Generalized Discrete Diffusion from Snapshots
We introduce Generalized Discrete Diffusion from Snapshots (GDDS), a unified framework for discrete diffusion modeling that supports arbitrary noising processes over large discrete state spaces. Our formulation encompasses all existing discrete diffusion approaches, while allowing significantly greater flexibility in the choice of corruption dynamics. The forward noising process relies on uniformization and enables fast arbitrary corruption. For the reverse process, we derive a simple evidence lower bound (ELBO) based on snapshot latents, instead of the entire noising path, that allows efficient training of standard generative modeling architectures with clear probabilistic interpretation...
| Authors | Oussama Zekri, Théo Uscidda, Nicolas Boullé et al. |
|---|---|
| Primary category | stat.ML |
| Published | 22 March 2026 |
| Last updated at source | 22 March 2026 |
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