Safe Latent Diffusion: Mitigating Inappropriate Degeneration in Diffusion Models (2211.05105v4)

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.

An Expert Analysis of Safe Latent Diffusion for Mitigating Inappropriate Content in Diffusion Models

The research paper titled "Safe Latent Diffusion: Mitigating Inappropriate Degeneration in Diffusion Models" addresses a significant challenge in the deployment of text-conditioned image generation models, particularly those utilizing diffusion models (DMs) like Stable Diffusion. As these models become increasingly integral to applications requiring high-quality text-to-image translation, the issue of generating inappropriate or biased content rooted in the unfiltered nature of extensive training datasets becomes critical. This paper introduces Safe Latent Diffusion (SLD) as a novel intervention for this challenge.

Overview of Safe Latent Diffusion

SLD is designed to actively reduce the generation of inappropriate content without the need for additional training or the use of external classifiers. It leverages the diffusion process's inherent properties, allowing for adjustments directly in the generation process. By using classifier-free guidance, SLD implements an ethical editing mechanism, ensuring that the output aligns with established standards of appropriateness. This mechanism considers inappropriate concepts defined in natural language and systematically suppresses their emergence during the generation process.

Technical Summary

The diffusion models employed in this research are conditioned over a textual prompt and further adjusted using safety guidance derived from existing learned representations within the model. This process involves:

• Guidance Mechanism: A vector-based manipulation within the latent space of the diffusion model to ensure output is guided not only by the text prompt but also away from predefined inappropriate concepts.
• Momentum and Warm-up: Introduced to ensure stability and gradual adjustments in editing, these hyper-parameters fine-tune the influence of inappropriateness suppression over the different stages of image generation.
• Configurations: The paper suggests multiple hyper-parameter sets, offering varying levels of strictness in intervention based on the application domain's sensitivity to content appropriateness.

Benchmarking with I2P Dataset

The researchers developed the Inappropriate Image Prompts (I2P) benchmark dataset to evaluate the performance of SLD. This dataset includes real-world text prompts known to generate inappropriate content with current diffusion models. The benchmarks reveal that SLD configurations considerably lower the probability of generating inappropriate outputs, outperforming methods like negative prompting.

Quantitative Results

The empirical evaluation highlights a significant reduction in the generation of inappropriate images across various categories:

• A reduction in probabilities of generating inappropriate content from over 40% to below 10% in certain categories.
• Expected maximum inappropriateness dramatically reduced when employing strong SLD configurations, showcasing the effectiveness even in batch image generative scenarios.

Implications for Future Research

The implications of this paper extend beyond practical applications in generative models. They provoke a broader discourse on ethical AI and responsible dataset curation. While SLD demonstrates a decrease in content issues stemming from biased datasets, it also underscores the necessity for refined curation and cautious deployment. Future research might focus on refining SLD to accommodate nuanced definitions of appropriateness that vary across cultures and contexts or integrating similar methods in other modalities, including text and audio generation.

As the use of multimodal models progresses, studies like these emphasize the need for concurrently developing technical safeguards while understanding societal norms to ensure the responsible AI that aligns with human ethical standards. The research delineates a path towards achieving a balance between the creative capabilities of AI and such ethical considerations.