Compositional Diffusion with Maximal-Noise Transitions for Long-Horizon Planning

Compositional diffusion planners generate long-horizon trajectories by composing short local plans that are denoised jointly and kept consistent on their overlaps. However, when local plan distributions are multimodal, averaging incompatible local modes can produce infeasible composed plans. We attribute the persistence of mode-averaging to the predicted noise direction retained in the sampling transition. To address this challenge, we propose *Maximal-Noise Compositional Diffusion* (MaxCD), a *training-free* method that modifies sampling transitions on the overlaps. MaxCD constructs the next noisy state from the denoised estimate and fresh Gaussian noise, removing the predicted noise direction without additional denoiser evaluations. We additionally introduce a spectral split that applies maximal-noise transitions to low-frequency components and retains the original transition on high-frequency components to preserve fine details. Experiments demonstrate that MaxCD substantially improves long-horizon planning performance on OGBench and the visual consistency of compositional panorama generation. Project website at https://max-comp-diffusion.github.io/.