We introduce a novel score-based diffusion framework named Twigs that incorporates multiple co-evolving flows for enriching conditional generation tasks. Specifically, a central or trunk diffusion process is associated with a primary variable (e.g., graph structure), and additional offshoot or stem processes are dedicated to dependent variables (e.g., graph properties or labels). A new strategy, which we call loop guidance, effectively orchestrates the flow of information between the trunk and the stem processes during sampling. This approach allows us to uncover intricate interactions and dependencies, and unlock new generative capabilities. We provide extensive experiments to demonstrate strong performance gains of the proposed method over contemporary baselines in the context of conditional graph generation, underscoring the potential of Twigs in challenging generative tasks such as inverse molecular design and molecular optimization.

翻译：我们提出了一种新颖的基于分数的扩散框架，命名为Twigs，该框架通过整合多个协同演化的流来增强条件生成任务。具体而言，一个中心或主干扩散过程与主要变量（如图结构）相关联，而额外的分支或茎干过程则专门处理相关变量（如图属性或标签）。我们提出了一种称为环路引导的新策略，该策略在采样过程中有效地协调主干与茎干过程间的信息流动。这种方法使我们能够揭示复杂的相互作用与依赖关系，并解锁新的生成能力。我们通过大量实验证明，在条件图生成任务中，所提方法相较于现有基线模型取得了显著的性能提升，这凸显了Twigs在逆向分子设计与分子优化等挑战性生成任务中的潜力。