Image AID via continuous-time reinforcement learning
Quick summary
arXiv:2605.13010v2 Announce Type: replace-cross Abstract: We study image inpainting with generative diffusion models. Existing methods typically either train dedicated task-specific models, or adapt a pretrained diffusion model separately for each masked image at deployment. We introduce a middle-ground model, termed Amortized Inpainting with Diffusion (AID), which keeps a pretrained diffusion backbone fixed, trains a small reusable guidance module offline, and then reuses it across masked images without per-instance optimization. We formulate it as a deterministic guidance problem with a supe
Key takeaways
- arXiv:2605.13010v2 Announce Type: replace-cross Abstract: We study image inpainting with generative diffusion models.
- Existing methods typically either train dedicated task-specific models, or adapt a pretrained diffusion model separately for each masked image at deployment.
- We introduce a middle-ground model, termed Amortized Inpainting with Diffusion (AID), which keeps a pretrained diffusion backbone fixed, trains a small reusable guidance module offline, and then reuses it across masked images without per-instance optimization.
Why it matters
The value of this work lies as much in how it was tested as in the claim itself. Sample design, baselines, uncertainty and replication help separate a laboratory result from real-world impact.
Member comments