Training-free linear inversion involves the use of a pretrained generative
model and -- through appropriate modifications to the generation process --
solving inverse problems without any finetuning of the generative model. While
recent prior methods have explored the use of diffusion models, they still
require the manual tuning of many hyperparameters for different inverse
problems. In this work, we propose a training-free method for image inversion
using pretrained flow models, leveraging the simplicity and efficiency of Flow
Matching models, using theoretically-justified weighting schemes and thereby
significantly reducing the amount of manual tuning. In particular, we draw
inspiration from two main sources: adopting prior gradient correction methods
to the flow regime, and a solver scheme based on conditional Optimal Transport
paths. As pretrained diffusion models are widely accessible, we also show how
to practically adapt diffusion models for our method. Empirically, our approach
requires no problem-specific tuning across an extensive suite of noisy linear
image inversion problems on high-dimensional datasets, ImageNet-64/128 and
AFHQ-256, and we observe that our flow-based method for image inversion
significantly improves upon closely-related diffusion-based linear inversion
methods.

通过预训练的流模型，利用理论上合理的加权方案，提出一种无需微调的图像逆向方法，显著减少了手动调整的工作量，并在高维数据集上进行了广泛测试，证明其在图像逆向中明显优于扩散模型的线性逆向方法。