[SEMINAR] Optimising the Social and Environmental Sustainability of Stable Diffusion Models
Title: Optimising the Social and Environmental Sustainability of Stable Diffusion Models
Speaker: Giordano D’Aloisio
When: Wednesday, 18th February, 14:30-15:30
Where: Alan Turing Seminar Room
Abstract: Text-to-image generation models are widely used across numerous domains. Among these models, Stable Diffusion (SD) – an open-source text-to-image generation model – has become the most popular, producing over 12 billion images annually. However, the widespread use of these models raises concerns regarding their social and environmental sustainability. To reduce the harm that SD models may have on society and the environment, we introduce SustainDiffusion, a search-based approach designed to enhance the social and environmental sustainability of SD models. SustainDiffusion searches the optimal combination of hyperparameters and prompt structures that can reduce gender and ethnic bias in generated images while also lowering the energy consumption required for image generation. Importantly, SustainDiffusion maintains image quality comparable to that of the original SD model. With SustainDiffusion, we demonstrate how enhancing the social and environmental sustainability of text-to-image generation models is possible without fine-tuning or changing the model’s architecture.
Preprint: https://arxiv.org/pdf/2507.15663
