Flash-BoN：擴散模型推理時擴展的即時草稿

摘要

文本到圖像生成中的推理時間擴展已從簡單的最佳N採樣（BoN）發展到引導式搜索方法，此類方法在中間去噪步驟中驗證並引導候選軌跡。這些方法專注於去噪過程中何時及多頻繁進行驗證，但很大程度上將生成過程本身的成本視為固定值。此外，標準的比較方法根據函數評估次數（NFEs）僅計算去噪前向傳遞，而忽略驗證器的開銷，這可能扭曲效率排名。我們證明，在掛鐘時間評估下，簡單的BoN已能匹配或超越多種引導式搜索技術，表明計算資源更應投入於廣泛探索，而非反覆的中間驗證。這促使我們提出Flash-BoN，其通過結合三種互補的加速手段（時間步長截斷、層跳越、激活代理），並針對每個模型一次性優化配置，生成大量低成本的草稿候選。隨後，高效的多階段驗證程序可識別最有潛力的草稿，並以完整質量進行精煉。在三項基準測試與三種模型規模下，Flash-BoN在固定掛鐘時間預算內持續優於所有基線，且優勢隨模型規模擴大而增長（AUC提升+8%）。我們進一步證明，此策略能與現有的正交技術（如基於反射的提示優化，AUC提升+16%）良好結合並加以改進。性能提升與候選多樣性增加相關，這也使草稿引導選擇能加速強化學習後訓練的收斂。

English

Inference-time scaling for text-to-image generation has progressed from simple Best-of-N (BoN) sampling to guided search methods that verify and steer candidate trajectories at intermediate denoising steps. These approaches focus on when and how often to verify during denoising but largely treat the cost of generation itself as fixed. Moreover, the standard practice of comparing methods by number of function evaluations (NFEs) counts only denoising forward passes and ignores verifier overhead, which can distort efficiency rankings. We show that under wall-clock evaluation, simple BoN already matches or outperforms several guided search techniques, suggesting that compute is better spent on broader exploration than on repeated intermediate verification. This motivates Flash-BoN, which generates a large pool of inexpensive draft candidates by combining three complementary acceleration knobs: timestep truncation, layer skipping, and activation proxies into a single configuration optimized once per model. An efficient multi-stage verification procedure then identifies the most promising draft, which is refined at full quality. Across three benchmarks and three model scales, Flash-BoN consistently outperforms all baselines under fixed wall-clock budgets, with gains that grow at larger model scales (+8% AUC). We further show that our strategy combines well and improves existing orthogonal techniques such as reflection-based prompt optimization (+16% AUC). The gains correlate with increased candidate diversity, which also enables draft-guided selection to accelerate RL post-training convergence.