CannyEdit: Selective Canny Control and Dual-Prompt Guidance for
  Training-Free Image Editing
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.
689ca649fab6fdd2e52aca4d
Weiyan Xie
claimed_verified
2025-08-14T13:38:13.947Z
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/avatars/f1bc3dc2fd7afd59cf53f0f2391ed16b.svg
Wy Xie
user
vaynexie
689ca649fab6fdd2e52aca4e
Han Gao
689ca649fab6fdd2e52aca4f
Didan Deng
689ca649fab6fdd2e52aca50
Kaican Li
689ca649fab6fdd2e52aca51
April Hua Liu
689ca649fab6fdd2e52aca52
Yongxiang Huang
689ca649fab6fdd2e52aca53
Nevin L. Zhang
CannyEdit: Selective Canny Control and Dual-Prompt Guidance for
  Training-Free Image Editing
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.
CannyEdit is a training-free framework that enhances text-to-image editing by balancing text adherence, context fidelity, and seamless integration through Selective Canny Control and Dual-Prompt Guidance.
Selective Canny Control Canny ControlNet inversion-phase ControlNet Dual-Prompt Guidance text-to-image regional image editing generative priors foundation models text adherence context fidelity editing seamlessness user studies AIGC experts