Stable diffusion generate natural images
In computer vision, image synthesis is one of the most spectacular recent developments, but also among those with the greatest computational demands. Scaling up likelihood-based models is now dominating the high-resolution synthesis of complicated natural settings. The encouraging results of GANs, on the other hand, have been proven to be primarily confined to data with relatively limited variability, as their adversarial learning technique does not easily scale to modelling complicated, multi-modal distributions. Recently, it has been demonstrated that diffusion models, which are created from a hierarchy of denoising autoencoders, generate outstanding results in picture synthesis. This article will help one to understand a diffusion model used for image synthesis. The following topics are
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Sourabh Mehta
Sourabh has worked as a full-time data scientist for an ISP organisation, experienced in analysing patterns and their implementation in product development. He has a keen interest in developing solutions for real-time problems with the help of data both in this universe and metaverse.
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