While conditional diffusion models are known to have good coverage of the data distribution, they still face limitations in output diversity, particularly when sampled with a high classifier-free guidance scale for optimal image quality or when trained on small datasets. We attribute this problem to the role of the conditioning signal in inference and offer an improved sampling strategy for diffusion models that can increase generation diversity, especially at high guidance scales, with minimal loss of sample quality. Our sampling strategy anneals the conditioning signal by adding scheduled, monotonically decreasing Gaussian noise to the conditioning vector during inference to balance diversity and condition alignment. Our Condition-Annealed Diffusion Sampler (CADS) can be used with any pretrained model and sampling algorithm, and we show that it boosts the diversity of diffusion models in various conditional generation tasks. Further, using an existing pretrained diffusion model, CADS achieves a new state-of-the-art FID of 1.70 and 2.31 for class-conditional ImageNet generation at 256$\times$256 and 512$\times$512 respectively.

通过使用一种改进的采样策略，我们提供了一种可以增加扩散模型的生成多样性的方法，尤其是在高指导尺度下，同时最小化样本质量损失。该方法通过在推理过程中向条件向量添加预定的、单调递减的高斯噪声来退火条件信号，以平衡多样性和条件对齐。我们的Condition-Annealed Diffusion Sampler (CADS)可以与任何预训练模型和采样算法一起使用，并且我们证明它在各种条件生成任务中提高了扩散模型的多样性。此外，通过使用现有的预训练扩散模型，CADS在256×256和512×512像素的类条件ImageNet生成任务中取得了新的最优FID值，分别为1.70和2.31。

CADS: 通过条件退火采样释放扩散模型的多样性