Update characters documentation, taxonomy, measured progress and validation

.gitattributes

@@ -58,3 +58,4 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 # Video files - compressed
 *.mp4 filter=lfs diff=lfs merge=lfs -text
 *.webm filter=lfs diff=lfs merge=lfs -text
+metadata/inference-records.jsonl filter=lfs diff=lfs merge=lfs -text

README.md

@@ -18,30 +18,16 @@ size_categories:
 pretty_name: Vivarium Greenscreen Characters — FLUX 9B
 configs:
 - config_name: default
-  data_files:
-  - split: train
-    path:
-    - data/*.tar
-- config_name: base
   data_files:
   - split: train
     path: data/*.tar
 ---
 
-# Vivarium warm-painted greenscreen people and special characters
-
-**Combined uploaded snapshot: 2,780/7,500 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 2,780/5,000 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.
-
-
-# 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.
 
-**Production snapshot: 198/500 complete people; 1980/5,000 images uploaded.** Pilot and production validation are recorded in the linked metadata. Counts describe the snapshot; incremental shard commits can be newer.
+**Production snapshot: 296/500 complete people; 2960/5,000 images uploaded.** Pilot and production validation are recorded in the linked metadata. Counts describe the snapshot; incremental shard commits can be newer.
 
 The [base model](https://huggingface.co/black-forest-labs/FLUX.2-klein-base-9B) and [character LoRA with examples and instructions](https://huggingface.co/laion/FLUX.2-klein-base-9B-vivarium-loras) are linked separately. The compatible [background dataset](https://huggingface.co/datasets/laion/Anim-E-Ghibli-Backgrounds-FLUX-9B) contains wide warm-painted environments for these foreground characters.
 
@@ -144,7 +130,7 @@ 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 \

metadata/hub-streaming-validation.json

@@ -1,8 +1,8 @@
 {
   "status": "passed",
-  "utc": "2026-10-06T18:16:42.173168+00:00",
+  "utc": "2026-10-06T19:40:17.090896+00:00",
   "repo": "laion/Vivarium-Greenscreen-Characters-FLUX-9B",
-  "revision": "85970489ed8706508b6d1fdc19a2449a0cad25c2",
+  "revision": "607d93fd1406e46b6af2145cd5cdcf8d09318507",
   "anonymous": true,
   "datasets_version": "5.1.0",
   "samples_verified": 16,

metadata/integrity-validation.json

@@ -1,11 +1,11 @@
 {
   "status": "passed",
   "scope": "closed-shard snapshot",
-  "utc": "2026-10-06T18:17:34.095645+00:00",
-  "images_verified": 1980,
-  "persons_verified": 198,
-  "shards_verified": 105,
-  "bytes_verified": 2196439040,
+  "utc": "2026-10-06T19:41:35.604662+00:00",
+  "images_verified": 2940,
+  "persons_verified": 294,
+  "shards_verified": 153,
+  "bytes_verified": 3265679360,
   "checks": [
     "TAR SHA256 and byte size",
     "three adjacent unique members per sample",
@@ -16,7 +16,7 @@
     "prompt/seed consistency",
     "PNG SHA256",
     "exact neutral reference linkage and416x624 resize",
-    "all ten emotions per closed person group",
+    "complete planned expressions per closed identity",
     "50-step CFG4 BF16 pinned model/LoRA",
     "no quantization or distillation"
   ]

metadata/performance.json

@@ -1,11 +1,11 @@
 {
   "hardware": "8 × NVIDIA A100-SXM4-80GB",
   "batch_size_per_gpu": 1,
-  "steady_samples": 1934,
-  "mean_seconds": 39.18896772094276,
-  "median_seconds": 39.89933709683828,
-  "neutral_mean_seconds": 31.580603175634707,
-  "reference_edit_mean_seconds": 40.00337193958764,
+  "steady_samples": 2914,
+  "mean_seconds": 39.18606356131111,
+  "median_seconds": 39.9017451396212,
+  "neutral_mean_seconds": 31.58531818194805,
+  "reference_edit_mean_seconds": 40.01003177474529,
   "timings_include": "50 denoising steps with real CFG4, reference VAE encode when applicable, VAE decode; CUDA synchronized",
   "timings_exclude": "Positive prompt preencoding, model loading, compile warmup, PNG/TAR writes and upload",
   "pilot_excluded": "Pilot GPU7 was shared with background production and used CPU offload; it is not a dedicated-GPU benchmark"

