Flow matching technique improves image generation with adaptive guidance control
Classifier-Free Guidance in Flow Matching: Non-Autonomous Potentials, Overshoot, and Posterior-Mean Control
Computer Vision and Pattern Recognition
Summary
Generating images with AI can be tricky when pushing the system too hard, causing the results to look less diverse or realistic. The authors study why stronger guidance in a method called flow matching can distort outputs. They suggest a way to keep guidance strong but balanced by controlling how the model changes the image generation paths. Their new method, called Posterior-Mean-Capped CFG, adapts its strength per image without extra computing steps. Tests show it keeps images varied and well-aligned when using high guidance.
What this means in practice
- •For image generation engineers: Enable creation of higher-quality conditional images by balancing guidance strength to maintain diversity and reduce distortion.
- •For machine learning platform developers: Incorporate adaptive guidance methods to improve conditional generation features without adding computational cost.
Authors
Jishen Peng, Zheng Ma
Abstract
Classifier-free guidance (CFG) improves conditional generation in Flow Matching, but strong guidance can distort the generated distribution and reduce diversity. We provide a geometric account of this behavior by viewing Flow Matching as a time-varying gradient flow and characterizing how CFG reshapes its underlying potential. This view explains how stronger alignment can be accompanied by mean displacement and trajectory concentration, and motivates controlling guidance through the model-implied terminal posterior mean. We therefore propose Posterior-Mean-Capped CFG (PMC-CFG), a training-free, per-sample method that adaptively retains the strongest feasible guidance without additional network evaluations. Experiments on synthetic and large-scale image-generation benchmarks show that PMC-CFG limits guidance-induced distortion and concentration while improving the alignment--diversity trade-off, with particularly strong benefits when nominal guidance is large.