In this paper, we study the text-guided image generation task. Our focus lies
in the modification of a reference image, given user text feedback, to imbue it
with specific desired properties. Despite recent strides in this field, a
persistent challenge remains that single-round optimization often overlooks
crucial details, particularly in the realm of fine-grained changes like shoes
or sleeves. This misalignment accumulation significantly hampers multi-round
customization during interaction. In an attempt to address this challenge, we
introduce a new self-supervised regularization into the existing framework,
i.e., multi-round regularization. It builds upon the observation that the
modification order does not affect the final result. As the name suggests, the
multi-round regularization encourages the model to maintain consistency across
different modification orders. Specifically, our proposed approach addresses
the issue where an initial failure to capture fine-grained details leads to
substantial discrepancies after multiple rounds, as opposed to traditional
one-round learning. Both qualitative and quantitative experiments show the
proposed method achieves high-fidelity generation quality over the text-guided
generation task, especially the local modification. Furthermore, we extend the
evaluation to semantic alignment with text by applying our method to
text-guided retrieval datasets, such as FahisonIQ, where it demonstrates
competitive performance.

本研究针对文本引导的图像生成任务，通过引入多轮规则化方法解决了单次优化忽略细节的问题，特别是在细微变化方面。我们的方法在文本引导的生成任务中展现了高保真度的生成质量，并在与文本的语义匹配方面得到了竞争性的表现。