Regularization by Texts for Latent Diffusion Inverse Solvers

Figure 1: Representative solutions obtained by TReg for various inverse problems. TReg optimizes both data consistency and the semantic alignment of the solution with textual cues, by reducing the solution space with text-conditional latent regularizer. This serves as an effective semantic guidance throughout the reconstruction process.