It is well known that many open-released foundational diffusion models have
difficulty in generating images that substantially depart from average
brightness, despite such images being present in the training data. This is due
to an inconsistency: while denoising starts from pure Gaussian noise during
inference, the training noise schedule retains residual data even in the final
timestep distribution, due to difficulties in numerical conditioning in
mainstream formulation, leading to unintended bias during inference. To
mitigate this issue, certain $\epsilon$-prediction models are combined with an
ad-hoc offset-noise methodology. In parallel, some contemporary models have
adopted zero-terminal SNR noise schedules together with
$\mathbf{v}$-prediction, which necessitate major alterations to pre-trained
models. However, such changes risk destabilizing a large multitude of
community-driven applications anchored on these pre-trained models. In light of
this, our investigation revisits the fundamental causes, leading to our
proposal of an innovative and principled remedy, called One More Step (OMS). By
integrating a compact network and incorporating an additional simple yet
effective step during inference, OMS elevates image fidelity and harmonizes the
dichotomy between training and inference, while preserving original model
parameters. Once trained, various pre-trained diffusion models with the same
latent domain can share the same OMS module.

通过在推理过程中整合简约网络并加入一个额外的简单而有效的步骤，One More Step（OMS）方法提高了图像的保真度，并协调训练和推理之间的差异，同时保留了原始模型参数。