title: FreeScale: Unleashing the Resolution of Diffusion Models via Tuning-Free
    Scale Fusion
summary: Visual diffusion models achieve remarkable progress, yet they are typically
  trained at limited resolutions due to the lack of high-resolution data and
  constrained computation resources, hampering their ability to generate
  high-fidelity images or videos at higher resolutions. Recent efforts have
  explored tuning-free strategies to exhibit the untapped potential
  higher-resolution visual generation of pre-trained models. However, these
  methods are still prone to producing low-quality visual content with repetitive
  patterns. The key obstacle lies in the inevitable increase in high-frequency
  information when the model generates visual content exceeding its training
  resolution, leading to undesirable repetitive patterns deriving from the
  accumulated errors. To tackle this challenge, we propose FreeScale, a
  tuning-free inference paradigm to enable higher-resolution visual generation
  via scale fusion. Specifically, FreeScale processes information from different
  receptive scales and then fuses it by extracting desired frequency components.
  Extensive experiments validate the superiority of our paradigm in extending the
  capabilities of higher-resolution visual generation for both image and video
  models. Notably, compared with the previous best-performing method, FreeScale
  unlocks the generation of 8k-resolution images for the first time.
paper_authors._id: 675f980c4d67dd90e85ecc6c
paper_authors.name: Haonan Qiu
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paper_authors.name: Shiwei Zhang
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paper_authors.fullname: Shiwei Zhang
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paper_authors.name: Yujie Wei
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paper_authors.fullname: Yujie Wei
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paper_authors.name: Ruihang Chu
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paper_authors.fullname: Ruihang Chu
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paper_authors.name: Hangjie Yuan
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paper_authors.fullname: Hangjie Yuan
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paper_authors.user: JacobYuan
paper_authors._id: 675f980c4d67dd90e85ecc71
paper_authors.name: Xiang Wang
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paper_authors.fullname: Xiang Wang
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paper_authors.user: xiangwang1223
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paper_authors.name: Yingya Zhang
paper_authors._id: 675f980c4d67dd90e85ecc73
paper_authors.name: Ziwei Liu
paper_authors.status: admin_assigned
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paper_authors.fullname: Ziwei Liu
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paper_authors.user: liuziwei7
paper_title: FreeScale: Unleashing the Resolution of Diffusion Models via Tuning-Free
    Scale Fusion
paper_summary: Visual diffusion models achieve remarkable progress, yet they are typically
  trained at limited resolutions due to the lack of high-resolution data and
  constrained computation resources, hampering their ability to generate
  high-fidelity images or videos at higher resolutions. Recent efforts have
  explored tuning-free strategies to exhibit the untapped potential
  higher-resolution visual generation of pre-trained models. However, these
  methods are still prone to producing low-quality visual content with repetitive
  patterns. The key obstacle lies in the inevitable increase in high-frequency
  information when the model generates visual content exceeding its training
  resolution, leading to undesirable repetitive patterns deriving from the
  accumulated errors. To tackle this challenge, we propose FreeScale, a
  tuning-free inference paradigm to enable higher-resolution visual generation
  via scale fusion. Specifically, FreeScale processes information from different
  receptive scales and then fuses it by extracting desired frequency components.
  Extensive experiments validate the superiority of our paradigm in extending the
  capabilities of higher-resolution visual generation for both image and video
  models. Notably, compared with the previous best-performing method, FreeScale
  unlocks the generation of 8k-resolution images for the first time.
paper_ai_summary: FreeScale, a tuning-free inference paradigm, enables high-resolution visual generation by fusing information from different scales, overcoming the limitations of existing methods.
paper_ai_keywords: visual diffusion models tuning-free strategies high-resolution visual generation pre-trained models repetitive patterns high-frequency information scale fusion receptive scales desired frequency components