![]() While VAEs can generate outputs such as images faster, the images generated by them are not as detailed as those of diffusion models. This allows the user to easily sample new latent representations that can be mapped through the decoder to generate novel data. The encoder and decoder work together to learn an efficient and simple latent data representation. This compressed representation preserves the information that’s needed for a decoder to reconstruct the original input data, while discarding any irrelevant information. When given an input, an encoder converts it into a smaller, more dense representation of the data. Variational autoencoders (VAEs): VAEs consist of two neural networks typically referred to as the encoder and decoder.Learn more about the mathematics of diffusion models in this blog post. However, because of the reverse sampling process, running foundation models is a slow, lengthy process. ![]() A diffusion model can take longer to train than a variational autoencoder (VAE) model, but thanks to this two-step process, hundreds, if not an infinite amount, of layers can be trained, which means that diffusion models generally offer the highest-quality output when building generative AI models.Īdditionally, diffusion models are also categorized as foundation models, because they are large-scale, offer high-quality outputs, are flexible, and are considered best for generalized use cases. ![]()
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