SAM-Flow: Source-Anchored Masked Flow for Training-Free Image Editing

• URL: http://arxiv.org/abs/2606.06228v1
• Date: Thu, 04 Jun 2026 14:36:19 GMT
• Title: SAM-Flow: Source-Anchored Masked Flow for Training-Free Image Editing
• Abstract summary: SAM-Flow is a source-anchored masked flow framework for localized training-free image editing.<n>It achieves accurate semantic editing while significantly improving background preservation.<n>It can be integrated with mainstream flow-matching backbones such as Stable Diffusion 3 and FLUX without any fine-tuning.
• Score: 11.868477331606911
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Training-free image editing has recently attracted increasing attention due to its ability to modify real images using powerful pre-trained diffusion and flow-matching models without additional training. However, existing inversion-based and differential-flow-based methods usually perform global latent transport, which inevitably propagates editing effects to non-target regions and leads to background leakage. To address this problem, we propose SAM-Flow, a source-anchored masked flow framework for localized training-free image editing. Instead of updating the whole latent representation, SAM-Flow first uses a scout image and token-grounded attention maps to localize the editable semantic regions. It then applies differential velocity updates only within these regions, while anchoring the remaining areas to the source-image latent trajectory. To further improve spatial stability and boundary naturalness, we introduce a time-varying source-anchored projection mechanism with dynamic soft masks, transition regions, and temporal mask accumulation. The proposed method is plug-and-play and can be integrated with mainstream flow-matching backbones such as Stable Diffusion 3 and FLUX without any fine-tuning. Extensive qualitative and quantitative experiments demonstrate that SAM-Flow achieves accurate semantic editing while significantly improving background preservation, providing a simple and general localized editing paradigm for training-free image editing. Code is available at: https://github.com/chwbob/Sam-Flow.

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