  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.
