138 lines
4.9 KiB
Python
138 lines
4.9 KiB
Python
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 diffusers.configuration_utils import ConfigMixin, register_to_config
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from diffusers.utils import BaseOutput
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from diffusers.utils.torch_utils import randn_tensor
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from diffusers.schedulers.scheduling_utils import SchedulerMixin
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@dataclass
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class DDPMSchedulerOutput(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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class DDPMCosineScheduler(SchedulerMixin, ConfigMixin):
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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[DDPMSchedulerOutput, Tuple]:
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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 DDPMSchedulerOutput(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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