metadata/progress.json

@@ -1,17 +1,17 @@
 {
-  "utc": "2026-10-06T18:19:10.510057+00:00",
+  "utc": "2026-10-06T19:43:17.735099+00:00",
   "people": {
-    "done": 198,
-    "queued": 293,
-    "running": 9
+    "done": 296,
+    "queued": 189,
+    "running": 15
   },
   "shards": {
-    "uploaded": 105
+    "uploaded": 154
   },
-  "images_in_closed_shards": 1980,
-  "images_generated": 2044,
+  "images_in_closed_shards": 2960,
+  "images_generated": 3070,
   "target_people": 500,
   "target_images": 5000,
-  "images_uploaded": 1980,
-  "buffer_free_gb": 299.943133184
+  "images_uploaded": 2960,
+  "buffer_free_gb": 298.794737664
 }

metadata/quality-review.json

@@ -739,7 +739,227 @@
     "person-0488-sad",
     "person-0488-annoyed",
     "person-0488-pain",
-    "person-0488-disgust"
+    "person-0488-disgust",
+    "person-0194-neutral",
+    "person-0194-joyful",
+    "person-0194-grateful",
+    "person-0194-playful",
+    "person-0194-angry",
+    "person-0194-afraid",
+    "person-0194-sad",
+    "person-0194-annoyed",
+    "person-0194-pain",
+    "person-0194-disgust",
+    "person-0014-neutral",
+    "person-0014-joyful",
+    "person-0014-grateful",
+    "person-0014-playful",
+    "person-0014-angry",
+    "person-0014-afraid",
+    "person-0014-sad",
+    "person-0014-annoyed",
+    "person-0014-pain",
+    "person-0014-disgust",
+    "person-0169-neutral",
+    "person-0169-joyful",
+    "person-0169-grateful",
+    "person-0169-playful",
+    "person-0169-angry",
+    "person-0169-afraid",
+    "person-0169-sad",
+    "person-0169-annoyed",
+    "person-0169-pain",
+    "person-0169-disgust",
+    "person-0492-neutral",
+    "person-0492-joyful",
+    "person-0492-grateful",
+    "person-0492-playful",
+    "person-0492-angry",
+    "person-0492-afraid",
+    "person-0492-sad",
+    "person-0492-annoyed",
+    "person-0492-pain",
+    "person-0492-disgust",
+    "person-0123-neutral",
+    "person-0123-joyful",
+    "person-0123-grateful",
+    "person-0123-playful",
+    "person-0123-angry",
+    "person-0123-afraid",
+    "person-0123-sad",
+    "person-0123-annoyed",
+    "person-0123-pain",
+    "person-0123-disgust",
+    "person-0448-neutral",
+    "person-0448-joyful",
+    "person-0448-grateful",
+    "person-0448-playful",
+    "person-0448-angry",
+    "person-0448-afraid",
+    "person-0448-sad",
+    "person-0448-annoyed",
+    "person-0448-pain",
+    "person-0448-disgust",
+    "person-0102-neutral",
+    "person-0102-joyful",
+    "person-0102-grateful",
+    "person-0102-playful",
+    "person-0102-angry",
+    "person-0102-afraid",
+    "person-0102-sad",
+    "person-0102-annoyed",
+    "person-0102-pain",
+    "person-0102-disgust",
+    "person-0425-neutral",
+    "person-0425-joyful",
+    "person-0425-grateful",
+    "person-0425-playful",
+    "person-0425-angry",
+    "person-0425-afraid",
+    "person-0425-sad",
+    "person-0425-annoyed",
+    "person-0425-pain",
+    "person-0425-disgust",
+    "person-0079-neutral",
+    "person-0079-joyful",
+    "person-0079-grateful",
+    "person-0079-playful",
+    "person-0079-angry",
+    "person-0079-afraid",
