PolyexponentialScheduler

Documentation

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

The PolyexponentialScheduler node is designed to generate a sequence of sigma values based on a polyexponential function. These sigma values are used to control the noise level at each step of a diffusion process, allowing for fine-tuned adjustments to the sampling behavior.

Input types

Required

• steps
  • Specifies the number of steps for which sigma values are to be generated, affecting the granularity of the diffusion process.
  • Comfy dtype: INT
  • Python dtype: int
• sigma_max
  • The maximum sigma value, setting the upper limit of noise to be introduced in the early stages of the diffusion process.
  • Comfy dtype: FLOAT
  • Python dtype: float
• sigma_min
  • The minimum sigma value, determining the lower limit of noise for the final stages, ensuring the process gradually refines the generated output.
  • Comfy dtype: FLOAT
  • Python dtype: float
• rho
  • A parameter influencing the shape of the polyexponential curve, thereby affecting the distribution of sigma values across the steps.
  • Comfy dtype: FLOAT
  • Python dtype: float

Output types

• sigmas
  • Comfy dtype: SIGMAS
  • A sequence of sigma values calculated based on the polyexponential function, tailored for each step of the diffusion process.
  • Python dtype: tuple

Usage tips

• Infra type: CPU
• Common nodes: unknown

Source code

class PolyexponentialScheduler:
    @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}),
                     "rho": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 100.0, "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, rho):
        sigmas = k_diffusion_sampling.get_sigmas_polyexponential(n=steps, sigma_min=sigma_min, sigma_max=sigma_max, rho=rho)
        return (sigmas, )