Solar activity is one of the main drivers of variability in our solar system and the key source of space weather phenomena that affect Earth and near Earth space. The extensive record of high resolution extreme ultraviolet (EUV) observations from the Solar Dynamics Observatory (SDO) offers an unprecedented, very large dataset of solar images. In this work, we make use of this comprehensive dataset to investigate capabilities of current state-of-the-art generative models to accurately capture the data distribution behind the observed solar activity states. Starting from StyleGAN-based methods, we uncover severe deficits of this model family in handling fine-scale details of solar images when training on high resolution samples, contrary to training on natural face images. When switching to the diffusion based generative model family, we observe strong improvements of fine-scale detail generation. For the GAN family, we are able to achieve similar improvements in fine-scale generation when turning to ProjectedGANs, which uses multi-scale discriminators with a pre-trained frozen feature extractor. We conduct ablation studies to clarify mechanisms responsible for proper fine-scale handling. Using distributed training on supercomputers, we are able to train generative models for up to 1024x1024 resolution that produce high quality samples indistinguishable to human experts, as suggested by the evaluation we conduct. We make all code, models and workflows used in this study publicly available at \url{https://github.com/SLAMPAI/generative-models-for-highres-solar-images}.

翻译：太阳活动是太阳系变化的主要驱动因素之一，也是影响地球及近地空间的空间天气现象的关键来源。太阳动力学观测站（SDO）提供的高分辨率极紫外（EUV）观测记录，构成了一个前所未有的、规模巨大的太阳图像数据集。在本研究中，我们利用这一综合性数据集，探究当前最先进的生成模型准确捕获太阳活动状态观测数据分布的能力。从基于StyleGAN的方法入手，我们发现该模型系列在处理高分辨率样本中太阳图像的精细尺度细节时存在严重缺陷，这与在自然人脸图像上的训练结果相反。当转向基于扩散的生成模型系列时，我们观察到精细尺度细节生成的能力显著提升。针对GAN系列，采用ProjectedGAN（使用预训练冻结特征提取器的多尺度判别器）后，我们能够在精细尺度生成方面实现类似改进。我们通过消融研究来阐明实现精细尺度恰当处理的关键机制。利用超级计算机进行分布式训练，我们成功训练了分辨率高达1024×1024的生成模型，其生成的高质量样本在评估中无法被人类专家区分。我们将本研究所用的所有代码、模型和工作流程公开于\url{https://github.com/SLAMPAI/generative-models-for-highres-solar-images}。