+    "person-0079-sad",
+    "person-0079-annoyed",
+    "person-0079-pain",
+    "person-0079-disgust",
+    "person-0404-neutral",
+    "person-0404-joyful",
+    "person-0404-grateful",
+    "person-0404-playful",
+    "person-0404-angry",
+    "person-0404-afraid",
+    "person-0404-sad",
+    "person-0404-annoyed",
+    "person-0404-pain",
+    "person-0404-disgust",
+    "person-0058-neutral",
+    "person-0058-joyful",
+    "person-0058-grateful",
+    "person-0058-playful",
+    "person-0058-angry",
+    "person-0058-afraid",
+    "person-0058-sad",
+    "person-0058-annoyed",
+    "person-0058-pain",
+    "person-0058-disgust",
+    "person-0381-neutral",
+    "person-0381-joyful",
+    "person-0381-grateful",
+    "person-0381-playful",
+    "person-0381-angry",
+    "person-0381-afraid",
+    "person-0381-sad",
+    "person-0381-annoyed",
+    "person-0381-pain",
+    "person-0381-disgust",
+    "person-0035-neutral",
+    "person-0035-joyful",
+    "person-0035-grateful",
+    "person-0035-playful",
+    "person-0035-angry",
+    "person-0035-afraid",
+    "person-0035-sad",
+    "person-0035-annoyed",
+    "person-0035-pain",
+    "person-0035-disgust",
+    "person-0360-neutral",
+    "person-0360-joyful",
+    "person-0360-grateful",
+    "person-0360-playful",
+    "person-0360-angry",
+    "person-0360-afraid",
+    "person-0360-sad",
+    "person-0360-annoyed",
+    "person-0360-pain",
+    "person-0360-disgust",
+    "person-0469-neutral",
+    "person-0469-joyful",
+    "person-0469-grateful",
+    "person-0469-playful",
+    "person-0469-angry",
+    "person-0469-afraid",
+    "person-0469-sad",
+    "person-0469-annoyed",
+    "person-0469-pain",
+    "person-0469-disgust",
+    "person-0289-neutral",
+    "person-0289-joyful",
+    "person-0289-grateful",
+    "person-0289-playful",
+    "person-0289-angry",
+    "person-0289-afraid",
+    "person-0289-sad",
+    "person-0289-annoyed",
+    "person-0289-pain",
+    "person-0289-disgust",
+    "person-0337-neutral",
+    "person-0337-joyful",
+    "person-0337-grateful",
+    "person-0337-playful",
+    "person-0337-angry",
+    "person-0337-afraid",
+    "person-0337-sad",
+    "person-0337-annoyed",
+    "person-0337-pain",
+    "person-0337-disgust",
+    "person-0145-neutral",
+    "person-0145-joyful",
+    "person-0145-grateful",
+    "person-0145-playful",
+    "person-0145-angry",
+    "person-0145-afraid",
+    "person-0145-sad",
+    "person-0145-annoyed",
+    "person-0145-pain",
+    "person-0145-disgust",
+    "person-0314-neutral",
+    "person-0314-joyful",
+    "person-0314-grateful",
+    "person-0314-playful",
+    "person-0314-angry",
+    "person-0314-afraid",
+    "person-0314-sad",
+    "person-0314-annoyed",
+    "person-0314-pain",
+    "person-0314-disgust",
+    "person-0266-neutral",
+    "person-0266-joyful",
+    "person-0266-grateful",
+    "person-0266-playful",
+    "person-0266-angry",
+    "person-0266-afraid",
+    "person-0266-sad",
+    "person-0266-annoyed",
+    "person-0266-pain",
+    "person-0266-disgust",
+    "person-0241-neutral",
+    "person-0241-joyful",
+    "person-0241-grateful",
