The content introduces a novel training-free method for solving linear inverse problems using pretrained flow models. By leveraging conditional OT probability paths, the authors demonstrate improved performance over diffusion-based methods across various datasets and tasks. The proposed algorithm is stable, simple, and requires no hyperparameter tuning.
Recent advances in generative models have led to the development of efficient solutions for noisy linear inverse problems without the need for extensive training. The proposed method combines ideas from diffusion models and flow matching to achieve superior results in image restoration tasks. By adapting pretrained models to utilize conditional OT probability paths, the approach demonstrates perceptual quality improvements in noisy settings.
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by Ashwini Pokl... في arxiv.org 03-12-2024
https://arxiv.org/pdf/2310.04432.pdfاستفسارات أعمق