Chain-of-Trajectories: Unlocking the Intrinsic Generative Optimality of Diffusion Models via Graph-Theoretic Planning
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.
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Ping Chen
claimed_verified
2026-03-18T08:45:22.425Z
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Ping Chen
redcping
user
redcping
69ba04cb5d2b9c28ea09efe9
Xiang Liu
69ba04cb5d2b9c28ea09efea
Xingpeng Zhang
69ba04cb5d2b9c28ea09efeb
Fei Shen
69ba04cb5d2b9c28ea09efec
Xun Gong
69ba04cb5d2b9c28ea09efed
Zhaoxiang Liu
69ba04cb5d2b9c28ea09efee
Zezhou Chen
69ba04cb5d2b9c28ea09efef
Huan Hu
69ba04cb5d2b9c28ea09eff0
Kai Wang
69ba04cb5d2b9c28ea09eff1
Shiguo Lian
Chain-of-Trajectories: Unlocking the Intrinsic Generative Optimality of Diffusion Models via Graph-Theoretic Planning
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.
Chain-of-Trajectories framework enables deliberative planning for diffusion models by using Diffusion DNA to dynamically allocate computational resources based on denoising difficulty.
diffusion models System 1 System 2 sampling schedule state dimensionality noise manifold trajectory planning Diffusion DNA graph planning directed acyclic graph Predict-Plan-Execute paradigm computational misallocation generative models output quality stability redundant computation