Diffusion Negative Preference Optimization Made Simple

Classifier-Free Guidance (CFG) improves diffusion sampling by encouraging conditional generations while discouraging unconditional ones. Existing preference alignment methods, however, focus only on positive preference pairs, limiting their ability to actively suppress undesirable outputs. Diffusion Negative Preference Optimization (Diff-NPO) approaches this limitation by introducing a separate negative model trained with inverted labels, allowing it to capture signals for suppressing undesirable generations. However, this design comes with two key drawbacks. First, maintaining two distinct models throughout training and inference substantially increases computational cost, making the approach less practical. Second, at inference time, Diff-NPO relies on weight merging between the positive and negative models, a process that dilutes the learned negative alignment and undermines its effectiveness. To overcome these issues, we introduce Diff-SNPO, a single-network framework that jointly learns from both positive and negative preferences. Our method employs a bounded preference objective to prevent winner-likelihood collapse, ensuring stable optimization. Diff-SNPO delivers strong alignment performance with significantly lower computational overhead, showing that explicit negative preference modeling can be simple, stable, and efficient within a unified diffusion framework. Code will be released.