title: Scaling Image and Video Generation via Test-Time Evolutionary Search
summary: 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
  Evolutionary 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.
paper_authors._id: 6833eeaf98515618764fc204
paper_authors.name: Haoran He
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2025-05-26T08:09:19.593Z
paper_authors._id: 6672937ceac0fb1b9e516595
paper_authors.avatarUrl: /avatars/5eea5657016572f60b0ecd0fa9a7dae4.svg
paper_authors.fullname: haoran he
paper_authors.type: user
paper_authors.user: haoranhe
paper_authors._id: 6833eeaf98515618764fc205
paper_authors.name: Jiajun Liang
paper_authors._id: 6833eeaf98515618764fc206
paper_authors.name: Xintao Wang
paper_authors._id: 6833eeaf98515618764fc207
paper_authors.name: Pengfei Wan
paper_authors._id: 6833eeaf98515618764fc208
paper_authors.name: Di Zhang
paper_authors._id: 6833eeaf98515618764fc209
paper_authors.name: Kun Gai
paper_authors._id: 6833eeaf98515618764fc20a
paper_authors.name: Ling Pan
paper_title: Scaling Image and Video Generation via Test-Time Evolutionary Search
paper_summary: 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
  Evolutionary 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.
paper_ai_summary: EvoSearch, an evolutionary search method, enhances test-time scaling for diffusion and flow-based generative models, improving image and video generation quality, diversity, and generalizability.
paper_ai_keywords: test-time scaling TTS image generation video generation diffusion models flow-based models denoising trajectory stochastic differential equation selection mutation EvoSearch