Skipping the Zeros in Diffusion Models for Sparse Data Generation

arXiv:2605.01817v2 Announce Type: replace Abstract: Diffusion models (DMs) excel on dense continuous data, but are not designed for sparse continuous data. They do not model exact zeros that represent the deliberate absence of a signal. As a result, they erase sparsity patterns and perform unnecessary computation on mostly zero entries. With Sparsity-Exploiting Diffusion (SED), we model only non-zero values, preserving sparsity. SED delivers computational savings while maintaining or improving generation quality by skipping zeros during training and inference. Across physics and biology benchm

Source: arXiv cs.LG — read the full report at the original publisher.

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