You can steer pretrained image models at inference time using multiple differentiable rewards and adaptive weighting—no retraining needed—to get better control over semantic accuracy, visual quality, and spatial grounding.
RewardFlow guides image generation by optimizing multiple reward signals during inference without modifying the model. It combines semantic, visual quality, and spatial rewards with a smart system that adjusts how much each reward matters based on the editing task, achieving better image editing and composition results.