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ioatol2
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#from: https://research.nvidia.com/labs/toronto-ai/AlignYourSteps/howto.html
import numpy as np
import torch

def loglinear_interp(t_steps, num_steps):
    """

    Performs log-linear interpolation of a given array of decreasing numbers.

    """
    xs = np.linspace(0, 1, len(t_steps))
    ys = np.log(t_steps[::-1])

    new_xs = np.linspace(0, 1, num_steps)
    new_ys = np.interp(new_xs, xs, ys)

    interped_ys = np.exp(new_ys)[::-1].copy()
    return interped_ys

NOISE_LEVELS = {"SD1": [14.6146412293, 6.4745760956,  3.8636745985,  2.6946151520, 1.8841921177,  1.3943805092,  0.9642583904,  0.6523686016, 0.3977456272,  0.1515232662,  0.0291671582],
                "SDXL":[14.6146412293, 6.3184485287,  3.7681790315,  2.1811480769, 1.3405244945,  0.8620721141,  0.5550693289,  0.3798540708, 0.2332364134,  0.1114188177,  0.0291671582],
                "SVD": [700.00, 54.5, 15.886, 7.977, 4.248, 1.789, 0.981, 0.403, 0.173, 0.034, 0.002]}

class AlignYourStepsScheduler:
    @classmethod
    def INPUT_TYPES(s):
        return {"required":
                    {"model_type": (["SD1", "SDXL", "SVD"], ),
                     "steps": ("INT", {"default": 10, "min": 10, "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_type, steps, denoise):
        total_steps = steps
        if denoise < 1.0:
            if denoise <= 0.0:
                return (torch.FloatTensor([]),)
            total_steps = round(steps * denoise)

        sigmas = NOISE_LEVELS[model_type][:]
        if (steps + 1) != len(sigmas):
            sigmas = loglinear_interp(sigmas, steps + 1)

        sigmas = sigmas[-(total_steps + 1):]
        sigmas[-1] = 0
        return (torch.FloatTensor(sigmas), )

NODE_CLASS_MAPPINGS = {
    "AlignYourStepsScheduler": AlignYourStepsScheduler,
}