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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. Elon Musk’s AI photo tool is generating realistic, fake ...

   www.aol.com/elon-musk-ai-photo-tool-140600421.html

   Elon Musk’s AI chatbot Grok on Tuesday began allowing users to create AI-generated images from text prompts and post them to X. Almost immediately, people began using the tool to flood the ...

4. Artificial intelligence art - Wikipedia

   en.wikipedia.org/wiki/Artificial_intelligence_art

   In 2024, Google's chatbot Gemini's AI image generator was criticized for perceived racial bias, with claims that Gemini deliberately underrepresented white people in its results. [103] Users reported that it generated images of white historical figures like the Founding Fathers , Nazi soldiers , and Vikings as other races, and that it refused ...

5. 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.

6. 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.

7. Generative adversarial network - Wikipedia

   en.wikipedia.org/wiki/Generative_adversarial_network

   The generator is decomposed into a pyramid of generators =, with the lowest one generating the image () at the lowest resolution, then the generated image is scaled up to (()), and fed to the next level to generate an image (+ (())) at a higher resolution, and so on. The discriminator is decomposed into a pyramid as well.