Safe Latent Diffusion: Mitigating Inappropriate Degeneration in Diffusion Models

• URL: http://arxiv.org/abs/2211.05105v4
• Date: Wed, 26 Apr 2023 11:47:44 GMT
• Title: Safe Latent Diffusion: Mitigating Inappropriate Degeneration in Diffusion Models
• Authors: Patrick Schramowski, Manuel Brack, Bj\"orn Deiseroth, Kristian Kersting
• Abstract summary: Text-conditioned image generation models suffer from degenerated and biased human behavior. We present safe latent diffusion (SLD) to help combat these undesired side effects. We show that SLD removes and suppresses inappropriate image parts during the diffusion process.
• Score: 18.701950647429
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Text-conditioned image generation models have recently achieved astonishing results in image quality and text alignment and are consequently employed in a fast-growing number of applications. Since they are highly data-driven, relying on billion-sized datasets randomly scraped from the internet, they also suffer, as we demonstrate, from degenerated and biased human behavior. In turn, they may even reinforce such biases. To help combat these undesired side effects, we present safe latent diffusion (SLD). Specifically, to measure the inappropriate degeneration due to unfiltered and imbalanced training sets, we establish a novel image generation test bed-inappropriate image prompts (I2P)-containing dedicated, real-world image-to-text prompts covering concepts such as nudity and violence. As our exhaustive empirical evaluation demonstrates, the introduced SLD removes and suppresses inappropriate image parts during the diffusion process, with no additional training required and no adverse effect on overall image quality or text alignment.

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