BWhat changed
Distilled trajectory-based image/video generators to predict several denoising steps per network evaluation, learning a mean-velocity representation without derivative regression.
Scientific scope
Distilled trajectory-based image/video generators to predict several denoising steps per network evaluation, learning a mean-velocity representation without derivative regression. Inherits diffusion/flow distillation, parallel decoding and mean-velocity ideas. Original four-to-eight-evaluation comparisons are empirical; no proof of compatibility with every possible pretrained model or universally superior diversity inferred.
Evidence
The linked original sources describe the stated contribution and its evidence limits. WRS has not independently rerun the experiments or certified a proof. Inherits diffusion/flow distillation, parallel decoding and mean-velocity ideas. Original four-to-eight-evaluation comparisons are empirical; no proof of compatibility with every possible pretrained model or universally superior diversity inferred.
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.
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