{"schema_version":"onlylabs.public_signal.v1","title":"Microsoft Model: microsoft/Mage-Flow-Edit-Base","description":"Microsoft model signal with public source context, captured evidence pages, related signals, and data-business radar classification.","url":"https://onlylabs.fyi/signals/d7f32671-78fa-4027-993c-bdc81ced9072","json_url":"https://onlylabs.fyi/signals/d7f32671-78fa-4027-993c-bdc81ced9072/signal.json","generated_at":"2026-07-26T18:50:41.590Z","evidence_latest_fetched_at":"2026-07-22T07:20:21.449034+00:00","signal_first_seen_at":"2026-07-22T07:20:08.299565+00:00","org":{"slug":"microsoft","name":"Microsoft","category":"frontier-lab","category_label":"Frontier lab","dossier_url":"https://onlylabs.fyi/labs/microsoft","dossier_json_url":"https://onlylabs.fyi/labs/microsoft/dossier.json"},"related_urls":{"signal":"https://onlylabs.fyi/signals/d7f32671-78fa-4027-993c-bdc81ced9072","signal_json":"https://onlylabs.fyi/signals/d7f32671-78fa-4027-993c-bdc81ced9072/signal.json","source":"https://huggingface.co/microsoft/Mage-Flow-Edit-Base","lab_dossier":"https://onlylabs.fyi/labs/microsoft","lab_dossier_json":"https://onlylabs.fyi/labs/microsoft/dossier.json","analysis":"https://onlylabs.fyi/analysis/microsoft","analysis_json":"https://onlylabs.fyi/analysis/microsoft/analysis.json","analysis_evidence_json":"https://onlylabs.fyi/analysis/microsoft/evidence.json","category":"https://onlylabs.fyi/frontier","category_json":"https://onlylabs.fyi/frontier.json","category_feed":"https://onlylabs.fyi/frontier/feed.xml","category_signals_json":"https://onlylabs.fyi/signals.json","topic":"https://onlylabs.fyi/topics/releases","topic_signals_json":"https://onlylabs.fyi/topics/releases/signals.json","topic_feed":"https://onlylabs.fyi/topics/releases/feed.xml","data_business":null},"answer_pack":{"answer":"Microsoft published microsoft/Mage-Flow-Edit-Base. 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Instead of scaling to tens of billions of parameters, Mage-Flow reaches state-of-the-art-competitive quality through careful **tokenizer–backbone–system co-design**, so it stays fast, memory-light, and easy to fine-tune under realistic compute budgets. The stack is built from **two shared, co-designed components**: - **Mage-VAE** — a lightweight, high-fidelity latent tokenizer (one-step diffusion encode/decode with anchor-latent KL regularization). - **NR-MMDiT** — a shared 4B **Native-Resolution Multimodal Diffusion Transformer**, trained with rectified flow matching in the Mage-VAE latent space. Together with native-resolution packing and a fused-kernel training infrastructure, this shared stack powers **two model instantiations**: **Mage-Flow** for text-to-image generation and **Mage-Flow-Edit** for instruction-based image editing. 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