Diffusion models generate realistic visual content, yet often fail to produce rare but plausible compositions. When prompted with combinations that are valid but underrepresented in training data, such as a snowy beach or a rainbow at night, the generation process frequently collapses toward more common alternatives. We identify this failure mode as default completion bias, where denoising trajectories are implicitly attracted toward high-frequency semantic configurations. Existing guidance mechanisms do not explicitly model this competing tendency and therefore struggle to prevent such collapse. We introduce Default Completion Repulsion (DCR), a training-free framework that explicitly models and suppresses default completion behavior. DCR constructs a counterfactual attractor by relaxing the rare compositional factor while preserving surrounding semantics, inducing an alternative denoising trajectory reflecting the model's preferred completion. We define the discrepancy between target and attractor trajectories as a counterfactual drift, and propose a projection-based repulsion mechanism that removes guidance components aligned with this drift direction. This suppresses undesired frequent completions while preserving other semantic components. DCR operates entirely within the standard diffusion sampling process without retraining or architectural modification. Experiments on rare compositional prompts show that DCR improves compositional fidelity while maintaining visual quality. Our analysis further shows that the framework exposes and counteracts intrinsic model biases, offering a new perspective on controllable generation beyond explicit constraint enforcement.

翻译：扩散模型能生成逼真的视觉内容，但常无法生成合理却稀有的组合。当使用训练数据中有效但代表性不足的组合（如雪地沙滩或夜晚彩虹）进行提示时，生成过程常坍缩至更常见的替代方案。我们将此失效模式识别为默认完成偏差，即去噪轨迹被隐式吸引向高频语义配置。现有引导机制未显式建模此竞争倾向，因此难以防止此类坍缩。我们提出默认完成排斥（DCR）——一种无需训练的框架，可显式建模并抑制默认完成行为。DCR通过放宽稀有组合因子同时保留周边语义，构建因果吸引子，诱导出反映模型偏好完成的替代去噪轨迹。我们将目标轨迹与吸引子轨迹的差异定义为因果漂移，并提出基于投影的排斥机制，移除与该漂移方向对齐的引导分量。此举可抑制非期望的频繁完成，同时保留其他语义分量。DCR完全运行于标准扩散采样流程中，无需重新训练或修改架构。在稀有组合提示上的实验表明，DCR在保持视觉质量的同时提升了组合保真度。我们的分析进一步显示，该框架可揭示并抵消模型内在偏差，为超越显式约束的可控生成提供了新视角。