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Stable Diffusion. Stable Diffusion is a deep learning, text-to-image model released in 2022 based on diffusion techniques. The generative artificial intelligence technology is the premier product of Stability AI and is considered to be a part of the ongoing artificial intelligence boom .
Text-to-image model. An image conditioned on the prompt "an astronaut riding a horse, by Hiroshige ", generated by Stable Diffusion, a large-scale text-to-image model 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.
Stability AI was founded in 2019 [citation needed] by Emad Mostaque, who also initially funded the company. [ 1] In August 2022 Stability AI rose to prominence with the release of its source and weights available text-to-image model Stable Diffusion. On March 23rd 2024 Emad Mostaque stepped down from his position as CEO.
Diffusion model parameters showed strong rank-order stability and mean-level changes in processing speed and speed–accuracy trade-offs that could be attributed to practice effects. At the same time, people differed little in these patterns across time. In addition, profiles of individual diffusion model parameters proved to be stable over time.
In machine learning, diffusion models, also known as diffusion probabilistic models or score-based generative models, are a class of latent variable generative models. A diffusion model consists of three major components: the forward process, the reverse process, and the sampling procedure. [ 1] The goal of diffusion models is to learn a ...
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DreamBooth is a deep learning generation model used to personalize existing text-to-image models by fine-tuning. It was developed by researchers from Google Research and Boston University in 2022. Originally developed using Google's own Imagen text-to-image model, DreamBooth implementations can be applied to other text-to-image models, where it ...
Diffusion model parameters showed strong rank-order stability and mean-level changes in processing speed and speed-accuracy trade-offs that could be attributed to practice effects. At the same time, people differed little in these patterns across time. In addition, profiles of individual diffusion model parameters proved to be stable over time.
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