[
    {
        "id": "osp-16449",
        "type": "article-journal",
        "title": "Deflating the Hessian: Rank-4 W4A4 Quantization for Multimodal Diffusion Transformers",
        "author": [
            {
                "family": "Wang",
                "given": "Shiwen"
            },
            {
                "family": "Zhao",
                "given": "Pengxiang"
            },
            {
                "family": "Yuan",
                "given": "Xiaoming"
            }
        ],
        "URL": "https://omanscience.com/en/articles/deflating-the-hessian-rank-4-w4a4-quantization-for-multimodal-diffusion-transformers",
        "language": "en",
        "issued": {
            "date-parts": [
                [
                    2026
                ]
            ]
        },
        "abstract": "In diffusion transformers, low-rank branches can mitigate 4-bit weight--activation (W4A4) post-training quantization (PTQ) loss by decomposing each weight into a low-bit residual and a high-precision low-rank component. Existing low-rank PTQ approaches, however, either optimize low-rank compensation and residual quantization separately, often requiring higher ranks, or rely on second-order weight updates without explicitly modeling activation quantization error, which becomes particularly pronounced under 4-bit quantization. To address these limitations, we present \\method{}, a unified framework modeling low-rank-assisted W4A4 PTQ as a coupled calibration problem and deriving optimization-based solvers from the joint objective. Eliminating the output-side low-rank factor yields a \\emph{deflated Hessian} that discounts residual errors already captured by the low-rank component, while an activation-noise surrogate is incorporated to suppress activation quantization error. Across five diffusion backbones, rank-4 \\method{} consistently outperforms rank-4 SVDQuant in PSNR and LPIPS. It further surpasses rank-32 SVDQuant on SANA-1.6B, FLUX.1-schnell, and FLUX.1-dev with an $8\\times$ smaller rank and up to $6.25\\times$ faster quantization. Furthermore, on the Qwen3-8B LLM, rank-4 \\method{} improves MMLU accuracy from 61.50\\% to 68.17\\% over rank-32 SVDQuant. Overall, \\method{} achieves better W4A4 performance with substantially lower rank and quantization cost."
    }
]