Create Raw Sigma Schedule 🎭🅐🅓

Documentation

• Class name: ADE_RawSigmaSchedule
• Category: Animate Diff 🎭🅐🅓/sample settings/sigma schedule
• Output node: False

This node is designed to generate a sigma schedule based on a given beta schedule. It translates the beta schedule into a sigma schedule, which is essential for controlling the noise levels in diffusion models, particularly in the context of animation and image synthesis.

Input types

Required

• raw_beta_schedule
  • Specifies the beta schedule to be converted into a sigma schedule. This parameter is crucial for determining the progression and intensity of noise levels throughout the diffusion process.
  • Comfy dtype: COMBO[STRING]
  • Python dtype: str
• linear_start
  • Defines the starting point of the linear interpolation used in the sigma schedule generation. It affects the initial noise level in the diffusion process.
  • Comfy dtype: FLOAT
  • Python dtype: float
• linear_end
  • Sets the endpoint for the linear interpolation in the sigma schedule, influencing the final noise level in the diffusion process.
  • Comfy dtype: FLOAT
  • Python dtype: float
• sampling
  • Determines the sampling method to be used in the sigma schedule, affecting how the noise levels are interpolated and applied. The type 'ModelSamplingType' should be understood as a placeholder for the actual sampling method chosen.
  • Comfy dtype: COMBO[STRING]
  • Python dtype: str
• lcm_original_timesteps
  • Specifies the number of original timesteps to be considered when using LCM sampling, impacting the granularity of the sigma schedule.
  • Comfy dtype: INT
  • Python dtype: int
• lcm_zsnr
  • A boolean flag indicating whether to apply Zero-SNR (zSNR) adjustments to the sigma schedule, enhancing the schedule's compatibility with certain diffusion processes.
  • Comfy dtype: BOOLEAN
  • Python dtype: bool

Output types

• sigma_schedule
  • Comfy dtype: SIGMA_SCHEDULE
  • The generated sigma schedule, which outlines the specific noise levels to be applied at each step of the diffusion process.
  • Python dtype: SigmaSchedule

Usage tips

• Infra type: CPU
• Common nodes: unknown

Source code

class RawSigmaScheduleNode:
    @classmethod
    def INPUT_TYPES(s):
        return {
            "required": {
                "raw_beta_schedule": (BetaSchedules.RAW_BETA_SCHEDULE_LIST,),
                "linear_start": ("FLOAT", {"default": 0.00085, "min": 0.0, "max": 1.0, "step": 0.000001}),
                "linear_end": ("FLOAT", {"default": 0.012, "min": 0.0, "max": 1.0, "step": 0.000001}),
                #"cosine_s": ("FLOAT", {"default": 8e-3, "min": 0.0, "max": 1.0, "step": 0.000001}),
                "sampling": (ModelSamplingType._FULL_LIST,),
                "lcm_original_timesteps": ("INT", {"default": 50, "min": 1, "max": 1000}),
                "lcm_zsnr": ("BOOLEAN", {"default": False}),
            }
        }

    RETURN_TYPES = ("SIGMA_SCHEDULE",)
    CATEGORY = "Animate Diff 🎭🅐🅓/sample settings/sigma schedule"
    FUNCTION = "get_sigma_schedule"

    def get_sigma_schedule(self, raw_beta_schedule: str, linear_start: float, linear_end: float,# cosine_s: float,
                           sampling: str, lcm_original_timesteps: int, lcm_zsnr: bool):
        new_config = ModelSamplingConfig(beta_schedule=raw_beta_schedule, linear_start=linear_start, linear_end=linear_end)
        if sampling != ModelSamplingType.LCM:
            lcm_original_timesteps=None
            lcm_zsnr=False
        model_type = ModelSamplingType.from_alias(sampling)    
        new_model_sampling = BetaSchedules._to_model_sampling(alias=BetaSchedules.AUTOSELECT, model_type=model_type, config_override=new_config, original_timesteps=lcm_original_timesteps)
        if lcm_zsnr:
            SigmaSchedule.apply_zsnr(new_model_sampling=new_model_sampling)
        return (SigmaSchedule(model_sampling=new_model_sampling, model_type=model_type),)