SpotEdit: Selective Region Editing in Diffusion Transformers
Diffusion Transformer models have significantly advanced image editing by encoding conditional images and integrating them into transformer layers. However, most edits involve modifying only small regions, while current methods uniformly process and denoise all tokens at every timestep, causing redundant computation and potentially degrading unchanged areas. This raises a fundamental question: Is it truly necessary to regenerate every region during editing? To address this, we propose SpotEdit, a training-free diffusion editing framework that selectively updates only the modified regions. SpotEdit comprises two key components: SpotSelector identifies stable regions via perceptual similarity and skips their computation by reusing conditional image features; SpotFusion adaptively blends these features with edited tokens through a dynamic fusion mechanism, preserving contextual coherence and editing quality. By reducing unnecessary computation and maintaining high fidelity in unmodified areas, SpotEdit achieves efficient and precise image editing.
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Zhibin Qin
6953692989916ff627aa4066
Zhenxiong Tan
claimed_verified
2025-12-31T20:56:14.448Z
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https://cdn-avatars.huggingface.co/v1/production/uploads/640ebdfefdeaae139086f4d8/2N94gbHubplYD8njmUTPf.jpeg
Zhenxiong Tan
Yuanshi
user
Yuanshi
6953692989916ff627aa4067
Zeqing Wang
claimed_verified
2025-12-31T20:56:16.436Z
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Zeqing Wang
INV-WZQ
user
INV-WZQ
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Songhua Liu
claimed_verified
2026-03-17T09:37:58.824Z
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/avatars/564c44cf45db794747a96f79f30ecd91.svg
Liu Songhua
Huage001
user
Huage001
6953692989916ff627aa4069
Xinchao Wang
SpotEdit: Selective Region Editing in Diffusion Transformers
Diffusion Transformer models have significantly advanced image editing by encoding conditional images and integrating them into transformer layers. However, most edits involve modifying only small regions, while current methods uniformly process and denoise all tokens at every timestep, causing redundant computation and potentially degrading unchanged areas. This raises a fundamental question: Is it truly necessary to regenerate every region during editing? To address this, we propose SpotEdit, a training-free diffusion editing framework that selectively updates only the modified regions. SpotEdit comprises two key components: SpotSelector identifies stable regions via perceptual similarity and skips their computation by reusing conditional image features; SpotFusion adaptively blends these features with edited tokens through a dynamic fusion mechanism, preserving contextual coherence and editing quality. By reducing unnecessary computation and maintaining high fidelity in unmodified areas, SpotEdit achieves efficient and precise image editing.