Test-Time Scaling of Text-to-Image Flow Matching Models with Noise-Rejuvenated Sequential Monte Carlo

Key points

• Test-time scaling of diffusion models with sequential Monte Carlo (SMC) has become a promising strategy for sampling under user-specified rewards, such as human preference scores.1
• However, the standard SMC framework typically relies on stochastic proposals and therefore does not directly transfer to flow matching models, in which inference is governed by deterministic ODEs.1
• We further introduce a look-ahead mechanism to refine resampled particles before weighting.1
• Experiments on Stable Diffusion 3, FLUX.1-dev, and Z-Image demonstrate consistent improvements in reward-guided generation, while ablation studies validate the contributions of noise rejuvenation and look-ahead refinement.1

Sources (1)

• [1]Test-Time Scaling of Text-to-Image Flow Matching Models with Noise-Rejuvenated Sequential Monte Carlo
  arXiv (AI, ML, NLP, CV, robotics, multi-agent) · Oct 18, 07:05 PM

  In this work, we introduce noise-rejuvenated SMC, an inference-time scaling method tailored for text-to-image flow matching models.

  Test-time scaling of diffusion models with sequential Monte Carlo (SMC) has become a promising strategy for sampling under user-specified rewards, such as human preference scores.

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