Dynamic Negative Guidance of Diffusion Models (2410.14398v3)

Abstract: Negative Prompting (NP) is widely utilized in diffusion models, particularly in text-to-image applications, to prevent the generation of undesired features. In this paper, we show that conventional NP is limited by the assumption of a constant guidance scale, which may lead to highly suboptimal results, or even complete failure, due to the non-stationarity and state-dependence of the reverse process. Based on this analysis, we derive a principled technique called Dynamic Negative Guidance, which relies on a near-optimal time and state dependent modulation of the guidance without requiring additional training. Unlike NP, negative guidance requires estimating the posterior class probability during the denoising process, which is achieved with limited additional computational overhead by tracking the discrete Markov Chain during the generative process. We evaluate the performance of DNG class-removal on MNIST and CIFAR10, where we show that DNG leads to higher safety, preservation of class balance and image quality when compared with baseline methods. Furthermore, we show that it is possible to use DNG with Stable Diffusion to obtain more accurate and less invasive guidance than NP.

• The paper introduces a dynamic guidance method that adjusts repulsion based on time and state to effectively prevent unwanted features.
• It utilizes a discrete Markov chain to dynamically estimate the posterior, ensuring more precise and adaptive denoising.
• Empirical results on datasets like MNIST, CIFAR10, and models such as Stable Diffusion demonstrate improved class removal and ethical content control.

Dynamic Negative Guidance of Diffusion Models

The paper discussed presents a paper on enhancing diffusion models through a method termed Dynamic Negative Guidance (DNG). Diffusion models (DMs) have garnered significant attention for their superior performance in generative tasks, such as text-to-image (T2I) generation. Despite their prowess, guiding the generation process to avoid certain features remains a challenge, particularly due to the limitations of conventional Negative Prompting (NP) techniques. This research addresses these limitations by proposing a more nuanced method of guiding diffusion models away from undesired characteristics without requiring additional training.

Problem Statement

The paper identifies a critical flaw in traditional Negative Prompting. NP operates on a constant guidance scale to keep unwanted features from being generated during the denoising process. However, this constant scale approach is limited by its inability to adapt to the non-stationary nature of the generative process, which can lead to suboptimal outcomes or complete failure when handling varying states and times during diffusion.

Dynamic Negative Guidance Approach

The authors propose Dynamic Negative Guidance (DNG) as a solution to the limitations of NP. DNG introduces a time and state-dependent modulation of guidance, allowing for near-optimal adjustments without additional training. Unlike NP, this technique involves estimating the posterior class probability throughout denoising by modeling the process as a discrete Markov chain. This approach allows the guidance scale to dynamically adapt, providing stronger influence near undesired regions while reducing unnecessary impact elsewhere.

The methodology of DNG is well-founded in theory and is designed to correct the major flaw of NP—that is, the static and sometimes misdirected repulsive guidance. By scaling the guidance according to the estimated posterior of encountering undesired features, DNG offers a robust framework for maintaining generation quality.

Key Results

Empirical evaluations demonstrate the superiority of DNG in class-removal tasks using datasets like MNIST and CIFAR10. DNG provides notable improvements in safety, class balance preservation, and image quality compared to baseline methods. Furthermore, it extends these benefits to more complex models like Stable Diffusion, demonstrating an ability to achieve more precise and less invasive guidance than NP.

Numerical results indicated DNG's effectiveness in generating distributions closely aligned to desired conditions without inadvertently regenerating unwanted features, particularly evident in both low-dimensional theoretical experiments and high-dimensional practical applications.

Implications and Future Directions

This research contributes significantly to improving how DMs are guided in the context of safety and ethical generation requirements, opening avenues for more nuanced and controllable models. DNG can be critical for applications requiring refined control, such as filtering NSFW content or aligning outputs with specific safety or ethical guidelines, without compromising the quality of the generated outputs.

Future research could expand on automated tuning of posterior estimation and guidance scaling, potentially integrating machine learning techniques to further refine these processes. Additionally, exploring DNG in other generative contexts beyond T2I, such as audio or video synthesis, could provide comprehensive insights into its versatility and robustness.

In conclusion, this paper introduces a compelling advancement in guiding diffusion models, addressing critical theoretical and practical challenges with a sophisticated approach that enhances performance and application diversity.