The provided paper introduces GenEAva, a novel framework for generating high-quality cartoon avatars with detailed facial expressions by fine-tuning a diffusion model on realistic faces and then applying stylization. To overcome limitations in existing datasets, the authors created GenEAva 1.0, the first expressive cartoon avatar dataset featuring 13,230 avatars across 135 distinct expressions with balanced demographics. Experiments demonstrate that GenEAvaproduces more expressive faces than current models like SDXL and ensures novel identities without memorizing training data, with user studies validating the preservation of identity and expression during the cartoon conversion. This work offers both a new method and a valuable resource for advancing research in expressive avatar creation.