Update characters documentation, taxonomy, measured progress and validation

README.md

@@ -117,105 +117,6 @@ configs:
     - data/train-g7-00017.tar
     - data/train-g7-00022.tar
     - data/train-g7-00023.tar
-- config_name: base
-  data_files:
-  - split: train
-    path:
-    - data/pilot-person-0002.tar
-    - data/pilot-person-0120.tar
-    - data/pilot-person-0253.tar
-    - data/train-g0-00000.tar
-    - data/train-g0-00001.tar
-    - data/train-g0-00002.tar
-    - data/train-g0-00006.tar
-    - data/train-g0-00012.tar
-    - data/train-g0-00015.tar
-    - data/train-g0-00016.tar
-    - data/train-g0-00018.tar
-    - data/train-g0-00023.tar
-    - data/train-g0-00024.tar
-    - data/train-g0-00026.tar
-    - data/train-g0-00027.tar
-    - data/train-g0-00029.tar
-    - data/train-g1-00000.tar
-    - data/train-g1-00004.tar
-    - data/train-g1-00008.tar
-    - data/train-g1-00012.tar
-    - data/train-g1-00013.tar
-    - data/train-g1-00015.tar
-    - data/train-g1-00019.tar
-    - data/train-g1-00025.tar
-    - data/train-g1-00029.tar
-    - data/train-g2-00004.tar
-    - data/train-g2-00008.tar
-    - data/train-g2-00009.tar
-    - data/train-g2-00011.tar
-    - data/train-g2-00016.tar
-    - data/train-g2-00019.tar
-    - data/train-g2-00021.tar
-    - data/train-g2-00022.tar
-    - data/train-g2-00026.tar
-    - data/train-g2-00027.tar
-    - data/train-g2-00028.tar
-    - data/train-g2-00029.tar
-    - data/train-g2-00031.tar
-    - data/train-g2-00032.tar
-    - data/train-g3-00001.tar
-    - data/train-g3-00003.tar
-    - data/train-g3-00007.tar
-    - data/train-g3-00011.tar
-    - data/train-g3-00012.tar
-    - data/train-g3-00018.tar
-    - data/train-g3-00021.tar
-    - data/train-g3-00022.tar
-    - data/train-g3-00023.tar
-    - data/train-g3-00026.tar
-    - data/train-g3-00030.tar
-    - data/train-g3-00031.tar
-    - data/train-g4-00004.tar
-    - data/train-g4-00008.tar
-    - data/train-g4-00013.tar
-    - data/train-g4-00015.tar
-    - data/train-g4-00019.tar
-    - data/train-g4-00021.tar
-    - data/train-g4-00022.tar
-    - data/train-g4-00025.tar
-    - data/train-g4-00026.tar
-    - data/train-g5-00002.tar
-    - data/train-g5-00006.tar
-    - data/train-g5-00008.tar
-    - data/train-g5-00010.tar
-    - data/train-g5-00011.tar
-    - data/train-g5-00012.tar
-    - data/train-g5-00014.tar
-    - data/train-g5-00015.tar
-    - data/train-g5-00017.tar
-    - data/train-g5-00020.tar
-    - data/train-g5-00021.tar
-    - data/train-g5-00022.tar
-    - data/train-g5-00026.tar
-    - data/train-g5-00027.tar
-    - data/train-g5-00028.tar
-    - data/train-g5-00030.tar
-    - data/train-g6-00000.tar
-    - data/train-g6-00002.tar
-    - data/train-g6-00004.tar
-    - data/train-g6-00007.tar
-    - data/train-g6-00010.tar
-    - data/train-g6-00013.tar
-    - data/train-g6-00015.tar
-    - data/train-g6-00018.tar
-    - data/train-g6-00020.tar
-    - data/train-g6-00021.tar
-    - data/train-g6-00026.tar
-    - data/train-g7-00000.tar
-    - data/train-g7-00004.tar
-    - data/train-g7-00011.tar
-    - data/train-g7-00012.tar
-    - data/train-g7-00016.tar
-    - data/train-g7-00017.tar
-    - data/train-g7-00022.tar
-    - data/train-g7-00023.tar
 - config_name: jobs
   data_files:
   - split: train
@@ -497,27 +398,10 @@ dataset_info:
     dtype: string
   - name: __url__
     dtype: string
-- config_name: base
-  features: *id001
 - config_name: jobs
   features: *id001
 ---
 
