Moonworks Lunara Aesthetic Dataset
It provides a curated dataset for researchers and practitioners in AI and art to study aesthetic quality and stylistic diversity, though it is incremental as it focuses on dataset creation rather than new methods.
The paper introduces the Moonworks Lunara Aesthetic Dataset, a first-of-its-kind collection of images generated to embody high-quality and diverse artistic styles, achieving substantially higher aesthetic scores than existing datasets.
The dataset spans diverse artistic styles, including regionally grounded aesthetics from the Middle East, Northern Europe, East Asia, and South Asia, alongside general categories such as sketch and oil painting. All images are generated using the Moonworks Lunara model and intentionally crafted to embody distinct, high-quality aesthetic styles, yielding a first-of-its-kind dataset with substantially higher aesthetic scores, exceeding even aesthetics-focused datasets, and general-purpose datasets by a larger margin. Each image is accompanied by a human-refined prompt and structured annotations that jointly describe salient objects, attributes, relationships, and stylistic cues. Unlike large-scale web-derived datasets that emphasize breadth over precision, the Lunara Aesthetic Dataset prioritizes aesthetic quality, stylistic diversity, and licensing transparency, and is released under the Apache 2.0 license to support research and unrestricted academic and commercial use.