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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.
This logo image consists only of simple geometric shapes or text. It does not meet the threshold of originality needed for copyright protection, and is therefore in the public domain . Although it is free of copyright restrictions, this image may still be subject to other restrictions .
An example of prompt usage for text-to-image generation, using Fooocus. Prompts for some text-to-image models can also include images and keywords and configurable parameters, such as artistic style, which is often used via keyphrases like "in the style of [name of an artist]" in the prompt [88] and/or selection of a broad aesthetic/art style.
This logo image consists only of simple geometric shapes or text. It does not meet the threshold of originality needed for copyright protection, and is therefore in the public domain . Although it is free of copyright restrictions, this image may still be subject to other restrictions .
Development of TextMaker started in 1987 under MS-DOS. [4]It was available as TextMaker 2002 for Sharp Zaurus, as TextMaker 2006 for FreeBSD [5] and Handheld PC 2000, as TextMaker 2010 for Pocket PCs with Windows CE 4.2. [2]
Petal Search supports searching for photos, reverse image search, and the standard text-based search. Visual search functions rely on Huawei’s visual search technology, HiTouch. [5] It can search for and detect multiple objects in an image at the same time.
Using procedural generation in games had origins in the tabletop role playing game (RPG) venue. [4] The leading tabletop system, Advanced Dungeons & Dragons, provided ways for the "dungeon master" to generate dungeons and terrain using random die rolls, expanded in later editions with complex branching procedural tables.
Anything inside the path will be included after the clipping path is applied; anything outside the path will be omitted from the output. Applying the clipping path results in a hard (aliased) or soft (anti-aliased) edge, depending on the image editor's capabilities. Clipping path. By convention, the inside of the path is defined by its direction.