论文标题
无分类器扩散指南
Classifier-Free Diffusion Guidance
论文作者
论文摘要
分类器指导是一种最近引入的方法，可以在训练后进行有条件扩散模型中的模式覆盖范围和样品保真度，与其他类型的生成模型中的低温采样或截断相同。分类器指南将扩散模型的得分估计与图像分类器的梯度相结合，因此需要训练与扩散模型分开的图像分类器。它还提出了一个问题，即在没有分类器的情况下是否可以执行指导。我们表明，在没有这样的分类器的情况下，纯生成模型确实可以执行指导：在我们所说的无分类器指导中，我们共同训练有条件的和无条件的扩散模型，并且我们结合了所得的条件和无条件得分估计，以在与使用分类器指导相似的样本质量和相似的样本质量和多样性之间达到折衷。
Classifier guidance is a recently introduced method to trade off mode coverage and sample fidelity in conditional diffusion models post training, in the same spirit as low temperature sampling or truncation in other types of generative models. Classifier guidance combines the score estimate of a diffusion model with the gradient of an image classifier and thereby requires training an image classifier separate from the diffusion model. It also raises the question of whether guidance can be performed without a classifier. We show that guidance can be indeed performed by a pure generative model without such a classifier: in what we call classifier-free guidance, we jointly train a conditional and an unconditional diffusion model, and we combine the resulting conditional and unconditional score estimates to attain a trade-off between sample quality and diversity similar to that obtained using classifier guidance.
