A two-step wrapper (invert candidate to latent, then run DPS from that latent) improves hard inpainting results, with mixed or negative superresolution results and toy-model theory.
Provable posterior sampling with denoising oracles via tilted transport
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ReGuidance: A Simple Diffusion Wrapper for Boosting Sample Quality on Hard Inverse Problems
A two-step wrapper (invert candidate to latent, then run DPS from that latent) improves hard inpainting results, with mixed or negative superresolution results and toy-model theory.