Regularization by Texts for Latent Diffusion Inverse Solvers

The recent advent of diffusion models has led to significant progress in solving inverse problems, leveraging these models as effective generative priors. Nonetheless, there remain challenges related to the ill-posed nature of such problems, often due to inherent ambiguities in measurements or intrinsic system symmetries. To address this, drawing inspiration from the human ability to resolve visual ambiguities through perceptual biases, here we introduce a novel latent diffusion inverse solver by regularization by texts (TReg). Specifically, TReg applies the textual description of the preconception of the solution during the reverse diffusion sampling, of which the description is dynamically reinforced through null-text optimization for adaptive negation. Our comprehensive experimental results demonstrate that TReg successfully mitigates ambiguity in the inverse problems, enhancing their effectiveness and accuracy.