arXiv AI
Sep 10

Let It Go or Learn to Self-Correct: Continuous Diffusion for Constrained Discrete Tasks

The paper examines how Denoising Diffusion Probabilistic Models (DDPMs) perform on globally constrained discrete tasks such as Sudoku and N-queens. It shows that standard diffusion sampling, which keeps updates close to the noisy state, often preserves early mistakes, whereas sampling directly from the model’s clean predictions dramatically improves validity (e.g., Sudoku from 31% to 95%). The authors further introduce self‑correction training, exposing the model to its own predictions to reduce inference errors, which enhances the performance of standard samplers across tasks.

By Mariia Drozdova, St\'ephane Liem Nguyen, Fran\c{c}ois Fleuret
arXiv Machine Learning
Oct 3

Learned End-to-End Guidance Schedules for Diffusion Models

The paper introduces Learned End-to-End Guidance Schedules (LEEGS) for diffusion models, which train a time‑dependent guidance schedule to balance data quality and requirement satisfaction while reducing sampling steps. LEEGS minimizes the guidance function over a small set of examples using stochastic gradient descent and employs a gradient approximation to cut training time by a factor of four. Experiments on tasks such as image inpainting, noisy image inverse problems, face‑ID‑guided generation, and PDE problems show that LEEGS outperforms baselines at the same computational budget or matches constant guidance with only 10% of the steps.

By Aneesh Barthakur, Mathias Niepert, Luiz F. O. Chamon
arXiv AI
Jun 10

MMD Guidance: Training-Free Distribution Adaptation for Diffusion Models via Maximum Mean Discrepancy Guidance

arXiv:2601. 08379v2 Announce Type: replace-cross Abstract: Pre-trained diffusion models have emerged as powerful generative priors for both unconditional and conditional sample generation, yet their outputs often deviate from the characteristics of user-specific target data.

By Matina Mahdizadeh Sani, Nima Jamali, Mohammad Jalali, Farzan Farnia