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schedulers
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Haoming
parent
361e7da0b7
commit
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@ -1,7 +1,9 @@
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import dataclasses
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import torch
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from math import atan, pi
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import k_diffusion
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import numpy as np
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import torch
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from scipy import stats
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from modules import shared
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@ -33,10 +35,7 @@ def uniform(n, sigma_min, sigma_max, inner_model, device):
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def sgm_uniform(n, sigma_min, sigma_max, inner_model, device):
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start = inner_model.sigma_to_t(torch.tensor(sigma_max))
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end = inner_model.sigma_to_t(torch.tensor(sigma_min))
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sigs = [
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inner_model.t_to_sigma(ts)
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for ts in torch.linspace(start, end, n + 1)[:-1]
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]
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sigs = [inner_model.t_to_sigma(ts) for ts in torch.linspace(start, end, n + 1)[:-1]]
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sigs += [0.0]
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return torch.FloatTensor(sigs).to(device)
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@ -122,7 +121,7 @@ def beta_scheduler(n, sigma_min, sigma_max, inner_model, device):
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beta = shared.opts.beta_dist_beta
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timesteps = 1 - np.linspace(0, 1, n)
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timesteps = [stats.beta.ppf(x, alpha, beta) for x in timesteps]
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sigmas = [sigma_min + (x * (sigma_max-sigma_min)) for x in timesteps]
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sigmas = [sigma_min + (x * (sigma_max - sigma_min)) for x in timesteps]
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sigmas += [0.0]
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return torch.FloatTensor(sigmas).to(device)
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@ -134,97 +133,60 @@ def turbo_scheduler(n, sigma_min, sigma_max, inner_model, device):
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sigmas = torch.cat([sigmas, sigmas.new_zeros([1])])
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return sigmas.to(device)
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def get_align_your_steps_sigmas_GITS(n, sigma_min, sigma_max, device):
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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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def get_bong_tangent_sigmas(steps, slope, pivot, start, end):
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smax = ((2 / pi) * atan(-slope * (0 - pivot)) + 1) / 2
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smin = ((2 / pi) * atan(-slope * ((steps - 1) - pivot)) + 1) / 2
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interped_ys = np.exp(new_ys)[::-1].copy()
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return interped_ys
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srange = smax - smin
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sscale = start - end
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if shared.sd_model.is_sdxl:
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sigmas = [14.615, 4.734, 2.567, 1.529, 0.987, 0.652, 0.418, 0.268, 0.179, 0.127, 0.029]
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else:
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sigmas = [14.615, 4.617, 2.507, 1.236, 0.702, 0.402, 0.240, 0.156, 0.104, 0.094, 0.029]
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sigmas = [((((2 / pi) * atan(-slope * (x - pivot)) + 1) / 2) - smin) * (1 / srange) * sscale + end for x in range(steps)]
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if n != len(sigmas):
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sigmas = np.append(loglinear_interp(sigmas, n), [0.0])
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else:
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sigmas.append(0.0)
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return sigmas
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return torch.FloatTensor(sigmas).to(device)
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def ays_11_sigmas(n, sigma_min, sigma_max, device='cpu'):
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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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def bong_tangent_scheduler(n, sigma_min, sigma_max, device, *, start=1.0, middle=0.5, end=0.0, pivot_1=0.6, pivot_2=0.6, slope_1=0.2, slope_2=0.2, pad=False):
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"""https://github.com/ClownsharkBatwing/RES4LYF/blob/main/sigmas.py#L4076"""
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n += 2
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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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midpoint = int((n * pivot_1 + n * pivot_2) / 2)
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pivot_1 = int(n * pivot_1)
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pivot_2 = int(n * pivot_2)
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interped_ys = np.exp(new_ys)[::-1].copy()
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return interped_ys
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slope_1 = slope_1 / (n / 40)
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slope_2 = slope_2 / (n / 40)
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if shared.sd_model.is_sdxl:
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sigmas = [14.615, 6.315, 3.771, 2.181, 1.342, 0.862, 0.555, 0.380, 0.234, 0.113, 0.029]
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else:
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sigmas = [14.615, 6.475, 3.861, 2.697, 1.886, 1.396, 0.963, 0.652, 0.399, 0.152, 0.029]
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stage_2_len = n - midpoint
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stage_1_len = n - stage_2_len
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if n != len(sigmas):
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sigmas = np.append(loglinear_interp(sigmas, n), [0.0])
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else:
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sigmas.append(0.0)
