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Large-scale image generation models, with impressive quality made possible by the vast amount of data available on the Internet, raise social concerns that these models may generate harmful or copyrighted content.
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Self-diagnosis and self-debiasing: A proposal for reducing corpus-based bias in nlp
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Exploring 12 million of the 2.3 billion images used to train stable diffusion’s image generator
Baio, A · 2022
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A prompt array keeps the bias away: Debiasing vision-language models with adversarial learning
Berg, H., Hall, S. M., Bhalgat, Y., Yang, W., Kirk, H. R., Shtedritski, A., and Bain, M · 2022
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Easily accessible text-to-image generation amplifies demographic stereotypes at large scale, 2022
Bianchi, F., Kalluri, P., Durmus, E., Ladhak, F., Cheng, M., Nozza, D., Hashimoto, T., Jurafsky, D., Zou, J., and Caliskan, A · 2022
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Prompt-to-prompt image editing with cross attention control
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Diffusers: State-of-the-art diffusion models
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Exploring the limits of domain-adaptive training for detoxifying large-scale language models
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Sega: Instructing diffusion using semantic dimensions
Brack, M., Friedrich, F., Hintersdorf, D., Struppek, L., Schramowski, P., and Kersting, K · 2023
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Erasing concepts from diffusion models
Gandikota, R., Materzynska, J., Fiotto-Kaufman, J., and Bau, D · 2023
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Stable bias: Analyzing societal representations in diffusion models
Luccioni, A. S., Akiki, C., Mitchell, M., and Jernite, Y · 2023
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Safe latent diffusion: Mitigating inappropriate degeneration in diffusion models
Schramowski, P., Brack, M., Deiseroth, B., and Kersting, K · 2023
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Forget-me-not: Learning to forget in text-to-image diffusion models
Zhang, E., Wang, K., Xu, X., Wang, Z., and Shi, H · 2023
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