Reflective Flow Sampling Enhancement

TL;DR

Reflective Flow Sampling (RF-Sampling) is a training-free inference enhancement method designed for flow-based text-to-image models like FLUX, improving quality and prompt alignment through a theoretically grounded approach.

Contribution

RF-Sampling provides a formal, training-free inference enhancement specifically for flow models, bridging the gap with conventional diffusion model improvements.

Findings

RF-Sampling improves generation quality on multiple benchmarks.

RF-Sampling enhances prompt-text alignment in flow models.

It exhibits test-time scaling ability on FLUX.

Abstract

The growing demand for text-to-image generation has led to rapid advances in generative modeling. Recently, text-to-image diffusion models trained with flow matching algorithms, such as FLUX, have achieved remarkable progress and emerged as strong alternatives to conventional diffusion models. At the same time, inference-time enhancement strategies have been shown to improve the generation quality and text-prompt alignment of text-to-image diffusion models. However, these techniques are mainly applicable to conventional diffusion models and usually fail to perform well on flow models. To bridge this gap, we propose Reflective Flow Sampling (RF-Sampling), a theoretically-grounded and training-free inference enhancement framework explicitly designed for flow models, especially for the CFG-distilled variants (i.e., models distilled from CFG guidance techniques), like FLUX. Departing from heuristic interpretations, we provide a formal derivation proving that RF-Sampling implicitly performs gradient ascent on the text-image alignment score. By leveraging a linear combination of textual representations and integrating them with flow inversion, RF-Sampling allows the model to explore noise spaces that are more consistent with the input prompt. Extensive experiments across multiple benchmarks demonstrate that RF-Sampling consistently improves both generation quality and prompt alignment. Moreover, RF-Sampling is also the first inference enhancement method that can exhibit test-time scaling ability to some extent on FLUX.