CannyEdit: Selective Canny Control and Dual-Prompt Guidance for
  Training-Free Image Editing
Authors: name: Weiyan Xie, name: Han Gao, name: Didan Deng, name: Kaican Li, name: April Hua Liu, name: Yongxiang Huang, name: Nevin L. Zhang

Recent advances in text-to-image (T2I) models have enabled training-free
regional image editing by leveraging the generative priors of foundation
models. However, existing methods struggle to balance text adherence in edited
regions, context fidelity in unedited areas, and seamless integration of edits.
We introduce CannyEdit, a novel training-free framework that addresses these
challenges through two key innovations: (1) Selective Canny Control, which
masks the structural guidance of Canny ControlNet in user-specified editable
regions while strictly preserving details of the source images in unedited
areas via inversion-phase ControlNet information retention. This enables
precise, text-driven edits without compromising contextual integrity. (2)
Dual-Prompt Guidance, which combines local prompts for object-specific edits
with a global target prompt to maintain coherent scene interactions. On
real-world image editing tasks (addition, replacement, removal), CannyEdit
outperforms prior methods like KV-Edit, achieving a 2.93 to 10.49 percent
improvement in the balance of text adherence and context fidelity. In terms of
editing seamlessness, user studies reveal only 49.2 percent of general users
and 42.0 percent of AIGC experts identified CannyEdit's results as AI-edited
when paired with real images without edits, versus 76.08 to 89.09 percent for
competitor methods.