POLARIS: Projection-Orthogonal Least Squares for Robust and Adaptive Inversion in Diffusion Models
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 ω 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.
692f17f7bfc6eea1b6fb6923
Wenshuo Chen
692f17f7bfc6eea1b6fb6924
Haosen Li
692f17f7bfc6eea1b6fb6925
Shaofeng Liang
692f17f7bfc6eea1b6fb6926
Lei Wang
692f17f7bfc6eea1b6fb6927
Haozhe Jia
692f17f7bfc6eea1b6fb6928
Kaishen Yuan
692f17f7bfc6eea1b6fb6929
Jieming Wu
692f17f7bfc6eea1b6fb692a
Bowen Tian
692f17f7bfc6eea1b6fb692b
Yutao Yue
POLARIS: Projection-Orthogonal Least Squares for Robust and Adaptive Inversion in Diffusion Models
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 ω 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, a novel approach based on diffusion models, minimizes noise approximation errors during image inversion by treating the guidance scale as a step-wise variable, thus enhancing the quality and accuracy of image editing and restoration tasks.
diffusion models Inversion-Denoising Paradigm reconstruction degradation approximate noise error error-compensation problem error-origin problem Projection-Orthogonal Least Squares for Robust and Adaptive Inversion (POLARIS) inversion latent quality guidance scale noise approximation errors downstream tasks