KV-Edit: Training-Free Image Editing for Precise Background Preservation
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
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Tianrui Zhu
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2025-02-25T07:24:35.845Z
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Tianrui Zhu
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xilluill
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Shiyi Zhang
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2025-02-26T08:30:48.262Z
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ElevenZ
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shiyi0408
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Jiawei Shao
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2025-02-26T08:50:09.030Z
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Jiawei Shao
user
jewelshaw
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Yansong Tang
KV-Edit: Training-Free Image Editing for Precise Background Preservation
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
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.
KV-Edit KV cache DiTs background tokens memory consumption space complexity inversion-free method DiT-based generative model image editing