Zheng Xingyu et al. at Beihang University, in arXiv 2607.01642, propose MrFlow — a completely training-free multi-resolution diffusion acceleration scheme. FLUX.1-dev and Qwen-Image have pushed text-to-image quality to open-source SOTA, but each image often requires dozens of denoising steps, making inference cost the main barrier to landing. Timestep distillation requires retraining for each base model; existing training-free multi-resolution schemes often exhibit obvious blur and artifacts when upsampling in latent space. MrFlow splits inference into an explicit four-step pipeline: low-resolution main body generation → pixel-space GAN super-resolution → low-strength noise injection → high-resolution detail refinement, no training, no runtime dynamic discrimination needed. On FLUX.1-dev and Qwen-Image, it achieves 10× end-to-end speedup with OneIG loss controlled within 1%; orthogonal stacking with timestep distillation can reach 25×. This is a rare "truly landable" solution in the past half year's diffusion acceleration routes — not tied to a model, not relying on custom kernels, especially friendly to the open-source community, hinting at a new paradigm of "acceleration can be stacked layer by layer". Code is open-sourced on GitHub (Xingyu-Zheng/MrFlow).