BasicScheduler

Documentation

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

The BasicScheduler node is designed to calculate and adjust the sigma values for a given model and scheduler over a specified number of steps, incorporating an optional denoise parameter to refine the process. It serves as a foundational element in custom sampling strategies, enabling precise control over the diffusion process in generative models.

Input types

Required

• model
  • Specifies the model for which sigma values are to be calculated, playing a crucial role in determining the diffusion process's behavior.
  • Comfy dtype: MODEL
  • Python dtype: comfy.model_management.Model
• scheduler
  • Defines the scheduler to be used for calculating sigma values, directly influencing the diffusion steps and their granularity.
  • Comfy dtype: COMBO[STRING]
  • Python dtype: str
• steps
  • Determines the number of diffusion steps to be used, affecting the resolution and granularity of the sigma values.
  • Comfy dtype: INT
  • Python dtype: int
• denoise
  • Optional parameter to adjust the effective number of steps based on denoising level, allowing for finer control over the diffusion process.
  • Comfy dtype: FLOAT
  • Python dtype: float

Output types

• sigmas
  • Comfy dtype: SIGMAS
  • The calculated sigma values for the specified model and scheduler, essential for controlling the diffusion process.
  • Python dtype: torch.FloatTensor

Usage tips

• Infra type: CPU
• Common nodes:
  • SamplerCustom
  • SplitSigmas
  • Reroute

Source code

class BasicScheduler:
    @classmethod
    def INPUT_TYPES(s):
        return {"required":
                    {"model": ("MODEL",),
                     "scheduler": (comfy.samplers.SCHEDULER_NAMES, ),
                     "steps": ("INT", {"default": 20, "min": 1, "max": 10000}),
                     "denoise": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 1.0, "step": 0.01}),
                      }
               }
    RETURN_TYPES = ("SIGMAS",)
    CATEGORY = "sampling/custom_sampling/schedulers"

    FUNCTION = "get_sigmas"

    def get_sigmas(self, model, scheduler, steps, denoise):
        total_steps = steps
        if denoise < 1.0:
            if denoise <= 0.0:
                return (torch.FloatTensor([]),)
            total_steps = int(steps/denoise)

        sigmas = comfy.samplers.calculate_sigmas(model.get_model_object("model_sampling"), scheduler, total_steps).cpu()
        sigmas = sigmas[-(steps + 1):]
        return (sigmas, )