v1.1 — reproduction package + segmenter-retrained checkpoints

v1.1 — reproduction package and segmenter-retrained checkpoints

Repository (commit 4ceddaa)

• reproduce/: rebuilds the paper's Hint-AUC tables from the released per-ratio metrics (A1_tables_from_released_metrics.py, 322/324 printed cells reproduced), the user-study tables and GLMM statistics (A2_userstudy_glmm.py, all values reproduced), a generic inference + 7-metric Hint-AUC pipeline for the released checkpoints (run_hauc_pipeline.sh, eval_per_ratio.py; HINT_ORDER=label = Table III protocol), training commands (B9_train_detfill.sh), and the verbatim launchers of every experiment of the paper and its revision (paper_experiments/). See reproduce/README.md for the coverage table and the known deviations.
• detfill: dataset_config.hint_order (area = Table II, label = Table III).
• Environment: torchmetrics==1.4.0 pinned (the SSIM implementation behind the published numbers; 1.8.x differs by up to 0.04 per image); perceptual-metric requirements in reproduce/requirements-metrics.txt.

Assets (this release) — DetFill scribble models (96 base channels, 200 epochs) trained on hints from alternative region segmenters; the "DanbooRegion" and "SLIC" training rows of the supplementary segmentation-dependency table. SHA-256 in SHA256SUMS.txt and checkpoints/README.md.

File	Size	Training segmenter
detfill_scribble_illust_danbooregion_200ep.pth	1.08 GB	DanbooRegion
detfill_scribble_illust_slic_200ep.pth	1.08 GB	SLIC

The v1.0 assets (paper checkpoints for scribble/dot and the stored test-split hint maps) remain on the v1.0 release.