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Diagram of the latent diffusion architecture used by Stable Diffusion The denoising process used by Stable Diffusion. The model generates images by iteratively denoising random noise until a configured number of steps have been reached, guided by the CLIP text encoder pretrained on concepts along with the attention mechanism, resulting in the desired image depicting a representation of the ...
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.
An improved flagship model, Flux 1.1 Pro was released on 2 October 2024. [ 25 ] [ 26 ] Two additional modes were added on 6 November, Ultra which can generate image at four times higher resolution and up to 4 megapixel without affecting generation speed and Raw which can generate hyper-realistic image in the style of candid photography .
Mathematical models developed in theoretical ecology predict complex food webs can be less stable than simpler webs. [ 1 ] : 75–77 [ 2 ] : 64 Life on Earth-Flow of Energy and Entropy Theoretical ecology is the scientific discipline devoted to the study of ecological systems using theoretical methods such as simple conceptual models ...
The Latent Diffusion Model (LDM) [1] is a diffusion model architecture developed by the CompVis (Computer Vision & Learning) [2] group at LMU Munich. [ 3 ] Introduced in 2015, diffusion models (DMs) are trained with the objective of removing successive applications of noise (commonly Gaussian ) on training images.
As of August 2023, more than 15 billion images had been generated using text-to-image algorithms, with 80% of these created by models based on Stable Diffusion. [171] If AI-generated content is included in new data crawls from the Internet for additional training of AI models, defects in the resulting models may occur. [172]
The goal of diffusion models is to learn a diffusion process for a given dataset, such that the process can generate new elements that are distributed similarly as the original dataset. A diffusion model models data as generated by a diffusion process, whereby a new datum performs a random walk with drift through the space of all possible data. [2]
Virtual Cell is an advanced software platform for modeling and simulating reaction kinetics, membrane transport and diffusion in the complex geometries of cells and multicellular tissues. VCell models have a hierarchical tree structure.