Denoising diffusion probabilistic models (DDPM) have shown remarkable performance in unconditional image generation. However, due to the stochasticity of the generative process in DDPM, it is challenging to generate images with the desired semantics. In this work, we propose Iterative Latent Variable Refinement (ILVR), a method to guide the generative process in DDPM to generate high-quality images based on a given reference image. Here, the refinement of the generative process in DDPM enables a single DDPM to sample images from various sets directed by the reference image. The proposed ILVR method generates high-quality images while controlling the generation. The controllability of our method allows adaptation of a single DDPM without any additional learning in various image generation tasks, such as generation from various downsampling factors, multi-domain image translation, paint-to-image, and editing with scribbles.

翻译：由于DDPM中基因化过程的随机性,用想要的语义来生成图像具有挑战性。在这项工作中,我们提议采用迭代式液态可变精度(ILVR)这一方法来指导DDPM中的基因化过程,以便根据给定的参考图像生成高质量的图像。在这里,DDPM中的基因化过程的完善使得一个单一的DDPM能够从参考图像指导的各组中提取图像样本。拟议的ILVR方法在控制生成的同时生成高质量的图像。我们的方法的可控性使得在不进行其他任何关于各种图像生成任务(例如从各种降色因素生成、多域图像翻译、油漆到图像以及用刻字词编辑等)的任何额外学习的情况下,能够对单一的DDPM进行改造。