This work presents an extensive hyperparameter search on Image Diffusion Models for Echocardiogram generation. The objective is to establish foundational benchmarks and provide guidelines within the realm of ultrasound image and video generation. This study builds over the latest advancements, including cutting-edge model architectures and training methodologies. We also examine the distribution shift between real and generated samples and consider potential solutions, crucial to train efficient models on generated data. We determine an Optimal FID score of $0.88$ for our research problem and achieve an FID of $2.60$. This work is aimed at contributing valuable insights and serving as a reference for further developments in the specialized field of ultrasound image and video generation.

翻译：本文对用于超声心动图生成的图像扩散模型进行了全面的超参数搜索。目标是在超声图像与视频生成领域建立基础基准并提供指导。本研究基于最新进展，包括前沿模型架构和训练方法。我们还探讨了真实样本与生成样本之间的分布偏移，并考虑了潜在解决方案——这对于在生成数据上训练高效模型至关重要。我们确定了研究问题的最优FID分数为$0.88$，并实现了$2.60$的FID值。本研究旨在为超声图像与视频生成这一专业领域的进一步发展提供有价值的见解，并作为参考。