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  2. 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.

  3. 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.

  4. DALL-E - Wikipedia

    en.wikipedia.org/wiki/DALL-E

    DALL-E was revealed by OpenAI in a blog post on 5 January 2021, and uses a version of GPT-3 [5] modified to generate images.. On 6 April 2022, OpenAI announced DALL-E 2, a successor designed to generate more realistic images at higher resolutions that "can combine concepts, attributes, and styles". [6]

  5. Generative artificial intelligence - Wikipedia

    en.wikipedia.org/wiki/Generative_artificial...

    AI generated images have become much more advanced. In March 2020, 15.ai, created by an anonymous MIT researcher, was a free web application that could generate convincing character voices using minimal training data. [42]

  6. OpenAI's CEO wants to solve AI's verification problem with ...

    www.aol.com/openais-ceo-wants-solve-ais...

    For people living in the US who still want to sign up without the promise of free money, there are 10 locations in four regions: Los Angeles, Miami, New York and San Francisco.

  7. Stable Diffusion - Wikipedia

    en.wikipedia.org/wiki/Stable_Diffusion

    Diagram of the latent diffusion architecture used by Stable Diffusion The denoising process used by Stable Diffusion. The model generates images by iteratively denoising random noise until a configured number of steps have been reached, guided by the CLIP text encoder pretrained on concepts along with the attention mechanism, resulting in the desired image depicting a representation of the ...