Can anyone explain differences between sampling methods and their uses to me in simple terms, because all the info I've found so far is either very contradicting or complex and goes over my head

First, you have to understand what samplers are. These are discretized differential equations. I'm not going to go into these at all in this post, but I've covered them before.

首先,你必须了解什么是采样器。这些是离散的微分方程。我不打算在这篇文章中讨论这些问题,但是我以前已经讨论过了。

DDIM and PLMS were the original samplers. They were part of Latent Diffusion's repository. They stand for the papers that introduced them, Denoising Diffusion Implicit Models and Pseudo Numerical Methods for Diffusion Models on Manifolds.

DDIM 和 PLMS 是最初的采样者。它们是潜在扩散资料库的一部分。它们代表了介绍它们的论文: 流形上扩散模型的去噪隐式模型和伪数值方法。

Almost all other samplers come from work done by @RiversHaveWings or Katherine Crowson, which is mostly contained in her work at this repository. She is listed as the principal researcher at Stability AI. Her notes for those samplers are as follows:

几乎所有其他的采样器都来自@RiversHaveWings 或 Katherine Crowson 的工作,这些工作大部分包含在她在这个仓库的工作中。她是稳定人工智能的首席研究员。她对这些采样者的说明如下:

The 'Karras' versions of these weren't made by Karras as far as I can tell, but instead are using a variance-exploding scheduler from the Karras paper, which of course is extra confusing given that most of the other samplers were inspired by that paper in the first place.

据我所知,“ Karras”版本并不是由 Karras 制作的,而是使用了 Karras 论文中的一个方差爆炸式调度程序,这当然更加令人困惑,因为大多数其他的采样程序都是受到了这篇论文的启发。