In subject-driven text-to-image synthesis, the synthesis process tends to be
heavily influenced by the reference images provided by users, often overlooking
crucial attributes detailed in the text prompt. In this work, we propose
Subject-Agnostic Guidance (SAG), a simple yet effective solution to remedy the
problem. We show that through constructing a subject-agnostic condition and
applying our proposed dual classifier-free guidance, one could obtain outputs
consistent with both the given subject and input text prompts. We validate the
efficacy of our approach through both optimization-based and encoder-based
methods. Additionally, we demonstrate its applicability in second-order
customization methods, where an encoder-based model is fine-tuned with
DreamBooth. Our approach is conceptually simple and requires only minimal code
modifications, but leads to substantial quality improvements, as evidenced by
our evaluations and user studies.

通过构建一个主题不可知条件并应用我们提出的双分类器自由指导，我们展示了我们的方法在生成与给定主题和输入文本提示一致的输出方面的有效性，并通过优化和编码方法验证了我们的方法的可行性，同时还展示了其在二阶定制方法中的适用性。