Files
blepping-comfyui_overly_com…/py/res_support.py
T
2024-05-30 07:47:43 -06:00

128 lines
3.4 KiB
Python

import math
import torch
from torch import FloatTensor
from typing import Optional, NamedTuple
# Copied from https://github.com/Clybius/ComfyUI-Extra-Samplers
def _gamma(
n: int,
) -> int:
"""
https://en.wikipedia.org/wiki/Gamma_function
for every positive integer n,
Γ(n) = (n-1)!
"""
return math.factorial(n - 1)
def _incomplete_gamma(s: int, x: float, gamma_s: Optional[int] = None) -> float:
"""
https://en.wikipedia.org/wiki/Incomplete_gamma_function#Special_values
if s is a positive integer,
Γ(s, x) = (s-1)!*∑{k=0..s-1}(x^k/k!)
"""
if gamma_s is None:
gamma_s = _gamma(s)
sum_: float = 0
# {k=0..s-1} inclusive
for k in range(s):
numerator: float = x**k
denom: int = math.factorial(k)
quotient: float = numerator / denom
sum_ += quotient
incomplete_gamma_: float = sum_ * math.exp(-x) * gamma_s
return incomplete_gamma_
# by Katherine Crowson
def _phi_1(neg_h: FloatTensor):
return torch.nan_to_num(torch.expm1(neg_h) / neg_h, nan=1.0)
# by Katherine Crowson
def _phi_2(neg_h: FloatTensor):
return torch.nan_to_num((torch.expm1(neg_h) - neg_h) / neg_h**2, nan=0.5)
# by Katherine Crowson
def _phi_3(neg_h: FloatTensor):
return torch.nan_to_num(
(torch.expm1(neg_h) - neg_h - neg_h**2 / 2) / neg_h**3, nan=1 / 6
)
def _phi(
neg_h: float,
j: int,
):
"""
For j={1,2,3}: you could alternatively use Kat's phi_1, phi_2, phi_3 which perform fewer steps
Lemma 1
https://arxiv.org/abs/2308.02157
ϕj(-h) = 1/h^j*∫{0..h}(e^(τ-h)*(τ^(j-1))/((j-1)!)dτ)
https://www.wolframalpha.com/input?i=integrate+e%5E%28%CF%84-h%29*%28%CF%84%5E%28j-1%29%2F%28j-1%29%21%29d%CF%84
= 1/h^j*[(e^(-h)*(-τ)^(-j)*τ(j))/((j-1)!)]{0..h}
https://www.wolframalpha.com/input?i=integrate+e%5E%28%CF%84-h%29*%28%CF%84%5E%28j-1%29%2F%28j-1%29%21%29d%CF%84+between+0+and+h
= 1/h^j*((e^(-h)*(-h)^(-j)*h^j*(Γ(j)-Γ(j,-h)))/(j-1)!)
= (e^(-h)*(-h)^(-j)*h^j*(Γ(j)-Γ(j,-h))/((j-1)!*h^j)
= (e^(-h)*(-h)^(-j)*(Γ(j)-Γ(j,-h))/(j-1)!
= (e^(-h)*(-h)^(-j)*(Γ(j)-Γ(j,-h))/Γ(j)
= (e^(-h)*(-h)^(-j)*(1-Γ(j,-h)/Γ(j))
requires j>0
"""
assert j > 0
gamma_: float = _gamma(j)
incomp_gamma_: float = _incomplete_gamma(j, neg_h, gamma_s=gamma_)
phi_: float = math.exp(neg_h) * neg_h**-j * (1 - incomp_gamma_ / gamma_)
return phi_
class RESDECoeffsSecondOrder(NamedTuple):
a2_1: float
b1: float
b2: float
def _de_second_order(
h: float,
c2: float,
simple_phi_calc=False,
) -> RESDECoeffsSecondOrder:
"""
Table 3
https://arxiv.org/abs/2308.02157
ϕi,j := ϕi,j(-h) = ϕi(-cj*h)
a2_1 = c2ϕ1,2
= c2ϕ1(-c2*h)
b1 = ϕ1 - ϕ2/c2
"""
if simple_phi_calc:
# Kat computed simpler expressions for phi for cases j={1,2,3}
a2_1: float = c2 * _phi_1(-c2 * h)
phi1: float = _phi_1(-h)
phi2: float = _phi_2(-h)
else:
# I computed general solution instead.
# they're close, but there are slight differences. not sure which would be more prone to numerical error.
a2_1: float = c2 * _phi(j=1, neg_h=-c2 * h)
phi1: float = _phi(j=1, neg_h=-h)
phi2: float = _phi(j=2, neg_h=-h)
phi2_c2: float = phi2 / c2
b1: float = phi1 - phi2_c2
b2: float = phi2_c2
return RESDECoeffsSecondOrder(
a2_1=a2_1,
b1=b1,
b2=b2,
)