AWhat changed
Constructed non-Markovian diffusion processes sharing a denoising training objective, enabling implicit reverse samplers and faster generation with fewer steps.
Scientific scope
Constructed non-Markovian diffusion processes sharing a denoising training objective, enabling implicit reverse samplers and faster generation with fewer steps. DDIM sampling trades computation against sample quality. It builds on diffusion training and is distinct from the original DDPM and later consistency-model training.
Evidence
The linked original sources describe the stated contribution and its evidence limits. WRS has not independently rerun the experiments or certified a proof. DDIM sampling trades computation against sample quality. It builds on diffusion training and is distinct from the original DDPM and later consistency-model training.
Contributors
- Source-listed authors and technical contributors · SCIENCE: Source-listed authors or explicitly credited technical contributors for the cited versions. Family entries retain source-specific attribution; a combined list is not one original-paper byline or equal individual work. Names alone do not establish resolved identities.
Provisional WRS assessment, edition 2026-10-16.1. Independent expert calibration is pending. This grade does not certify correctness or assign ownership.
Full scientific record · Browse contribution grades · Request a correction