FlashVideo:Flowing Fidelity to Detail for Efficient High-Resolution
  Video Generation
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 .
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Shilong Zhang
admin_assigned
2025-02-10T16:14:33.747Z
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Shilong Zhang
user
shilongz
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Wenbo Li
admin_assigned
2025-02-10T16:14:43.524Z
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wenboli
user
wenboli
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Shoufa Chen
claimed_verified
2025-02-10T09:49:46.264Z
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Shoufa Chen
user
ShoufaChen
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Chongjian Ge
admin_assigned
2025-02-10T16:14:54.589Z
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ChongjianGE
user
RhettGee
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Peize Sun
admin_assigned
2025-02-10T16:15:01.448Z
640dc9bf8512ec51d7f0ac1a
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peizesun
user
peizesun
67a9901cc0310368e248892e
Yida Zhang
67a9901cc0310368e248892f
Yi Jiang
claimed_verified
2025-03-02T20:18:49.413Z
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https://cdn-avatars.huggingface.co/v1/production/uploads/6344dcb1cd37e44d9ed46508/J92UKSxKR3iziD2WJfih4.jpeg
Yi Jiang
user
JiangYi
67a9901cc0310368e2488930
Zehuan Yuan
admin_assigned
2025-02-10T16:15:29.017Z
661a80af3557013b638061d5
/avatars/4c551aeb223e257a5fc45b5b6c7ded49.svg
Zehuan Yuan
user
sweetrabor
67a9901cc0310368e2488931
Binyue Peng
67a9901cc0310368e2488932
Ping Luo
FlashVideo:Flowing Fidelity to Detail for Efficient High-Resolution
  Video Generation
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 .
A two-stage framework, FlashVideo, for text-to-video generation improves computational efficiency while maintaining high resolution and detail fidelity.
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