Pith. sign in

REVIEW 3 cited by

I-Max: Maximize the Resolution Potential of Pre-trained Rectified Flow Transformers with Projected Flow

Not yet reviewed by Pith; the record is open.

Re-run · record.json Download PDF Read on arXiv ↗

This paper has not been read by Pith yet. Machine review is queued; the pith claim, tier, and objections will appear here once it completes.

SPECIMEN: schema-true, not a live event

T0 review · schema-true

One-sentence machine reading of the paper's core claim.

pith:XXXXXXXX · record.json · timestamp

arxiv 2410.07536 v2 pith:5HGPZUIN submitted 2024-10-10 cs.CV

Ruoyi Du , Dongyang Liu , Le Zhuo , Qin Qi , Hongsheng Li , Zhanyu Ma , Peng Gao This is my paper · ORCID
classification cs.CV
keywords resolutionextrapolationflowi-maxinferencemaximizemethodspotential
verification ladder T0 review T1 audit T2 compute T3 formal
0 comments
read the original abstract

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.

Discussion (0). Continue with ORCID to comment.

Forward citations

Cited by 3 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. CineScale: Free Lunch in High-Resolution Cinematic Visual Generation

    cs.CV 2025-08 conditional novelty 6.0 of 10

    CineScale extends pre-trained diffusion models to 8k image and 4k video generation with mostly tuning-free inference plus a small LoRA adaptation for video.

  2. LSSGen: Leveraging Latent Space Scaling in Flow and Diffusion for Efficient Text to Image Generation

    cs.CV 2025-07 conditional novelty 6.0 of 10

    A latent-space scaling framework that replaces pixel-space upscaling with a trainable latent upsampler and noise compensation, yielding faster high-resolution text-to-image generation.

  3. Tuning-Free Latent Diffusion Models for Ultrahigh-Resolution Image Editing

    cs.CV 2026-07 conditional novelty 5.0 of 10

    UltraDiffEdit enables tuning-free image editing at up to 8K resolution on a single consumer GPU by combining multi-patch latent encoding, boundary-aware denoising, and hybrid local-global-upsample sampling.

Pith tools