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The name diffusion is from the thermodynamic diffusion, since they were first developed with inspiration from thermodynamics. [13] [14] Models in Stable Diffusion series before SD 3 all used a variant of diffusion models, called latent diffusion model (LDM), developed in 2021 by the CompVis (Computer Vision & Learning) [15] group at LMU Munich ...
An image conditioned on the prompt an astronaut riding a horse, by Hiroshige, generated by Stable Diffusion 3.5, a large-scale text-to-image model first released in 2022. A text-to-image model is a machine learning model which takes an input natural language description and produces an image matching that description.
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]
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.
DISPLAY-2 (Greece) – A vapour cloud dispersion model for neutral or denser-than-air pollution plumes over irregular, obstructed terrain on a local scale. It accommodates jet releases as well as two-phase (i.e., liquid-vapor mixtures) releases. This model was also developed at the National Centre of Scientific Research "DEMOKRITOS" of Greece.
The Fréchet inception distance (FID) is a metric used to assess the quality of images created by a generative model, like a generative adversarial network (GAN) [1] or a diffusion model. [ 2 ] [ 3 ] The FID compares the distribution of generated images with the distribution of a set of real images (a "ground truth" set).
As of August 2023, more than 15 billion images had been generated using text-to-image algorithms, with 80% of these created by models based on Stable Diffusion. [187] If AI-generated content is included in new data crawls from the Internet for additional training of AI models, defects in the resulting models may occur. [188]
Diffusion process is stochastic in nature and hence is used to model many real-life stochastic systems. Brownian motion , reflected Brownian motion and Ornstein–Uhlenbeck processes are examples of diffusion processes.