Projected Coupled Diffusion for Test-Time Constrained Joint Generation

• URL: http://arxiv.org/abs/2508.10531v2
• Date: Tue, 30 Sep 2025 07:38:20 GMT
• Title: Projected Coupled Diffusion for Test-Time Constrained Joint Generation
• Authors: Hao Luan, Yi Xian Goh, See-Kiong Ng, Chun Kai Ling,
• Abstract summary: We propose Projected Coupled Diffusion (PCD), a novel test-time framework for constrained joint generation.<n>PCD introduces a coupled guidance term into the generative dynamics to encourage coordination between diffusion models and incorporates a projection step at each diffusion step to enforce hard constraints.<n>Our results show improved coupling effects and guaranteed constraint satisfaction without incurring excessive computational costs.
• Score: 49.69610867216755
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Modifications to test-time sampling have emerged as an important extension to diffusion algorithms, with the goal of biasing the generative process to achieve a given objective without having to retrain the entire diffusion model. However, generating jointly correlated samples from multiple pre-trained diffusion models while simultaneously enforcing task-specific constraints without costly retraining has remained challenging. To this end, we propose Projected Coupled Diffusion (PCD), a novel test-time framework for constrained joint generation. PCD introduces a coupled guidance term into the generative dynamics to encourage coordination between diffusion models and incorporates a projection step at each diffusion step to enforce hard constraints. Empirically, we demonstrate the effectiveness of PCD in application scenarios of image-pair generation, object manipulation, and multi-robot motion planning. Our results show improved coupling effects and guaranteed constraint satisfaction without incurring excessive computational costs.

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