  In the rapidly progressing field of generative models, the development of
efficient and high-fidelity text-to-image diffusion systems represents a
significant frontier. This study introduces YaART, a novel production-grade
text-to-image cascaded diffusion model aligned to human preferences using
Reinforcement Learning from Human Feedback (RLHF). During the development of
YaART, we especially focus on the choices of the model and training dataset
sizes, the aspects that were not systematically investigated for text-to-image
cascaded diffusion models before. In particular, we comprehensively analyze how
these choices affect both the efficiency of the training process and the
quality of the generated images, which are highly important in practice.
Furthermore, we demonstrate that models trained on smaller datasets of
higher-quality images can successfully compete with those trained on larger
datasets, establishing a more efficient scenario of diffusion models training.
From the quality perspective, YaART is consistently preferred by users over
many existing state-of-the-art models.
