We discover that common diffusion noise schedules do not enforce the last timestep to have zero signal-to-noise ratio (SNR), and some implementations of diffusion samplers do not start from the last timestep. Such designs are flawed and do not reflect the fact that the model is given pure Gaussian noise at inference, creating a discrepancy between training and inference. We show that the flawed design causes real problems in existing implementations. In Stable Diffusion, it severely limits the model to only generate images with medium brightness and prevents it from generating very bright and dark samples. We propose a few simple fixes: (1) rescale the noise schedule to enforce zero terminal SNR; (2) train the model with v prediction; (3) change the sampler to always start from the last timestep; (4) rescale classifier-free guidance to prevent over-exposure. These simple changes ensure the diffusion process is congruent between training and inference and allow the model to generate samples more faithful to the original data distribution.

通过检测斑点扩散噪音调度方案，研究发现某些实现方式的差异在训练和推理中会导致模型输出失真，提出了改进措施，包括：规范化噪声调度方案、加入v预测、更改采样器以从最后一个时间步开始采样、对无分类器的指导进行缩放，使扩散过程在训练和推理中保持一致，从而更忠实地生成样本。

常用扩散噪声计划和样本步长存在缺陷