+    "person-0241-playful",
+    "person-0241-angry",
+    "person-0241-afraid",
+    "person-0241-sad",
+    "person-0241-annoyed",
+    "person-0241-pain",
+    "person-0241-disgust",
+    "person-0218-neutral",
+    "person-0218-joyful",
+    "person-0218-grateful",
+    "person-0218-playful",
+    "person-0218-angry",
+    "person-0218-afraid",
+    "person-0218-sad",
+    "person-0218-annoyed",
+    "person-0218-pain",
+    "person-0218-disgust"
   ],
   "findings": [
     "Focused reference edit prompts produce clear natural neutral/joyful/angry differences for baby,schoolgirl and17-year-old girl while maintaining closely matched identity,hair,outfit and pose. Baby sadness was inspected separately when present.",
@@ -754,7 +974,8 @@
     "All ten variants reviewed for skin-preservation wording in schoolchildren0114/0160 and younger teen0208: facial tone remains closely matched across expressions. Also reviewed formal schoolchild0157,rock-inspired older adult0391,deep-brown senior0414,and older seniors0458/0479. Outfits and pose stay consistent; occasional slight lightening is possible in the older reference-prompt version.",
     "At the 1000-image milestone, all ten expressions visually inspected for twelve additional identities0003/0025/0091/0136/0256/0232/0372/0482/0349/0280/0438/0460. Baby sportswear,creative overalls,formal skirt,rock-inspired denim,crafts and senior workwear render fully clothed; identities and outfits remain closely matched. Skin-preservation wording also holds in reviewed darker-skinned adults and seniors. Minor facial-detail and lighting variation remain possible.",
     "Person0301 simple everyday clothing reviewed in all ten emotions. Visual review now covers all twenty planned outfit-style labels,all22 age/gender cells,and all8 intended heritage labels; these are coverage of prompted labels rather than ethnicity inference from images.",
-    "At the2000-image milestone,reviewed another22 complete person groups,one newly finished group per age/gender cell. Hair,outfit,pose and framing remain closely matched; joyful/grateful/playful/anger and mild negative expressions remain natural. No new series-level defect requiring regeneration was observed."
+    "At the2000-image milestone,reviewed another22 complete person groups,one newly finished group per age/gender cell. Hair,outfit,pose and framing remain closely matched; joyful/grateful/playful/anger and mild negative expressions remain natural. No new series-level defect requiring regeneration was observed.",
+    "At the3000-image milestone,inspected22 newly completed ten-expression groups across all22 age/gender cells. Consistent clothes,hair,pose and greenscreen framing; facial changes remain mild and natural. No additional group-level defect requiring regeneration was found."
   ],
   "status": "ongoing stratified visual review; accepted production",
   "limitations": [
@@ -764,65 +985,65 @@
     "Reference edits can alter skin tone or lighting; explicit preservation wording reduces observed changes but does not guarantee identical pixels.",
