Ctrl-Z Sampling: Scaling Diffusion Sampling with Controlled Random Zigzag Explorations

Diffusion models generate conditional samples by progres-sively denoising Gaussian noise, yet the denoising trajectory can stall atvisually plausible but low-quality outcomes with conditional misalign-ment or structural artifacts. We interpret this behavior as local optimain a surrogate quality landscape: Once early denoising commits to a sub-optimal global structure, later steps mainly sharpen details and seldomcorrect the underlying mistake. While existing inference-time approachesexplore alternative diffusion states via re-noising with fixed strength ordirection, they exhibit limited capacity to escape steep quality plateaus.We propose Controlled Random Zigzag Sampling (Ctrl-Z Sampling), ascalable sampling strategy that detects plateaus in quality landscapevia a surrogate score, and allocates exploration only when a plateau isdetected. Upon detection, Ctrl-Z Sampling rolls back to noisier states,samples a set of alternative continuations, and updates the trajectorywhen a candidate improves the score, otherwise escalating the explo-ration depth to escape the current plateau. The proposed method ismodel-agnostic and broadly compatible with existing diffusion frame-works. Experiments show that Ctrl-Z Sampling consistently improvesgeneration quality over other inference-time scaling samplers across dif-ferent NFE budgets, offering a scalable compute-quality trade-off. Codeavailable at: https://github.com/ShunqiM/Ctrl-Z-Sampling.