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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 ...
A Chrome extension followed in 2010, [5] which was released for Blink-based Opera 15 in 2013 [6] [7] and as a Firefox WebExtension in 2017. [ 8 ] [ 9 ] Similar extensions for Safari [ 10 ] and for Presto -based Opera [ 11 ] are distributed as 'Stylish' by other developers with Barnabe's approval.
15.ai was a free non-commercial web application that used artificial intelligence to generate text-to-speech voices of fictional characters from popular media.Created by an artificial intelligence researcher known as 15 during their time at the Massachusetts Institute of Technology, the application allowed users to make characters from video games, television shows, and movies speak custom ...
Adobe Firefly is a generative machine learning text-to-image model included as part of Adobe Creative Cloud.It is currently being tested in an open beta phase. [1] [2] [3]Adobe Firefly is developed using Adobe's Sensei platform.
Frutiger Aero visuals in user interface design (KDE Plasma 4 from 2011)Frutiger Aero (/ f r uː t ɪ ɡ ə r ɛ ə r ə ʊ /), sometimes known as Web 2.0 Gloss, [1] is a retrospective name applied to a design trend observed mainly in user interfaces, product design, and Internet aesthetics from the mid-2000s to the early 2010s. [2]
Name Creator Input format Languages (alphabet order) OS support First public release date Latest stable version Software license; Ddoc: Walter Bright: Text D Windows, OS X, Linux and BSD 2005/09/19 DMD 2.078.3 Boost (opensource) Document! X Innovasys Text, Binary C++/CLI only, C#, IDL, Java, VB, VBScript, PL/SQL Windows only 1998 2014.1 ...
The GAN uses a "generator" to create new images and a "discriminator" to decide which created images are considered successful. [43] Unlike previous algorithmic art that followed hand-coded rules, generative adversarial networks could learn a specific aesthetic by analyzing a dataset of example images.
The 5.1 model is more opinionated than version 5, applying more of its own stylization to images, while the 5.1 RAW model adds improvements while working better with more literal prompts. The version 5.2 included a new "aesthetics system", and the ability to "zoom out" by generating surroundings to an existing image. [16]