Latent Pollution Model: The Hidden Carbon Footprint in 3D Image Synthesis

TL;DR

This paper investigates the hidden environmental impact of 3D image synthesis using latent diffusion models, revealing significant carbon emissions during training and data generation, and emphasizes the need for sustainable AI practices.

Contribution

It provides the first comprehensive analysis of carbon footprints in 2D and 3D latent diffusion models during training and generation phases, highlighting environmental concerns.

Findings

Large image synthesis contributes most to emissions.

Training emissions comparable to driving 10-90 km.

Generation emissions equivalent to driving up to 3345 km.

Abstract

Contemporary developments in generative AI are rapidly transforming the field of medical AI. These developments have been predominantly driven by the availability of large datasets and high computing power, which have facilitated a significant increase in model capacity. Despite their considerable potential, these models demand substantially high power, leading to high carbon dioxide (CO2) emissions. Given the harm such models are causing to the environment, there has been little focus on the carbon footprints of such models. This study analyzes carbon emissions from 2D and 3D latent diffusion models (LDMs) during training and data generation phases, revealing a surprising finding: the synthesis of large images contributes most significantly to these emissions. We assess different scenarios including model sizes, image dimensions, distributed training, and data generation steps. Our findings reveal substantial carbon emissions from these models, with training 2D and 3D models comparable to driving a car for 10 km and 90 km, respectively. The process of data generation is even more significant, with CO2 emissions equivalent to driving 160 km for 2D models and driving for up to 3345 km for 3D synthesis. Additionally, we found that the location of the experiment can increase carbon emissions by up to 94 times, and even the time of year can influence emissions by up to 50%. These figures are alarming, considering they represent only a single training and data generation phase for each model. Our results emphasize the urgent need for developing environmentally sustainable strategies in generative AI.