AI绘画的「负向提示」正在拖后腿
扩散模型生成图片时常用「无分类器引导」(CFG)来平衡创意与忠实度，但最新研究发现，在模型蒸馏(把大模型能力压缩到小模型)时，直接模仿老师的CFG输出会导致「负分支不对称」:学生模型在正向提示上学得越好，负向提示反而越差，最终生成质量受限。研究者提出「正方向匹配」，把正向预测和CFG方向分开约束，在视频控制任务上效果更稳定。这不是你明天能用的技巧，但解释了为什么有些AI绘画小模型总感觉「怪怪的」——问题可能出在负向提示的副作用上。
📄 原文摘要(英文)
On-policy distillation (OPD) adapts diffusion models by querying a teacher along trajectories generated by the current student, but how it should behave under classifier-free guidance (CFG), a default component of modern diffusion systems, remains poorly understood. Existing OPD methods naturally extend velocity matching to the CFG-composed prediction, directly matching teacher and student guided velocities. We show that this objective is under-identified at the branch level: positive- and negative-branch errors can compensate in the guided prediction. Through two contrasting cases, we find that naive matching remains effective under shared negative conditioning, where both branch errors decrease jointly. When the model's native CFG schema retains privileged information in the teacher's negative branch that is unavailable to the student, however, this joint reduction breaks down and the composed objective induces antagonistic branch-error dynamics, reducing the positive-branch error while increasing the negative-branch error. We term this failure mode Negative Branch Asymmetry (NBA). To address NBA, we introduce Positive--Direction Matching (PDM), a branch-aware OPD objective that separately constrains the positive prediction and the CFG conditional direction. We apply PDM to dense-to-sparse video control, where naive guided matching is highly sensitive to inference guidance scales, while branch-aware supervision enables more robust and effective knowledge transfer.