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  2. Microsoft Copilot - Wikipedia

    en.wikipedia.org/wiki/Microsoft_Copilot

    Several default chatbots are available in Microsoft Copilot, including the standard Copilot chatbot as well as Microsoft Designer, which is oriented towards the use of its Image Creator to generate images based on text prompts. Others include "Travel Planner", "Cooking Assistant", and "Fitness Trainer".

  3. 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.

  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. DALL-E - Wikipedia

    en.wikipedia.org/wiki/DALL-E

    Microsoft unveiled their implementation of DALL-E 2 in their Designer app and Image Creator tool included in Bing and Microsoft Edge. [13] The API operates on a cost-per-image basis, with prices varying depending on image resolution. Volume discounts are available to companies working with OpenAI's enterprise team. [14]

  6. Freepik - Wikipedia

    en.wikipedia.org/wiki/Freepik

    Stock Images and Videos is a library of millions of high-quality stock images, design elements, videos, and audio files, available via free or premium subscriptions. [ 29 ] Templates and Mockups offers customizable templates and mockups for branding, presentations, and product showcases.

  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.