SamplerLCMDualNoise

Documentation

• Class name: SamplerLCMDualNoise
• Category: sampling/custom_sampling/samplers
• Output node: False

The SamplerLCMDualNoise node provides a mechanism for generating samples using a dual noise approach within a custom sampling framework. It leverages a combination of weights, normalization steps, and the option to reuse or parallelize noise generation to enhance the sampling process.

Input types

Required

• weight
  • Specifies the blending weight between two noise-induced samples, influencing the balance and variation in the sampling output.
  • Comfy dtype: FLOAT
  • Python dtype: float
• normalize_steps
  • Determines the number of normalization steps to apply, affecting the smoothness and quality of the generated samples.
  • Comfy dtype: INT
  • Python dtype: int
• reuse_lcm_noise
  • Controls whether the same noise is reused across sampling steps, impacting the diversity and consistency of samples.
  • Comfy dtype: BOOLEAN
  • Python dtype: bool
• parallel
  • Enables parallel processing of noise generation, potentially speeding up the sampling process.
  • Comfy dtype: BOOLEAN
  • Python dtype: bool

Output types

• sampler
  • Comfy dtype: SAMPLER
  • Produces a sampler configured with dual noise characteristics for generating samples.
  • Python dtype: comfy.samplers.KSAMPLER

Usage tips

• Infra type: CPU
• Common nodes: unknown

Source code

class SamplerLCMDualNoise:
    @classmethod
    def INPUT_TYPES(cls):
        return {
            "required": {
                "weight": ("FLOAT", {"default": 0.8, "min": 0.0, "max": 1.0, "step": 0.001, "round": False}),
                "normalize_steps": ("INT", {"default": 0, "min": 0, "max": 50, "step": 1}),
                "reuse_lcm_noise": ("BOOLEAN", {"default": False}),
                "parallel": ("BOOLEAN", {"default": False}),
            }
        }

    RETURN_TYPES = ("SAMPLER",)
    CATEGORY = "sampling/custom_sampling/samplers"
    FUNCTION = "get_sampler"

    def get_sampler(self, weight, normalize_steps, reuse_lcm_noise, parallel):
        return (comfy.samplers.KSAMPLER(sample_lcm_dual_noise, extra_options={"weight": weight, "normalize_steps": normalize_steps, "reuse_lcm_noise": reuse_lcm_noise, "parallel": parallel}),)