I-Max: Maximize the Resolution Potential of Pre-trained Rectified Flow
  Transformers with Projected Flow
Authors: name: Ruoyi Du, name: Dongyang Liu, name: Le Zhuo, name: Qin Qi, name: Hongsheng Li, name: Zhanyu Ma, name: Peng Gao

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.