The Inversion-Denoising Paradigm, which is based on diffusion models, excels in diverse image editing and restoration tasks. We revisit its mechanism and reveal a critical, overlooked factor in reconstruction degradation: the approximate noise error. This error stems from approximating the noise at step t with the prediction at step t-1, resulting in severe error accumulation throughout the inversion process. We introduce Projection-Orthogonal Least Squares for Robust and Adaptive Inversion (POLARIS), which reformulates inversion from an error-compensation problem into an error-origin problem. Rather than optimizing embeddings or latent codes to offset accumulated drift, POLARIS treats the guidance scale {\omega} as a step-wise variable and derives a mathematically grounded formula to minimize inversion error at each step. Remarkably, POLARIS improves inversion latent quality with just one line of code. With negligible performance overhead, it substantially mitigates noise approximation errors and consistently improves the accuracy of downstream tasks.
POLARIS：用于扩散模型中鲁棒自适应反演的投影正交最小二乘法 / POLARIS: Projection-Orthogonal Least Squares for Robust and Adaptive Inversion in Diffusion Models
这篇论文发现扩散模型在图像编辑中效果变差的关键原因在于噪声近似误差的累积，并提出了一种名为POLARIS的简单高效方法，通过动态调整一个关键参数来从根源上最小化每一步的误差，从而显著提升图像重建和后续编辑任务的质量。