SSI-DM: Singularity Skipping Inversion of Diffusion Models

• URL: http://arxiv.org/abs/2602.02193v1
• Date: Mon, 02 Feb 2026 14:59:58 GMT
• Title: SSI-DM: Singularity Skipping Inversion of Diffusion Models
• Authors: Chen Min, Enze Jiang, Jishen Peng, Zheng Ma,
• Abstract summary: existing methods produce non-Gaussian noise with poor editability due to the inaccuracy in early noising steps.<n>We propose Singularity Skipping Inversion of Diffusion Models (SSI-DM), which bypasses this singular region by adding small noise before standard inversion.<n>As a plug-and-play technique compatible with general diffusion models, our method achieves superior performance on public image datasets.
• Score: 4.736184528345518
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Inverting real images into the noise space is essential for editing tasks using diffusion models, yet existing methods produce non-Gaussian noise with poor editability due to the inaccuracy in early noising steps. We identify the root cause: a mathematical singularity that renders inversion fundamentally ill-posed. We propose Singularity Skipping Inversion of Diffusion Models (SSI-DM), which bypasses this singular region by adding small noise before standard inversion. This simple approach produces inverted noise with natural Gaussian properties while maintaining reconstruction fidelity. As a plug-and-play technique compatible with general diffusion models, our method achieves superior performance on public image datasets for reconstruction and interpolation tasks, providing a principled and efficient solution to diffusion model inversion.

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