[2604.24885] VibeToken: Scaling 1D Image Tokenizers and Autoregressive Models for Dynamic Resolution Generations

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Computer Science > Computer Vision and Pattern Recognition

arXiv:2604.24885 (cs)

[Submitted on 27 Apr 2026]

Title:VibeToken: Scaling 1D Image Tokenizers and Autoregressive Models for Dynamic Resolution Generations

Authors:Maitreya Patel, Jingtao Li, Weiming Zhuang, Yezhou Yang, Lingjuan Lv<br>View a PDF of the paper titled VibeToken: Scaling 1D Image Tokenizers and Autoregressive Models for Dynamic Resolution Generations, by Maitreya Patel and 4 other authors

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Abstract:We introduce an efficient, resolution-agnostic autoregressive (AR) image synthesis approach that generalizes to arbitrary resolutions and aspect ratios, narrowing the gap to diffusion models at scale. At its core is VibeToken, a novel resolution-agnostic 1D Transformer-based image tokenizer that encodes images into a dynamic, user-controllable sequence of 32-256 tokens, achieving a state-of-the-art efficiency and performance trade-off. Building on VibeToken, we present VibeToken-Gen, a class-conditioned AR generator with out-of-the-box support for arbitrary resolutions while requiring significantly fewer compute resources. Notably, VibeToken-Gen synthesizes 1024x1024 images using only 64 tokens and achieves 3.94 gFID; by comparison, a diffusion-based state-of-the-art alternative requires 1,024 tokens and attains 5.87 gFID. In contrast to fixed-resolution AR models such as LlamaGen -- whose inference FLOPs grow quadratically with resolution (11T FLOPs at 1024x1024) -- VibeToken-Gen maintains a constant 179G FLOPs (63.4x efficient) independent of resolution. We hope VibeToken can help unlock the wide adoption of AR visual generative models in production use cases.

Comments:<br>Accepted at CVPR'26 | Project Page: this https URL

Subjects:

Computer Vision and Pattern Recognition (cs.CV); Machine Learning (cs.LG)

Cite as:<br>arXiv:2604.24885 [cs.CV]

(or<br>arXiv:2604.24885v1 [cs.CV] for this version)

https://doi.org/10.48550/arXiv.2604.24885

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arXiv-issued DOI via DataCite

Submission history<br>From: Maitreya Patel [view email]<br>[v1]<br>Mon, 27 Apr 2026 18:08:05 UTC (4,877 KB)

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