language: en
license: cc-by-nc-sa-4.0
library_name: pytorch
pipeline_tag: image-to-image
tags:
- diffusion
- image-restoration
- remote-sensing
- underwater
KAUSTics-Net: selected result checkpoints
Pixel-space conditional velocity-diffusion model for through-water UAV restoration. This model has no latent autoencoder. Use the accompanying KAUSTics-Net GitHub code; these files are not Diffusers pipeline checkpoints.
Contents
| Directory | Seed checkpoints | Initialization / selected epoch |
|---|---|---|
kaustics/ |
seed1.pt, seed2.pt, seed3.pt |
Scratch KAUSTics training; epochs975/925/925 |
sea_undistort/ |
seed1.pt, seed2.pt, seed3.pt |
Corresponding KAUSTics seed, sampler-aware fine-tuning; epochs95/100/100 |
Only the six selected model weights are included. No optimizer states, intermediate epochs, pilots, ablations or competitor weights. selected.json records SHA256 checksums, original archival checkpoint checksums and unchanged tensor digests. The lightweight release exports CPU tensors plus primitive metadata; load with torch.load(..., map_location='cpu', weights_only=True).
Inference
Use infer.py --checkpoint checkpoints/kaustics/seed1.pt --input image.png --output predictions/example in the code release. Input/output256×256 RGB; input normalization[-1,1];1000-step cosine schedule;25-step DDIM at strength0.95 and classifier-free guidance2.0. Preserve image filenames for deterministic per-image noise.
KAUSTics training uses source-disjoint26/3/3 UAV source IDs. Sea-Undistort fine-tuning uses840 all-effects training scenes and240 all-effects validation scenes; all three120-scene test variants are scored against original GT. See the code release’s docs/PROTOCOL.md for loss weights, selection, stopping rules, benchmark exposure and portability limits.
These weights target the paper’s dataset/task. Restoration quality and geometric accuracy on unrelated real UAV scenes are not guaranteed. Sea-Undistort adaptation shows a fidelity/perceptual trade-off; it is not a claim of universally improved image quality.
License
The model weights are licensed under Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0).
Citation
If you use this release in your research, please cite our arXiv preprint.
@article{scilla2026kausticsnet,
title = {{KAUSTics-Net}: Diffusion-Based Through-Water {UAV} Restoration with a Semi-Synthetic Dataset},
author = {Scilla, Dario and Lopez, Omar A. and Johansen, Kasper and Nieuwenhuis, Brian O. and McCabe, Matthew F.},
journal = {arXiv preprint arXiv:2611.03120},
year = {2026},
doi = {10.48550/arXiv.2611.03120},
url = {https://arxiv.org/abs/2611.03120}
}