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  2. Wikipedia:Random - Wikipedia

    en.wikipedia.org/wiki/Wikipedia:Random

    On Wikipedia and other sites running on MediaWiki, Special:Random can be used to access a random article in the main namespace; this feature is useful as a tool to generate a random article. Depending on your browser, it's also possible to load a random page using a keyboard shortcut (in Firefox , Edge , and Chrome Alt-Shift + X ).

  3. Lavarand - Wikipedia

    en.wikipedia.org/wiki/Lavarand

    Lavarand, also known as the Wall of Entropy, is a hardware random number generator designed by Silicon Graphics that worked by taking pictures of the patterns made by the floating material in lava lamps, extracting random data from the pictures, and using the result to seed a pseudorandom number generator. [1]

  4. Google Image Labeler - Wikipedia

    en.wikipedia.org/wiki/Google_Image_Labeler

    Google Image Labeler is a feature, in the form of a game, of Google Images that allows the user to label random images to help improve the quality of Google's image search results.

  5. Random walker algorithm - Wikipedia

    en.wikipedia.org/wiki/Random_walker_algorithm

    The random walker algorithm is an algorithm for image segmentation. In the first description of the algorithm, [ 1 ] a user interactively labels a small number of pixels with known labels (called seeds), e.g., "object" and "background".

  6. Flux (text-to-image model) - Wikipedia

    en.wikipedia.org/wiki/Flux_(text-to-image_model)

    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.

  7. Text-to-image model - Wikipedia

    en.wikipedia.org/wiki/Text-to-image_model

    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.

  8. Fotor - Wikipedia

    en.wikipedia.org/wiki/Fotor

    Fotor: A free easy-to-use photo editing and graphic design tool, available in web, desktop, and mobile versions. It provides a full suite of tools that cover most image editing needs. Fotor also includes advanced AI-powered tools such as background remover, image enlarger, and object remover, which make complex edits simple.

  9. Ideogram (text-to-image model) - Wikipedia

    en.wikipedia.org/wiki/Ideogram_(text-to-image_model)

    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.