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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. Artificial intelligence art - Wikipedia

    en.wikipedia.org/wiki/Artificial_intelligence_art

    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 [91] and/or selection of a broad aesthetic/art style.

  4. 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.

  5. This Creator Used AI To Generate 39 Posters For Horror Movie ...

    www.aol.com/lifestyle/creator-used-ai-generate...

    Suddenly, Disney feels darker and more sinister, yet it somehow fits. Opposites like this could surprisingly work, but we’re probably not going to see a real version anytime soon. Still

  6. Disney Creates New Tech Hub For AI and AR - AOL

    www.aol.com/finance/disney-creates-tech-hub-ai...

    Walt Disney Co (NYSE:DIS) has announced the creation of a new group, the Office of Technology Enablement (OTE), dedicated to guiding the company’s approach to emerging technologies like ...

  7. Text-to-image personalization - Wikipedia

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

    Text-to-Image personalization is a task in deep learning for computer graphics that augments pre-trained text-to-image generative models. In this task, a generative model that was trained on large-scale data (usually a foundation model ), is adapted such that it can generate images of novel, user-provided concepts.