You can steer pretrained image models at inference time by optimizing multiple reward signals together, enabling better control over generation without model retraining.
RewardFlow guides image generation by combining multiple reward signals during inference, steering diffusion models toward desired outputs without retraining. It uses language-vision reasoning and adaptive weighting to balance competing objectives like semantic accuracy, visual quality, and object consistency.