Scaling Image and Video Generation via Test-Time Evolutionary Search
Journal:
arXiv
Published Date:
May 23, 2025
Abstract
As the marginal cost of scaling computation (data and parameters) during
model pre-training continues to increase substantially, test-time scaling (TTS)
has emerged as a promising direction for improving generative model performance
by allocating additional computation at inference time. While TTS has
demonstrated significant success across multiple language tasks, there remains
a notable gap in understanding the test-time scaling behaviors of image and
video generative models (diffusion-based or flow-based models). Although recent
works have initiated exploration into inference-time strategies for vision
tasks, these approaches face critical limitations: being constrained to
task-specific domains, exhibiting poor scalability, or falling into reward
over-optimization that sacrifices sample diversity. In this paper, we propose
\textbf{Evo}lutionary \textbf{Search} (EvoSearch), a novel, generalist, and
efficient TTS method that effectively enhances the scalability of both image
and video generation across diffusion and flow models, without requiring
additional training or model expansion. EvoSearch reformulates test-time
scaling for diffusion and flow models as an evolutionary search problem,
leveraging principles from biological evolution to efficiently explore and
refine the denoising trajectory. By incorporating carefully designed selection
and mutation mechanisms tailored to the stochastic differential equation
denoising process, EvoSearch iteratively generates higher-quality offspring
while preserving population diversity. Through extensive evaluation across both
diffusion and flow architectures for image and video generation tasks, we
demonstrate that our method consistently outperforms existing approaches,
achieves higher diversity, and shows strong generalizability to unseen
evaluation metrics. Our project is available at the website
https://tinnerhrhe.github.io/evosearch.
