mlsanityData to 14 Oct 2026

Climate Implications of Diffusion-based Generative Visual AI Systems and their Mass Adoption

arXiv 2505.18892 ↗First posted 24 May 2025cs.CY cs.AIcited by 3

On the map: Diffusion models → Diffusion foundations and sampling

Abstract (arXiv)

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.

Key facts. Cited by 3 papers in the map (data through 14 Oct 2026). Placed in Diffusion foundations and sampling, one of 14 directions in Diffusion models. Builds on 4 of its 31 references that are in the map. First posted on arXiv 24 May 2025.

Builds on

4 of 31 references.

References · 4

  1. 2019Energy and Policy Considerations for Deep Learning in NLPcited by 1,254
  2. 2019Green AIcited by 627
  3. 2019Quantifying the Carbon Emissions of Machine Learningcited by 521
  4. 2023Counting Carbon: A Survey of Factors Influencing the Emissions of Machine Learningcited by 42

Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2025: 1; 2026: 2.

3 papers.

Similar, not cited

Close in wording, no citation either way

  1. 2023State of the Art on Diffusion Models for Visual Computing
  2. 2024Latent Pollution Model: The Hidden Carbon Footprint in 3D Image Synthesis
  3. 2025SustainDiffusion: Optimising the Social and Environmental Sustainability of Stable Diffusion Models
  4. 2026Environmental Impact of Generative and Agentic AI: An in-Depth Analysis and Green Solutions
  5. 2022A Survey on Generative Diffusion Model
  6. 2025The Hidden Cost of an Image: Quantifying the Energy Consumption of AI Image Generation
  7. 2023Speed Is All You Need: On-Device Acceleration of Large Diffusion Models via GPU-Aware Optimizations
  8. 2023AI Imagery and the Overton Window