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The hidden cost of an image: Quantifying the energy consumption of Artificial Intelligence image generation
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- TRV-2026-2533raw data (JSON)
- Version
- v1Published
- First published
- Evidence type
- Benchmark or lab evaluation
- Sources
- 1 sourcePeer-reviewed
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With the growing adoption of Artificial Intelligence (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 through diffusion models. Our experiment compares 17 state-of-the-art image generative models by considering mul…
The problem
In a test of 17 diffusion image models, energy per image differed by up to 46x between models, and int8 quantization raised energy use for most models, by up to 64.54%.
What this doesn’t fix
Only models free for academic use and runnable offline were tested, so subscription services such as DALL-E and Midjourney were excluded, and all runs used one workstation with an RTX 4090.
Sources
1 on file- Engineering Applications of Artificial Intelligencedoi:10.1016/j.engappai.2026.116340
Cite this record
v1Truvace Impact Record TRV-2026-2533, v1: “The hidden cost of an image: Quantifying the energy consumption of Artificial Intelligence image generation.” Truvace, 2026-11-12. /record/TRV-2026-2533 (accessed on the day you copy it). sha256 39c6f39d1ec03f15…
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