  This paper delves into the text-guided image editing task, focusing on
modifying a reference image according to user-specified textual feedback to
embody specific attributes. Despite recent advancements, a persistent challenge
remains that the single-round generation often overlooks crucial details,
particularly in the realm of fine-grained changes like shoes or sleeves. This
issue compounds over multiple rounds of interaction, severely limiting
customization quality. In an attempt to address this challenge, we introduce a
new self-supervised regularization, \ie, multi-round regularization, which is
compatible with existing methods. Specifically, the multi-round regularization
encourages the model to maintain consistency across different modification
orders. It builds upon the observation that the modification order generally
should not affect the final result. Different from traditional one-round
generation, the mechanism underpinning the proposed method is the error
amplification of initially minor inaccuracies in capturing intricate details.
Qualitative and quantitative experiments affirm that the proposed method
achieves high-fidelity editing quality, especially the local modification, in
both single-round and multiple-round generation, while also showcasing robust
generalization to irregular text inputs. The effectiveness of our semantic
alignment with textual feedback is further substantiated by the retrieval
improvements on FahisonIQ and Fashion200k.
