Diffusion-model post-training has long been dragged down by memory and throughput — 12B-level FLUX.1-dev wanting to do customization or RL fine-tuning has prohibitively high cost. FourTune (arXiv:2607.05711) led by MIT's Song Han gives a clean solution: end-to-end compressing weights, activations, and gradients all to 4-bit (W4A4G4), plus a LoRA + frozen numerical-stabilizer coexisting three-branch hybrid pipeline, with block-level quantization and custom fused kernels, hard-running training stably under native 4-bit compute. On FLUX.1-dev 12B, memory usage is cut 2.25×, end-to-end throughput improved 2.27×, and on customization, RL, and distillation tasks the quality matches full-precision fine-tuning — no point loss from quantization. This complements diffusion-quantization routes for the inference side like OrbitQuant and FAIR-Calib, while FourTune directly targets the highest-cost link of post-training, further lowering the customization threshold for 12B-level diffusion models. The W4A4G4 paradigm can also transfer to video diffusion models like Wan and CogVideoX, upgrading 4-bit post-training from an engineering trick to a system-level solution.