Procedural noise is a fundamental component of computer graphics pipelines,
offering a flexible way to generate textures that exhibit "natural" random
variation. Many different types of noise exist, each produced by a separate
algorithm. In this paper, we present a single generative model which can learn
to generate multiple types of noise as well as blend between them. In addition,
it is capable of producing spatially-varying noise blends despite not having
access to such data for training. These features are enabled by training a
denoising diffusion model using a novel combination of data augmentation and
network conditioning techniques. Like procedural noise generators, the model's
behavior is controllable via interpretable parameters and a source of
randomness. We use our model to produce a variety of visually compelling noise
textures. We also present an application of our model to improving inverse
procedural material design; using our model in place of fixed-type noise nodes
in a procedural material graph results in higher-fidelity material
reconstructions without needing to know the type of noise in advance.

通过训练一种去噪扩散模型，采用数据增强和网络调节技术的新颖组合，我们提出了一个单一生成模型，可以学习生成多种类型的噪声并在它们之间进行混合，同时可以生成具有空间变化的噪声混合，而无需训练这些数据。通过控制参数和随机性，该模型的行为可由用户控制。我们还将该模型应用于改进反向程序性材料设计；使用该模型代替程序性材料图中的固定类型噪声节点可以得到更高保真度的材料重建，无需事先了解噪声类型。