  Diffusion models have achieved remarkable success in image and video
generation. In this work, we demonstrate that diffusion models can also
\textit{generate high-performing neural network parameters}. Our approach is
simple, utilizing an autoencoder and a standard latent diffusion model. The
autoencoder extracts latent representations of a subset of the trained network
parameters. A diffusion model is then trained to synthesize these latent
parameter representations from random noise. It then generates new
representations that are passed through the autoencoder's decoder, whose
outputs are ready to use as new subsets of network parameters. Across various
architectures and datasets, our diffusion process consistently generates models
of comparable or improved performance over trained networks, with minimal
additional cost. Notably, we empirically find that the generated models are not
memorizing the trained networks. Our results encourage more exploration on the
versatile use of diffusion models.
