Inference-Time Scaling of Diffusion Models via Progressive Seed Pruning
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TL;DR - Progressive Seed Pruning improves diffusion-model inference by evaluating many noise seeds early and progressively eliminating weak trajectories. It delivers better image-generation quality than competing selection methods at matched compute.
- Scores intermediate denoised estimates to identify promising seeds before full generation.
- Front-loads exploration while keeping total model evaluations fixed, trading additional memory for more effective compute use.
- Outperforms best-of-N, importance-sampling, and tree-search baselines across diffusion and flow-matching backbones.
- Improves automated GenEval scores and human-rated prompt alignment.
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Inference-Time Scaling of Diffusion Models via Progressive Seed Pruning
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TL;DR - Progressive Seed Pruning improves diffusion-model inference by evaluating many noise seeds early and progressively eliminating weak trajectories. It delivers better image-generation quality than competing selection methods at matched compute.
- Scores intermediate denoised estimates to identify promising seeds before full generation.
- Front-loads exploration while keeping total model evaluations fixed, trading additional memory for more effective compute use.
- Outperforms best-of-N, importance-sampling, and tree-search baselines across diffusion and flow-matching backbones.
- Improves automated GenEval scores and human-rated prompt alignment.