SwiftExplorer: Training-free Diffusion Model Alignment with Swift Diversity Exploration

2026-09-06

AI Summary

为解决扩散模型难以对齐特定目标且训练成本高问题，提出SwiftExplorer方法，通过探索机制和质量效率仲裁机制提高生成多样性与质量。

Abstract

Diffusion models have general generative abilities but struggle to align with specific objectives. Fine-tuning can improve alignment, yet its training cost is often prohibitive. This led to training-free methods that apply objective-guided terms in sampling to bias the generation distribution toward designated regions, e.g., high-reward areas. However, these methods face two issues: (1) the strong directional bias narrows the pretrained distribution and generation diversity, and (2) indiscriminate constant guidance fails to prune redundant signals, hurting both quality and efficiency. To address the above challenges, we propose SwiftExplorer, a plugin that mitigates distribution collapse caused by excessive diversity loss and reduces compute costs. First, we adopt an Inheritance-Restart exploration mechanism to avoid early convergence, while exploration also increases the likelihood of high-reward trajectories. Additionally, it balances diversity and fidelity, adding diversity without causing a distribution over-shift. Second, our Quality-Efficiency arbitration mechanism improves guidance by removing incorrect signals, and it reduces computation by dynamically stopping generation when completeness and marginal reward gain are optimal. In an extensive number of experiments and different types of evaluation metrics, the proposed SwiftExplorer achieves excellent performance on all metrics, including preference, fidelity, diversity, and richness.

Problem

• diffusion models
• alignment
• diversity
• guidance
• efficiency

Innovation

• Inheritance-Restart
• Quality-Efficiency arbitration
• diversity and fidelity balance
• training-free alignment
• dynamic stopping