{"ID":5676769,"CreatedAt":"2026-07-03T03:29:23.032456456Z","UpdatedAt":"2026-07-12T23:57:57.250035847Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2607.02461","arxiv_id":"2607.02461","title":"OrbitQuant: Data-Agnostic Quantization for Image and Video Diffusion Transformers","abstract":"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.","short_abstract":"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...","url_abs":"https://arxiv.org/abs/2607.02461","url_pdf":"https://arxiv.org/pdf/2607.02461v1","authors":"[\"Donghyun Lee\",\"Jitesh Chavan\",\"Duy Nguyen\",\"Sam Huang\",\"Liming Jiang\",\"Priyadarshini Panda\",\"Timo Mertens\",\"Saurabh Shukla\"]","published":"2026-07-02T17:27:34Z","proceeding":"cs.CV","tasks":"[\"cs.CV\",\"cs.AI\",\"cs.LG\"]","methods":"[\"Diffusion Model\",\"Transformer\"]","has_code":false}