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  2. Blended artificial intelligence - Wikipedia

    en.wikipedia.org/wiki/Blended_artificial...

    Blended artificial intelligence (blended AI) refers to the blending of different artificial intelligence techniques or approaches to achieve more robust and practical solutions. It involves integrating multiple AI models, algorithms , and technologies to leverage their respective strengths and compensate for their weaknesses.

  3. 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]

  4. 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.

  5. Do AI-generated images reinforce gender and racial ... - AOL

    www.aol.com/ai-generated-images-reinforce-gender...

    Evaluate only the first image generated by each AI image generator. ... The gender was not clearly pronounced in two of the images (deepai and hotpot.ai), but both generators created people with ...

  6. 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.

  7. Neural style transfer - Wikipedia

    en.wikipedia.org/wiki/Neural_Style_Transfer

    The first two example-based style transfer algorithms were image analogies [1] and image quilting. [2] Both of these methods were based on patch-based texture synthesis algorithms. Given a training pair of images–a photo and an artwork depicting that photo–a transformation could be learned and then applied to create new artwork from a new ...