We introduce RewardFlow, an inversion-free framework that steers pretrained diffusion and flow-matching models at inference time through multi-reward Langevin dynamics. RewardFlow unifies complementary differentiable rewards for semantic alignment, perceptual fidelity, localized grounding, object consistency, and human preference, and further introduces a differentiable VQA-based reward that provides fine-grained semantic supervision through language-vision reasoning. To coordinate these heterogeneous objectives, we design a prompt-aware adaptive policy that extracts semantic primitives from the instruction, infers edit intent, and dynamically modulates reward weights and step sizes throughout sampling. Across several image editing and compositional generation benchmarks, RewardFlow delivers state-of-the-art edit fidelity and compositional alignment.

翻译：我们提出RewardFlow，一种无逆推框架，通过多奖励朗之万动力学在推理阶段引导预训练扩散模型与流匹配模型。该框架统一了用于语义对齐、感知保真度、局部锚定、对象一致性与人类偏好的可微奖励，并进一步引入基于可微VQA的奖励，通过语言-视觉推理提供细粒度语义监督。为协调这些异构目标，我们设计了提示感知自适应策略，从指令中提取语义基元、推断编辑意图，并在采样过程中动态调节奖励权重与步长。在多个图像编辑与组合生成基准测试中，RewardFlow实现了最先进的编辑保真度与组合对齐性能。