How I Met Your Bias: Investigating Bias Amplification in Diffusion Models
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arXiv
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| Autores principales: | , , , , |
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| Formato: | Preprint |
| Publicado: |
2025
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| Acceso en línea: | |
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| _version_ | 1866918260938637312 |
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| author | Roos, Nathan Iakovleva, Ekaterina Gjergji, Ani Pastore, Vito Paolo Tartaglione, Enzo |
| author_facet | Roos, Nathan Iakovleva, Ekaterina Gjergji, Ani Pastore, Vito Paolo Tartaglione, Enzo |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2512_20233 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | How I Met Your Bias: Investigating Bias Amplification in Diffusion Models Roos, Nathan Iakovleva, Ekaterina Gjergji, Ani Pastore, Vito Paolo Tartaglione, Enzo Machine Learning Computer Vision and Pattern Recognition 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. |
| title | How I Met Your Bias: Investigating Bias Amplification in Diffusion Models |
| topic | Machine Learning Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2512.20233 |