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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.
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.
Users reported that it generated images of white historical figures like the Founding Fathers, Nazi soldiers, and Vikings as other races, and that it refused to process prompts such as "happy white people" and "ideal nuclear family". [103] [104] Google later apologized for "missing the mark" and took Gemini's image generator offline for updates ...
Adobe Firefly is developed using Adobe's Sensei platform. Firefly is trained with images from Creative Commons, Wikimedia and Flickr Commons as well as 300 million images and videos in Adobe Stock and the public domain. [4] [5] [6] It uses image data sets to generate various designs. [7] It learns from user feedback by adjusting its designs ...
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.
The A-Bian dolls are kawaii likeness of sports figure, famous individuals, and now political figures that use kawaii images as a means of self-promotion and potential votes. [37] The creation of the A-Bian doll has allowed Taiwanese President Chen Shui-bian staffers to create a new culture where the "kawaii" image of a politician can be used to ...
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Similarly, an image model prompted with the text "a photo of a CEO" might disproportionately generate images of white male CEOs, [116] if trained on a racially biased data set. A number of methods for mitigating bias have been attempted, such as altering input prompts [117] and reweighting training data. [118]
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