We investigate the task of adapting image generative models to different datasets without finetuneing. To this end, we introduce Semantica, an image-conditioned diffusion model capable of generating images based on the semantics of a conditioning image. Semantica is trained exclusively on web-scale image pairs, that is it receives a random image from a webpage as conditional input and models another random image from the same webpage. Our experiments highlight the expressivity of pretrained image encoders and necessity of semantic-based data filtering in achieving high-quality image generation. Once trained, it can adaptively generate new images from a dataset by simply using images from that dataset as input. We study the transfer properties of Semantica on ImageNet, LSUN Churches, LSUN Bedroom and SUN397.

我们研究了在不进行微调的情况下将图像生成模型适应于不同数据集的任务。为此，我们介绍了Semantica，这是一种基于图像语义条件的扩散模型，能够根据条件图像的语义生成图像。Semantica仅通过网络规模的图像配对进行训练，即它接收来自网页的随机图像作为条件输入，并对来自同一网页的另一张随机图像进行建模。我们的实验突出了预训练图像编码器的表现力，以及在实现高质量图像生成时基于语义的数据过滤的必要性。一旦训练完成，它可以通过简单地使用该数据集中的图像作为输入自适应地生成新图像。我们研究了在ImageNet、LSUN Churches、LSUN Bedroom和SUN397上的Semantica的转移性质。

Semantica: 一种适应性图像条件扩散模型