Mage-Flow: An Efficient Native-Resolution Foundation Model for Image Generation and Editing
Authors: name: Xinjie Zhang, name: Peng Zhang, name: Shicheng Zheng, name: Jinghao Guo, name: Zhaoyang Jia, name: Yifei Shen, name: Xun Guo, name: Yuxuan Luo, name: Jiahao Li, name: Wenxuan Xie, name: Fanyi Pu, name: Xiaoyi Zhang, name: Kaichen Zhang, name: Zongyu Guo, name: Tianci Bi, name: Dongnan Gui, name: Zhening Liu, name: Zimo Wen, name: Zihan Zheng, name: Senqiao Yang, name: Xiao Li, name: Jinglu Wang, name: Bin Li, name: Yan Lu
Organization: Microsoft

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 stack is built from two co-designed components: Mage-VAE, a lightweight high-fidelity latent tokenizer, and a Native-Resolution Multimodal Diffusion Transformer trained with rectified flow matching. Mage-VAE uses one-step diffusion-style encoding and decoding with anchor-latent regularization, preserving the reconstruction quality of strong public VAEs while reducing tokenization cost by more than an order of magnitude. Together with native-resolution packing and stack-level CUDA kernel fusion, the stack supports flexible-resolution training and improves end-to-end training throughput by about 2.5times. Built on this foundation, we develop a complete model family with Base, RL-aligned, and Turbo variants for both generation and editing. Diffusion-NFT improves prompt following, text rendering, aesthetic quality, and editing fidelity, while few-step distillation with adversarial perceptual guidance produces 4-step Turbo models for low-latency inference. Despite its compact scale, Mage-Flow and Mage-Flow-Edit achieves competitive performance across standard generation and editing benchmarks. More importantly, the Turbo variants make high-resolution generation and editing practical for interactive use: at 1024^2 resolution on a single NVIDIA A100 GPU, Mage-Flow-Turbo generates an image in 0.59s, and Mage-Flow-Edit-Turbo edits an image in 1.02s, while maintaining a small memory footprint. These results show that careful tokenizer--backbone--system co-design can deliver strong high-resolution generation and editing within an efficient 4B model family.