| import math |
| from dataclasses import dataclass |
| from typing import List, Optional, Tuple, Union |
|
|
| import torch |
|
|
| from diffusers.configuration_utils import ConfigMixin, register_to_config |
| from diffusers.utils import BaseOutput |
| from diffusers.utils.torch_utils import randn_tensor |
| from diffusers.schedulers.scheduling_utils import SchedulerMixin |
|
|
|
|
| @dataclass |
| class DDPMSchedulerOutput(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 |
|
|
|
|
| class DDPMCosineScheduler(SchedulerMixin, ConfigMixin): |
|
|
| @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[DDPMSchedulerOutput, Tuple]: |
| 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 DDPMSchedulerOutput(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 |
|
|
