Mage-Flow

Mage-Flow An Efficient Native-Resolution Foundation Model for Image Generation and Editing Mage-Flow** is a compact **4B-scale 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**, 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. Each ships in **Base**, **RL-aligned**, and **4-step Turbo** variants. ✨ Highlights **Compact & competitive.** A single 4B family for generation *and* editing that matches or beats much larger open systems (Qwen-Image 20B, Z-Image 6B, FLUX.2 32B, FireRed-Image-Edit 20B). **Efficient tokenizer.** Mage-VAE matches FLUX.2-VAE reconstruction fidelity while using **~12× / ~22× fewer encode / decode MACs per pixel**, removing the VAE as the high-resolution bottleneck. **Native resolution.** One checkpoint generates from **512 to 2048** on any aspect ratio, including extreme **4:1** (e.g. `512×2048`, `2048×512`). **System-level speed.** Native-resolution packing (FlashAttention var-len + per-sample 2D RoPE) + fused CUDA kernels cut per-step training time from **~1.93 s → ~0.78 s** (**~2.5× faster training**); CFG's conditional/unconditional branches run in **one** packed forward. **Full family.** **Base**, **RL-aligned**