Dynamic Negative Guidance of Diffusion Models
Felix Koulischer Johannes Deleu Gabriel Raya Thomas Demeester Luca Ambrogioni
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 ***D**ynamic **N**egative **G**uidance*, 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.
Classifier-free guidance, Negative prompting, Diffusion model guidance
A novel theoretically grounded dynamic negative guidance scheme is proposed as alternative to the widespread, yet severly understudied, negative prompting algorithm.