|
|
| 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,
|
| }
|
|
|