     "Painted stylization can make adjacent age groups look similar,especially teens and young adults; age_years is the prompted age rather than a visual ground-truth estimate."
   ],
-  "utc": "2026-10-06T18:19:06.391520+00:00",
+  "utc": "2026-10-06T19:40:29.915815+00:00",
   "coverage": {
     "age_and_gender": {
-      "baby/male": 4,
-      "baby/female": 3,
-      "preschool/male": 3,
-      "preschool/female": 3,
-      "younger_schoolchild/male": 3,
-      "younger_schoolchild/female": 4,
-      "older_schoolchild/male": 4,
-      "older_schoolchild/female": 4,
-      "younger_teen/male": 2,
-      "younger_teen/female": 4,
-      "older_teen/male": 4,
-      "older_teen/female": 4,
-      "young_adult/male": 3,
-      "young_adult/female": 3,
-      "middle_adult/male": 2,
-      "middle_adult/female": 4,
-      "older_adult/male": 3,
-      "older_adult/female": 3,
-      "younger_senior/male": 3,
-      "younger_senior/female": 3,
-      "older_senior/male": 4,
-      "older_senior/female": 4
+      "older_senior/male": 5,
+      "older_teen/female": 5,
+      "older_adult/female": 4,
+      "young_adult/female": 4,
+      "older_senior/female": 5,
+      "older_teen/male": 5,
+      "young_adult/male": 4,
+      "baby/male": 5,
+      "preschool/male": 4,
+      "preschool/female": 4,
+      "older_schoolchild/female": 5,
+      "younger_schoolchild/female": 5,
+      "older_schoolchild/male": 5,
+      "younger_schoolchild/male": 4,
+      "younger_teen/male": 3,
+      "younger_senior/male": 4,
+      "baby/female": 4,
+      "younger_senior/female": 4,
+      "older_adult/male": 4,
+      "younger_teen/female": 5,
+      "middle_adult/female": 5,
+      "middle_adult/male": 3
     },
     "intended_ethnicity": {
-      "white_caucasian": 33,
-      "african_american": 5,
-      "japanese": 7,
-      "arab": 7,
-      "southeast_asian": 6,
-      "latino": 5,
-      "indigenous_american": 4,
-      "indian": 7
+      "southeast_asian": 8,
+      "japanese": 8,
+      "white_caucasian": 44,
+      "african_american": 7,
+      "arab": 8,
+      "indian": 8,
+      "indigenous_american": 7,
+      "latino": 6
     },
     "outfit_style": {
-      "baby_everyday": 2,
-      "baby_layers": 3,
-      "baby_sporty": 2,
-      "child_sporty": 4,
+      "workwear": 4,
+      "simple_everyday": 5,
+      "formal": 4,
+      "smart_casual": 7,
+      "everyday_dress": 3,
+      "sporty": 10,
+      "outdoors": 8,
+      "baby_everyday": 3,
       "child_formal": 4,
       "child_school": 3,
-      "child_everyday": 4,
-      "child_creative": 3,
-      "child_outdoors": 3,
-      "sporty": 10,
-      "rock_inspired": 4,
       "crafts": 3,
+      "child_creative": 6,
+      "child_outdoors": 4,
+      "child_sporty": 4,
+      "formal_skirt": 2,
+      "rock_inspired": 6,
       "casual_layers": 8,
-      "workwear": 3,
-      "formal": 3,
-      "smart_casual": 5,
-      "simple_everyday": 2,
-      "outdoors": 5,
-      "everyday_dress": 2,
-      "formal_skirt": 1
+      "baby_sporty": 3,
+      "child_everyday": 6,
+      "baby_layers": 3
     }
   },
-  "people_reviewed": 74,
-  "images_reviewed": 740
+  "people_reviewed": 96,
+  "images_reviewed": 960
 }

