KSamplerAdvancedProvider

Documentation

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

The KSamplerAdvancedProvider node is designed to offer advanced sampling capabilities within a generative modeling framework. It likely extends or enhances the functionality of basic sampling by providing more sophisticated methods or options for sampling, tailored to specific needs or higher complexity scenarios.

Input types

Required

• cfg
  • Specifies the classifier free guidance value, which influences the direction and strength of the generative model's output, guiding it towards desired characteristics.
  • Comfy dtype: FLOAT
  • Python dtype: float
• sampler_name
  • Determines the specific sampling algorithm to be used, allowing for customization of the sampling process based on the model's requirements or desired outcomes.
  • Comfy dtype: COMBO[STRING]
  • Python dtype: str
• scheduler
  • Defines the noise schedule for the sampling process, impacting how noise is added or removed during generation to control the quality and characteristics of the output.
  • Comfy dtype: COMBO[STRING]
  • Python dtype: str
• sigma_factor
  • A multiplier for the noise schedule, adjusting the level of noise applied in the sampling process, thereby fine-tuning the output's fidelity or diversity.
  • Comfy dtype: FLOAT
  • Python dtype: float
• basic_pipe
  • Represents the basic pipeline input for sampling, serving as the foundational data or model configuration upon which the sampling process operates.
  • Comfy dtype: BASIC_PIPE
  • Python dtype: str

Optional

• sampler_opt
  • [OPTIONAL] Allows for the use of an externally defined sampler instead of the node's internal sampler, offering flexibility in the sampling approach.
  • Comfy dtype: SAMPLER
  • Python dtype: str
• scheduler_func_opt
  • [OPTIONAL] Specifies a custom function for generating the noise schedule, providing an alternative to the predefined scheduler options for more tailored sampling dynamics.
  • Comfy dtype: SCHEDULER_FUNC
  • Python dtype: str

Output types

• ksampler_advanced
  • Comfy dtype: KSAMPLER_ADVANCED
  • Produces an advanced sampler configuration, capable of being utilized in complex generative tasks that require nuanced control over the sampling process.
  • Python dtype: str

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", ),
                                "scheduler_func_opt": ("SCHEDULER_FUNC",),
                            }
                }

    TOOLTIPS = {
        "input": {
            "cfg": "classifier free guidance value",
            "sampler_name": "sampler",
            "scheduler": "noise schedule",
            "sigma_factor": "Multiplier of noise schedule",
            "basic_pipe": "basic_pipe input for sampling",
            "sampler_opt": "[OPTIONAL] Uses the passed sampler instead of internal impact_sampler.",
            "scheduler_func_opt": "[OPTIONAL] Noise schedule generation function. If this is set, the scheduler widget will be ignored.",
        },
        "output": ("sampler wrapper. (Can be used when generating a regional_prompt.)", )
    }

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

    CATEGORY = "ImpactPack/Sampler"

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