通过动态调整噪声搜索，提升扩散模型生成质量
Guided Trajectory Optimization with Sparse Scaling for Test-Time Diffusion
- 用奖励引导策略主动寻找优质噪声区域
- 在测试时仅优化关键步骤，性能提升15.6%
- 适合追求高质量图像生成的研究者与开发者
高效测试时缩放（TTS）范式为提升扩散模型生成性能提供了新路径。然而，现有方法受限于静态预设的噪声池，且在去噪轨迹中缺乏灵活的噪声探索能力。为此，我们提出一种新型奖励引导轨迹缩放方法（RTS），充分释放扩散模型的生成潜力。与现有方法不同，RTS通过两项核心创新实现高质量图像合成：1）采用奖励引导的噪声优化策略，主动引导搜索至有前景区域；2）结合稀疏测试时缩放框架与基于PCA的曲率分析方案，优先选择整个去噪空间中的关键中间步骤，有效压缩搜索空间。实验表明，该方法在GenEval Score上较基线提升15.6%，ImageReward得分提高60.4%，达到新SOTA水平，并为扩散模型架构下的更有效测试时缩放提供实用指导。
原文摘要 · Abstract (English)
The efficient Test-Time Scaling (TTS) paradigm offers a promising perspective for enhancing the generation performance of diffusion models. However, current solutions are limited to a static, pre-defined noise pool and suffer from inflexible noise exploration across the denoising trajectory. To bridge this gap, we propose RTS, a novel Reward-guided Trajectory Scaling method to fully unlock the generative potential of diffusion models. Unlike existing methods, RTS facilitates the synthesis of refined, high-fidelity images via two core innovations: 1) a reward-guided noise optimization strategy to actively direct the search towards promising regions; and 2) a sparse test-time scaling framework together with a PCA-driven curvature analysis scheme to prioritize key intermediate steps in the entire denoising space, effectively compressing the search space. Experiments show our approach outperforms baselines by 15.6% across GenEval Score, and a 60.4% enhancement in ImageReward score, setting a new SOTA while providing a practical guideline for more effective test-time scaling across diffusion-specific architectures.
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