Merge sigmas gradually : takes sigmas_1 and sigmas_2 as an input and merge them by starting with sigmas_1 times the weight and sigmas_2 times 1-the weight, like if you want to start with karras and end with simple.

Multiply sigmas: simply multiply the sigmas by what you want.

Split and concatenate sigmas: takes sigmas_1 and sigmas_2 as an input and merge them by starting with sigmas_1 until the chosen step, then the rest with sigmas_2

Get sigmas as float: Just get first - last step to be able to inject noise inside a latent with noise injection nodes.

Graph sigmas: make a graph of the sigmas.

Manual scheduler: uses eval() to create a custom schedule. The math module is fully imported. Available variables are:

sigmin: sigma min
sigmax: sigma max
phi
pi comes from math
x equals 1 for the first step and 0 for the last step.
y equals 0 for the first step and 1 for the last step.
s or steps: total amount of steps.
j from 0 to total steps -1.

The Golden Scheduler: Uses phi as the exponent. Hence the name 😊. The formula is pretty simple:

(1-x/(steps-1))**phi*sigmax+(x/(steps-1))**phi*sigmin for x in range(steps)

or if you want to use it in the manual node:

x**phi*sigmax+(1-x)**phi*sigmin

It works pretty well with dpmpp2m, euler and lms!

Here is a comparison, side by side with karras. Karras being on the right (or below depending on your screen):

Using pi as the exponent is nice too (default formula in the manual node):