探索开源模型生态系统：艺术图像生成中创作者实践的实证研究
Navigating the Open-Source Model Ecosystem: An Empirical Study of Creator Practices in Artistic Image Generation
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中文总结 AI 辅助
本文针对开源图像生成生态系统中创作者模型使用行为展开实证研究，构建含600万张图像及生成元数据的数据集，关联基础模型与LoRA模型使用情况，揭示其优劣势，公开数据集，为创作者和研究人员提供参考。
中文摘要 AI 辅助
强大图像生成模型的开源创造了一个充满活力的生态系统，创作者在此精心挑选并组合大量社区贡献的模型，这与使用Midjourney等闭源工具形成鲜明对比。然而，对于这些新兴的创作工作流程我们知之甚少。为填补这一空白，本文首次对该开源图像生成生态系统内创作者模型使用行为进行大规模实证研究。我们构建了一个包含600万张图像及其嵌入生成元数据的新颖数据集，通过将22400个基础模型和154000个LoRA模型的使用与图像关联，研究结果突出了该生态系统的独特优势和内在障碍，为使其更具可持续性和创新性提供了有价值的见解。此外，我们还公开了数据集，为创作者提供实用参考，也便于研究人员进一步开展研究。
英文摘要
The open-sourcing of powerful image generation models has created a vibrant ecosystem where creators curate and combine a vast array of community-contributed models. This practice stands in sharp contrast to using closed-source tools like Midjourney. Yet, little is known about these emerging creative workflows. To bridge this gap, this paper presents the first large-scale empirical study of creator model usage behavior within this open-source image generation ecosystem. We construct a novel dataset of 6 million images with their embedded generation metadata -- a detailed recipe of the creation process, including the models used and the prompts. By linking the usage of 22.4K base models and 154K LoRA models to the images, our findings underscore the ecosystem's unique strengths and its inherent obstacles. This provides valuable insights for making this ecosystem more sustainable and innovative. Moreover, we make our dataset publicly available, providing creators with practical references for producing better artworks and researchers to facilitate further studies.
发表机构
- The Hong Kong University of Science and Technology (Guangzhou)（香港科学与技术大学（广州））
机构由 AI 辅助整理，请以论文原文为准。
