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arXiv · 2510.21512

Towards a Golden Classifier-Free Guidance Path via Foresight Fixed Point Iterations

Kaibo Wang·Jianda Mao·Tong Wu·Yang Xiang
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Abstract

Classifier-Free Guidance (CFG) is an essential component of text-to-image diffusion models, and understanding and advancing its operational mechanisms remains a central focus of research. Existing approaches stem from divergent theoretical interpretations, thereby limiting the design space and obscuring key design choices. To address this, we propose a unified perspective that reframes conditional guidance as fixed point iterations, seeking to identify a golden path where latents produce consistent outputs under both conditional and unconditional generation. We demonstrate that CFG and its variants constitute a special case of single-step short-interval iteration, which is theoretically proven to exhibit inefficiency. To this end, we introduce Foresight Guidance (FSG), which prioritizes solving longer-interval subproblems in early diffusion stages with increased iterations. Extensive experiments across diverse datasets and model architectures validate the superiority of FSG over state-of-the-art methods in both image quality and computational efficiency. Our work offers novel perspectives for conditional guidance and unlocks the potential of adaptive design.

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Kaibo Wang, Jianda Mao, Tong Wu, Yang Xiang. 2025-10-24. Towards a Golden Classifier-Free Guidance Path via Foresight Fixed Point Iterations. https://arxiv.org/abs/2510.21512

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