  Recently, text-to-image (T2I) editing has been greatly pushed forward by
applying diffusion models. Despite the visual promise of the generated images,
inconsistencies with the expected textual prompt remain prevalent. This paper
aims to systematically improve the text-guided image editing techniques based
on diffusion models, by addressing their limitations. Notably, the common idea
in diffusion-based editing firstly reconstructs the source image via inversion
techniques e.g., DDIM Inversion. Then following a fusion process that carefully
integrates the source intermediate (hidden) states (obtained by inversion) with
the ones of the target image. Unfortunately, such a standard pipeline fails in
many cases due to the interference of texture retention and the new characters
creation in some regions. To mitigate this, we incorporate human annotation as
an external knowledge to confine editing within a ``Mask-informed'' region.
Then we carefully Fuse the edited image with the source image and a constructed
intermediate image within the model's Self-Attention module. Extensive
empirical results demonstrate the proposed ``MaSaFusion'' significantly
improves the existing T2I editing techniques.
