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It is well known that many open-released foundational diffusion models havedifficulty in generating images that substantially depart from averagebrightness, despite such images being present in the training data. This is dueto an inconsistency: while denoising starts from pure Gaussian noise duringinference, the training noise schedule retains residual data even in the finaltimestep distribution, due to difficulties in numerical conditioning inmainstream formulation, leading to unintended bias during inference. Tomitigate this issue, certain $\epsilon$-prediction models are combined with anad-hoc offset-noise methodology. In parallel, some contemporary models haveadopted zero-terminal SNR noise schedules together with$\mathbf{v}$-prediction, which necessitate major alterations to pre-trainedmodels. However, such changes risk destabilizing a large multitude ofcommunity-driven applications anchored on these pre-trained models. In light ofthis, our investigation revisits the fundamental causes, leading to ourproposal of an innovative and principled remedy, called One More Step (OMS). Byintegrating a compact network and incorporating an additional simple yeteffective step during inference, OMS elevates image fidelity and harmonizes thedichotomy between training and inference, while preserving original modelparameters. Once trained, various pre-trained diffusion models with the samelatent domain can share the same OMS module.

One More Step: A Versatile Plug-and-Play Module for Rectifying Diffusion Schedule Flaws and Enhancing Low-Frequency Controls