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cs.LG2026
Plug-and-Play Guidance for Discrete Diffusion Models via Gradient-Informed Logit Correction
Hongkun Dou, Zike Chen, Fengji Li +2
Controllable generation with discrete diffusion models is often hindered by high computational overhead or the need for retraining. In this paper, we present \underline{\textbf{G}}…
cs.LG2026
Constrained Particle Seeking: Solving Diffusion Inverse Problems with Just Forward Passes
Hongkun Dou, Zike Chen, Zeyu Li +3
Diffusion models have gained prominence as powerful generative tools for solving inverse problems due to their ability to model complex data distributions. However, existing method…
cs.LG2025
You Only Look One Step: Accelerating Backpropagation in Diffusion Sampling with Gradient Shortcuts
Hongkun Dou, Zeyu Li, Xingyu Jiang +4
Diffusion models (DMs) have recently demonstrated remarkable success in modeling large-scale data distributions. However, many downstream tasks require guiding the generated conten…