_cumprod.to(device) |
| return alpha_cumprod.clamp(0.0001, 0.9999) |
|
|
| def scale_model_input(self, sample: torch.Tensor, timestep: Optional[int] = None) -> torch.Tensor: |
| """ |
| Ensures interchangeability with schedulers that need to scale the denoising model input depending on the |
| current timestep. |
| |
| Args: |
| sample (`torch.Tensor`): input sample |
| timestep (`int`, optional): current timestep |
| |
| Returns: |
| `torch.Tensor`: scaled input sample |
| """ |
| return sample |
|
|
| def set_timesteps( |
| self, |
| num_inference_steps: int = None, |
| timesteps: Optional[List[int]] = None, |
| device: Union[str, torch.device] = None, |
| ): |
| """ |
| Sets the discrete timesteps used for the diffusion chain. Supporting function to be run before inference. |
| |
| Args: |
| num_inference_steps (`Dict[float, int]`): |
| the number of diffusion steps used when generating samples with a pre-trained model. If passed, then |
| `timesteps` must be `None`. |
| device (`str` or `torch.device`, optional): |
| the device to which the timesteps are moved to. {2 / 3: 20, 0.0: 10} |
| """ |
| if timesteps is None: |
| timesteps = torch.linspace(1.0, 0.0, num_inference_steps + 1, device=device) |
| if not isinstance(timesteps, torch.Tensor): |
| timesteps = torch.Tensor(timesteps).to(device) |
| self.timesteps = timesteps |
|
|
| def step( |
| self, |
| model_output: torch.Tensor, |
| timestep: int, |
| sample: torch.Tensor, |
| generator=None, |
| return_dict: bool = True, |
| ) -> Union[DDPMWuerstchenSchedulerOutput, 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 (`torch.Tensor`): direct output from learned diffusion model. |
| timestep (`int`): current discrete timestep in the diffusion chain. |
| sample (`torch.Tensor`): |
| current instance of sample being created by diffusion process. |
| generator: random number generator. |
| return_dict (`bool`): option for returning tuple rather than DDPMWuerstchenSchedulerOutput class |
| |
| Returns: |
| [`DDPMWuerstchenSchedulerOutput`] or `tuple`: [`DDPMWuerstchenSchedulerOutput`] if `return_dict` is True, |
| otherwise a `tuple`. When returning a tuple, the first element is the sample tensor. |
| |
| """ |
| dtype = model_output.dtype |
| device = model_output.device |
| t = timestep |
|
|
| prev_t = self.previous_timestep(t) |
|
|
| alpha_cumprod = self._alpha_cumprod(t, device).view(t.size(0), *[1 for _ in sample.shape[1:]]) |
| alpha_cumprod_prev = self._alpha_cumprod(prev_t, device).view(prev_t.size(0), *[1 for _ in sample.shape[1:]]) |
| alpha = alpha_cumprod / alpha_cumprod_prev |
|
|
| mu = (1.0 / alpha).sqrt() * (sample - (1 - alpha) * model_output / (1 - alpha_cumprod).sqrt()) |
|
|
| std_noise = randn_tensor(mu.shape, generator=generator, device=model_output.device, dtype=model_output.dtype) |
| std = ((1 - alpha) * (1.0 - alpha_cumprod_prev) / (1.0 - alpha_cumprod)).sqrt() * std_noise |
| pred = mu + std * (prev_t != 0).float().view(prev_t.size(0), *[1 for _ in sample.shape[1:]]) |
|
|
| if not return_dict: |
| return (pred.to(dtype),) |
|
|
| return DDPMWuerstchenSchedulerOutput(prev_sample=pred.to(dtype)) |
|
|
| def add_noise( |
| self, |
| original_samples: torch.Tensor, |
| noise: torch.Tensor, |
| timesteps: torch.Tensor, |
| ) -> torch.Tensor: |
| device = original_samples.device |
| dtype = original_samples.dtype |
| alpha_cumprod = self._alpha_cumprod(timesteps, device=device).view( |
| timesteps.size(0), *[1 for _ in original_samples.shape[1:]] |
| ) |
| noisy_samples = alpha_cumprod.sqrt() * original_samples + (1 - alpha_cumprod).sqrt() * noise |
| return noisy_samples.to(dtype=dtype) |
|
|
| def __len__(self): |
| return self.config.num_train_timesteps |
|
|
| def previous_timestep(self, timestep): |
| index = (self.timesteps - timestep[0]).abs().argmin().item() |
| prev_t = self.timesteps[index + 1][None].expand(timestep.shape[0]) |
| return prev_t |
|
|
