I-Max: Maximize the Resolution Potential of Pre-trained Rectified Flow
  Transformers with Projected Flow
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.
670d1ad6756dc949a69f79e5
Ruoyi Du
admin_assigned
2024-10-14T15:46:34.443Z
64a54586c0f13de8e7093314
/avatars/389e43e9a32cf2fc95f8f3a23b8f0508.svg
Ruoyi Du
user
RuoyiDu
670d1ad6756dc949a69f79e6
Dongyang Liu
admin_assigned
2024-10-14T15:46:43.133Z
646f1bef075e11ca78da3bb7
https://cdn-avatars.huggingface.co/v1/production/uploads/646f1bef075e11ca78da3bb7/gNS-ikyZXYeMrf4a7HTQE.jpeg
Dongyang Liu (Chris Liu)
user
Cxxs
670d1ad6756dc949a69f79e7
Le Zhuo
claimed_verified
2025-09-13T15:06:44.452Z
6358a167f56b03ec9147074d
/avatars/e54ea7bf0c240cf76d538296efb3976c.svg
Le Zhuo
user
JackyZhuo
670d1ad6756dc949a69f79e8
Qin Qi
670d1ad6756dc949a69f79e9
Hongsheng Li
670d1ad6756dc949a69f79ea
Zhanyu Ma
670d1ad6756dc949a69f79eb
Peng Gao
I-Max: Maximize the Resolution Potential of Pre-trained Rectified Flow
  Transformers with Projected Flow
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.
I-Max framework enhances resolution extrapolation for Text-to-Image Rectified Flow Transformers, improving stability and image quality without parameter tuning.
Rectified Flow Transformers RFTs diffusion models resolution extrapolation Projected Flow strategy inference toolkit Text-to-Image Lumina-Next-2K Flux.1-dev image detail emergence artifact correction