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

• URL: http://arxiv.org/abs/2510.21512v1
• Date: Fri, 24 Oct 2025 14:39:07 GMT
• Title: Towards a Golden Classifier-Free Guidance Path via Foresight Fixed Point Iterations
• Abstract summary: We propose a unified perspective that reframes conditional guidance as fixed point iterations.<n>We introduce Foresight Guidance (FSG), which prioritizes solving longer-interval subproblems in early diffusion stages.<n>Our work offers novel perspectives for conditional guidance and unlocks the potential of adaptive design.
• Score: 12.366757123129402
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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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