Understanding and Mitigating Copying in Diffusion Models (2305.20086v1)

Abstract: Images generated by diffusion models like Stable Diffusion are increasingly widespread. Recent works and even lawsuits have shown that these models are prone to replicating their training data, unbeknownst to the user. In this paper, we first analyze this memorization problem in text-to-image diffusion models. While it is widely believed that duplicated images in the training set are responsible for content replication at inference time, we observe that the text conditioning of the model plays a similarly important role. In fact, we see in our experiments that data replication often does not happen for unconditional models, while it is common in the text-conditional case. Motivated by our findings, we then propose several techniques for reducing data replication at both training and inference time by randomizing and augmenting image captions in the training set.

• The paper reveals that specific text conditioning can trigger image replication, highlighting a key challenge in diffusion models.
• The paper evaluates mitigation strategies, showing that training-time interventions like using multiple captions significantly reduce replication.
• The paper employs metrics such as FID and SSCD to quantify improved image diversity and quality after applying the proposed strategies.

Exploring the Effect of Text Conditioning and Mitigation Strategies on Data Replication in Diffusion Models

Introduction

Diffusion models, particularly those generating images like Stable Diffusion, have shown remarkable capabilities in producing high-quality synthetic images. However, these models often face challenges with data replication, generating images too similar to their training dataset, potentially without users’ knowledge. This paper explores the effects of text conditioning on data replication in diffusion models and proposes several mitigation strategies to reduce such replication at both training and inference times.

Related Work

The phenomenon of data replication in diffusion models and its implications have been previously studied, with a focus on empirical investigations into generative adversarial networks (GANs) and generative LLMs. This paper builds on these findings, offering a novel analysis of the impact of text conditioning on replication and proposing mitigation techniques that augment the image captions in the training dataset.

Experimental Setup

The paper employs large pre-trained models fine-tuned on subsets of the LAION dataset, with special attention given to the text conditioning of images. The evaluation metrics used include the Frechet Inception Distance (FID) for image quality and diversity and a dataset similarity score based on the SSCD algorithm to quantify replication.

Data Duplication and Replication

Initial findings suggest that while data duplication within the training set plays a role in replication, it does not fully account for the observed behavior. In particular, text conditioning significantly influences replication, with specificity in captions acting as a trigger for recalling training data.

The Effect of Model Conditioning

Experiments with varying types of text conditioning revealed that the specificity and diversity of captions directly affect the model’s tendency to replicate images from the training data. This is a critical insight, underscoring the importance of conditioning in managing replication behavior.

Mitigation Strategies

Several strategies were tested to mitigate data replication, including randomizing the text input via multiple captions, Gaussian noise, random caption replacement, among others. The paper found that training-time interventions, particularly using multiple captions for each image, were most effective in reducing replication. Inference-time strategies also yielded positive results but were less impactful compared to training-time adjustments.

Conclusion and Recommendations

This paper highlights the significant influence of text conditioning on data replication in diffusion models and presents effective strategies for mitigating such behaviors. It provides a comprehensive suite of recommendations for preparing datasets, adjusting training protocols, and applying post-training mitigations to curtail replication. As diffusion models continue to evolve, these findings contribute to a growing body of knowledge aimed at enhancing the ethical and responsible utilization of generative AI technologies.

Future Work

Given the intricate dynamics between text conditioning, data duplication, and model behavior, further research is essential to uncover additional factors influencing replication. Future studies could explore the interplay between image complexity and replication tendency or investigate the long-term effects of proposed mitigation strategies in diverse application scenarios.

Acknowledgements

Support for this research was provided by a number of organizations, highlighting the collaborative effort in addressing the challenges posed by diffusion models and working towards solutions that enhance model fidelity while respecting copyright considerations.