-# Complete character collection and professional outfit plan
-
-**Planned total: 840 identities, 12,140 images.** The expression/special collection contains 7,500 planned images. A further **464 adult humans × 10 professional outfits = 4,640 sprites** reuse those identities and their neutral references; they do not add new personality IDs. See [all 100 jobs and the exact outfit allocation](JOB_OUTFITS.md). Job sprites uploaded so far: **16/4,640**. The `jobs` streaming configuration becomes available when its first TAR is uploaded.
-
-The [teen/child detector recheck](SAFETY_RECHECK.md) reports actual image checksums, both LAION classifier scores, operational thresholds and any replacements. Intended ages, professions and demographic attributes describe prompting, not inferred real-world classifications.
-
-# Vivarium warm-painted greenscreen people and special characters
-
-**Combined uploaded snapshot: 1,810/7,500 images.** The collection comprises an original 500-person series, an extension of 240 adult people with the same ten emotions, and 100 fantasy / science-fiction specials with one neutral portrait each. All images are 832×1,248, in the selected Ghibli-inspired warm-painted style, with solid greenscreen backgrounds. The original collection has 1,810/5,000 images uploaded; the extension has 0/2,500.
-
-See the detailed [extension allocation and 25-class special taxonomy](EXTENSIONS.md), including all 100 variants, exact prompt plans, CSVs, inference examples and measured progress. The base model is [FLUX.2-klein-base-9B](https://huggingface.co/black-forest-labs/FLUX.2-klein-base-9B); the [rank-64 character LoRA](https://huggingface.co/laion/FLUX.2-klein-base-9B-vivarium-loras) is linked with examples and inference instructions. Native generation uses **50 steps, real CFG 4 and BF16**.
-
-Named streaming configurations separate the original `base`, additional `humans_extra`, and `specials` families as their TARs become available. `default` reads all currently uploaded families. Demographic labels are intended prompt attributes; special creatures are not assigned human ethnicities. Use identity IDs for train/evaluation splitting.
-
-
 # Complete character collection and professional outfit plan
 
 **Planned total: 840 identities, 12,140 images.** The expression/special collection contains 7,500 planned images. A further **464 adult humans × 10 professional outfits = 4,640 sprites** reuse those identities and their neutral references; they do not add new personality IDs. See [all 100 jobs and the exact outfit allocation](JOB_OUTFITS.md). Job sprites uploaded so far: **16/4,640**. The `jobs` streaming configuration becomes available when its first TAR is uploaded.
@@ -525,7 +409,7 @@ Named streaming configurations separate the original `base`, additional `humans_
 The [teen/child detector recheck](SAFETY_RECHECK.md) reports actual image checksums, both LAION classifier scores, operational thresholds and any replacements. Intended ages, professions and demographic attributes describe prompting, not inferred real-world classifications.
 
 
-# Original 500-person collection
+# Vivarium warm-painted greenscreen characters
 
 **500 fictional people ×10 natural facial expressions =5,000 portrait images**, each832×1,248 pixels. The selected aesthetic is the **ghibli-warm-painted** direction: restrained facial proportions, delicate drawn lines, warm painted shading and a solid chroma-green backdrop. Generation uses the **undistilled9B base**, the **rank64 character LoRA**, **50 steps**, real **CFG4**, and **BF16**, with no quantization or distilled checkpoint.
 
@@ -632,7 +516,7 @@ image, metadata, prompt = sample["png"], sample["json"], sample["txt"]
 print(metadata["personality_id"], metadata["emotion"], image.size)
 ```
 
-The `base` configuration selects only `data/*.tar`; the default also includes available extension TAR families. JSON indexes are excluded from training examples. An alternate standard-library TAR/HTTP reader avoids extracting image files:
+The card config selects only `data/*.tar` so index files are not mistaken for training examples. An alternate standard-library TAR/HTTP reader avoids extracting image files:
 
 ```bash
 python tools/read_webdataset.py pilot-person-0253.tar \

SAFETY_RECHECK.md

@@ -8,6 +8,8 @@ Two official LAION image models are used: the [CLIP ViT-L/14 binary classifier](
 
 The deliberately strict operational threshold is **0.10**: an image is queued for replacement if either model/view reaches it. Scores at least0.50 are recorded separately as positive. These thresholds are project criteria, not documented guarantees or a calibration for this illustrated domain. The two models can disagree; a flagged score does not establish that an image depicts sexual content. Model scores can produce false positives and false negatives.
 
