无需微调或梯度计算，用分块采样实现扩散模型的奖励对齐。
CoDe: Blockwise Control for Denoising Diffusion Models
- 在去噪过程中分块施加控制，实现推理时无梯度引导。
- 相比现有方法，在奖励对齐与指令遵循上表现更优，推理成本更低。
- 适合需要快速部署、避免微调的生成任务场景。
将扩散模型与下游任务对齐通常需要微调新模型或在推理时使用基于梯度的引导。本文提出一种简单高效的推理时无梯度引导方法——受控去噪（CoDe），无需可微引导函数或模型微调。CoDe是一种应用于中间去噪步骤的分块采样方法，可实现与下游奖励的对齐。实验表明，尽管方法简单，CoDe在奖励对齐、提示指令遵循与推理开销之间取得了良好平衡，性能媲美当前最优基线。代码已开源：https://github.com/anujinho/code。
原文摘要 · Abstract (English)
Aligning diffusion models to downstream tasks often requires finetuning new models or gradient-based guidance at inference time to enable sampling from the reward-tilted posterior. In this work, we explore a simple inference-time gradient-free guidance approach, called controlled denoising (CoDe), that circumvents the need for differentiable guidance functions and model finetuning. CoDe is a blockwise sampling method applied during intermediate denoising steps, allowing for alignment with downstream rewards. Our experiments demonstrate that, despite its simplicity, CoDe offers a favorable trade-off between reward alignment, prompt instruction following, and inference cost, achieving a competitive performance against the state-of-the-art baselines. Our code is available at: https://github.com/anujinho/code.
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