Mage-Flow: Microsoft's 4B Native-Resolution Image Model

Microsoft Asia has released Mage-Flow, a compact 4B-parameter generative stack for efficient text-to-image generation and instruction-based image editing. 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, keeping it fast, memory-light, and easy to fine-tune under realistic compute budgets.

• Model: microsoft/Mage-Flow on Hugging Face
• Paper: arXiv 2607.19064
• Code: github.com/microsoft/Mage
• License: MIT

What It Does

Mage-Flow 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). Matches FLUX.2-VAE reconstruction fidelity while using ~12x / ~22x fewer encode / decode MACs per pixel.
• NR-MMDiT — a shared 4B Native-Resolution Multimodal Diffusion Transformer with rectified flow matching in the Mage-VAE latent space, using Qwen3-VL as the text encoder.

Together, these power two model instantiations — Mage-Flow for text-to-image and Mage-Flow-Edit for instruction-based image editing — each shipping in Base, RL-aligned, and 4-step Turbo variants.

Key Features

• Compact yet competitive — a single 4B family that matches or beats much larger open systems (Qwen-Image 20B, Z-Image 6B, FLUX.2 32B, FireRed-Image-Edit 20B)
• Native resolution — one checkpoint generates from 512 to 2048 on any aspect ratio, including extreme 4:1 (e.g. 512x2048, 2048x512)
• System-level speed — native-resolution packing + fused CUDA kernels raise MFU from ~33% to ~77% (~2.5x faster training); CFG's conditional/unconditional branches run in one packed forward
• Full model family — 6 checkpoints on Hugging Face covering generation and editing at Base, RL-aligned, and Turbo levels
• Interactive latency — at 1024x1024 on a single A100: Mage-Flow-Turbo 0.59 s/image, Mage-Flow-Edit-Turbo 1.02 s/edit, peak memory ~18-20 GB
• Versatile editing — supports semantic content editing, appearance transformation, image restoration, and structure-aware outputs within a unified pipeline

Model Zoo

All checkpoints are self-contained diffusers-style repos on Hugging Face:

Performance Highlights

Mage-Flow achieves GenEval 0.90 — the highest among all open-source models, surpassing FLUX.2 (0.87), Qwen-Image (0.87), and Z-Image (0.84). On DPG-Bench, Mage-Flow-Base scores 86.26, competitive with much larger models.

For image editing, Mage-Flow-Edit-Turbo scores GEdit-EN 8.271 and GEdit-CN 8.264, ranking first or second among all open-source editing models across multiple benchmarks.

Availability

All Mage-Flow checkpoints are released under the MIT license on Hugging Face, making them freely available for research and commercial use.

ComfyUI Support

Mage-Flow now has native ComfyUI support — the integration PR (#15026) has been merged into ComfyUI core, and Comfy-Org/Mage-Flow hosts repackaged model files for direct use in ComfyUI, including int8 ConvRot quantized variants.

Place the files in the following directories:

📂 ComfyUI/
├── 📂 models/
│   ├── 📂 diffusion_models/
│   │   ├── mage_flow_bf16.safetensors
│   │   ├── mage_flow_int8_convrot.safetensors
│   │   ├── mage_flow_turbo_bf16.safetensors
│   │   ├── mage_flow_turbo_int8_convrot.safetensors
│   │   ├── mage_flow_edit_bf16.safetensors
│   │   ├── mage_flow_edit_int8_convrot.safetensors
│   │   ├── mage_flow_edit_turbo_bf16.safetensors
│   │   └── mage_flow_edit_turbo_int8_convrot.safetensors
│   ├── 📂 text_encoders/
│   │   └── qwen3vl_4b_bf16.safetensors
│   ├── 📂 vae/
│       └── mage_flow_vae_bf16.safetensors

See the project page for more details, galleries, and benchmarks.