[
    {
        "id": "osp-25088",
        "type": "article-journal",
        "title": "Spectral Amplitude Purification in Distribution Matching for Diffusion Distillation",
        "author": [
            {
                "family": "Zhou",
                "given": "Zhenyu"
            },
            {
                "family": "Wang",
                "given": "Can"
            },
            {
                "family": "Chen",
                "given": "Chun"
            },
            {
                "family": "Zheng",
                "given": "Zeyu"
            },
            {
                "family": "Chen",
                "given": "Defang"
            }
        ],
        "URL": "https://omanscience.com/en/articles/spectral-amplitude-purification-in-distribution-matching-for-diffusion-distillation",
        "language": "en",
        "issued": {
            "date-parts": [
                [
                    2026
                ]
            ]
        },
        "abstract": "Distribution Matching Distillation (DMD) enables high-quality diffusion sampling in only a few steps, but its optimization dynamics remain dominated by coarse, low-frequency signals, delaying the recovery of fine-grained details. We identify a pronounced concentration of spectral amplitudes at low frequencies in the DMD directional error, where dominant low-frequency components overwhelm weaker mid- and high-frequency signals. To address this issue, we propose Spectral Amplitude Purification for Distribution Matching Distillation (SAP-DMD), a plug-and-play approach that adaptively modulates the amplitude spectrum of the DMD directional field. By suppressing the dominant tail of the amplitude spectrum, SAP-DMD reduces low-frequency dominance and promotes more effective recovery of fine structures and textures. Experiments on PixArt-$α$, SD3, and SD3.5 demonstrate that SAP-DMD accelerates training convergence and improves generation quality under both 2-step and 4-step sampling."
    }
]