The field of text-to-image models is moving towards improved understanding and representation of complex scenes and historical contexts. Recent research has focused on evaluating the ability of these models to accurately depict different historical periods and understand compositional relationships between objects. This has led to the development of new methodologies and benchmarks for assessing the performance of text-to-image models. Furthermore, there has been significant progress in using generative models for scene understanding and inverse generative modeling, enabling the inference of scene structure and object relationships from natural images. Notably, some papers have made innovative contributions to the field, such as proposing training-free frameworks for stylized abstraction and demonstrating the importance of domain effects in diffusion classifiers. Noteworthy papers include:

• Synthetic History: Evaluating Visual Representations of the Past in Diffusion Models, which presents a systematic methodology for evaluating historical representation in generated imagery.
• Diffusion Classifiers Understand Compositionality, but Conditions Apply, which provides a comprehensive study of the discriminative capabilities of diffusion classifiers on compositional tasks.