AI画图太慢？新方法让模型瘦身不降质
现在的AI画图模型（比如FLUX、Wan）效果惊艳，但跑一次要很久，因为模型太大、采样步骤多。通常的压缩方法（量化）需要针对每个模型、每个提示词重新校准，很麻烦。这篇论文提出一个巧妙的思路：先把数据旋转到一个标准化的空间里，让所有输入（不同时间步、不同提示）的数值分布变得一致，然后只用一套固定的量化规则就能搞定。这样，无论模型是画图还是生成视频，都不需要重新调整。实验证明，在多个主流模型上，它能在极低精度（如2位权重、4位激活）下保持可用画质。虽然你明天用不上，但它让AI生成更快、更省资源，是前沿技术的重要一步。
📄 原文摘要(英文)
Diffusion transformers (DiTs) achieve state-of-the-art image and video generation, but their multi-step sampling and growing parameter count make inference expensive. Post-training quantization (PTQ) is the natural remedy, yet DiT activations shift across timesteps, prompts, and guidance branches, forcing prior methods to re-fit calibration data for every new checkpoint or modality. We present OrbitQuant, a data-agnostic weight-activation quantizer that bypasses range estimation by quantizing in a normalized, rotated basis. In this basis, a randomized permuted block-Hadamard (RPBH) rotation concentrates each coordinate around one fixed, known marginal regardless of the input, so a single Lloyd-Max codebook serves all timesteps, prompts, and layers of a given input dimension. We extend the same quantizer to weight rows offline, absorbing the rotation into the weights so that it cancels inside each linear layer and only a forward rotation on the activations remains at runtime. The same recipe transfers from image to video with no per-modality tuning. Across FLUX.1, Z-Image-Turbo, Wan 2.1, and CogVideoX, it sets the state of the art for PTQ at several low-bit settings. It also pushes PTQ of image diffusion transformers to W2A4 with usable generation quality.