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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.
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 .
Sensei has been used to power a variety of features in Adobe's creative software, such as object selection in Photoshop and image auto-enhancement in Lightroom. Firefly expanded its capabilities to Illustrator, Premiere Pro, and Express, particularly for generating photos, videos and audio to enhance or alter specific parts of the media.
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.
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 ...
Example of prompt engineering for text-to-image generation, with Fooocus. In 2022, text-to-image models like DALL-E 2, Stable Diffusion, and Midjourney were released to the public. [47] These models take text prompts as input and use them to generate AI-generated images.
Generative AI features have been integrated into a variety of existing commercially available products such as Microsoft Office (Microsoft Copilot), [85] Google Photos, [86] and the Adobe Suite (Adobe Firefly). [87] Many generative AI models are also available as open-source software, including Stable Diffusion and the LLaMA [88] language model.
Given an existing image, DALL-E 2 can produce "variations" of the image as individual outputs based on the original, as well as edit the image to modify or expand upon it. DALL-E 2's "inpainting" and "outpainting" use context from an image to fill in missing areas using a medium consistent with the original, following a given prompt.
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