metadata/remote-verification.json

@@ -1,13 +1,13 @@
 {
   "status": "passed",
   "scope": "uploaded-shard snapshot",
-  "utc": "2026-10-06T18:16:35.839433+00:00",
+  "utc": "2026-10-06T19:40:11.355118+00:00",
   "repo": "laion/Vivarium-Greenscreen-Characters-FLUX-9B",
-  "revision": "85970489ed8706508b6d1fdc19a2449a0cad25c2",
+  "revision": "607d93fd1406e46b6af2145cd5cdcf8d09318507",
   "anonymous_access": true,
-  "shards_verified": 105,
-  "images_verified": 1980,
-  "bytes_verified": 2196439040,
+  "shards_verified": 153,
+  "images_verified": 2940,
+  "bytes_verified": 3265679360,
   "checks": [
     "Remote byte size",
     "Remote LFS SHA256",

metadata/shards.json

@@ -175,6 +175,72 @@
     "created": "2026-10-06T18:16:20.062640+00:00",
     "uploaded": "2026-10-06T18:16:26.022501+00:00"
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+    "created": "2026-10-06T18:29:33.908785+00:00",
+    "uploaded": "2026-10-06T18:29:38.289413+00:00"
+  },
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+    "bytes": 21770240,
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+    "created": "2026-10-06T18:42:48.579492+00:00",
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+    "created": "2026-10-06T18:53:21.254719+00:00",
+    "uploaded": "2026-10-06T18:53:26.244883+00:00"
+  },
+  {
+    "name": "train-g6-00016.tar",
+    "worker": 6,
+    "count": 20,
+    "bytes": 22016000,
+    "sha256": "376d4d373c9aaebc8032348840ffcdc49a1030851db2125cd434903b62f5c84c",
+    "status": "uploaded",
+    "revision": "09222bf92b83fb20330c0fb09f4fd5e337eb1b5e",
+    "created": "2026-10-06T19:06:25.596146+00:00",
+    "uploaded": "2026-10-06T19:06:31.443425+00:00"
+  },
+  {
+    "name": "train-g6-00017.tar",
+    "worker": 6,
+    "count": 20,
+    "bytes": 23582720,
+    "sha256": "9db4aa37c844aad34482b822e855db4e3e029ea8430ee742bcd7f858d7f8a58e",
+    "status": "uploaded",
+    "revision": "0db0288a93d26b5d4556aa207332b6b6aa20a3f8",
+    "created": "2026-10-06T19:19:29.840327+00:00",
+    "uploaded": "2026-10-06T19:19:31.568491+00:00"
+  },
+  {
+    "name": "train-g6-00018.tar",
+    "worker": 6,
+    "count": 20,
+    "bytes": 21739520,
+    "sha256": "812665c4fe49c1ca3b6630bf9494bb2e3bc5a7c9ed19c9739ef8ca9c69e34f1d",
+    "status": "uploaded",
+    "revision": "3ad869ae768bd3b6765f81c5e08da7e9bf12f8ec",
+    "created": "2026-10-06T19:32:33.918430+00:00",
+    "uploaded": "2026-10-06T19:32:35.849543+00:00"
+  },
   {
     "name": "train-g7-00000.tar",
     "worker": 7,
@@ -1153,5 +1615,82 @@
     "revision": "c062158099c8f02f2ca707a7b7fecfc47021d7ca",
     "created": "2026-10-06T18:07:37.165240+00:00",
     "uploaded": "2026-10-06T18:07:38.984672+00:00"
