Over-aligning image generation models to a generalized aesthetic preference conflicts with user intent, particularly when "anti-aesthetic" outputs are requested for artistic or critical purposes. This adherence prioritizes developer-centered values, compromising user autonomy and aesthetic pluralism. We test this bias by constructing a wide-spectrum aesthetics dataset and evaluating state-of-the-art generation and reward models. This position paper finds that aesthetic-aligned generation models frequently default to conventionally beautiful outputs, failing to respect instructions for low-quality or negative imagery. Crucially, reward models penalize anti-aesthetic images even when they perfectly match the explicit user prompt. We confirm this systemic bias through image-to-image editing and evaluation against real abstract artworks.

翻译：将图像生成模型过度对齐于普适审美偏好会与用户意图产生冲突，尤其是在为艺术或批判目的请求"反审美"输出时。这种对齐机制优先考虑以开发者为中心的价值观，损害了用户自主性与审美多元性。我们通过构建广谱审美数据集并评估最先进的生成模型与奖励模型来检验这种偏差。本立场论文发现，审美对齐的生成模型往往默认输出符合传统审美的内容，无法响应关于低质量或负面图像的生成指令。关键在于，即使反审美图像完全符合用户明确提示，奖励模型仍会对其施加惩罚。我们通过图像到图像编辑任务以及与真实抽象艺术作品的对比评估，证实了这种系统性偏差的存在。