title: FlashVideo:Flowing Fidelity to Detail for Efficient High-Resolution
    Video Generation
summary: DiT diffusion models have achieved great success in text-to-video generation,
  leveraging their scalability in model capacity and data scale. High content and
  motion fidelity aligned with text prompts, however, often require large model
  parameters and a substantial number of function evaluations (NFEs). Realistic
  and visually appealing details are typically reflected in high resolution
  outputs, further amplifying computational demands especially for single stage
  DiT models. To address these challenges, we propose a novel two stage
  framework, FlashVideo, which strategically allocates model capacity and NFEs
  across stages to balance generation fidelity and quality. In the first stage,
  prompt fidelity is prioritized through a low resolution generation process
  utilizing large parameters and sufficient NFEs to enhance computational
  efficiency. The second stage establishes flow matching between low and high
  resolutions, effectively generating fine details with minimal NFEs.
  Quantitative and visual results demonstrate that FlashVideo achieves
  state-of-the-art high resolution video generation with superior computational
  efficiency. Additionally, the two-stage design enables users to preview the
  initial output before committing to full resolution generation, thereby
  significantly reducing computational costs and wait times as well as enhancing
  commercial viability .
paper_authors._id: 67a9901cc0310368e2488929
paper_authors.name: Shilong Zhang
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paper_authors.fullname: Shilong Zhang
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paper_authors.name: Wenbo Li
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paper_authors.name: Shoufa Chen
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paper_authors.fullname: Shoufa Chen
paper_authors.type: user
paper_authors.user: ShoufaChen
paper_authors._id: 67a9901cc0310368e248892c
paper_authors.name: Chongjian Ge
paper_authors.status: admin_assigned
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paper_authors.fullname: ChongjianGE
paper_authors.type: user
paper_authors.user: RhettGee
paper_authors._id: 67a9901cc0310368e248892d
paper_authors.name: Peize Sun
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paper_authors.fullname: peizesun
paper_authors.type: user
paper_authors.user: peizesun
paper_authors._id: 67a9901cc0310368e248892e
paper_authors.name: Yida Zhang
paper_authors._id: 67a9901cc0310368e248892f
paper_authors.name: Yi Jiang
paper_authors.status: claimed_verified
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paper_authors.avatarUrl: https://cdn-avatars.huggingface.co/v1/production/uploads/6344dcb1cd37e44d9ed46508/J92UKSxKR3iziD2WJfih4.jpeg
paper_authors.fullname: Yi Jiang
paper_authors.type: user
paper_authors.user: JiangYi
paper_authors._id: 67a9901cc0310368e2488930
paper_authors.name: Zehuan Yuan
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2025-02-10T16:15:29.017Z
paper_authors._id: 661a80af3557013b638061d5
paper_authors.avatarUrl: /avatars/4c551aeb223e257a5fc45b5b6c7ded49.svg
paper_authors.fullname: Zehuan Yuan
paper_authors.type: user
paper_authors.user: sweetrabor
paper_authors._id: 67a9901cc0310368e2488931
paper_authors.name: Binyue Peng
paper_authors._id: 67a9901cc0310368e2488932
paper_authors.name: Ping Luo
paper_title: FlashVideo:Flowing Fidelity to Detail for Efficient High-Resolution
    Video Generation
paper_summary: DiT diffusion models have achieved great success in text-to-video generation,
  leveraging their scalability in model capacity and data scale. High content and
  motion fidelity aligned with text prompts, however, often require large model
  parameters and a substantial number of function evaluations (NFEs). Realistic
  and visually appealing details are typically reflected in high resolution
  outputs, further amplifying computational demands especially for single stage
  DiT models. To address these challenges, we propose a novel two stage
  framework, FlashVideo, which strategically allocates model capacity and NFEs
  across stages to balance generation fidelity and quality. In the first stage,
  prompt fidelity is prioritized through a low resolution generation process
  utilizing large parameters and sufficient NFEs to enhance computational
  efficiency. The second stage establishes flow matching between low and high
  resolutions, effectively generating fine details with minimal NFEs.
  Quantitative and visual results demonstrate that FlashVideo achieves
  state-of-the-art high resolution video generation with superior computational
  efficiency. Additionally, the two-stage design enables users to preview the
  initial output before committing to full resolution generation, thereby
  significantly reducing computational costs and wait times as well as enhancing
  commercial viability .
paper_ai_summary: A two-stage framework, FlashVideo, for text-to-video generation improves computational efficiency while maintaining high resolution and detail fidelity.
paper_ai_keywords: DiT diffusion models text-to-video generation computational efficiency high resolution prompt fidelity flow matching fine details function evaluations model capacity data scale computational demands state-of-the-art