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News/Few-step Generative Models as Lossy Compression
arxiv
PublishedJune 10, 2026 at 4:00 AM
▲bullish

Few-step Generative Models as Lossy Compression

Source
arxiv.orgfull article ↗
Read on arxiv→
Publisher summary· verbatim

arXiv:2606.10450v1 Announce Type: cross Abstract: DiffC provides a principled way to reuse pre-trained diffusion models for lossy compression, but its encoding and decoding procedures remain slow because they require many discretized forward and reverse steps. We study whether few-step generative mo

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Discussion
Mentioned models
05
  • 01
    DiffC
  • 02
    Rectified Flow
  • 03
    Consistency Trajectory Models (CTM)
  • 04
    MeanFlow
  • 05
    EDM
Source
↗
arxiv
Read original ↗All from arxiv →
Tags
04
#compression#diffusion#generative models#computer vision

No replies yet. Be first.

Mentioned models
05
  • 01
    DiffC
  • 02
    Rectified Flow
  • 03
    Consistency Trajectory Models (CTM)
  • 04
    MeanFlow
  • 05
    EDM
Source
↗
arxiv
Read original ↗All from arxiv →
Tags
04
#compression#diffusion#generative models#computer vision

Related coverage

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Originally published on arxiv ↗
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