ArXiv

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

2026-06-25 · 17:59 UTC ·Pradhaan S Bhat, Rishubh Parihar, Abhijnya Bhat... ·1 min read
Authors
Pradhaan S Bhat, Rishubh Parihar, Abhijnya Bhat...
Categories
cs.CV
arXiv
https://arxiv.org/abs/2606.27371v1
PDF
https://arxiv.org/pdf/2606.27371v1

Brief

A training-free, plug-and-play feature self-guidance method mitigates diversity collapse in pretrained flow models by dispersing internal features during batch generation and then projecting them back to the data manifold via manifold regularization. The approach avoids external reward models, adds marginal inference overhead, and reportedly increases generation diversity while maintaining fidelity across multiple conditional flow tasks.

Why it matters

Proposes a training-free, plug-and-play feature self-guidance that disperses internal features during batch generation and uses a manifold regularization step to project dispersed features back onto the data manifold, aiming to mitigate diversity collapse in pretrained flow models.

Key details

  • Demonstrates significant improvements in sample diversity while preserving fidelity across conditional flow tasks (multi-step and few-step text-to-image, depth-to-image, and reference-image generation) with only marginal inference cost and no external reward models.
  • Paper by Pradhaan S Bhat, Rishubh Parihar, Abhijnya Bhat, and R. Venkatesh Babu; accepted to ECCV 2026, posted on arXiv 2026-06-25 as arXiv:2606.27371v1 (project page: https://dont-settle-at-the-mode.github.io/).
Source evidence

Abstract

State-of-the-art flow models generate stunning images from text or image prompts. However, they suffer from diversity collapse when generating multiple samples under the same conditioning. Existing methods address this issue via either latent guidance, which has limited effectiveness, or sample selection, which relies on external reward models that incur significant inference-time overhead. In this work, we introduce an efficient, training-free self-guidance mechanism to mitigate diversity collapse without requiring additional reward models. Specifically, we disperse the internal features of the flow model during batch generation with feature self-guidance. Further, to keep the features close to the manifold, we introduce a manifold regularization step that projects these dispersed features back onto the data manifold, ensuring diverse generation without sacrificing alignment with the input conditions. Our method integrates seamlessly as a plug-and-play module into pretrained flow models, adding only a marginal inference cost. Experiments demonstrate significant improvements in diversity while preserving fidelity across several conditional flow models, including multi-step and few-step text-to-image, depth-to-image, and reference image generation.

Comment: Accepted by ECCV 2026. Project page: https://dont-settle-at-the-mode.github.io/