# Diffused Responsibility: Analyzing the Energy Consumption of Generative Text-to-Audio Diffusion Models

arXiv: https://arxiv.org/abs/2505.07615 · HTML: https://mlsanity.com/paper/2505.07615/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Placed in Audio, speech and music, one of 14 directions in Diffusion models.
- Builds on 21 of its 40 references that are in the map.
- First posted on arXiv 12 May 2025.

## Placed in

- Diffusion models / [Audio, speech and music](https://mlsanity.com/diffusion/audio-speech-and-music/)

## Builds on (most cited first, 21 of 21)

- [Diffusion Models: A Comprehensive Survey of Methods and Applications](https://mlsanity.com/paper/2209.00796/) (2022)
- [The Flan Collection: Designing Data and Methods for Effective Instruction Tuning](https://mlsanity.com/paper/2301.13688/) (2023)
- [Green AI](https://mlsanity.com/paper/1907.10597/) (2019)
- [AudioLDM: Text-to-Audio Generation with Latent Diffusion Models](https://mlsanity.com/paper/2301.12503/) (2023)
- [Clotho: An Audio Captioning Dataset](https://mlsanity.com/paper/1910.09387/) (2019)
- [AudioLDM 2: Learning Holistic Audio Generation with Self-supervised Pretraining](https://mlsanity.com/paper/2308.05734/) (2023)
- [Make-An-Audio: Text-To-Audio Generation with Prompt-Enhanced Diffusion Models](https://mlsanity.com/paper/2301.12661/) (2023)
- [Fr'echet Audio Distance: A Metric for Evaluating Music Enhancement Algorithms](https://mlsanity.com/paper/1812.08466/) (2018)
- [Stable Audio Open](https://mlsanity.com/paper/2407.14358/) (2024)
- [The Computational Limits of Deep Learning](https://mlsanity.com/paper/2007.05558/) (2020)
- [Power Hungry Processing: Watts Driving the Cost of AI Deployment?](https://mlsanity.com/paper/2311.16863/) (2023)
- [Carbontracker: Tracking and Predicting the Carbon Footprint of Training Deep Learning Models](https://mlsanity.com/paper/2007.03051/) (2020)
- [Deep Learning for Audio Signal Processing](https://mlsanity.com/paper/1905.00078/) (2019)
- [Adapting Frechet Audio Distance for Generative Music Evaluation](https://mlsanity.com/paper/2311.01616/) (2023)
- [Make-An-Audio 2: Temporal-Enhanced Text-to-Audio Generation](https://mlsanity.com/paper/2305.18474/) (2023)
- [Foundation Models for Music: A Survey](https://mlsanity.com/paper/2408.14340/) (2024)
- [Energy Consumption of Deep Generative Audio Models](https://mlsanity.com/paper/2107.02621/) (2021)
- [Performance and energy balance: a comprehensive study of state-of-the-art sound event detection systems](https://mlsanity.com/paper/2310.03455/) (2023)
- [PAGURI: a user experience study of creative interaction with text-to-music models](https://mlsanity.com/paper/2407.04333/) (2024)
- [From Computation to Consumption: Exploring the Compute-Energy Link for Training and Testing Neural Networks for SED Systems](https://mlsanity.com/paper/2409.05080/) (2024)
- [Energy Consumption Trends in Sound Event Detection Systems](https://mlsanity.com/paper/2409.08763/) (2024)

## Built on by (most cited first, 2 of 2)

- [Mitigating data replication in text-to-audio generative diffusion models through anti-memorization guidance](https://mlsanity.com/paper/2509.14934/) (2025)
- [Exposing the Cost of Deep Learning Audio Development](https://mlsanity.com/paper/2610.01619/) (2026)

## Abstract (arXiv)

Text-to-audio models have recently emerged as a powerful technology for generating sound from textual descriptions. However, their high computational demands raise concerns about energy consumption and environmental impact. In this paper, we conduct an analysis of the energy usage of 7 state-of-the-art text-to-audio diffusion-based generative models, evaluating to what extent variations in generation parameters affect energy consumption at inference time. We also aim to identify an optimal balance between audio quality and energy consumption by considering Pareto-optimal solutions across all selected models. Our findings provide insights into the trade-offs between performance and environmental impact, contributing to the development of more efficient generative audio models.