← 研踪 PaperTrailarXiv:2611.10066 · v1
VLM as an Editing Pilot: VLM-Steered Velocity Control for Flow-Based Image Editing
首次提交 2026-11-06 17:23
版本更新 2026-11-06 17:23
雷达观测 2026-11-10 10:12
摘要
Text-guided image editing requires realizing the requested semantic change while preserving content unrelated to the instruction. FlowEdit provides an efficient inversion-free solution, but its source-anchored coupling can become overly restrictive, causing zero-edit, where the output remains close to the source and the requested change fails to emerge. We find that source coupling directly governs the preservation--editing trade-off, while a fixed coupling schedule cannot adapt to timestep-specific editing needs. Motivated by this observation, we propose V2C-Edit, a training-free VLM-steered velocity control framework. V2C-Edit constructs complementary coupling-controlled velocities and dynamically selects among them using Instruction-Level Similarity (ILS), which evaluates whether the predicted transformation follows the editing instruction. Adaptive spatial masking further preserves regions unrelated to the edit. Experiments on MagicBrush and PIE-Bench with FLUX and Stable Diffusion 3 show that V2C-Edit consistently improves instruction alignment over FlowEdit while maintaining competitive structural preservation, and that ILS better reflects editing effectiveness than target-caption CLIP similarity. Our code is available at https://github.com/Kuanshen77/V2C-Edit.
期刊信息：WACV 2027
观测到的版本
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v1 · 2026-11-06 17:23 · 最新观测版本
VLM as an Editing Pilot: VLM-Steered Velocity Control for Flow-Based Image Editing
Text-guided image editing requires realizing the requested semantic change while preserving content unrelated to the instruction. FlowEdit provides an efficient inversion-free solution, but its source-anchored coupling can become overly restrictive, causing zero-edit, where the output remains close to the source and the requested change fails to emerge. We find that source coupling directly governs the preservation--editing trade-off, while a fixed coupling schedule cannot adapt to timestep-specific editing needs. Motivated by this observation, we propose V2C-Edit, a training-free VLM-steered velocity control framework. V2C-Edit constructs complementary coupling-controlled velocities and dynamically selects among them using Instruction-Level Similarity (ILS), which evaluates whether the predicted transformation follows the editing instruction. Adaptive spatial masking further preserves regions unrelated to the edit. Experiments on MagicBrush and PIE-Bench with FLUX and Stable Diffusion 3 show that V2C-Edit consistently improves instruction alignment over FlowEdit while maintaining competitive structural preservation, and that ILS better reflects editing effectiveness than target-caption CLIP similarity. Our code is available at https://github.com/Kuanshen77/V2C-Edit.
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