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Moonworks Lunara Aesthetic II: An Image Variation Dataset

arXiv 2602.01666 ↗First posted 2 Feb 2026cs.CVcited by 0

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Abstract (arXiv)

We introduce Lunara Aesthetic II, a publicly released, ethically sourced image dataset designed to support controlled evaluation and learning of contextual consistency in modern image generation and editing systems. The dataset comprises 2,854 anchor-linked variation pairs derived from original art and photographs created by Moonworks. Each variation pair applies contextual transformations, such as illumination, weather, viewpoint, scene composition, color tone, or mood; while preserving a stable underlying identity. Lunara Aesthetic II operationalizes identity-preserving contextual variation as a supervision signal while also retaining Lunara's signature high aesthetic scores. Results show high identity stability, strong target attribute realization, and a robust aesthetic profile that exceeds large-scale web datasets. Released under the Apache 2.0 license, Lunara Aesthetic II is intended for benchmarking, fine-tuning, and analysis of contextual generalization, identity preservation, and edit robustness in image generation and image-to-image systems with interpretable, relational supervision. The dataset is publicly available at: https://huggingface.co/datasets/moonworks/lunara-aesthetic-image-variations.

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References · 7

  1. 2022LAION-5B: An open large-scale dataset for training next generation image-text modelscited by 4,131
  2. 2022InstructPix2Pix: Learning to Follow Image Editing Instructionscited by 2,591
  3. 2025Z-Image: An Efficient Image Generation Foundation Model with Single-Stream Diffusion Transformercited by 247
  4. 2023D-CALM: A Dynamic Clustering-based Active Learning Approach for Mitigating Biascited by 12
  5. 2024Active Learning for Robust and Representative LLM Generation in Safety-Critical Scenarioscited by 10
  6. 2024Coherence-Driven Multimodal Safety Dialogue with Active Learning for Embodied Agentscited by 7
  7. 2024An Active Learning Framework for Inclusive Generation by Large Language Modelscited by 5

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