Towards a Golden Classifier-Free Guidance Path via Foresight Fixed Point Iterations
This work addresses a key bottleneck in conditional guidance for diffusion models, offering a novel method that is incremental but provides strong specific gains for researchers and practitioners in generative AI.
The paper tackled the inefficiency of Classifier-Free Guidance (CFG) in text-to-image diffusion models by proposing Foresight Guidance (FSG), which uses longer-interval fixed point iterations to improve performance, resulting in superior image quality and computational efficiency across diverse datasets and architectures.
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.