Image editing affords increased control over the aesthetics and content of
generated images. Pre-existing works focus predominantly on text-based
instructions to achieve desired image modifications, which limit edit precision
and accuracy. In this work, we propose an inference-time editing optimisation,
designed to extend beyond textual edits to accommodate multiple editing
instruction types (e.g. spatial layout-based; pose, scribbles, edge maps). We
propose to disentangle the editing task into two competing subtasks: successful
local image modifications and global content consistency preservation, where
subtasks are guided through two dedicated loss functions. By allowing to adjust
the influence of each loss function, we build a flexible editing solution that
can be adjusted to user preferences. We evaluate our method using text, pose
and scribble edit conditions, and highlight our ability to achieve complex
edits, through both qualitative and quantitative experiments.

我们提出了一种推理时间编辑优化方法，旨在通过两个专门的损失函数，成功地完成本地图像修改和全局内容一致性保留的竞争子任务，实现文本、姿势和涂鸦等多种编辑指令类型的灵活编辑解决方案，并通过定性和定量实验证明了我们能够实现复杂的编辑。