title: KV-Edit: Training-Free Image Editing for Precise Background Preservation
summary: Background consistency remains a significant challenge in image editing
  tasks. Despite extensive developments, existing works still face a trade-off
  between maintaining similarity to the original image and generating content
  that aligns with the target. Here, we propose KV-Edit, a training-free approach
  that uses KV cache in DiTs to maintain background consistency, where background
  tokens are preserved rather than regenerated, eliminating the need for complex
  mechanisms or expensive training, ultimately generating new content that
  seamlessly integrates with the background within user-provided regions. We
  further explore the memory consumption of the KV cache during editing and
  optimize the space complexity to O(1) using an inversion-free method. Our
  approach is compatible with any DiT-based generative model without additional
  training. Experiments demonstrate that KV-Edit significantly outperforms
  existing approaches in terms of both background and image quality, even
  surpassing training-based methods. Project webpage is available at
  https://xilluill.github.io/projectpages/KV-Edit
paper_authors._id: 67bd6d2bbf6d46017e619f31
paper_authors.name: Tianrui Zhu
paper_authors.status: extracted_confirmed
paper_authors.statusLastChangedAt: 2025-02-25T07:24:35.845Z
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paper_authors.fullname: Tianrui Zhu
paper_authors.type: user
paper_authors.user: xilluill
paper_authors._id: 67bd6d2bbf6d46017e619f32
paper_authors.name: Shiyi Zhang
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2025-02-26T08:30:48.262Z
paper_authors._id: 6315d306a9456afe2b9bf34a
paper_authors.avatarUrl: /avatars/7285b4e7d84b528d1a50f8ee4eb10727.svg
paper_authors.fullname: ElevenZ
paper_authors.type: user
paper_authors.user: shiyi0408
paper_authors._id: 67bd6d2bbf6d46017e619f33
paper_authors.name: Jiawei Shao
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2025-02-26T08:50:09.030Z
paper_authors._id: 646c6985d072747f7ebf352a
paper_authors.avatarUrl: /avatars/8aaf92045687b21b56c257db62bf4fa5.svg
paper_authors.fullname: Jiawei Shao
paper_authors.type: user
paper_authors.user: jewelshaw
paper_authors._id: 67bd6d2bbf6d46017e619f34
paper_authors.name: Yansong Tang
paper_title: KV-Edit: Training-Free Image Editing for Precise Background Preservation
paper_summary: Background consistency remains a significant challenge in image editing
  tasks. Despite extensive developments, existing works still face a trade-off
  between maintaining similarity to the original image and generating content
  that aligns with the target. Here, we propose KV-Edit, a training-free approach
  that uses KV cache in DiTs to maintain background consistency, where background
  tokens are preserved rather than regenerated, eliminating the need for complex
  mechanisms or expensive training, ultimately generating new content that
  seamlessly integrates with the background within user-provided regions. We
  further explore the memory consumption of the KV cache during editing and
  optimize the space complexity to O(1) using an inversion-free method. Our
  approach is compatible with any DiT-based generative model without additional
  training. Experiments demonstrate that KV-Edit significantly outperforms
  existing approaches in terms of both background and image quality, even
  surpassing training-based methods. Project webpage is available at
  https://xilluill.github.io/projectpages/KV-Edit
paper_ai_summary: KV-Edit uses the KV cache in DiT-based models to preserve background tokens, enabling high-quality image editing with seamless background consistency and low memory usage.
paper_ai_keywords: KV-Edit KV cache DiTs background tokens memory consumption space complexity inversion-free method DiT-based generative model image editing