Diffusion probabilistic models have been successful in generating high-quality and diverse images. However, traditional models, whose input and output are high-resolution images, suffer from excessive memory requirements, making them less practical for edge devices. Previous approaches for generative adversarial networks proposed a patch-based method that uses positional encoding and global content information. Nevertheless, designing a patch-based approach for diffusion probabilistic models is non-trivial. In this paper, we resent a diffusion probabilistic model that generates images on a patch-by-patch basis. We propose two conditioning methods for a patch-based generation. First, we propose position-wise conditioning using one-hot representation to ensure patches are in proper positions. Second, we propose Global Content Conditioning (GCC) to ensure patches have coherent content when concatenated together. We evaluate our model qualitatively and quantitatively on CelebA and LSUN bedroom datasets and demonstrate a moderate trade-off between maximum memory consumption and generated image quality. Specifically, when an entire image is divided into 2 x 2 patches, our proposed approach can reduce the maximum memory consumption by half while maintaining comparable image quality.

翻译：扩散概率模型在生成高质量、多样化的图像方面取得了成功。然而，传统模型的输入和输出均为高分辨率图像，导致其内存需求过高，难以在边缘设备上实际应用。先前针对生成对抗网络的研究提出了一种基于分块的方法，利用位置编码和全局内容信息。然而，为扩散概率模型设计分块方法并非易事。本文提出了一种逐块生成图像的扩散概率模型。我们针对分块生成提出了两种条件化方法：首先，利用独热编码实现位置条件化，确保各分块位于正确位置；其次，提出全局内容条件化（Global Content Conditioning，GCC），以保障分块拼接后内容连贯。我们在CelebA和LSUN卧室数据集上对模型进行了定性和定量评估，结果表明最大内存消耗与生成图像质量之间存在适度权衡。具体而言，当整幅图像被划分为2×2分块时，所提方法可在保持可比较图像质量的同时，将最大内存消耗降低一半。