Don't Settle at the Mode! Mitigating Diversity Collapse in Pretrained Flow Models via Feature Self-Guidance

arXiv·medium signal

Addresses diversity collapse, where state-of-the-art flow models produce near-identical samples when generating multiple outputs under the same conditioning. The proposed Feature Self-Guidance restores sample variety without retraining. Practical for image-generation products that need varied outputs from a single prompt.