  Large-scale diffusion models have achieved remarkable performance in
generative tasks. Beyond their initial training applications, these models have
proven their ability to function as versatile plug-and-play priors. For
instance, 2D diffusion models can serve as loss functions to optimize 3D
implicit models. Rectified flow, a novel class of generative models, enforces a
linear progression from the source to the target distribution and has
demonstrated superior performance across various domains. Compared to
diffusion-based methods, rectified flow approaches surpass in terms of
generation quality and efficiency, requiring fewer inference steps. In this
work, we present theoretical and experimental evidence demonstrating that
rectified flow based methods offer similar functionalities to diffusion models
- they can also serve as effective priors. Besides the generative capabilities
of diffusion priors, motivated by the unique time-symmetry properties of
rectified flow models, a variant of our method can additionally perform image
inversion. Experimentally, our rectified flow-based priors outperform their
diffusion counterparts - the SDS and VSD losses - in text-to-3D generation. Our
method also displays competitive performance in image inversion and editing.
