Exploring the Hyperparameter Space of Image Diffusion Models for
  Echocardiogram Generation
Hadrien Reynaud Bernhard Kainz
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.