stable-diffusion-webui/modules/sd_schedulers.py

133 lines
4.3 KiB
Python

import dataclasses
import torch
import k_diffusion
import numpy as np
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
@dataclasses.dataclass
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)
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),
]
schedulers_map = {**{x.name: x for x in schedulers}, **{x.label: x for x in schedulers}}