  Text-driven image synthesis has made significant advancements with the
development of diffusion models, transforming how visual content is generated
from text prompts. Despite these advances, text-driven image editing, a key
area in computer graphics, faces unique challenges. A major challenge is making
simultaneous edits across multiple objects or attributes. Applying these
methods sequentially for multi-aspect edits increases computational demands and
efficiency losses. In this paper, we address these challenges with significant
contributions. Our main contribution is the development of MultiEdits, a method
that seamlessly manages simultaneous edits across multiple attributes. In
contrast to previous approaches, MultiEdits not only preserves the quality of
single attribute edits but also significantly improves the performance of
multitasking edits. This is achieved through an innovative attention
distribution mechanism and a multi-branch design that operates across several
processing heads. Additionally, we introduce the PIE-Bench++ dataset, an
expansion of the original PIE-Bench dataset, to better support evaluating
image-editing tasks involving multiple objects and attributes simultaneously.
This dataset is a benchmark for evaluating text-driven image editing methods in
multifaceted scenarios. Dataset and code are available at
https://mingzhenhuang.com/projects/MultiEdits.html.
