G2D2: Gradient-guided Discrete Diffusion for image inverse problem solving
Recent literature has effectively utilized diffusion models trained on continuous variables as priors for solving inverse problems. Notably, discrete diffusion models with discrete latent codes have shown strong performance, particularly in modalities suited for discrete compressed representations, such as image and motion generation. However, their discrete and non-differentiable nature has limited their application to inverse problems formulated in continuous spaces. This paper presents a novel method for addressing linear inverse problems by leveraging image-generation models based on discrete diffusion as priors. We overcome these limitations by approximating the true posterior distribution with a variational distribution constructed from categorical distributions and continuous relaxation techniques. Furthermore, we employ a star-shaped noise process to mitigate the drawbacks of traditional discrete diffusion models with absorbing states, demonstrating that our method performs comparably to continuous diffusion techniques. To the best of our knowledge, this is the first approach to use discrete diffusion model-based priors for solving image inverse problems.
Code (0)
등록된 구현이 없습니다.
Tasks
Image GenerationMotion GenerationMethods 이 논문이 사용한 방법론
Similar Papers 제목 키워드 기반
Discrete Langevin-Inspired Posterior Sampling
We study posterior sampling for inverse problems in discrete state spaces using discrete diffusion models as generative priors. While continuous diffusion models have become widely used for inverse problems, their discre…
Image RestorationTest-Time Anchoring for Discrete Diffusion Posterior Sampling
While continuous diffusion models have achieved remarkable success, discrete diffusion offers a unified framework for jointly modeling text and images. Beyond unification, discrete diffusion provides faster inference, fi…
Question AnsweringProtein Design with Guided Discrete Diffusion
A popular approach to protein design is to combine a generative model with a discriminative model for conditional sampling. The generative model samples plausible sequences while the discriminative model guides a search …
Bayesian OptimizationDenoisingProtein DesignDiffusion State-Guided Projected Gradient for Inverse Problems
Recent advancements in diffusion models have been effective in learning data priors for solving inverse problems. They leverage diffusion sampling steps for inducing a data prior while using a measurement guidance gradie…
Image RestorationLearning To Sample From Diffusion Models Via Inverse Reinforcement Learning
Diffusion models generate samples through an iterative denoising process guided by a pretrained neural network. Once the denoiser is fixed, the sampling algorithm itself (noise schedules, guidance scales, stochasticity p…
Reinforcement Learning