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.

翻译：众所周知，许多开源的基础扩散模型在生成显著偏离平均亮度的图像时存在困难，尽管此类图像存在于训练数据中。这是由于存在不一致性：推理过程从纯高斯噪声开始去噪，而训练噪声调度在主流公式的数值条件限制下，即使在最终时间步分布中仍保留残余数据，导致推理过程中产生意外偏差。为缓解这一问题，某些采用$\epsilon$预测的模型与特设的偏移噪声方法结合使用。与此同时，一些当代模型采用了零终端信噪比噪声调度和$\mathbf{v}$预测方法，但这需要对预训练模型进行重大修改。然而，此类修改可能会破坏大量基于这些预训练模型的社区驱动应用的稳定性。鉴于此，本研究重新审视了根本原因，并提出了一种创新且基于原理的解决方案——一步之外（OMS）。通过集成紧凑网络并在推理过程中增加一个简单有效的步骤，OMS在保持原始模型参数不变的同时，提升了图像保真度，并调和了训练与推理之间的二分法。一旦训练完成，同一隐空间领域的多个预训练扩散模型可共享同一个OMS模块。