Training-Free Constrained Generation With Stable Diffusion Models

NeurIPSSpotlight2025

Authors
Stefano Zampini, Jacob K Christopher, Luca Oneto, Davide Anguita, Ferdinando Fioretto
Venue
NeurIPS 2025
Track
Spotlight

TL;DR

Stable diffusion models represent the state-of-the-art in data synthesis across diverse domains and hold transformative potential for applications in science and engineering, e…

Opening excerpt from the authors’ abstract. source

Read the paper

Topics

diffusion

← All NeurIPS 2025 Spotlight papers · Browse the whole archive