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DistillAlign improves video distillation via distributional alignment

Researchers have introduced DistillAlign, a novel approach to autoregressive video distillation that addresses limitations in existing multi-stage pipelines. The method emphasizes distributional alignment between student and teacher models, proposing a joint distillation technique that combines Distribution Matching Distillation (DMD) with Consistency Distillation. This approach aims to improve generation quality, coverage, and diversity, notably outperforming baselines that use larger teacher models. AI

IMPACT Enhances video generation quality and diversity by improving distillation techniques.

RANK_REASON The cluster contains a research paper detailing a new method for video distillation.

Read on Hugging Face Daily Papers →

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DistillAlign improves video distillation via distributional alignment

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The cluster contains a research paper detailing a new method for video distillation.
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COVERAGE [2]

  1. Hugging Face Daily Papers TIER_1 English(EN) ·

    DistillAlign: Coordinating Mode Covering and Mode Seeking in Autoregressive Video Distillation

    Existing autoregressive video distillation methods commonly adopt a Distribution Matching Distillation (DMD)-based multi-stage pipeline. However, they typically decouple the initialization and DMD stages -- which then pursue different target distributions -- and judge the interme…

  2. arXiv cs.CV TIER_1 English(EN) · Jiaxing Li, Kai Zou, Cindy Zhou, Kaichen Huang, Junyao Gao, Zile Wang, Yang Liu, Bin Liu, Bo An, Yangguang Li ·

    DistillAlign: Coordinating Mode Covering and Mode Seeking in Autoregressive Video Distillation

    arXiv:2607.26811v1 Announce Type: new Abstract: Existing autoregressive video distillation methods commonly adopt a Distribution Matching Distillation (DMD)-based multi-stage pipeline. However, they typically decouple the initialization and DMD stages -- which then pursue differe…