+  },
+  {
+    "name": "train-g7-00011.tar",
+    "worker": 7,
+    "count": 20,
+    "bytes": 21954560,
+    "sha256": "f932dda844fca7aa7a6fb608d86eaa371f432218d86366136bd4aad5f81da8f5",
+    "status": "uploaded",
+    "revision": "b698639f781a11cdc75e8dbe6c5956b9bce7ac27",
+    "created": "2026-10-06T18:20:52.524742+00:00",
+    "uploaded": "2026-10-06T18:20:58.718623+00:00"
+  },
+  {
+    "name": "train-g7-00012.tar",
+    "worker": 7,
+    "count": 20,
+    "bytes": 21422080,
+    "sha256": "48470c34ae01773bfa96812f60581f2ff13d61ac796cc97a2fb6a1f626ce2acf",
+    "status": "uploaded",
+    "revision": "a4c1090c06674b843c8194e9cde2e2acc1eede98",
+    "created": "2026-10-06T18:34:07.934048+00:00",
+    "uploaded": "2026-10-06T18:34:10.542185+00:00"
+  },
+  {
+    "name": "train-g7-00013.tar",
+    "worker": 7,
+    "count": 20,
+    "bytes": 21698560,
+    "sha256": "81f5face0ca29c9515b05d42ad0913d81604c1c0b768ab8bcb090b8237bab64d",
+    "status": "uploaded",
+    "revision": "8c33320a86a9f3e310e8a3f89c61f8d2193672cd",
+    "created": "2026-10-06T18:47:23.458654+00:00",
+    "uploaded": "2026-10-06T18:47:25.336573+00:00"
+  },
+  {
+    "name": "train-g7-00014.tar",
+    "worker": 7,
+    "count": 20,
+    "bytes": 22036480,
+    "sha256": "0d381a40ca3b2d308024a73874cd9878b9f893f9b27c90d686dbfd67c7897cb0",
+    "status": "uploaded",
+    "revision": "079ca2a8c0b68267739add20389a400c8881a75f",
+    "created": "2026-10-06T19:00:39.329398+00:00",
+    "uploaded": "2026-10-06T19:00:40.715344+00:00"
+  },
+  {
+    "name": "train-g7-00015.tar",
+    "worker": 7,
+    "count": 20,
+    "bytes": 22558720,
+    "sha256": "d9ce4f49b5d8d55a8b7ac8ecf32b2ea599ce6993b7aedc9cebfe6e12fadb5a91",
+    "status": "uploaded",
+    "revision": "522cc28b69dde7d19af6e7d4708cab3eda2a9413",
+    "created": "2026-10-06T19:13:55.138405+00:00",
+    "uploaded": "2026-10-06T19:14:00.797571+00:00"
+  },
+  {
+    "name": "train-g7-00016.tar",
+    "worker": 7,
+    "count": 20,
+    "bytes": 22097920,
+    "sha256": "5eb7bcc142176dee62e700f3a42eaf9d7015f17324c00826a2f01252f2cb6b0e",
+    "status": "uploaded",
+    "revision": "6ab5fa5136ae04b86c884fd9156ec1429ec2fba8",
+    "created": "2026-10-06T19:27:10.683781+00:00",
+    "uploaded": "2026-10-06T19:27:15.313137+00:00"
+  },
+  {
+    "name": "train-g7-00017.tar",
+    "worker": 7,
+    "count": 20,
+    "bytes": 21606400,
+    "sha256": "f59fd61c00d2c217001d1f8c67855b8816d2fa45031292f594b15ffe67cc8cb9",
+    "status": "uploaded",
+    "revision": "30d0b02adcbeeb564a93ce8999bf9b53e194910a",
+    "created": "2026-10-06T19:40:26.260073+00:00",
+    "uploaded": "2026-10-06T19:40:30.423212+00:00"
   }
 ]

