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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| Original publisher | arXiv cs.AI |
| Canonical URL | https://arxiv.org/abs/2605.15661 |
| Publication time | Mon, 18 May 2026 00:00:00 -0400 |
| Retrieval time | 2026-05-18T04:04:54.418Z |
| Last seen | 2026-05-18T04:04:54.418Z |
| Headline source | Publisher (no WeSearch rewrite) |
| Excerpt source | publisher body |
| Excerpt method | First ~120 words (~800 chars) of extracted publisher body, fair-use limited. |
| Summary | WeSearch · cerebras-chat (WeSearch summarizer) |
| Summary source text | contentText |
| Citation coverage | Summary is a WeSearch-generated derivative; primary citation is the original publisher URL. |
| Cluster | iaf7lGK-0Nf0 |
| Cluster logic | Grouped by semantic title/content similarity across sources within a rolling window. Same-publisher template collisions are excluded from coverage comparison. |
| Ranking reason | Story pages are not engagement-ranked. Hub feeds use recency, with optional source-diversified chronological ordering (cap consecutive stories per source). No personalized ranking. |
| Publisher visit | Yes — open original |
| Substitutes article? | No — link-out required for full text |
Rights status (four layers)
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| Indexing | May the item be indexed (stored, ranked, made findable)? | Allowed |
| Snippet | May a short excerpt of the publisher's text be shown? | Allowed |
| AI summary | May WeSearch generate its own short summary of the article? | Limited |
| Retrieval / RAG | May the content be exposed for third-party retrieval-augmented generation? | Not asserted |
| Model training | May the content be used to train AI models? | Not asserted |
| Commercial reuse | May the content be reused commercially? | Not permitted |
Basis: Derived from the published RSS/Atom feed. Contact: [email protected]. Reviewed: 2026-07-24.
Opening excerpt (first ~120 words) tap to expand
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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Excerpt limited to ~120 words for fair-use compliance. The full article is at arXiv cs.AI.
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