title: I-Max: Maximize the Resolution Potential of Pre-trained Rectified Flow
    Transformers with Projected Flow
summary: Rectified Flow Transformers (RFTs) offer superior training and inference
  efficiency, making them likely the most viable direction for scaling up
  diffusion models. However, progress in generation resolution has been
  relatively slow due to data quality and training costs. Tuning-free resolution
  extrapolation presents an alternative, but current methods often reduce
  generative stability, limiting practical application. In this paper, we review
  existing resolution extrapolation methods and introduce the I-Max framework to
  maximize the resolution potential of Text-to-Image RFTs. I-Max features: (i) a
  novel Projected Flow strategy for stable extrapolation and (ii) an advanced
  inference toolkit for generalizing model knowledge to higher resolutions.
  Experiments with Lumina-Next-2K and Flux.1-dev demonstrate I-Max's ability to
  enhance stability in resolution extrapolation and show that it can bring image
  detail emergence and artifact correction, confirming the practical value of
  tuning-free resolution extrapolation.
paper_authors._id: 670d1ad6756dc949a69f79e5
paper_authors.name: Ruoyi Du
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2024-10-14T15:46:34.443Z
paper_authors._id: 64a54586c0f13de8e7093314
paper_authors.avatarUrl: /avatars/389e43e9a32cf2fc95f8f3a23b8f0508.svg
paper_authors.fullname: Ruoyi Du
paper_authors.type: user
paper_authors.user: RuoyiDu
paper_authors._id: 670d1ad6756dc949a69f79e6
paper_authors.name: Dongyang Liu
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2024-10-14T15:46:43.133Z
paper_authors._id: 646f1bef075e11ca78da3bb7
paper_authors.avatarUrl: https://cdn-avatars.huggingface.co/v1/production/uploads/646f1bef075e11ca78da3bb7/gNS-ikyZXYeMrf4a7HTQE.jpeg
paper_authors.fullname: Dongyang Liu (Chris Liu)
paper_authors.type: user
paper_authors.user: Cxxs
paper_authors._id: 670d1ad6756dc949a69f79e7
paper_authors.name: Le Zhuo
paper_authors._id: 670d1ad6756dc949a69f79e8
paper_authors.name: Qin Qi
paper_authors._id: 670d1ad6756dc949a69f79e9
paper_authors.name: Hongsheng Li
paper_authors._id: 670d1ad6756dc949a69f79ea
paper_authors.name: Zhanyu Ma
paper_authors._id: 670d1ad6756dc949a69f79eb
paper_authors.name: Peng Gao
paper_title: I-Max: Maximize the Resolution Potential of Pre-trained Rectified Flow
    Transformers with Projected Flow
paper_summary: Rectified Flow Transformers (RFTs) offer superior training and inference
  efficiency, making them likely the most viable direction for scaling up
  diffusion models. However, progress in generation resolution has been
  relatively slow due to data quality and training costs. Tuning-free resolution
  extrapolation presents an alternative, but current methods often reduce
  generative stability, limiting practical application. In this paper, we review
  existing resolution extrapolation methods and introduce the I-Max framework to
  maximize the resolution potential of Text-to-Image RFTs. I-Max features: (i) a
  novel Projected Flow strategy for stable extrapolation and (ii) an advanced
  inference toolkit for generalizing model knowledge to higher resolutions.
  Experiments with Lumina-Next-2K and Flux.1-dev demonstrate I-Max's ability to
  enhance stability in resolution extrapolation and show that it can bring image
  detail emergence and artifact correction, confirming the practical value of
  tuning-free resolution extrapolation.
paper_ai_keywords: Rectified Flow Transformers (RFTs) diffusion models resolution extrapolation generative stability Text-to-Image I-Max framework Projected Flow strategy inference toolkit Lumina-Next-2K Flux.1-dev image detail emergence artifact correction tuning-free resolution extrapolation