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  2. Text-to-image model - Wikipedia

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

    In 2016, Reed, Akata, Yan et al. became the first to use generative adversarial networks for the text-to-image task. [5] [7] With models trained on narrow, domain-specific datasets, they were able to generate "visually plausible" images of birds and flowers from text captions like "an all black bird with a distinct thick, rounded bill".

  3. Gemini (language model) - Wikipedia

    en.wikipedia.org/wiki/Gemini_(language_model)

    Input images may be of different resolutions, while video is inputted as a sequence of images. Audio is sampled at 16 kHz and then converted into a sequence of tokens by the Universal Speech Model. Gemini's dataset is multimodal and multilingual, consisting of "web documents, books, and code, and includ[ing] image, audio, and video data". [40]

  4. Adobe Firefly - Wikipedia

    en.wikipedia.org/wiki/Adobe_Firefly

    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 ...

  5. Generative pre-trained transformer - Wikipedia

    en.wikipedia.org/wiki/Generative_pre-trained...

    Generative pretraining (GP) was a long-established concept in machine learning applications. [16] [17] [18] It was originally used as a form of semi-supervised learning, as the model is trained first on an unlabelled dataset (pretraining step) by learning to generate datapoints in the dataset, and then it is trained to classify a labelled dataset.

  6. 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 ...

  7. Prompt engineering - Wikipedia

    en.wikipedia.org/wiki/Prompt_engineering

    Prompt engineering is the process of structuring an instruction that can be interpreted and understood by a generative AI model. [1] [2] A prompt is natural language text describing the task that an AI should perform: [3] a prompt for a text-to-text language model can be a query such as "what is Fermat's little theorem?", [4] a command such as "write a poem about leaves falling", [5] or a ...

  8. OpenAI - Wikipedia

    en.wikipedia.org/wiki/OpenAI

    Former headquarters at the Pioneer Building in San Francisco. In December 2015, OpenAI was founded by Sam Altman, Elon Musk, Ilya Sutskever, Greg Brockman, Trevor Blackwell, Vicki Cheung, Andrej Karpathy, Durk Kingma, John Schulman, Pamela Vagata, and Wojciech Zaremba, with Sam Altman and Elon Musk as the co-chairs. $1 billion in total was pledged by Sam Altman, Greg Brockman, Elon Musk, Reid ...

  9. Microsoft Copilot - Wikipedia

    en.wikipedia.org/wiki/Microsoft_Copilot

    In October, the image-generating tool was updated to use the more recent DALL-E 3. [38] Although Bing blocks prompts including various keywords that could generate inappropriate images, within days many users reported being able to bypass those constraints, such as to generate images of popular cartoon characters committing terrorist attacks. [39]

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