What is recorded here?
Distilled trajectory-based image/video generators to predict several denoising steps per network evaluation, learning a mean-velocity representation without derivative regression.
Source-linked catalogue note. A detailed explanatory guide has not yet been prepared for this entry.
Scientific scope
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.
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People and systems
Neta Shaul; Chao Liu; Arash Vahdat; Julius Berner
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.
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English catalogue narrative
Parallel Decoding Distillation: multiple denoising steps per evaluation
Distilled trajectory-based image/video generators to predict several denoising steps per network evaluation, learning a mean-velocity representation without derivative regression.
Scientific scope
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.
Reading status
This is a source-linked WRS catalogue note. A full explanatory guide and independent scientific reconstruction have not been completed for this entry. Read the identified original work for its complete narrative and arguments.
People and systems
Neta Shaul; Chao Liu; Arash Vahdat; Julius Berner
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.