RewardFlow: Generate Images by Optimizing What You Reward
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.
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Onkar Susladkar
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Dong-Hwan Jang
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Tushar Prakash
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Adheesh Juvekar
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Vedant Shah
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Ayush Barik
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Nabeel Bashir
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Muntasir Wahed
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Ritish Shrirao
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Ismini Lourentzou
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2026-04-13T07:33:40.159Z
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/avatars/1244fc1b305c9c6383df9bb5e4707347.svg
Ismini Lourentzou
isminoula
user
isminoula
RewardFlow: Generate Images by Optimizing What You Reward
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.
RewardFlow enables pretrained diffusion and flow-matching models to be guided during inference through multi-reward Langevin dynamics without requiring inversion, achieving superior performance in image editing and compositional generation.
diffusion models flow-matching models Langevin dynamics semantic alignment perceptual fidelity localized grounding object consistency human preference differentiable VQA-based reward prompt-aware adaptive policy image editing compositional generation