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Log-Linear Schedule

discrete_diffusion.noise_schedules.log_linear

Log-linear noise schedule implementation.

LogLinear

Bases: NoiseSchedule

Log-linear noise schedule: alpha(t) = 1 - (1-eps)*t.

Source code in src/discrete_diffusion/noise_schedules/log_linear.py 
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class LogLinear(NoiseSchedule):
  """Log-linear noise schedule: alpha(t) = 1 - (1-eps)*t."""

  def __init__(self, eps: float = 1e-3, **kwargs):
    super().__init__()
    self.eps = float(eps)

  def alpha_t(self, t: torch.Tensor) -> torch.Tensor:
    scaled_t = (1 - self.eps) * t
    return 1 - scaled_t

  def alpha_prime_t(self, t: torch.Tensor) -> torch.Tensor:
    return -(1 - self.eps) * torch.ones_like(t)