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Cascading diffusion model stacks multiple diffusion models one after another, in the style of Progressive GAN. The lowest level is a standard diffusion model that generate 32x32 image, then the image would be upscaled by a diffusion model specifically trained for upscaling, and the process repeats. [53]
GANs are implicit generative models, [8] which means that they do not explicitly model the likelihood function nor provide a means for finding the latent variable corresponding to a given sample, unlike alternatives such as flow-based generative model.
The Fréchet inception distance (FID) is a metric used to assess the quality of images created by a generative model, like a generative adversarial network (GAN) [1] or a diffusion model. [2] [3] The FID compares the distribution of generated images with the distribution of a set of real images (a "ground truth" set).
The original GAN method is based on the GAN game, a zero-sum game with 2 players: generator and discriminator. The game is defined over a probability space (,,), The generator's strategy set is the set of all probability measures on (,), and the discriminator's strategy set is the set of measurable functions : [,].
Many generative AI models are also available as open-source software, including Stable Diffusion and the LLaMA [88] language model. Smaller generative AI models with up to a few billion parameters can run on smartphones , embedded devices, and personal computers .
Diffusion models, generative models used to create synthetic data based on existing data, [53] were first proposed in 2015, [54] but they only became better than GANs in early 2021. [55] Latent diffusion model was published in December 2021 and became the basis for the later Stable Diffusion (August 2022).
The Inception Score (IS) is an algorithm used to assess the quality of images created by a generative image model such as a generative adversarial network (GAN). [1] The score is calculated based on the output of a separate, pretrained Inception v3 image classification model applied to a sample of (typically around 30,000) images generated by the generative model.
Regardless of precise definition, the terminology is constitutional because a generative model can be used to "generate" random instances , either of an observation and target (,), or of an observation x given a target value y, [2] while a discriminative model or discriminative classifier (without a model) can be used to "discriminate" the ...