Bigger is not Always Better: Scaling Properties of Latent Diffusion Models

arXiv:2404.01367v1 Announce Type: cross
Abstract: We study the scaling properties of latent diffusion models (LDMs) with an emphasis on their sampling efficiency. While improved network architecture and inference algorithms have shown to effectively boost sampling efficiency of diffusion models, the role of model size -- a critical determinant of sampling efficiency -- has not been thoroughly examined. Through empirical analysis of established text-to-image diffusion models, we conduct an in-depth investigation into how model size influences sampling efficiency across varying sampling …

abstract algorithms architecture arxiv bigger boost cs.cv cs.lg diffusion diffusion models efficiency inference latent diffusion models network network architecture role sampling scaling study type