ExponentialScheduler

Documentation

• Class name: ExponentialScheduler
• Category: sampling/custom_sampling/schedulers
• Output node: False

The ExponentialScheduler node is designed to generate a sequence of sigma values following an exponential schedule for diffusion processes. It calculates these values based on the number of steps and the specified minimum and maximum sigma values, providing a foundational mechanism for controlling the noise level across the diffusion steps.

Input types

Required

• steps
  • Specifies the total number of steps for the diffusion process, directly influencing the length of the generated sigma sequence.
  • Comfy dtype: INT
  • Python dtype: int
• sigma_max
  • Defines the maximum sigma value, setting the upper limit of noise to be introduced at the beginning of the diffusion process.
  • Comfy dtype: FLOAT
  • Python dtype: float
• sigma_min
  • Determines the minimum sigma value, establishing the lower noise limit towards the end of the diffusion process.
  • Comfy dtype: FLOAT
  • Python dtype: float

Output types

• sigmas
  • Comfy dtype: SIGMAS
  • A sequence of sigma values calculated based on an exponential schedule, used to control the noise level in each step of the diffusion process.
  • Python dtype: Tuple[float]

Usage tips

• Infra type: CPU
• Common nodes: unknown

Source code

class ExponentialScheduler:
    @classmethod
    def INPUT_TYPES(s):
        return {"required":
                    {"steps": ("INT", {"default": 20, "min": 1, "max": 10000}),
                     "sigma_max": ("FLOAT", {"default": 14.614642, "min": 0.0, "max": 5000.0, "step":0.01, "round": False}),
                     "sigma_min": ("FLOAT", {"default": 0.0291675, "min": 0.0, "max": 5000.0, "step":0.01, "round": False}),
                    }
               }
    RETURN_TYPES = ("SIGMAS",)
    CATEGORY = "sampling/custom_sampling/schedulers"

    FUNCTION = "get_sigmas"

    def get_sigmas(self, steps, sigma_max, sigma_min):
        sigmas = k_diffusion_sampling.get_sigmas_exponential(n=steps, sigma_min=sigma_min, sigma_max=sigma_max)
        return (sigmas, )