Upload 1393 files

.gitattributes

@@ -1,3 +1,4 @@
 *.png filter=lfs diff=lfs merge=lfs -text
 *.npy filter=lfs diff=lfs merge=lfs -text
 *.npz filter=lfs diff=lfs merge=lfs -text
+*.parquet filter=lfs diff=lfs merge=lfs -text

README.md

@@ -10,7 +10,7 @@ configs:
   default: true
   data_files:
   - split: train
-    path: data/train/**
+    path: data/train-*.parquet
 task_categories:
 - text-to-image
 tags:
@@ -58,7 +58,8 @@ The original five formats and the three additions were measured in separate sess
 
 ## Dataset organization
 
-- `data/train/metadata.jsonl` is the Hugging Face ImageFolder index. Each row links an image to its prompt, seed, format, checkpoint provenance, raw scientific artifacts, and flattened metric values.
+- `data/train-00000-of-00001.parquet` is the Dataset Viewer source: one self-contained table with the embedded image plus every prompt, seed, format, checkpoint provenance, raw-artifact path, and flattened metric column.
+- `data/train/metadata.jsonl` and `data/train/images/` are the equivalent ImageFolder tree kept for direct file access. Each metadata row links an image to its prompt, seed, format, checkpoint provenance, raw scientific artifacts, and flattened metric values.
 - `raw/` contains `decoded_float32.npy`, `final_latent_float32.npy`, `trajectory.npz`, and capture `metadata.json` for every scored run.
 - `metrics/` contains raw per-image, per-parameter, paired-statistics, summary, trajectory, latency, and performance tables.
 - `comparison_sheets/` contains eight-format sheets, BF16-relative difference maps, and automatically selected detail crops.

