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import dataclasses | |
import torch | |
import k_diffusion | |
import numpy as np | |
from scipy import stats | |
from modules import shared | |
def to_d(x, sigma, denoised): | |
"""Converts a denoiser output to a Karras ODE derivative.""" | |
return (x - denoised) / sigma | |
k_diffusion.sampling.to_d = to_d | |
class Scheduler: | |
name: str | |
label: str | |
function: any | |
default_rho: float = -1 | |
need_inner_model: bool = False | |
aliases: list = None | |
def uniform(n, sigma_min, sigma_max, inner_model, device): | |
return inner_model.get_sigmas(n).to(device) | |
def sgm_uniform(n, sigma_min, sigma_max, inner_model, device): | |
start = inner_model.sigma_to_t(torch.tensor(sigma_max)) | |
end = inner_model.sigma_to_t(torch.tensor(sigma_min)) | |
sigs = [ | |
inner_model.t_to_sigma(ts) | |
for ts in torch.linspace(start, end, n + 1)[:-1] | |
] | |
sigs += [0.0] | |
return torch.FloatTensor(sigs).to(device) | |
def get_align_your_steps_sigmas(n, sigma_min, sigma_max, device): | |
# https://research.nvidia.com/labs/toronto-ai/AlignYourSteps/howto.html | |
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 | |
if shared.sd_model.is_sdxl: | |
sigmas = [14.615, 6.315, 3.771, 2.181, 1.342, 0.862, 0.555, 0.380, 0.234, 0.113, 0.029] | |
else: | |
# Default to SD 1.5 sigmas. | |
sigmas = [14.615, 6.475, 3.861, 2.697, 1.886, 1.396, 0.963, 0.652, 0.399, 0.152, 0.029] | |
if n != len(sigmas): | |
sigmas = np.append(loglinear_interp(sigmas, n), [0.0]) | |
else: | |
sigmas.append(0.0) | |
return torch.FloatTensor(sigmas).to(device) | |
def kl_optimal(n, sigma_min, sigma_max, device): | |
alpha_min = torch.arctan(torch.tensor(sigma_min, device=device)) | |
alpha_max = torch.arctan(torch.tensor(sigma_max, device=device)) | |
step_indices = torch.arange(n + 1, device=device) | |
sigmas = torch.tan(step_indices / n * alpha_min + (1.0 - step_indices / n) * alpha_max) | |
return sigmas | |
def simple_scheduler(n, sigma_min, sigma_max, inner_model, device): | |
sigs = [] | |
ss = len(inner_model.sigmas) / n | |
for x in range(n): | |
sigs += [float(inner_model.sigmas[-(1 + int(x * ss))])] | |
sigs += [0.0] | |
return torch.FloatTensor(sigs).to(device) | |
def normal_scheduler(n, sigma_min, sigma_max, inner_model, device, sgm=False, floor=False): | |
start = inner_model.sigma_to_t(torch.tensor(sigma_max)) | |
end = inner_model.sigma_to_t(torch.tensor(sigma_min)) | |
if sgm: | |
timesteps = torch.linspace(start, end, n + 1)[:-1] | |
else: | |
timesteps = torch.linspace(start, end, n) | |
sigs = [] | |
for x in range(len(timesteps)): | |
ts = timesteps[x] | |
sigs.append(inner_model.t_to_sigma(ts)) | |
sigs += [0.0] | |
return torch.FloatTensor(sigs).to(device) | |
def ddim_scheduler(n, sigma_min, sigma_max, inner_model, device): | |
sigs = [] | |
ss = max(len(inner_model.sigmas) // n, 1) | |
x = 1 | |
while x < len(inner_model.sigmas): | |
sigs += [float(inner_model.sigmas[x])] | |
x += ss | |
sigs = sigs[::-1] | |
sigs += [0.0] | |
return torch.FloatTensor(sigs).to(device) | |
def beta_scheduler(n, sigma_min, sigma_max, inner_model, device): | |
# From "Beta Sampling is All You Need" [arXiv:2407.12173] (Lee et. al, 2024) """ | |
alpha = shared.opts.beta_dist_alpha | |
beta = shared.opts.beta_dist_beta | |
timesteps = 1 - np.linspace(0, 1, n) | |
timesteps = [stats.beta.ppf(x, alpha, beta) for x in timesteps] | |
sigmas = [sigma_min + (x * (sigma_max-sigma_min)) for x in timesteps] | |
sigmas += [0.0] | |
return torch.FloatTensor(sigmas).to(device) | |
def turbo_scheduler(n, sigma_min, sigma_max, inner_model, device): | |
unet = inner_model.inner_model.forge_objects.unet | |
timesteps = torch.flip(torch.arange(1, n + 1) * float(1000.0 / n) - 1, (0,)).round().long().clip(0, 999) | |
sigmas = unet.model.predictor.sigma(timesteps) | |
sigmas = torch.cat([sigmas, sigmas.new_zeros([1])]) | |
return sigmas.to(device) | |
schedulers = [ | |
Scheduler('automatic', 'Automatic', None), | |
Scheduler('uniform', 'Uniform', uniform, need_inner_model=True), | |
Scheduler('karras', 'Karras', k_diffusion.sampling.get_sigmas_karras, default_rho=7.0), | |
Scheduler('exponential', 'Exponential', k_diffusion.sampling.get_sigmas_exponential), | |
Scheduler('polyexponential', 'Polyexponential', k_diffusion.sampling.get_sigmas_polyexponential, default_rho=1.0), | |
Scheduler('sgm_uniform', 'SGM Uniform', sgm_uniform, need_inner_model=True, aliases=["SGMUniform"]), | |
Scheduler('kl_optimal', 'KL Optimal', kl_optimal), | |
Scheduler('align_your_steps', 'Align Your Steps', get_align_your_steps_sigmas), | |
Scheduler('simple', 'Simple', simple_scheduler, need_inner_model=True), | |
Scheduler('normal', 'Normal', normal_scheduler, need_inner_model=True), | |
Scheduler('ddim', 'DDIM', ddim_scheduler, need_inner_model=True), | |
Scheduler('beta', 'Beta', beta_scheduler, need_inner_model=True), | |
Scheduler('turbo', 'Turbo', turbo_scheduler, need_inner_model=True), | |
] | |
schedulers_map = {**{x.name: x for x in schedulers}, **{x.label: x for x in schedulers}} | |