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tan_sigmas_1 = get_bong_tangent_sigmas(stage_1_len, slope_1, pivot_1, start, middle)
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tan_sigmas_2 = get_bong_tangent_sigmas(stage_2_len, slope_2, pivot_2 - stage_1_len, middle, end)
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return torch.FloatTensor(sigmas).to(device)
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tan_sigmas_1 = tan_sigmas_1[:-1]
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if pad:
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tan_sigmas_2 = tan_sigmas_2 + [0]
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def ays_32_sigmas(n, sigma_min, sigma_max, device='cpu'):
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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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if shared.sd_model.is_sdxl:
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sigmas = [14.61500000000000000, 11.14916180000000000, 8.505221270000000000, 6.488271510000000000, 5.437074020000000000, 4.603986190000000000, 3.898547040000000000, 3.274074570000000000, 2.743965270000000000, 2.299686590000000000, 1.954485140000000000, 1.671087150000000000, 1.428781520000000000, 1.231810090000000000, 1.067896490000000000, 0.925794430000000000, 0.802908860000000000, 0.696601210000000000, 0.604369030000000000, 0.528525520000000000, 0.467733440000000000, 0.413933790000000000, 0.362581860000000000, 0.310085170000000000, 0.265189250000000000, 0.223264610000000000, 0.176538770000000000, 0.139591920000000000, 0.105873810000000000, 0.055193690000000000, 0.028773340000000000, 0.015000000000000000]
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else:
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sigmas = [14.61500000000000000, 11.23951352000000000, 8.643630810000000000, 6.647294240000000000, 5.572508620000000000, 4.716485460000000000, 3.991960650000000000, 3.519560900000000000, 3.134904660000000000, 2.792287880000000000, 2.487736280000000000, 2.216638650000000000, 1.975083510000000000, 1.779317200000000000, 1.614753350000000000, 1.465409530000000000, 1.314849000000000000, 1.166424970000000000, 1.034755470000000000, 0.915737440000000000, 0.807481690000000000, 0.712023610000000000, 0.621739000000000000, 0.530652020000000000, 0.452909600000000000, 0.374914550000000000, 0.274618190000000000, 0.201152900000000000, 0.141058730000000000, 0.066828810000000000, 0.031661210000000000, 0.015000000000000000]
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if n != len(sigmas):
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sigmas = np.append(loglinear_interp(sigmas, n), [0.0])
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else:
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sigmas.append(0.0)
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return torch.FloatTensor(sigmas).to(device)
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tan_sigmas = torch.tensor(tan_sigmas_1 + tan_sigmas_2)
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return tan_sigmas.to(device)
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schedulers = [
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Scheduler('automatic', 'Automatic', None),
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Scheduler('uniform', 'Uniform', uniform, need_inner_model=True),
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Scheduler('karras', 'Karras', k_diffusion.sampling.get_sigmas_karras, default_rho=7.0),
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Scheduler('exponential', 'Exponential', k_diffusion.sampling.get_sigmas_exponential),
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Scheduler('polyexponential', 'Polyexponential', k_diffusion.sampling.get_sigmas_polyexponential, default_rho=1.0),
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Scheduler('sgm_uniform', 'SGM Uniform', sgm_uniform, need_inner_model=True, aliases=["SGMUniform"]),
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Scheduler('kl_optimal', 'KL Optimal', kl_optimal),
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Scheduler('align_your_steps', 'Align Your Steps', get_align_your_steps_sigmas),
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Scheduler('simple', 'Simple', simple_scheduler, need_inner_model=True),
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Scheduler('normal', 'Normal', normal_scheduler, need_inner_model=True),
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Scheduler('ddim', 'DDIM', ddim_scheduler, need_inner_model=True),
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Scheduler('beta', 'Beta', beta_scheduler, need_inner_model=True),
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Scheduler('turbo', 'Turbo', turbo_scheduler, need_inner_model=True),
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Scheduler('align_your_steps_GITS', 'Align Your Steps GITS', get_align_your_steps_sigmas_GITS),
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Scheduler('align_your_steps_11', 'Align Your Steps 11', ays_11_sigmas),
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Scheduler('align_your_steps_32', 'Align Your Steps 32', ays_32_sigmas),
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Scheduler("automatic", "Automatic", None),
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Scheduler("uniform", "Uniform", uniform, need_inner_model=True),
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Scheduler("karras", "Karras", k_diffusion.sampling.get_sigmas_karras, default_rho=7.0),
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Scheduler("exponential", "Exponential", k_diffusion.sampling.get_sigmas_exponential),
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Scheduler("polyexponential", "Polyexponential", k_diffusion.sampling.get_sigmas_polyexponential, default_rho=1.0),
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Scheduler("sgm_uniform", "SGM Uniform", sgm_uniform, need_inner_model=True, aliases=["SGMUniform"]),
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Scheduler("kl_optimal", "KL Optimal", kl_optimal),
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Scheduler("align_your_steps", "Align Your Steps", get_align_your_steps_sigmas),
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Scheduler("simple", "Simple", simple_scheduler, need_inner_model=True),
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Scheduler("normal", "Normal", normal_scheduler, need_inner_model=True),
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Scheduler("ddim", "DDIM", ddim_scheduler, need_inner_model=True),
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Scheduler("beta", "Beta", beta_scheduler, need_inner_model=True),
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Scheduler("turbo", "Turbo", turbo_scheduler, need_inner_model=True),
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Scheduler("bong_tangent", "Bong Tangent", bong_tangent_scheduler),
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]
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schedulers_map = {**{x.name: x for x in schedulers}, **{x.label: x for x in schedulers}}
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