Guiding a Diffusion Model by Swapping Its Tokens

• URL: http://arxiv.org/abs/2604.08048v1
• Date: Thu, 09 Apr 2026 09:54:49 GMT
• Title: Guiding a Diffusion Model by Swapping Its Tokens
• Abstract summary: We propose a simple method to enable CFG-like guidance for both conditional and unconditional generation.<n>The key idea is to generate a perturbed prediction via simple token swap operations.<n>Our approach selectively exchanges and recomposes token latents, allowing finer control over perturbation.
• Score: 16.588428780117752
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

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