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Ideogram was founded in 2022 by Mohammad Norouzi, William Chan, Chitwan Saharia, and Jonathan Ho to develop a better text-to-image model. [3]It was first released with its 0.1 model on August 22, 2023, [4] after receiving $16.5 million in seed funding, which itself was led by Andreessen Horowitz and Index Ventures.
MicroEMACS is a small, portable Emacs-like text editor originally written by Dave Conroy in 1985, and further developed by Daniel M. Lawrence (1958–2010 [2] [3]) and was maintained by him. MicroEMACS has been ported to many operating systems , including CP/M , [ 4 ] MS-DOS , Microsoft Windows , VMS , Atari ST , AmigaOS , OS-9 , NeXTSTEP , and ...
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.
Flux (also known as FLUX.1) is a text-to-image model developed by Black Forest Labs, based in Freiburg im Breisgau, Germany. Black Forest Labs were founded by former employees of Stability AI. As with other text-to-image models, Flux generates images from natural language descriptions, called prompts.
Hemlock is a free Emacs text editor for most POSIX-compliant Unix systems. It follows the tradition of the Lisp Machine editor ZWEI and the ITS/TOPS-20 implementation of Emacs, but differs from XEmacs or GNU Emacs, the most popular Emacs variants, in that it is written in Common Lisp rather than Emacs Lisp and C—although it borrows features from the later editors.
GNU Emacs is a text editor and suite of free software tools. Its development began in 1984 by GNU Project founder Richard Stallman, [5] based on the Emacs editor developed for Unix operating systems.
Text-to-Image personalization is a task in deep learning for computer graphics that augments pre-trained text-to-image generative models. In this task, a generative model that was trained on large-scale data (usually a foundation model ), is adapted such that it can generate images of novel, user-provided concepts.