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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 ...
The intersection of AI and employment concerns among underrepresented groups globally remains a critical facet. While AI promises efficiency enhancements and skill acquisition, concerns about job displacement and biased recruiting processes persist among these groups, as outlined in surveys by Fast Company. To leverage AI for a more equitable ...
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.
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.
DALL-E was revealed by OpenAI in a blog post on 5 January 2021, and uses a version of GPT-3 [5] modified to generate images.. On 6 April 2022, OpenAI announced DALL-E 2, a successor designed to generate more realistic images at higher resolutions that "can combine concepts, attributes, and styles". [6]
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.
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.
The GAN uses a "generator" to create new images and a "discriminator" to decide which created images are considered successful. [46] Unlike previous algorithmic art that followed hand-coded rules, generative adversarial networks could learn a specific aesthetic by analyzing a dataset of example images.
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