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      "statement": "# Parallel Decoding Distillation: multiple denoising steps per evaluation\n\nDistilled trajectory-based image/video generators to predict several denoising steps per network evaluation, learning a mean-velocity representation without derivative regression.\n\n## Scientific scope\n\nInherits 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.\n\n## Reading status\n\nThis 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.\n\n## People and systems\n\nNeta Shaul; Chao Liu; Arash Vahdat; Julius Berner\n\nSource-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.\n\n\n\n## Original sources\n\n- [Parallel Decoding Distillation for Fast Image and Video Generation](https://arxiv.org/abs/2607.26004v1)\n",
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