SamplerLMS

Documentation

Class name: SamplerLMS
Category: sampling/custom_sampling/samplers
Output node: False

The SamplerLMS node provides a mechanism to generate a sampler based on the LMS (Least Mean Squares) algorithm, allowing for the customization of the sampling process through the adjustment of the order parameter. This node is designed to facilitate the creation of samplers that utilize the LMS method for applications within custom sampling strategies.

Input types

Required

order
- The 'order' parameter specifies the order of the LMS algorithm used in the sampling process. Adjusting this parameter allows for the tuning of the sampler's behavior, impacting the quality and characteristics of the generated samples.
- Comfy dtype: INT
- Python dtype: int

Output types

sampler
- Comfy dtype: SAMPLER
- This output represents a sampler configured according to the LMS algorithm and the specified order parameter. It is ready to be used in sampling tasks within the framework.
- Python dtype: comfy.samplers.KSampler

Usage tips

Infra type: CPU
Common nodes: unknown

Source code

class SamplerLMS:
    @classmethod
    def INPUT_TYPES(s):
        return {"required":
                    {"order": ("INT", {"default": 4, "min": 1, "max": 100}),
                      }
               }
    RETURN_TYPES = ("SAMPLER",)
    CATEGORY = "sampling/custom_sampling/samplers"

    FUNCTION = "get_sampler"

    def get_sampler(self, order):
        sampler = comfy.samplers.ksampler("lms", {"order": order})
        return (sampler, )

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