Verifier Threshold: An Efficient Test-Time Scaling Approach for Image Generation

TL;DR

This paper introduces Verifier-Threshold, a method that reallocates test-time compute for image generation models, significantly improving efficiency while maintaining performance.

Contribution

It proposes a simple, automatic compute reallocation technique that outperforms existing heuristics in test-time image generation.

Findings

2-4x reduction in computational time on GenEval benchmark

Maintains performance while improving efficiency

Outperforms state-of-the-art methods in test-time compute allocation

Abstract

Image generation has emerged as a mainstream application of large generative models. Just as test-time compute and reasoning have improved language model capabilities, similar benefits have been observed for image generation models. In particular, searching over noise samples for diffusion and flow models has been shown to scale well with test-time compute. While recent works explore allocating non-uniform inference-compute budgets across denoising steps, existing approaches rely on greedy heuristics and often allocate the compute budget ineffectively. In this work, we study this problem and propose a simple fix. We propose Verifier-Threshold, which automatically reallocates test-time compute and delivers substantial efficiency improvements. For the same performance on the GenEval benchmark, we achieve a 2-4x reduction in computational time over the state-of-the-art method.