  With the advancement of image-to-image diffusion models guided by text,
significant progress has been made in image editing. However, a persistent
challenge remains in seamlessly incorporating objects into images based on
textual instructions, without relying on extra user-provided guidance. Text and
images are inherently distinct modalities, bringing out difficulties in fully
capturing the semantic intent conveyed through language and accurately
translating that into the desired visual modifications. Therefore, text-guided
image editing models often produce generations with residual object attributes
that do not fully align with human expectations. To address this challenge, the
models should comprehend the image content effectively away from a disconnect
between the provided textual editing prompts and the actual modifications made
to the image. In our paper, we propose a novel method called Locate and Forget
(LaF), which effectively locates potential target concepts in the image for
modification by comparing the syntactic trees of the target prompt and scene
descriptions in the input image, intending to forget their existence clues in
the generated image. Compared to the baselines, our method demonstrates its
superiority in text-guided image editing tasks both qualitatively and
quantitatively.
