VAGS: Velocity Adaptive Guidance Scale for Image Editing and Generation
The article introduces the Velocity Adaptive Guidance Scale (VAGS), a new method for image editing and generation. VAGS improves upon traditional classifier-free guidance by adapting the guidance scale based on the dynamics of the model. This approach enhances structural fidelity and generation quality without requiring fine-tuning or additional networks.
- ▪VAGS is a training-free method that adjusts guidance strength based on the alignment of velocity fields.
- ▪The method shows consistent improvements over fixed classifier-free guidance and other training-free variants.
- ▪VAGS can be applied to both image editing and generation tasks, demonstrating versatility in its application.
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Computer Science > Computer Vision and Pattern Recognition arXiv:2605.15661 (cs) [Submitted on 15 May 2026] Title:VAGS: Velocity Adaptive Guidance Scale for Image Editing and Generation Authors:Yan Luo, Ahmadou Aidara, Jingyi Lu, Jeremy Moebel, Kai Han, Mengyu Wang View a PDF of the paper titled VAGS: Velocity Adaptive Guidance Scale for Image Editing and Generation, by Yan Luo and 5 other authors View PDF HTML (experimental) Abstract:Classifier-free guidance (CFG) is the primary control over how strongly text semantics move a flow-based sampler, yet standard practice holds its scale fixed across the entire ODE trajectory.
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