+The [baseline class-output analysis](metadata/minor-safety/baseline-classification-summary.json) separates these additional project flags from the models' ordinary classifications. Across all2,760 original images and both views, EfficientNet's highest-scoring class is always SFW:5,406 drawing views and114 neutral views, with no hentai/porn/sexy top labels. The maximum CLIP NSFW output is0.0545109. Thus all original top-label/binary classifications are SFW; the1,662 flags come from the extra0.10 sum threshold. This illustrated-domain sensitivity is an observed limitation, not evidence of1,662 sexual images.
+
 New teen/child TARs are held until the exact image checksums pass. During repair the default/base Datasets configurations whitelist already-uploaded, checksum-approved original TARs; entire flagged TARs are temporarily omitted. The [stream-filter snapshot](metadata/minor-safety/public-stream-filter.json) records that reduced subset. Previously published TARs with unresolved flags have also been temporarily removed from current main, as recorded in the [public quarantine audit](metadata/minor-safety/public-quarantine.json); old Git revisions remain historical versions. Screened replacements return under the same TAR names. The complete500-person plan remains the target, not a claim about this temporarily filtered stream. Full wildcard streaming is restored after the recheck and all replacement uploads complete. Replacements use fully covered, age-appropriate opaque clothing and are checked again. If a neutral reference changes, all ten emotions are regenerated against the new reference. When the original neutral already passes, its exact pixels and other passing expressions are normally retained; only flagged expressions are regenerated against that same neutral. Difficult cases may instead receive a coherent fully covering outfit variant for the entire ten-image family, recorded in the [recipe overrides](metadata/minor-safety/profile-recipe-overrides.json). Every released group still has ten passing expressions. Published replacement audits distinguish changed and retained PNG hashes, parameters and the corresponding TAR changes. Individual image assets remain inside TARs.
 
 New neutral-reference searches prefer scores below0.07 to leave more room for later expression edits. Within a16-candidate search, the best neutral below the required0.10 threshold can also be used; this preference does not relax the release policy. Already accepted candidate PNGs are checkpointed with exact prompt, seed and reference linkage. Full PNG/TAR integrity validation and anonymous reference-index checks are separate from classifier judgments.

metadata/minor-safety/baseline-classification-summary.json

@@ -0,0 +1,17 @@
+{
+  "status": "complete_baseline_output_analysis",
+  "utc": "2026-10-06T23:32:38.099896+00:00",
+  "images": 2760,
+  "views": 5520,
+  "efficientnet_top_class_counts": {
+    "drawing": 5406,
+    "neutral": 114
+  },
+  "efficientnet_top_nsfw_class_count": 0,
+  "clip_max_nsfw_score": 0.05451091751456261,
+  "clip_at_least_0_10": 0,
+  "strict_0_10_sum_filter_images": 1662,
+  "alternative_0_30_efficientnet_0_10_clip_images": 37,
+  "alternative_profiles": 7,
+  "interpretation": "All EfficientNet top-label classifications are drawing or neutral and every CLIP output is below0.10. The original0.10 sum threshold is an additional project heuristic, not a positive classifier label. Neither score threshold is calibrated for this illustrated domain. Active policy remains unchanged pending the optional preference question."
+}

metadata/minor-safety/current-validation.json

@@ -1,6 +1,6 @@
 {
   "status": "checking",
-  "utc": "2026-10-06T23:36:41.386668+00:00",
+  "utc": "2026-10-06T23:37:43.272557+00:00",
   "expected_images": 2760,
   "current_images_checked": 2760,
   "current_images_passed": 1257,

metadata/minor-safety/progress.json

@@ -1,6 +1,6 @@
 {
   "status": "checking",
-  "utc": "2026-10-06T23:36:41.386668+00:00",
+  "utc": "2026-10-06T23:37:43.272557+00:00",
   "expected_images": 2760,
   "current_images_checked": 2760,
   "current_images_passed": 1257,

metadata/minor-safety/public-replacement-stream-validation.json

@@ -0,0 +1,14 @@
+{
+  "status": "passed",
+  "utc": "2026-10-06T23:24:03.352959+00:00",
+  "anonymous": true,
+  "revision": "ef8c9f1d8d2ad0a4955195d1b94aea5e50cb258d",
+  "samples": 101,
+  "exact_png_sha256": true,
+  "full_decode": true,
+  "prompt_seed_consistency": true,
+  "checked_replacement_metadata": 1,
+  "repaired_image_ids": [
+    "person-0010-neutral"
+  ]
+}

metadata/minor-safety/public-stream-filter.json

@@ -1,6 +1,6 @@
 {
   "status": "active_while_repairing",
-  "utc": "2026-10-06T23:36:52.438461+00:00",
+  "utc": "2026-10-06T23:37:47.621130+00:00",
   "included_shards": 95,
   "included_base_images": 1810,
   "withheld_shards": 0,

metadata/progress.json

@@ -1,5 +1,5 @@
 {
-  "utc": "2026-10-06T23:36:45.328255+00:00",
+  "utc": "2026-10-06T23:37:46.436818+00:00",
   "people": {
     "done": 500
   },
@@ -13,5 +13,5 @@
   "target_people": 500,
   "target_images": 5000,
   "images_uploaded": 1810,
-  "buffer_free_gb": 290.461696
+  "buffer_free_gb": 290.455707648
 }