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Numerical diffusion is a difficulty with computer simulations of continua (such as fluids) wherein the simulated medium exhibits a higher diffusivity than the true medium. This phenomenon can be particularly egregious when the system should not be diffusive at all, for example an ideal fluid acquiring some spurious viscosity in a numerical model.
The Latent Diffusion Model (LDM) [1] is a diffusion model architecture developed by the CompVis (Computer Vision & Learning) [2] group at LMU Munich. [ 3 ] Introduced in 2015, diffusion models (DMs) are trained with the objective of removing successive applications of noise (commonly Gaussian ) on training images.
It handles instantaneous, continuous, and pool releases, and can model gases, particulates, and liquids. The model has a three regime structure: that of single building (area density < 5%), urban array (area density > 5%) and open. The model can be coupled with the US model SCIPUFF to replace the open regime and extend the model's prediction range.
AUTOMATIC1111 Stable Diffusion Web UI (SD WebUI, A1111, or Automatic1111 [3]) is an open source generative artificial intelligence program that allows users to generate images from a text prompt. [4] It uses Stable Diffusion as the base model for its image capabilities together with a large set of extensions and features to customize its output.
The diffusion equation is a parabolic partial differential equation. In physics, it describes the macroscopic behavior of many micro-particles in Brownian motion , resulting from the random movements and collisions of the particles (see Fick's laws of diffusion ).
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In particular, experiments on the diffusion of foreign material in a turbulent water stream, [6] vertical structure of water in lake bodies, [7] and lowest part of the atmosphere [8] found experimental evidence that eddy diffusion is indeed stronger than molecular diffusion and generally obeys the theory originally developed by G. I. Taylor ...
The goal of diffusion models is to learn a diffusion process for a given dataset, such that the process can generate new elements that are distributed similarly as the original dataset. A diffusion model models data as generated by a diffusion process, whereby a new datum performs a random walk with drift through the space of all possible data. [2]