SwiftExplorer: Training-free Diffusion Model Alignment with Swift Diversity Exploration
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 ...
| Authors | Renye Yan, Jikang Cheng, You Wu et al. |
|---|---|
| Primary category | cs.LG |
| Published | 6 September 2026 |
| Last updated at source | 6 September 2026 |
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