Flipping the Flow: Promoting Diversity in Flow Models via Velocity Inversion

Flow-matching models such as FLUX achieve strong generative quality but often concentrate samples around a few modes, limiting diversity. Existing approaches address this through batch-interaction, optimization-based, or guidance-based methods. Batch-interaction and optimization-based methods improve diversity but require joint sample generation and often additional computation, precluding independent sampling. Guidance-based methods incur little computational overhead but typically rely on text-embedding perturbation or classifier-free guidance (CFG) modulation. In this work, we analyze how scaling the velocity field affects differential entropy, showing that negating a contractive velocity field increases entropy and drives trajectories toward low-density regions. Based on this insight, we discover that a surprisingly simple mechanism—reversing the velocity field during early sampling steps—serves as a remarkably effective tool for mode coverage. Our training-free method requires no model modification, text-embedding access, or CFG variation, enabling application across standard and distilled models, spanning image (FLUX, Z-Image Turbo), video (LTX), and 3D generation (TRELLIS). On GenEval, our approach exceeds the diversity range of noise-initialization methods, rivals batch-interaction methods without joint sampling, and outperforms guidance-based methods under worst-case quality.