MAG-Edit: Localized Image Editing in Complex Scenarios via
  Mask-Based Attention-Adjusted
  Guidance
Authors: name: Qi Mao, name: Lan Chen, name: Yuchao Gu, name: Zhen Fang, name: Mike Zheng Shou
Organization: Multimedia Intelligent Processing Group in Communication University of China

Recent diffusion-based image editing approaches have exhibited impressive
editing capabilities in images with simple compositions. However, localized
editing in complex scenarios has not been well-studied in the literature,
despite its growing real-world demands. Existing mask-based inpainting methods
fall short of retaining the underlying structure within the edit region.
Meanwhile, mask-free attention-based methods often exhibit editing leakage and
misalignment in more complex compositions. In this work, we develop
MAG-Edit, a training-free, inference-stage optimization method,
which enables localized image editing in complex scenarios. In particular,
MAG-Edit optimizes the noise latent feature in diffusion models by maximizing
two mask-based cross-attention constraints of the edit token, which in turn
gradually enhances the local alignment with the desired prompt. Extensive
quantitative and qualitative experiments demonstrate the effectiveness of our
method in achieving both text alignment and structure preservation for
localized editing within complex scenarios.