KSamplerAdvancedProvider

Documentation

• Class name: KSamplerAdvancedProvider
• Category: ImpactPack/Sampler
• Output node: False

This node provides an advanced KSampler configuration, enabling the customization of sampling processes with additional parameters and options for more complex and tailored sampling strategies.

Input types

Required

• cfg
  • Specifies the configuration value for the sampler, influencing its behavior and performance characteristics.
  • Comfy dtype: FLOAT
  • Python dtype: float
• sampler_name
  • Determines the specific sampler to be used, chosen from a predefined set of samplers.
  • Comfy dtype: COMBO[STRING]
  • Python dtype: str
• scheduler
  • Selects the scheduling algorithm to manage the sampling process, affecting the progression and adjustment of sampling parameters.
  • Comfy dtype: COMBO[STRING]
  • Python dtype: str
• sigma_factor
  • Adjusts the sigma factor, modifying the noise level applied during the sampling process for fine-tuning purposes.
  • Comfy dtype: FLOAT
  • Python dtype: float
• basic_pipe
  • Provides the basic pipeline components necessary for the sampling operation, including the model and conditioning information.
  • Comfy dtype: BASIC_PIPE
  • Python dtype: tuple

Optional

• sampler_opt
  • Optional sampler configurations, allowing for further customization of the sampling process.
  • Comfy dtype: SAMPLER
  • Python dtype: dict

Output types

• ksampler_advanced
  • Comfy dtype: KSAMPLER_ADVANCED
  • An advanced KSampler instance configured with the specified parameters for complex sampling tasks.
  • Python dtype: KSamplerAdvancedWrapper

Usage tips

• Infra type: CPU
• Common nodes: unknown

Source code

class KSamplerAdvancedProvider:
    @classmethod
    def INPUT_TYPES(s):
        return {"required": {
                                "cfg": ("FLOAT", {"default": 8.0, "min": 0.0, "max": 100.0}),
                                "sampler_name": (comfy.samplers.KSampler.SAMPLERS, ),
                                "scheduler": (core.SCHEDULERS, ),
                                "sigma_factor": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 10.0, "step": 0.01}),
                                "basic_pipe": ("BASIC_PIPE", )
                             },
                "optional": {
                                "sampler_opt": ("SAMPLER", )
                            }
                }

    RETURN_TYPES = ("KSAMPLER_ADVANCED",)
    FUNCTION = "doit"

    CATEGORY = "ImpactPack/Sampler"

    def doit(self, cfg, sampler_name, scheduler, basic_pipe, sigma_factor=1.0, sampler_opt=None):
        model, _, _, positive, negative = basic_pipe
        sampler = KSamplerAdvancedWrapper(model, cfg, sampler_name, scheduler, positive, negative, sampler_opt=sampler_opt, sigma_factor=sigma_factor)
        return (sampler, )