checksums/SHA256SUMS

@@ -1,9 +1,9 @@
-94602cdf01b140cca3574491f0ac0b2677a7176401a0b61c7d9318922b72cd2e  .gitattributes
+26dd9f691c984a8676e4ff0dccf50239d35c174e863d6884c3d43818b0ab577c  .gitattributes
 38d9694f003d651d39799317c174d65cb27784ac8042f60ad9189c138172353a  .gitignore
 afbd7232877f7fb929d8e7a2b3b396a41179a3106ee6c1c99f90aa470062f969  CITATION.cff
 fe888a3acbf08a05c3e976f76aa635f2a7034d09589955117c890f3f3a05c0a6  LICENSE-CODE
 10232ffb753de2527c89b1f4a69bb1479e846892c3b3e26a8550866d0f0fe440  LICENSE-DATA
-1746cf85f90d495021261f7207ab63ee725f0597422739a80bc0ab02ab492ff4  README.md
+59717bd9aeefec2b035370c1bde0c0dbb4dd5e5a5bdb1465bfac523b2259883b  README.md
 7860f02cff561fc62a93cabc452ea76a20452063e8207f97fecedb1a74fbadb2  TECHNICAL_REPORT.md
 f020293711e8ad7eab2758442e5479b39648cd6c725fd076403f66a029c60b5c  comparison_sheets/complete.json
 d71bfb4a773b3998636d68998ad885b52907b285a703cb2348976f4b613ba4de  comparison_sheets/contact_sheet_replicate0.png
@@ -98,6 +98,7 @@ a1495f17df7382e30c740db4d07574f34e5e31cb20020ac7fc9f71a4fea002bb  comparison_she
 b2705f64ed3fca264b0e7d6c6d3a01fa2f977477fd0a19ca94537552b5bba036  comparison_sheets/full/p15_scientific_poster__r0.png
 d3d58661e35a3b4b6b0c4fe11f6f09f2c66dab482c89f4264a872806e3ead10a  comparison_sheets/full/p15_scientific_poster__r1.png
 a4335e2c6da478e4eab3e2ccaa933553d0b4810179064893b57ed52001a1677a  comparison_sheets/index.json
+08b9c4cb964aed1251a1835eda6943397cdf94dac89e14161086761a743b94d6  data/train-00000-of-00001.parquet
 fd760338f2c01688676cd31a47421de131674706ae9d18b364bc5d5346ce6de2  data/train/images/bf16/p01_portrait__r0__bf16.png
 5f332fe49f9202be8ebc0d43f122fcf1725a384fcfa9d14a5caee0471f005e08  data/train/images/bf16/p01_portrait__r1__bf16.png
 55fb84113e54887184c8f216ac376cb6af3f311f2a44b4dbb8a24861576d5014  data/train/images/bf16/p02_hands_group__r0__bf16.png
@@ -338,7 +339,7 @@ da70a8d418a91f55751116780c301800932b6d63d33854ebeb5172b573f3308e  data/train/ima
 e3d89d39b4b38950bb231b8c832aa0993f3acaddb8f96eaab86de65a7e892776  data/train/images/nvfp4/p14_spatial_counts__r1__nvfp4.png
 48ceeafe80e131505c285c415b07fb3992c2e81414f480dba501cf4cecc983df  data/train/images/nvfp4/p15_scientific_poster__r0__nvfp4.png
 5d02dc85e0917b273aed6ae1870e91477d2d25f1a5bc2d491b8f2810160f9253  data/train/images/nvfp4/p15_scientific_poster__r1__nvfp4.png
-de260453b35ba2eafdf2f209894c394f276dfde524d8c7d6569ec25f6c6c7a84  data/train/metadata.jsonl
+35d2c757a7b7ce928c2326e094821adf7fbbf0cfb338b6e0800af1030ac0529f  data/train/metadata.jsonl
 ad7fcb76448f0e1b5d8b8ce4f8be0d5098c547df11dbf593acd8cbd3e2b7fc2b  metrics/analysis_advanced_complete.json
 a236850178c844a95dee1f2584fe4d6e9b79c640ee3412df6e527fc5bd46444e  metrics/analysis_core_complete.json
 2881b83ffe14a855ad536d10b028abfa5e73f56798083de0b389b252c3543500  metrics/image_advanced.csv
@@ -1358,9 +1359,10 @@ b2482808ae8ad7dc723bbee028d76650909449bf03b19118f046a3e9f0721ea7  reproduction/b
 e77eff0f644162b32fdbb19e0787b1381a007b41897a4bacb9b47c18791b6a4d  reproduction/benchmark/workflows/krea2_benchmark_interactive_gguf.json
 150a133f4ff5508ca9828fd436fd3db854fbc7218c8fcd9217395eabdb51e3b3  reproduction/download_models.py
 ac43fe8c1625b5754ab3d829591758a50607320f4f694c32784e5ce026c2bc59  reproduction/requirements-release.txt
-77b872302f3de58416d29954cd6b3b863f5388776795a60410bfa4ba3e44724b  scripts/prepare_release.py
+56ecc0611beb0a2ed147b3eed17da521b2c21c0394776082ced988ec8ae63dc7  scripts/build_parquet.py
+b393da3601e6cc985e4db4f392260ff26ea760e63a0d818bedf4ae7c39d82acf  scripts/prepare_release.py
 15a332d8bd9031c64539d7bd64df1f55619987ae9e07d06540585fc302d8ac82  scripts/upload_to_huggingface.py
-55c4f6d373d3fe4e7fde9e7c8a88c142de15a0dbaedc8f7be91f3cd997a77ce9  scripts/validate_release.py
+003d039c79e007f6d36cb46279ae2cd1d5509200b4c3ad13d2f3faa28651c878  scripts/validate_release.py
 99eb77d4339d532e6fc11cfd50cd55b4f8db54f58f1ddcd93cf74361a9252012  tables/decision_table.csv
 74f759089450fe7f074d67a389988251422d78260bdd728d040cf7fde7a365fb  tables/format_ranking.csv
 1434a8b74c4e55dc0dcbe7b0aac9fae34c4fc8fc088051724aaf3b139fc7392c  tables/metadata_schema.json

data/train-00000-of-00001.parquet

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:08b9c4cb964aed1251a1835eda6943397cdf94dac89e14161086761a743b94d6
+size 277020290

