Recent progress with conditional image diffusion models has been stunning, and this holds true whether we are speaking about models conditioned on a text description, a scene layout, or a sketch. Unconditional image diffusion models are also improving but lag behind, as do diffusion models which are conditioned on lower-dimensional features like class labels. We propose to close the gap between conditional and unconditional models using a two-stage sampling procedure. In the first stage we sample an embedding describing the semantic content of the image. In the second stage we sample the image conditioned on this embedding and then discard the embedding. Doing so lets us leverage the power of conditional diffusion models on the unconditional generation task, which we show improves FID by 25-50% compared to standard unconditional generation.

翻译：最近，基于条件图像扩散模型的进展令人瞩目，无论是针对文本描述、场景布局还是草图的条件模型，其表现均十分出色。无条件的图像扩散模型虽在改进，但仍落后于条件模型，同样落后的是基于类别标签等低维特征的条件扩散模型。我们提出通过两阶段采样流程来缩小条件模型与无条件模型之间的差距。第一阶段，我们采样一个描述图像语义内容的嵌入向量；第二阶段，我们基于该嵌入向量条件采样图像，随后丢弃该嵌入。这种方法使我们能够在无条件生成任务中利用条件扩散模型的强大能力，实验表明，与标准无条件生成相比，FID指标提升了25-50%。