Related papers: How I Met Your Bias: Investigating Bias Amplification in Diffusion Models

How I Met Your Bias: Investigating Bias Amplification in Diffusion Models

URL: http://arxiv.org/abs/2512.20233v1
Date: Tue, 23 Dec 2025 10:46:48 GMT
Title: How I Met Your Bias: Investigating Bias Amplification in Diffusion Models
Authors: Nathan Roos, Ekaterina Iakovleva, Ani Gjergji, Vito Paolo Pastore, Enzo Tartaglione,
Abstract summary: Diffusion-based generative models demonstrate state-of-the-art performance across various image synthesis tasks.<n>Previous research has viewed bias amplification as an inherent characteristic of diffusion models.<n>We empirically demonstrate that samplers for diffusion models have a significant and measurable effect on bias amplification.
Score: 11.80771961784834
License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
Abstract: Diffusion-based generative models demonstrate state-of-the-art performance across various image synthesis tasks, yet their tendency to replicate and amplify dataset biases remains poorly understood. Although previous research has viewed bias amplification as an inherent characteristic of diffusion models, this work provides the first analysis of how sampling algorithms and their hyperparameters influence bias amplification. We empirically demonstrate that samplers for diffusion models -- commonly optimized for sample quality and speed -- have a significant and measurable effect on bias amplification. Through controlled studies with models trained on Biased MNIST, Multi-Color MNIST and BFFHQ, and with Stable Diffusion, we show that sampling hyperparameters can induce both bias reduction and amplification, even when the trained model is fixed. Source code is available at https://github.com/How-I-met-your-bias/how_i_met_your_bias.

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