Diffusion models operate in a reflexive System 1 mode, constrained by a fixed, content-agnostic sampling schedule. This rigidity arises from the curse of state dimensionality, where the combinatorial explosion of possible states in the high-dimensional noise manifold renders explicit trajectory planning intractable and leads to systematic computational misallocation. To address this, we introduce Chain-of-Trajectories (CoTj), a train-free framework enabling System 2 deliberative planning. Central to CoTj is Diffusion DNA, a low-dimensional signature that quantifies per-stage denoising difficulty and serves as a proxy for the high-dimensional state space, allowing us to reformulate sampling as graph planning on a directed acyclic graph. Through a Predict-Plan-Execute paradigm, CoTj dynamically allocates computational effort to the most challenging generative phases. Experiments across multiple generative models demonstrate that CoTj discovers context-aware trajectories, improving output quality and stability while reducing redundant computation. This work establishes a new foundation for resource-aware, planning-based diffusion modeling. The code is available at https://github.com/UnicomAI/CoTj.

翻译：扩散模型以反射性的系统1模式运行，受限于固定的、内容无关的采样调度。这种刚性源于状态维度的诅咒：高维噪声流形中可能状态的组合爆炸使得显式轨迹规划难以处理，并导致系统性的计算资源错配。为解决此问题，我们提出了轨迹链（CoTj），这是一个无需训练即可实现系统2审慎规划的框架。CoTj的核心是扩散DNA，这是一种低维特征签名，用于量化每个阶段的去噪难度，并作为高维状态空间的代理，使我们能够将采样问题重新表述为在有向无环图上的图规划问题。通过“预测-规划-执行”范式，CoTj能够动态地将计算资源分配给最具挑战性的生成阶段。在多种生成模型上的实验表明，CoTj能够发现上下文感知的轨迹，在提高输出质量和稳定性的同时减少冗余计算。这项工作为基于规划的、资源感知的扩散建模奠定了新的基础。代码可在 https://github.com/UnicomAI/CoTj 获取。