AI画图提速25倍，不训练不换硬件
现在的AI画图（比如FLUX.1）想跑快，要么换硬件，要么花大钱训练小模型。这篇直接绕开：先让AI在低分辨率下快速画出主体结构（这一步省了90%的计算），再用一个轻量级的老模型（GAN）把图放大到高清，最后加一点噪声让AI重新补细节。全程不用训练，不碰硬件，在FLUX.1和Qwen-Image上实测提速10倍，画质几乎不掉；如果配合已有的蒸馏技术，能冲到25倍。它不是你明天就能装进手机用的，但给所有跑图太慢的人指了一条路：别死磕大模型，把“画草图”和“补细节”拆开干。
📄 原文摘要(英文)
Hardware-agnostic strategies for accelerating text-to-image diffusion, such as timestep distillation and feature caching, can reduce inference time without custom kernels or system-level optimization. Among them, multi-resolution generation strategies have recently received broad attention, attaining more than 5x speedup without any training. However, the design of performing upsampling in the latent space, together with the selective modification of partial regions, causes these methods to exhibit noticeable blurring or artifacts. To this end, we propose MrFlow, a training-free multi-resolution acceleration strategy for pretrained flow-matching models built upon a staged low-to-high-resolution pipeline. MrFlow first rapidly generates the main structure at low resolution, then performs super-resolution in the pixel space using a lightweight pretrained GAN-based model, subsequently injects low-strength noise to enable high-frequency resampling, and finally refines the details at high resolution. Quantitative and qualitative results on FLUX.1-dev and Qwen-Image show that MrFlow exploits the quadratic token reduction and reduced step requirement of low-resolution sampling to achieve 10x end-to-end acceleration while keeping OneIG within a 1% gap relative to that before acceleration, significantly surpassing other training-free acceleration strategies, and requiring no training or runtime dynamic identification whatsoever. MrFlow can further be directly combined orthogonally with pre-trained timestep distillation strategies, achieving even higher generation acceleration of up to 25x.