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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. Midjourney - Wikipedia

    en.wikipedia.org/wiki/Midjourney

    Midjourney is a generative artificial intelligence program and service created and hosted by the San Francisco-based independent research lab Midjourney, Inc. Midjourney generates images from natural language descriptions, called prompts, similar to OpenAI's DALL-E and Stability AI's Stable Diffusion.

  4. DeepSeek releases ‘revolutionary’ AI image generator amid ...

    www.aol.com/deepseek-releases-revolutionary-ai...

    DeepSeek’s Janus-Pro is free-to-use without restriction, unlike image generators built by US rivals like OpenAI and Stability AI. It also marks a breakthrough in the way such models are trained ...

  5. NovelAI - Wikipedia

    en.wikipedia.org/wiki/NovelAI

    NovelAI is an online cloud-based, SaaS model, and a paid subscription service for AI-assisted storywriting [2] [3] [4] and text-to-image synthesis, [5] originally launched in beta on June 15, 2021, [6] with the image generation feature being implemented later on October 3, 2022.

  6. Fotor - Wikipedia

    en.wikipedia.org/wiki/Fotor

    Fotor: A free easy-to-use photo editing and graphic design tool, available in web, desktop, and mobile versions. It provides a full suite of tools that cover most image editing needs. Fotor also includes advanced AI-powered tools such as background remover, image enlarger, and object remover, which make complex edits simple.

  7. Artificial intelligence art - Wikipedia

    en.wikipedia.org/wiki/Artificial_intelligence_art

    The GAN uses a "generator" to create new images and a "discriminator" to decide which created images are considered successful. [32] Unlike previous algorithmic art that followed hand-coded rules, generative adversarial networks could learn a specific aesthetic by analyzing a dataset of example images.