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TokenRepel: Generating Diverse Image Sets Without Compromising Quality

Paper recorded by Signals 4 on 2026-10-29 in cs.CV. Abstract reproduced from arXiv; link to the original below.

Published 2026-10-29 on arXiv · recorded by Signals 4 on 2026-10-30

Category: cs.CV · 计算机视觉 · first seen 2026-10-30

Abstract

We present an inference-time approach that enhances the visual diversity of image sets generated by text-to-image models, without compromising the average image quality. Our method leverages the intrinsic geometry of hidden representations in pretrained models to diversify images generated from the same prompt. First, we introduce a token repulsion mechanism that increases the angle between featur

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#75 most recent of 710 cs.CV papers we have recorded · ↑ newer: PhysAg3D: Bridging 3D Generative and Agentic Models for High-Fidelity · ↓ older: What Survives Label Downsampling? Exact Region Survival for Coarse Seg
Cite this page: TokenRepel: Generating Diverse Image Sets Without Compromising Quality: the #75 most recent of 710 cs.CV papers we have recorded (as of 2026-10-29). Source: Signals 4 (Signals API) — https://data.jiangzhang.ca/signals4/t/papers/tokenrepel-generating-diverse-image-sets-without-compromising-quality.html
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