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  2. 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.

  3. Text-to-image model - Wikipedia

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

    A text-to-image model is a machine learning model which takes an input natural language description and produces an image matching that description. Text-to-image models began to be developed in the mid-2010s during the beginnings of the AI boom, as a result of advances in deep neural networks.

  4. Stable Diffusion - Wikipedia

    en.wikipedia.org/wiki/Stable_Diffusion

    Stable Diffusion is a deep learning, text-to-image model released in 2022 based on diffusion techniques. The generative artificial intelligence technology is the premier product of Stability AI and is considered to be a part of the ongoing artificial intelligence boom.

  5. Google’s new AI tool uses image prompts instead of text

    www.aol.com/google-ai-tool-uses-image-214855525.html

    Google’s newest artificial intelligence tool, “Whisk,” lets people upload photos to get back a combined, AI-generated image – even without users inputting any text to explain what they ...

  6. Generative artificial intelligence - Wikipedia

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

    Generative AI can be either unimodal or multimodal; unimodal systems take only one type of input, whereas multimodal systems can take more than one type of input. [48] For example, one version of OpenAI 's GPT-4 accepts both text and image inputs.

  7. DALL-E - Wikipedia

    en.wikipedia.org/wiki/DALL-E

    The input to the Transformer model is a sequence of tokenized image caption followed by tokenized image patches. The image caption is in English, tokenized by byte pair encoding (vocabulary size 16384), and can be up to 256 tokens long. Each image is a 256×256 RGB image, divided into 32×32 patches of 4×4 each.