通过优化采样轨迹降低扩散模型预测误差，提升生成质量。
Variance-Reduction Guidance: Sampling Trajectory Optimization for Diffusion Models
- 统计测量预测误差，设计无须微调的轨迹优化方法
- 相同步数下生成质量显著提升，适用于条件与无条件生成
- 无需修改模型，可直接用于现有扩散模型
扩散模型作为新兴的生成模型，其采样过程包含多步迭代，每一步均需从噪声样本中预测噪声。当模型预测偏离真实值时，产生预测误差，该误差在采样过程中累积，导致生成质量下降。本文提出一种新型统计测量方法来量化预测误差，并引入方差减少引导（Variance-Reduction Guidance, VRG）策略以缓解此问题。VRG无需模型微调或修改，仅在预定义采样轨迹基础上搜索更优轨迹，在保持相同采样步数的前提下实现更高生成质量。该方法适用于条件与无条件生成任务。在多个数据集和基线模型上的实验表明，VRG能显著提升扩散模型的生成效果。代码已公开于 https://github.com/shifengxu/VRG。
原文摘要 · Abstract (English)
Diffusion models have become emerging generative models. Their sampling process involves multiple steps, and in each step the models predict the noise from a noisy sample. When the models make prediction, the output deviates from the ground truth, and we call such a deviation as \textit{prediction error}. The prediction error accumulates over the sampling process and deteriorates generation quality. This paper introduces a novel technique for statistically measuring the prediction error and proposes the Variance-Reduction Guidance (VRG) method to mitigate this error. VRG does not require model fine-tuning or modification. Given a predefined sampling trajectory, it searches for a new trajectory which has the same number of sampling steps but produces higher quality results. VRG is applicable to both conditional and unconditional generation. Experiments on various datasets and baselines demonstrate that VRG can significantly improve the generation quality of diffusion models. Source code is available at https://github.com/shifengxu/VRG.
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