scripts/build_parquet.py

@@ -0,0 +1,47 @@
+from __future__ import annotations
+
+import argparse
+from pathlib import Path
+
+import pyarrow.parquet as pq
+from datasets import load_dataset
+from datasets.table import embed_table_storage
+
+
+def main() -> int:
+    parser = argparse.ArgumentParser(description="Build a viewer-friendly Parquet split with embedded images")
+    parser.add_argument("--root", type=Path, default=Path(__file__).resolve().parents[1])
+    parser.add_argument("--out", type=Path, default=None, help="Output parquet path")
+    parser.add_argument("--row-group-size", type=int, default=100)
+    args = parser.parse_args()
+    root = args.root.resolve()
+    out = args.out or (root / "data" / "train-00000-of-00001.parquet")
+    out.parent.mkdir(parents=True, exist_ok=True)
+
+    dataset = load_dataset(str(root), split="train")
+    ordered = ["image"] + [name for name in dataset.column_names if name != "image"]
+    dataset = dataset.select_columns(ordered)
+
+    table = dataset.with_format("arrow")[:]
+    table = embed_table_storage(table)
+
+    writer = pq.ParquetWriter(str(out), table.schema)
+    try:
+        for batch in table.to_batches(max_chunksize=args.row_group_size):
+            writer.write_batch(batch, row_group_size=args.row_group_size)
+    finally:
+        writer.close()
+
+    check = pq.read_table(str(out))
+    first = check.column("image")[0].as_py()
+    embedded = isinstance(first, dict) and first.get("bytes") is not None
+    print(
+        f"wrote {out} ({out.stat().st_size / 1024**2:.2f} MiB, {check.num_rows} rows, "
+        f"{check.metadata.num_row_groups if hasattr(check, 'metadata') else 'n/a'}); "
+        f"images_embedded={embedded}"
+    )
+    return 0 if embedded else 1
+
+
+if __name__ == "__main__":
+    raise SystemExit(main())

scripts/validate_release.py

@@ -77,6 +77,8 @@ def main() -> int:
             required_paths.append(path)
             fail(not path.is_file(), f"missing {key}: {path}", errors)
     fail(len(list((root / "data" / "train" / "images").rglob("*.png"))) != expected_rows, f"expected {expected_rows} PNG images", errors)
+    parquet_path = root / "data" / "train-00000-of-00001.parquet"
+    fail(not parquet_path.is_file(), f"missing viewer parquet: {parquet_path}", errors)
     fail(len(list((root / "raw").rglob("*.npy"))) != expected_rows * 2, f"expected {expected_rows * 2} NPY files", errors)
     fail(len(list((root / "raw").rglob("*.npz"))) != expected_rows, f"expected {expected_rows} NPZ files", errors)
     fail(len(list((root / "comparison_sheets").rglob("*.*"))) != 93, "expected 93 comparison artifacts", errors)
@@ -149,14 +151,24 @@ def main() -> int:
             fail(not path.is_file(), f"checksum target missing: {relative}", errors)
             if path.is_file():
                 fail(checksum(path) != expected, f"checksum mismatch: {relative}", errors)
+        try:
+            import pyarrow.parquet as pq
+
+            parquet_file = pq.ParquetFile(str(parquet_path))
+            fail(parquet_file.metadata.num_rows != expected_rows, f"parquet rows: {parquet_file.metadata.num_rows}", errors)
+            first_image = parquet_file.read_row_group(0, columns=["image"]).column("image")[0].as_py()
+            fail(not (isinstance(first_image, dict) and first_image.get("bytes")), "parquet image column is not embedded", errors)
+        except Exception as exc:
+            errors.append(f"parquet inspection failed: {exc}")
         try:
             from datasets import load_dataset
 
             dataset = load_dataset(str(root), split="train")
-            fail(dataset.num_rows != expected_rows, f"ImageFolder rows: {dataset.num_rows}", errors)
+            fail(dataset.num_rows != expected_rows, f"dataset rows: {dataset.num_rows}", errors)
+            fail("image" not in dataset.features, "dataset is missing the image feature", errors)
             _ = dataset[0]["image"]
         except Exception as exc:
-            errors.append(f"ImageFolder load failed: {exc}")
+            errors.append(f"dataset load failed: {exc}")
         try:
             import yaml