扩散模型生成艺术系统大规模使用，正显著推高全球能耗。
Climate Implications of Diffusion-based Generative Visual AI Systems and their Mass Adoption
- 分析扩散模型艺术系统增长与使用模式，量化其能源消耗趋势。
- 估算大众普及后可能大幅增加全球电力消耗。
- 呼吁关注隐性碳排放，适合关注AI可持续发展的研究者参考。
快速发展的数字技术（如区块链、加密货币挖矿和NFT铸造）的气候影响已广受关注，其大量使用GPU导致的能耗问题令人担忧。然而，我们推测由于文本提示驱动的扩散生成式AI艺术系统具有更广泛的消费者吸引力，其GPU使用同样需引起重视。随着高度复杂的生成艺术系统数量激增，并被消费者和创意专业人士迅速采纳，这些系统对气候的影响亟需认真评估。本文报告了基于扩散模型的视觉AI系统的发展趋势、使用模式、增长情况及其气候影响。我们的估算表明，这些工具的大规模采用可能显著增加全球能源消耗。论文最后探讨了潜在解决方案、未来研究方向及面临的挑战，包括公开数据的匮乏。
原文摘要 · Abstract (English)
Climate implications of rapidly developing digital technologies, such as blockchains and the associated crypto mining and NFT minting, have been well documented and their massive GPU energy use has been identified as a cause for concern. However, we postulate that due to their more mainstream consumer appeal, the GPU use of text-prompt based diffusion AI art systems also requires thoughtful considerations. Given the recent explosion in the number of highly sophisticated generative art systems and their rapid adoption by consumers and creative professionals, the impact of these systems on the climate needs to be carefully considered. In this work, we report on the growth of diffusion-based visual AI systems, their patterns of use, growth and the implications on the climate. Our estimates show that the mass adoption of these tools potentially contributes considerably to global energy consumption. We end this paper with our thoughts on solutions and future areas of inquiry as well as associated difficulties, including the lack of publicly available data.
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