Inference-Time Scaling of Diffusion Models via Progressive Pruning Search

Di!usion and flow-matching models dominate conditionalimage generation, yet inference-time scaling for these models is far lessdeveloped than for autoregressive language models. Because final qual-ity is highly sensitive to the initial noise seed, many approaches spendextra compute on seed search or resampling under a black-box reward,but typically maintaining a constant memory footprint throughout in-ference. We show that relaxing this constraint enables an underexploredinference-time scaling axis: by front-loading exploration, evaluating manyseeds early, and pruning aggressively, we can use a fixed compute budgetmore e!ectively. Progressive Seed Pruning (PSP) scores intermediate de-noised estimates and progressively narrows the candidate set so that onlypromising trajectories are fully denoised, while keeping the total num-ber of model evaluations fixed. Across di!usion and flow-matching back-bones, PSP consistently improves reward-guided selection and achieveshigher GenEval scores (automated) and better human evaluation onprompt-alignment than best-of-N , importance-sampling, and tree-searchbaselines at matched compute. Project page: vision.caltech.edu/psp.Code: github.com/rogerioagjr/psp