# SustainDiffusion: Optimising the Social and Environmental Sustainability of Stable Diffusion Models

arXiv: https://arxiv.org/abs/2507.15663 · HTML: https://mlsanity.com/paper/2507.15663/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Placed in Image generation, text-to-image and editing, one of 14 directions in Diffusion models.
- Builds on 29 of its 94 references that are in the map.
- First posted on arXiv 21 Jul 2025.

## Placed in

- Diffusion models / [Image generation, text-to-image and editing](https://mlsanity.com/diffusion/image-generation-text-to-image-and-editing/)

## Builds on (most cited first, 29 of 29)

- [Photorealistic Text-to-Image Diffusion Models with Deep Language Understanding](https://mlsanity.com/paper/2205.11487/) (2022)
- [A Survey on Bias and Fairness in Machine Learning](https://mlsanity.com/paper/1908.09635/) (2019)
- [Green AI](https://mlsanity.com/paper/1907.10597/) (2019)
- [Easily Accessible Text-to-Image Generation Amplifies Demographic Stereotypes at Large Scale](https://mlsanity.com/paper/2211.03759/) (2022)
- [Stable Bias: Analyzing Societal Representations in Diffusion Models](https://mlsanity.com/paper/2303.11408/) (2023)
- [Social Biases through the Text-to-Image Generation Lens](https://mlsanity.com/paper/2304.06034/) (2023)
- [How well can Text-to-Image Generative Models understand Ethical Natural Language Interventions?](https://mlsanity.com/paper/2210.15230/) (2022)
- [Large-scale Text-to-Image Generation Models for Visual Artists' Creative Works](https://mlsanity.com/paper/2210.08477/) (2022)
- [Data-Centric Green AI: An Exploratory Empirical Study](https://mlsanity.com/paper/2204.02766/) (2022)
- [Smiling Women Pitching Down: Auditing Representational and Presentational Gender Biases in Image Generative AI](https://mlsanity.com/paper/2305.10566/) (2023)
- [Mitigating stereotypical biases in text to image generative systems](https://mlsanity.com/paper/2310.06904/) (2023)
- [The Weights can be Harmful: Pareto Search versus Weighted Search in Multi-Objective Search-Based Software Engineering](https://mlsanity.com/paper/2202.03728/) (2022)
- [Unveiling Energy Efficiency in Deep Learning: Measurement, Prediction, and Scoring across Edge Devices](https://mlsanity.com/paper/2310.18329/) (2023)
- [A Synthesis of Green Architectural Tactics for ML-Enabled Systems](https://mlsanity.com/paper/2312.09610/) (2023)
- [The Hypervolume Indicator: Problems and Algorithms](https://mlsanity.com/paper/2005.00515/) (2020)
- [On the Tradeoff between Energy, Precision, and Accuracy in Federated Quantized Neural Networks](https://mlsanity.com/paper/2111.07911/) (2021)
- [Greening AI-enabled Systems with Software Engineering: A Research Agenda for Environmentally Sustainable AI Practices](https://mlsanity.com/paper/2506.01774/) (2025)
- [Exploring LLM-Driven Explanations for Quantum Algorithms](https://mlsanity.com/paper/2409.19028/) (2024)
- [Unveiling the Energy Vampires: A Methodology for Debugging Software Energy Consumption](https://mlsanity.com/paper/2412.10063/) (2024)
- [What Does a Software Engineer Look Like? Exploring Societal Stereotypes in LLMs](https://mlsanity.com/paper/2501.03569/) (2025)
- [Comparative Analysis of Carbon Footprint in Manual vs. LLM-Assisted Code Development](https://mlsanity.com/paper/2505.04521/) (2025)
- [Understanding Fairness in Software Engineering: Insights from Stack Exchange](https://mlsanity.com/paper/2402.19038/) (2024)
- [GreenStableYolo: Optimizing Inference Time and Image Quality of Text-to-Image Generation](https://mlsanity.com/paper/2407.14982/) (2024)
- [On the Compression of Language Models for Code: An Empirical Study on CodeBERT](https://mlsanity.com/paper/2412.13737/) (2024)
- [EfficientBioAI: Making Bioimaging AI Models Efficient in Energy, Latency and Representation](https://mlsanity.com/paper/2306.06152/) (2023)
- [A 28.6 mJ/iter Stable Diffusion Processor for Text-to-Image Generation with Patch Similarity-based Sparsity Augmentation and Text-based Mixed-Precision](https://mlsanity.com/paper/2403.04982/) (2024)
- [How Do Generative Models Draw a Software Engineer? A Case Study on Stable Diffusion Bias](https://mlsanity.com/paper/2501.09014/) (2025)
- [Investigation of Energy-efficient AI Model Architectures and Compression Techniques for "Green" Fetal Brain Segmentation](https://mlsanity.com/paper/2405.15778/) (2024)
- [It is Giving Major Satisfaction: Why Fairness Matters for Software Practitioners](https://mlsanity.com/paper/2410.02482/) (2024)

## Abstract (arXiv)

Background: 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. Aims: 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. Method: 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. Results: We conduct a comprehensive empirical evaluation of SustainDiffusion, testing it against six different baselines using 56 different prompts. Our results demonstrate that SustainDiffusion can reduce gender bias in SD3 by 68%, ethnic bias by 59%, and energy consumption (calculated as the sum of CPU and GPU energy) by 48%. Additionally, the outcomes produced by SustainDiffusion are consistent across multiple runs and can be generalised to various prompts. Conclusions: 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.