无需分类器即可实现扩散模型的条件生成控制。
Classifier-Free Diffusion Guidance
- 联合训练条件与无条件扩散模型，用两者得分差做引导
- 在生成质量与多样性间取得与传统方法相当的平衡
- 适合想简化架构、避免额外训练分类器的研究者
分类器引导是一种近期提出的后训练方法，用于在条件扩散模型中权衡模式覆盖与样本保真度，其原理与其它生成模型中的低温度采样或截断类似。该方法需独立训练一个图像分类器，并将其梯度与扩散模型的得分估计结合。本文提出，引导可不依赖分类器实现：在称为无分类器引导的方法中，联合训练条件与无条件扩散模型，通过组合两者的得分估计，实现与分类器引导相当的生成质量与多样性权衡。
原文摘要 · Abstract (English)
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.
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