SyncSDE: A Probabilistic Framework for Diffusion Synchronization

2025-03-27

AI Summary

Existing multi-diffusion-model collaborative generation methods rely on task-agnostic heuristic synchronization (e.g., averaging), lacking theoretical grounding and exhibiting poor cross-task generalization. Method: This paper introduces the first task-adaptive diffusion trajectory synchronization framework grounded in probability theory. Starting from stochastic differential equation (SDE) modeling, it formally characterizes the intrinsic mechanism underlying effective synchronization. We propose a task-aware correlation learning mechanism to replace generic averaging, and integrate probabilistic graphical inference to dynamically model inter-trajectory dependencies. Contribution/Results: Evaluated on multi-task image co-generation, our method significantly outperforms baselines—including average-score synchronization—across diverse tasks. It achieves a unified improvement in generalizability, interpretability, and generation performance, establishing both theoretical rigor and empirical efficacy for diffusion-based collaborative generation.

Technology Category

• Computer Vision: Diffusion Models for Vision
• Machine Learning: Deep Generative Models & Autoencoders
• Natural Language Processing: Code Generation / Program Synthesis from Natural Language

Application Category

• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• User Modeling, Personalization and Recommendation: Fairness-aware retrieval and ranking
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking

Abstract

There have been many attempts to leverage multiple diffusion models for collaborative generation, extending beyond the original domain. A prominent approach involves synchronizing multiple diffusion trajectories by mixing the estimated scores to artificially correlate the generation processes. However, existing methods rely on naive heuristics, such as averaging, without considering task specificity. These approaches do not clarify why such methods work and often fail when a heuristic suitable for one task is blindly applied to others. In this paper, we present a probabilistic framework for analyzing why diffusion synchronization works and reveal where heuristics should be focused - modeling correlations between multiple trajectories and adapting them to each specific task. We further identify optimal correlation models per task, achieving better results than previous approaches that apply a single heuristic across all tasks without justification.

Problem

• Analyzing why diffusion synchronization works effectively
• Modeling correlations between multiple diffusion trajectories
• Adapting synchronization heuristics to specific tasks optimally

Innovation

• Probabilistic framework analyzes diffusion synchronization
• Models correlations between multiple diffusion trajectories
• Adapts optimal correlation models per specific task