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import numpy as np |
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import torch |
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def loglinear_interp(t_steps, num_steps): |
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""" |
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Performs log-linear interpolation of a given array of decreasing numbers. |
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""" |
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xs = np.linspace(0, 1, len(t_steps)) |
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ys = np.log(t_steps[::-1]) |
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new_xs = np.linspace(0, 1, num_steps) |
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new_ys = np.interp(new_xs, xs, ys) |
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interped_ys = np.exp(new_ys)[::-1].copy() |
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return interped_ys |
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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], |
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"SDXL":[14.6146412293, 6.3184485287, 3.7681790315, 2.1811480769, 1.3405244945, 0.8620721141, 0.5550693289, 0.3798540708, 0.2332364134, 0.1114188177, 0.0291671582], |
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"SVD": [700.00, 54.5, 15.886, 7.977, 4.248, 1.789, 0.981, 0.403, 0.173, 0.034, 0.002]} |
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class AlignYourStepsScheduler: |
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@classmethod |
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def INPUT_TYPES(s): |
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return {"required": |
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{"model_type": (["SD1", "SDXL", "SVD"], ), |
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"steps": ("INT", {"default": 10, "min": 10, "max": 10000}), |
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"denoise": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 1.0, "step": 0.01}), |
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} |
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} |
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RETURN_TYPES = ("SIGMAS",) |
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CATEGORY = "sampling/custom_sampling/schedulers" |
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FUNCTION = "get_sigmas" |
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def get_sigmas(self, model_type, steps, denoise): |
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total_steps = steps |
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if denoise < 1.0: |
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if denoise <= 0.0: |
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return (torch.FloatTensor([]),) |
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total_steps = round(steps * denoise) |
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sigmas = NOISE_LEVELS[model_type][:] |
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if (steps + 1) != len(sigmas): |
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sigmas = loglinear_interp(sigmas, steps + 1) |
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sigmas = sigmas[-(total_steps + 1):] |
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sigmas[-1] = 0 |
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return (torch.FloatTensor(sigmas), ) |
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NODE_CLASS_MAPPINGS = { |
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"AlignYourStepsScheduler": AlignYourStepsScheduler, |
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} |
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