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Mage-Flow: An Efficient Native-Resolution Foundation Model for Image Generation and Editing research paper by Microsoft, 2026

Microsoft · Jul 21, 2026 · Inference and efficiency · 2 citations · 77 upvotes · unverified

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What it shows

Mage-Flow is a compact 4B generative stack combining a lightweight latent tokenizer and a native-resolution diffusion transformer to enable efficient high-resolution text-to-image generation and instruction-based editing.

UnverifiedHugging Face's summary; not yet checked by hand.

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About this paper
Authors
Xinjie Zhang, Peng Zhang, Shicheng Zheng and 21 more
arXiv
2607.19064 · PDF
Venue
arXiv.org
Citations
2, 0 influential · Semantic Scholar
Upvotes
77 · Hugging Face
Lab
Microsoft · on Companies · on Acquisitions · on Quarterly · on Paydays · on Releases · on TechConf

Changes

What changed
Influential citationsfirst count: 0Sep 25, 2026
Citationsfirst count: 2Sep 25, 2026
New paperFound by the weekly scan, unverifiedSep 25, 2026

Sources: each lab's own papers and arXiv, with citation and upvote counts from Semantic Scholar and Hugging Face. One-line summaries are for orientation, not a substitute for the paper. Logos via logo.dev; trademarks belong to their owners.

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