Venue

Date

Share

G2D2: Gradient-Guided Discrete Diffusion for Image Inverse Problem Solving

Naoki Murata

Chieh-Hsin Lai

Yuhta Takida

Toshimitsu Uesaka

Bac Nguyen

Stefano Ermon*

Yuki Mitsufuji

* External authors

TMLR-25

2025

Abstract

Recent literature has effectively leveraged 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 generative 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 with lower GPU memory consumption.

Related Publications

Music Arena: Live Evaluation for Text-to-Music

NeurIPS, 2025
Yonghyun Kim, Wayne Chi, Anastasios N. Angelopoulos, Wei-Lin Chiang, Koichi Saito, Shinji Watanabe, Yuki Mitsufuji, Chris Donahue

We present Music Arena, an open platform for scalable human preference evaluation of text-to-music (TTM) models. Soliciting human preferences via listening studies is the gold standard for evaluation in TTM, but these studies are expensive to conduct and difficult to compare…

Large-Scale Training Data Attribution for Music Generative Models via Unlearning

NeurIPS, 2025
Woosung Choi, Junghyun Koo*, Kin Wai Cheuk, Joan Serrà, Marco A. Martínez-Ramírez, Yukara Ikemiya, Naoki Murata, Yuhta Takida, Wei-Hsiang Liao, Yuki Mitsufuji

This paper explores the use of unlearning methods for training data attribution (TDA) in music generative models trained on large-scale datasets. TDA aims to identify which specific training data points contributed to the generation of a particular output from a specific mod…

Blind Inverse Problem Solving Made Easy by Text-to-Image Latent Diffusion

NeurIPS, 2025
Michail Dontas, Yutong He, Naoki Murata, Yuki Mitsufuji, J. Zico Kolter*, Ruslan Salakhutdinov*

Blind inverse problems, where both the target data and forward operator are unknown, are crucial to many computer vision applications. Existing methods often depend on restrictive assumptions such as additional training, operator linearity, or narrow image distributions, thu…

  • HOME
  • Publications
  • G2D2: Gradient-Guided Discrete Diffusion for Image Inverse Problem Solving

JOIN US

Shape the Future of AI with Sony AI

We want to hear from those of you who have a strong desire
to shape the future of AI.