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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.
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.
Image Featurizer: CLIP's image encoder can be adapted as a pre-trained image featurizer. This can then be fed into other AI models. [1] Text-to-Image Generation: Models like Stable Diffusion use CLIP's text encoder to transform text prompts into embeddings for image generation. [3]
XAI counters the "black box" tendency of machine learning, where even the AI's designers cannot explain why it arrived at a specific decision. [6] [7] XAI hopes to help users of AI-powered systems perform more effectively by improving their understanding of how those systems reason. [8] XAI may be an implementation of the social right to ...
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.
A white box (or glass box, clear box, or open box) is a subsystem whose internals can be viewed but usually not altered. [1] The term is used in systems engineering, software engineering, and in intelligent user interface design, [2] [3] where it is closely related to recent interest in explainable artificial intelligence.
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Bayesian optimization of a function (black) with Gaussian processes (purple). Three acquisition functions (blue) are shown at the bottom. [8]Bayesian optimization is typically used on problems of the form (), where is a set of points, , which rely upon less (or equal to) than 20 dimensions (,), and whose membership can easily be evaluated.