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README.md

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 ---
-license: mit
+language: en
+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.
+
+Authors: Dario Scilla, Omar A. Lopez, Kasper Johansen, Brian O. Nieuwenhuis and Matthew F. McCabe. Paper: *KAUSTics-Net: Diffusion-Based Through-Water UAV Restoration with a Semi-Synthetic Dataset* (revision manuscript).
+
+Licensing for the authors’ model weights will be specified by the authors before publication; this prepared card does not assign a license. Dataset terms remain separate.

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