OrbitQuant: Data-Agnostic Quantization for Image and Video Diffusion Transformers
This work addresses the high inference cost of diffusion transformers for practitioners deploying image and video generation models, offering a calibration-free quantization that generalizes across timesteps, prompts, and modalities.
OrbitQuant introduces a data-agnostic quantization method for diffusion transformers that eliminates the need for calibration data by quantizing in a normalized, rotated basis, achieving state-of-the-art post-training quantization across multiple models and modalities, including W2A4 for image diffusion transformers.
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.