Guiding a Diffusion Model by Swapping Its Tokens

TL;DR

This paper introduces Self-Swap Guidance (SSG), a method that enables classifier-free guidance for both conditional and unconditional diffusion model generation by swapping tokens to steer sampling.

Contribution

It proposes a simple token swap technique to extend CFG to unconditional generation, improving image fidelity and robustness across datasets.

Findings

Outperforms previous condition-free methods in image quality and prompt alignment.

Enhances robustness by reducing side-effects across various perturbation strengths.

Can be integrated into any diffusion model as a plug-in for immediate improvements.

Abstract

Classifier-Free Guidance (CFG) is a widely used inference-time technique to boost the image quality of diffusion models. Yet, its reliance on text conditions prevents its use in unconditional generation. We propose a simple method to enable CFG-like guidance for both conditional and unconditional generation. The key idea is to generate a perturbed prediction via simple token swap operations, and use the direction between it and the clean prediction to steer sampling towards higher-fidelity distributions. In practice, we swap pairs of most semantically dissimilar token latents in either spatial or channel dimensions. Unlike existing methods that apply perturbation in a global or less constrained manner, our approach selectively exchanges and recomposes token latents, allowing finer control over perturbation and its influence on generated samples. Experiments on MS-COCO 2014, MS-COCO 2017, and ImageNet datasets demonstrate that the proposed Self-Swap Guidance (SSG), when applied to popular diffusion models, outperforms previous condition-free methods in image fidelity and prompt alignment under different set-ups. Its fine-grained perturbation granularity also improves robustness, reducing side-effects across a wider range of perturbation strengths. Overall, SSG extends CFG to a broader scope of applications including both conditional and unconditional generation, and can be readily inserted into any diffusion model as a plug-in to gain immediate improvements.