ProEdit: Inversion-based Editing From Prompts Done Right
Inversion-based visual editing provides an effective and training-free way to edit an image or a video based on user instructions. Existing methods typically inject source image information during the sampling process to maintain editing consistency. However, this sampling strategy overly relies on source information, which negatively affects the edits in the target image (e.g., failing to change the subject's atributes like pose, number, or color as instructed). In this work, we propose ProEdit to address this issue both in the attention and the latent aspects. In the attention aspect, we introduce KV-mix, which mixes KV features of the source and the target in the edited region, mitigating the influence of the source image on the editing region while maintaining background consistency. In the latent aspect, we propose Latents-Shift, which perturbs the edited region of the source latent, eliminating the influence of the inverted latent on the sampling. Extensive experiments on several image and video editing benchmarks demonstrate that our method achieves SOTA performance. In addition, our design is plug-and-play, which can be seamlessly integrated into existing inversion and editing methods, such as RF-Solver, FireFlow and UniEdit.
6951e89b746a34b55dd548bd
Zhi Ouyang
claimed_verified
2026-01-02T15:40:39.911Z
67e262c315046d00df13c291
https://cdn-avatars.huggingface.co/v1/production/uploads/no-auth/rKhGxzBabGsfhGgbVycWE.png
Zhi Ouyang
Gemini1
user
Gemini1
6951e89b746a34b55dd548be
Dian Zheng
claimed_verified
2025-12-29T14:23:01.346Z
67e60ae6ac37824273d74389
https://cdn-avatars.huggingface.co/v1/production/uploads/noauth/YvPKZ_0gyJnvNwM1zK3JS.png
Dian Zheng
zhengli1013
user
zhengli1013
6951e89b746a34b55dd548bf
Xiao-Ming Wu
6951e89b746a34b55dd548c0
Jian-Jian Jiang
6951e89b746a34b55dd548c1
Kun-Yu Lin
6951e89b746a34b55dd548c2
Jingke Meng
6951e89b746a34b55dd548c3
Wei-Shi Zheng
ProEdit: Inversion-based Editing From Prompts Done Right
Inversion-based visual editing provides an effective and training-free way to edit an image or a video based on user instructions. Existing methods typically inject source image information during the sampling process to maintain editing consistency. However, this sampling strategy overly relies on source information, which negatively affects the edits in the target image (e.g., failing to change the subject's atributes like pose, number, or color as instructed). In this work, we propose ProEdit to address this issue both in the attention and the latent aspects. In the attention aspect, we introduce KV-mix, which mixes KV features of the source and the target in the edited region, mitigating the influence of the source image on the editing region while maintaining background consistency. In the latent aspect, we propose Latents-Shift, which perturbs the edited region of the source latent, eliminating the influence of the inverted latent on the sampling. Extensive experiments on several image and video editing benchmarks demonstrate that our method achieves SOTA performance. In addition, our design is plug-and-play, which can be seamlessly integrated into existing inversion and editing methods, such as RF-Solver, FireFlow and UniEdit.