Image AID via continuous-time reinforcement learning
Amortized Inpainting with Diffusion (AID) separates inference cost from training complexity by keeping a pretrained diffusion backbone fixed and training only a small guidance module offline. This allows the model to be reused across many images without per-instance optimization, bridging dedicated models and test-time adaptation.