OrbitQuant: Data-Agnostic Quantization for Image and Video Diffusion Transformers

Diffusion transformers producing state-of-the-art images and videos are computationally expensive, and standard quantization techniques for compressing them must be re-calibrated for every new model or modality since activation patterns shift constantly. OrbitQuant solves this by rotating activations into a normalized basis where their statistical distribution becomes fixed and predictable, enabling a single reusable codebook across all timesteps and prompts. This data-agnostic approach transfers seamlessly between image and video models without retuning. Tested on models like FLUX.1 and CogVideoX, it achieves state-of-the-art low-bit compression, making efficient deployment of large generative media models more practical. Authors: Donghyun Lee, Jitesh Chavan, Duy Nguyen, Sam Huang, Liming Jiang, Priyadarshini Panda, Timo Mertens, Saurabh Shukla Paper: https://arxiv.org/abs/2607.02461v1