Novel architectures have recently improved generative image synthesis leading
to excellent visual quality in various tasks. Of particular note is the field
of ``AI-Art'', which has seen unprecedented growth with the emergence of
powerful multimodal models such as CLIP. By combining speech and image
synthesis models, so-called ``prompt-engineering'' has become established, in
which carefully selected and composed sentences are used to achieve a certain
visual style in the synthesized image. In this note, we present an alternative
approach based on retrieval-augmented diffusion models (RDMs). In RDMs, a set
of nearest neighbors is retrieved from an external database during training for
each training instance, and the diffusion model is conditioned on these
informative samples. During inference (sampling), we replace the retrieval
database with a more specialized database that contains, for example, only
images of a particular visual style. This provides a novel way to prompt a
general trained model after training and thereby specify a particular visual
style. As shown by our experiments, this approach is superior to specifying the
visual style within the text prompt. We open-source code and model weights at
this https URL .

通过检索增强的扩散模型，我们提出了一种替代性方法，改进了生成图像合成技术，优于基于文本提示的视觉样式规定。