tools/background_production.py

@@ -20,6 +20,8 @@ ROOT = Path(__file__).resolve().parents[1]
 STATE = ROOT / 'experiments/background-production'
 BUFFER = Path('/tmp/vivarium-background-production')
 REPO = 'laion/Anim-E-Ghibli-Backgrounds-FLUX-9B'
+REPO_PREFIX = 'data'
+SHARD_PREFIX = 'train'
 BASE_REV = '32773329fbe7e81a90ef971740e8ba4b0364ecf3'
 LORA_REV = '5bd654514cc35a1c0d0de878c3a514019896a44e'
 LORA_SHA = '253dd23291a64514f7cb107714f2c20118efef930781d620a83417b441198982'
@@ -112,7 +114,7 @@ def worker(gpu, chunk):
     pipe = create_native_pipeline('backgrounds', device='cuda:0', compile_mode='default', cfg_mode='batched')
     load_seconds = time.perf_counter() - start
     event(con, gpu, 'loaded', json.dumps(dict(seconds=load_seconds, gpu=torch.cuda.get_device_name(0))))
-    existing = list(BUFFER.glob(f'train-g{gpu}-*.tar'))
+    existing = list(BUFFER.glob(f'{SHARD_PREFIX}-g{gpu}-*.tar'))
     sequence = max([int(p.stem.rsplit('-', 1)[1]) for p in existing] + [-1]) + 1
     # Include remotely uploaded/deleted shards when resuming.
     rows = con.execute('SELECT name FROM shards WHERE worker=?', (gpu,)).fetchall()
@@ -131,7 +133,7 @@ def worker(gpu, chunk):
                 time.sleep(3)
                 continue
             if tar is None:
-                name = f'train-g{gpu}-{sequence:05d}.tar'
+                name = f'{SHARD_PREFIX}-g{gpu}-{sequence:05d}.tar'
                 partial = BUFFER / (name + '.partial')
                 final = BUFFER / name
                 tar = tarfile.open(partial, 'w', format=tarfile.USTAR_FORMAT)
@@ -230,10 +232,10 @@ def upload(delete_after=False):
             try:
                 if sha(local) != row['sha256']:
                     raise ValueError('Local shard checksum mismatch: ' + row['name'])
-                result = api.upload_file(path_or_fileobj=local, path_in_repo='data/'+row['name'],
+                result = api.upload_file(path_or_fileobj=local, path_in_repo=REPO_PREFIX+'/'+row['name'],
                     repo_id=REPO, repo_type='dataset', commit_message=f"Add {row['count']} backgrounds: {row['name']}")
                 remote = api.dataset_info(REPO, revision=result.oid, files_metadata=True)
-                file = next(f for f in remote.siblings if f.rfilename == 'data/'+row['name'])
+                file = next(f for f in remote.siblings if f.rfilename == REPO_PREFIX+'/'+row['name'])
                 remote_digest = file.lfs.sha256 if file.lfs else None
                 if file.size != row['bytes'] or remote_digest != row['sha256']:
                     raise ValueError('Remote size/SHA256 mismatch: ' + row['name'])

tools/infer_character_tar.py

@@ -1,4 +1,4 @@
-"""Generate one planned person and ten expression samples directly into a TAR."""
+"""Generate all planned expressions for one human or special directly into a TAR."""
 import argparse
 import hashlib
 import io
@@ -21,7 +21,9 @@ def main():
     p.add_argument('--no-compile',action='store_true');a=p.parse_args()
     if a.plan:plan=json.loads(a.plan.read_text())
     else:
-        url=f'https://huggingface.co/datasets/laion/Vivarium-Greenscreen-Characters-FLUX-9B/resolve/{a.revision}/metadata/person-plan.json'
+        extension=a.person.startswith('special-') or (a.person.startswith('person-') and int(a.person.split('-')[1])>500)
+        member='metadata/extension/person-plan.json' if extension else 'metadata/person-plan.json'
+        url=f'https://huggingface.co/datasets/laion/Vivarium-Greenscreen-Characters-FLUX-9B/resolve/{a.revision}/{member}'
         with urlopen(url,timeout=90) as f:plan=json.load(f)
     person=next(x for x in plan if x['personality_id']==a.person)
     pipe=create_native_pipeline('characters',base_path=a.base_path,compile_mode=None if a.no_compile else 'default',adapter_scale=person.get('lora_scale',1.0))

tools/read_webdataset.py

@@ -36,12 +36,13 @@ if __name__ == '__main__':
     p.add_argument('shard',help='Local TAR path, https URL, or shard filename in --repo')
     p.add_argument('--repo',default='laion/Anim-E-Ghibli-Backgrounds-FLUX-9B')
     p.add_argument('--revision',default='main')
+    p.add_argument('--prefix',default='data',help='Repository shard directory, e.g. city-data or extension-data')
     p.add_argument('--limit',type=int,default=5)
     a=p.parse_args()
     if Path(a.shard).is_file():
         source=open(a.shard,'rb')
     else:
-        url=a.shard if a.shard.startswith('https://') else f'https://huggingface.co/datasets/{a.repo}/resolve/{a.revision}/data/{a.shard}'
+        url=a.shard if a.shard.startswith('https://') else f'https://huggingface.co/datasets/{a.repo}/resolve/{a.revision}/{a.prefix}/{a.shard}'
         source=urlopen(url,timeout=90)
     with source:
         for i,(image,metadata,text) in enumerate(samples(source)):