Title: The Hidden Cost of an Image: Quantifying the Energy Consumption of AI Image Generation Show Chinese title

Title: 图像的隐性成本：量化人工智能图像生成的能源消耗

Abstract: With the growing adoption of AI image generation, in conjunction with the ever-increasing environmental resources demanded by AI, we are urged to answer a fundamental question: What is the environmental impact hidden behind each image we generate? In this research, we present a comprehensive empirical experiment designed to assess the energy consumption of AI image generation. Our experiment compares 17 state-of-the-art image generation models by considering multiple factors that could affect their energy consumption, such as model quantization, image resolution, and prompt length. Additionally, we consider established image quality metrics to study potential trade-offs between energy consumption and generated image quality. Results show that image generation models vary drastically in terms of the energy they consume, with up to a 46x difference. Image resolution affects energy consumption inconsistently, ranging from a 1.3x to 4.7x increase when doubling resolution. U-Net-based models tend to consume less than Transformer-based one. Model quantization instead results to deteriorate the energy efficiency of most models, while prompt length and content have no statistically significant impact. Improving image quality does not always come at the cost of a higher energy consumption, with some of the models producing the highest quality images also being among the most energy efficient ones. Show Chinese abstract

Abstract: 随着AI图像生成的日益普及，以及AI对环境资源需求的不断增加，我们被敦促回答一个基本问题：隐藏在我们生成的每张图片背后的环境影响是什么？ 在这项研究中，我们设计了一个全面的经验实验，旨在评估AI图像生成的能量消耗。 我们的实验通过考虑可能影响模型能耗的多个因素（如模型量化、图像分辨率和提示长度）来比较17种最先进的图像生成模型。 此外，我们考虑了已建立的图像质量度量标准，以研究能耗与生成图像质量之间的潜在权衡。 结果显示，图像生成模型在能耗方面存在巨大差异，最高可达46倍。 图像分辨率对能耗的影响不一致，当分辨率翻倍时，能耗增加了1.3倍到4.7倍。 基于U-Net的模型倾向于比基于Transformer的模型消耗更少的能量。 模型量化反而导致大多数模型的能量效率下降，而提示长度和内容没有统计学上显著的影响。 提高图像质量并不总是以更高的能量消耗为代价，一些生成最高质量图像的模型同时也属于最节能的模型。