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uniuyuni/
nagi_sharpen

Name: nagi_sharpen
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nagi_sharpen — checkpoints

Trained checkpoints for nagi_sharpen, a memory-bounded tiled defocus / soft-focus restoration tool built on PyTorch.

The code lives on GitHub; this repository holds only the model weights, which are kept out of the source repo.

Files

File	Role
`nagi_sharpen_stage4.pt`	Baseline defocus / soft-focus restoration checkpoint from the early experiments.
`nagi_sharpen_stage5_dpdd_guard.pt`	DPDD fine-tune candidate selected by validation-aware training. Best DPDD validation result; not the production default.
`nagi_sharpen_stage9_multiscale384.pt`	Synthetic partial-defocus candidate for more visible subjective correction. Use with the `stage9-visual` / `stage9-strong` presets.
`nagi_sharpen_stage11_realistic.pt`	Realistic synthetic-defocus candidate; improves realistic and mixed synthetic validation over stage9. Presets `stage11-visual` / `stage11-strong`.
`nagi_sharpen_stage12_realistic384.pt`	Continued from stage11 with 384px realistic synthetic-defocus training. Presets `stage12-visual` / `stage12-strong`.
`nagi_sharpen_stage13_largeblur384.pt`	Continued from stage12 with stronger large-radius realistic synthetic defocus. Presets `stage13-visual`, `stage13-strong`, `stage13-adaptive`, `stage13-defocus-map` and their strong variants.
`nagi_sharpen_stage14_balanced_long.pt`	Best Stage14 balanced candidate by pipeline synthetic/paired validation. Presets `stage14` / `stage14-balanced`.
`nagi_sharpen_stage14_pipeline500.pt`	Stronger Stage14 visual-review candidate: higher synthetic defocus gain, slightly negative paired validation. Presets `stage14-pipeline500` / `stage14-strong-preview`.
`nagi_sharpen_stage15_guarded_refine.pt`	Recommended. Low-LR refinement from `stage14_balanced_long`; best pipeline-scored natural defocus result. Presets `stage15` (general natural cleanup) and `stage15-petal-natural` (subtle flower-edge cleanup).
`MANIFEST.json`	Machine-readable index of the checkpoints above, as used by the source repo.

Generative presets (stage15-petal-generate, stage16–stage18) are experimental and are not defaults.

Usage

Download the checkpoints into the source tree, then run the CLI:

python

from huggingface_hub import snapshot_download

snapshot_download(
    "uniuyuni/nagi_sharpen",
    allow_patterns=["*.pt", "MANIFEST.json"],
    local_dir="nagi_sharpen/checkpoints",
)

python -m nagi_sharpen deblur input.jpg output.jpg --preset stage15

See the GitHub repository's docs/NATURAL_PRESETS.md for the current recommended presets and docs/EXPERIMENTS.md for the training history behind each stage.

License

Released under the Apache License 2.0, same as the source repository. Commercial use of these weights is permitted.

Acknowledgements

These checkpoints were trained on the DPDD dataset (Abuolaim & Brown, Defocus Deblurring Using Dual-Pixel Data, ECCV 2020), released under the MIT License, and on synthetic defocus generated from its sharp targets. Please cite the DPDD paper if you use this work in research:

bibtex

@inproceedings{abuolaim2020defocus,
  title     = {Defocus Deblurring Using Dual-Pixel Data},
  author    = {Abuolaim, Abdullah and Brown, Michael S.},
  booktitle = {European Conference on Computer Vision (ECCV)},
  year      = {2020}
}