arxiv
PublishedJuly 23, 2026 at 4:00 AM
—neutral
Mage-Flow: An Efficient Native-Resolution Foundation Model for Image Generation and Editing
Publisher summary· verbatim
arXiv:2607.19064v2 Announce Type: replace-cross Abstract: Large-scale visual generators are increasingly capable but costly to train, fine-tune, and deploy. We introduce Mage-Flow, a compact 4B-scale generative stack for efficient text-to-image generation and instruction-based image editing. The sta
Stay posted· Newsletter
Three short emails a week: Monday, Wednesday and Friday. Top movers, new models and the most-referenced story.
Your email is used only to send The Bubble Brief. Privacy policy
Discussion
No replies yet. Be first.
Related coverage
More from ARXIV
arxivGlobeBench: Evaluating Text-Based World Model Capabilities for Scalable Environment Simulations1harxivAlgorithm Modelization: Transforming Optimization Algorithms into Evolvable Intelligence1harxivSimOPD: Relational Knowledge Transfer for On-Policy Distillation1harxivGenerative Policy Improvement from Observational Data1hThe Bubble Brief
MON · WED · FRIRead AI insights every Monday, Wednesday and Friday — top movers, new models, story of the week.
Your email is used only to send The Bubble Brief. Privacy policy
Originally published on arxiv ↗