title: QVGen: Pushing the Limit of Quantized Video Generative Models
summary: Video diffusion models (DMs) have enabled high-quality video synthesis. Yet,
  their substantial computational and memory demands pose serious challenges to
  real-world deployment, even on high-end GPUs. As a commonly adopted solution,
  quantization has proven notable success in reducing cost for image DMs, while
  its direct application to video DMs remains ineffective. In this paper, we
  present QVGen, a novel quantization-aware training (QAT) framework tailored for
  high-performance and inference-efficient video DMs under extremely low-bit
  quantization (e.g., 4-bit or below). We begin with a theoretical analysis
  demonstrating that reducing the gradient norm is essential to facilitate
  convergence for QAT. To this end, we introduce auxiliary modules (Phi) to
  mitigate large quantization errors, leading to significantly enhanced
  convergence. To eliminate the inference overhead of Phi, we propose a
  rank-decay strategy that progressively eliminates Phi. Specifically, we
  repeatedly employ singular value decomposition (SVD) and a proposed rank-based
  regularization gamma to identify and decay low-contributing
  components. This strategy retains performance while zeroing out inference
  overhead. Extensive experiments across 4 state-of-the-art (SOTA) video DMs,
  with parameter sizes ranging from 1.3B sim14B, show that QVGen is the
  first to reach full-precision comparable quality under 4-bit settings.
  Moreover, it significantly outperforms existing methods. For instance, our
  3-bit CogVideoX-2B achieves improvements of +25.28 in Dynamic Degree and
  +8.43 in Scene Consistency on VBench.
paper_authors._id: 682c27b10f622b7afc25df1f
paper_authors.name: Yushi Huang
paper_authors.status: claimed_verified
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paper_authors.fullname: Yushi Huang
paper_authors.type: user
paper_authors.user: Harahan
paper_authors._id: 682c27b10f622b7afc25df20
paper_authors.name: Ruihao Gong
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2025-05-20T08:53:49.624Z
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paper_authors.fullname: Gong
paper_authors.type: user
paper_authors.user: Ruihao
paper_authors._id: 682c27b10f622b7afc25df21
paper_authors.name: Jing Liu
paper_authors._id: 682c27b10f622b7afc25df22
paper_authors.name: Yifu Ding
paper_authors._id: 682c27b10f622b7afc25df23
paper_authors.name: Chengtao Lv
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2025-05-20T08:53:14.848Z
paper_authors._id: 64e9bfc3f494f8b2a061a010
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paper_authors.fullname: 吕呈滔
paper_authors.type: user
paper_authors.user: lvchengtao
paper_authors._id: 682c27b10f622b7afc25df24
paper_authors.name: Haotong Qin
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2025-05-20T08:52:56.938Z
paper_authors._id: 65c49589c0b1921e19260a8d
paper_authors.avatarUrl: /avatars/7ce9af8c627f2a0c3db6bde82290ee1f.svg
paper_authors.fullname: Haotong Qin
paper_authors.type: user
paper_authors.user: HaotongQin
paper_authors._id: 682c27b10f622b7afc25df25
paper_authors.name: Jun Zhang
paper_title: QVGen: Pushing the Limit of Quantized Video Generative Models
paper_summary: Video diffusion models (DMs) have enabled high-quality video synthesis. Yet,
  their substantial computational and memory demands pose serious challenges to
  real-world deployment, even on high-end GPUs. As a commonly adopted solution,
  quantization has proven notable success in reducing cost for image DMs, while
  its direct application to video DMs remains ineffective. In this paper, we
  present QVGen, a novel quantization-aware training (QAT) framework tailored for
  high-performance and inference-efficient video DMs under extremely low-bit
  quantization (e.g., 4-bit or below). We begin with a theoretical analysis
  demonstrating that reducing the gradient norm is essential to facilitate
  convergence for QAT. To this end, we introduce auxiliary modules (Phi) to
  mitigate large quantization errors, leading to significantly enhanced
  convergence. To eliminate the inference overhead of Phi, we propose a
  rank-decay strategy that progressively eliminates Phi. Specifically, we
  repeatedly employ singular value decomposition (SVD) and a proposed rank-based
  regularization gamma to identify and decay low-contributing
  components. This strategy retains performance while zeroing out inference
  overhead. Extensive experiments across 4 state-of-the-art (SOTA) video DMs,
  with parameter sizes ranging from 1.3B sim14B, show that QVGen is the
  first to reach full-precision comparable quality under 4-bit settings.
  Moreover, it significantly outperforms existing methods. For instance, our
  3-bit CogVideoX-2B achieves improvements of +25.28 in Dynamic Degree and
  +8.43 in Scene Consistency on VBench.
paper_ai_summary: QVGen is a quantization-aware training framework that enhances the performance and efficiency of video diffusion models under low-bit quantization, achieving high-quality video synthesis comparable to full-precision models.
paper_ai_keywords: video diffusion models quantization quantization-aware training QAT gradient norm auxiliary modules singular value decomposition rank-based regularization Dynamic Degree Scene Consistency