AI RESEARCH
Energy Scaling Laws for Diffusion Models: Quantifying Compute in Image Generation
arXiv CS.LG
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ArXi:2511.17031v2 Announce Type: replace The rapidly growing computational demands of diffusion models for image generation have raised significant concerns about energy consumption and environmental impact. While existing approaches to energy optimization focus on architectural improvements or hardware acceleration, there is a lack of principled methods to predict energy consumption across different model configurations and hardware setups. We propose an adaptation of Kaplan scaling laws to predict GPU energy consumption for diffusion models based on computational complexity (FLOPs.