Datasets:

SumomoLee
/

personal-source

Update personal-source: artist @ prefix, folder-name artists, cleaned tags, re-captioned and re-translated NL, 31 early-year images removed, rules doc

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---
license: other
license_name: personal-use-only
pretty_name: personal-source
task_categories:
- text-to-image
- image-to-text
size_categories:
- 10K<n<100K
configs:
- config_name: default
  data_files:
  - split: train
    path: data/train-*.parquet
extra_gated_prompt: >-
  This is a private personal image collection with annotations. Access is granted manually, for the owner's collaborators only.
  You must not redistribute the images or annotations.
extra_gated_fields:
  Name: text
  Affiliation: text
  Reason for access: text
  I will not redistribute any part of this dataset: checkbox
---
# personal-source (v7)

Personal training set (images and all annotations) used by the Krea2 / Anima personal LoRA and full fine-tuning runs, version v7
(2026-10-09 update). Gated, manual approval. This card contains aggregate statistics only.

- **19,578** images in **90** subset folders, **99** parquet shards (~500 MB, zstd for text columns; image bytes stored as-is), total 48.9 GB.
- Existing images: 17,123 (original pixels from the personal-source archives). Added in v7: 2,455.
- Removed from the set: two artist folders, one teaser collection, one low-quality character image, and 53 images: 31 early-year images of the newly added artist folders (real source year before 2020 or before the per-artist cutoff), 21 betanonbeet images dated before 2025-01-01, and one todo image.
- Update 2026-10-12: artist tags are `@`-prefixed in `tags` / `tag_groups_json` (AI folders excluded) and folder artists were added where missing; affected captions were regenerated and re-translated.
- Verified: row count = 19578, sha256 and md5 of every stored image match, every image decodes (PIL `verify()` + full `load()`, width/height match): 19578 / 19578.

## Schema (one row per image)

| column | type | description |
| --- | --- | --- |
| `image` | struct{bytes,path} | original image file bytes, exactly as stored in the source archive / download (not re-encoded); `path` is the file name |
| `folder` | string | subset folder of the personal dataset (artist / concept / character / collection folder, 90 values) |
| `file_name` | string | original file name (stem + extension); `folder` + file stem is unique |
| `mime_type` | string | MIME type detected from the image bytes (PIL) |
| `width / height` | int | pixel size of the stored image |
| `file_size` | int | byte size of the stored image |
| `md5 / sha256` | string | hash of the stored image bytes (sha256 is also the image id used in the training tables) |
| `source` | string | where the pixels come from: `archive` (original personal-source archives, existing images), `hub`, `r2`, `cdn`, `x`, `gelbooru` (images added in v7) |
| `new_group` | string | selection group of an image added in v7 (null for existing images) |
| `is_new_v7` | bool | true = added in the 2026-10-09 update (v7), false = existing before the update |
| `danbooru_id / gelbooru_id / tweet_id` | int / int / string | post ids where known |
| `post_url` | string | canonical post URL built from the ids above (Danbooru / Gelbooru / X) |
| `source_url` | string | original source URL recorded by the booru post (pixiv / twitter / ...), where known |
| `source_date / source_date_method` | string | real source date (YYYY-MM-DD) and how it was derived (booru API, tweet snowflake, pixiv CDN path, file name, ...); null when only an imputed year exists |
| `year / year_source` | int / string | annotation year used as the `year NNNN` prompt token in training, and the rule that produced it |
| `rating` | string | g / s / q / e where known (booru rating or PixAI-derived) |
| `tags` | string | tag string as used in training, format `prefix ||| tail` where prefix = subject counts, artist, `year NNNN`, quality tag, complexity tag and tail = character, copyright, general/meta tags, rating |
| `tags_source` | string | `training_new_meta` = the string stored in the v7 training table (new-meta format); `composed_from_final_tags` = old-format training strings are recomposed into the new format from the final merged tag groups (booru/user tags + PixAI) and the final year / quality / complexity labels |
| `tag_groups_json` | string (JSON) | tag groups behind `tags` (artist, character, copyright, general, rating) |
| `tags_v7_train` | string | the raw alternate caption stored in the v7 training table (may still be the old 3-segment format); null when no tag row exists |
| `pixai_status / pixai_tags_json` | string / string (JSON) | PixAI tagger output: lists of [tag, score] for general / character / copyright / style / meta / rating |
| `pixai_film_grain / pixai_chromatic_aberration` | float | PixAI detector scores |
| `caption_en` | string | natural-language caption (English), heretic-Qwen3.8 v7 (OCR text and final labels in the prompt) |
| `caption_zh / caption_ja` | string | translations of `caption_en` (Index-Translate-35B-A3B-preview) |
| `caption_source` | string | caption generation run tag |
| `quality_label / quality_score / quality_score_source` | string / float / string | final quality label used in training (masterpiece / good / normal / bad / worst) and its score (0-1); source = `crystal` (production metadata) or `personal_run` (noobreward + aes25 + aim + popularity pipeline) |
| `complexity_tag / complexity_score` | string / float | `high visual complexity` / `low visual complexity` (null = middle, not tagged), and the overall complexity score (0-100) |
| `aes25_score / aim_score / noob_p_good` | float | individual quality model outputs |
| `booru_score / booru_fav_count` | int | booru popularity where known |
| `crystal_matched` | bool | image matched a row of the production_0925_crystal Danbooru metadata |
| `ocr_allowed / ocr_status / ocr_has_text / ocr_text` | bool / string / bool / string | OCR annotation (VLM). Not run (null) for the `add`, `add-n`, `todo`, `todo-n` folders; `ocr_text` null unless status is `complete`, empty string = no text |
| `ocr_used_in_caption` | bool | OCR text was part of the caption prompt |
| `repeats_d01 .. repeats_d04` | int | num_repeats of the caption branch in each resolution tier (768 / 1024 / 1280 / 1536 views); the tag branch uses 2x; null where the image has no such tier |
| `train_rows_v7 / val_rows_v7 / split_v7` | int / int / string | rows the image contributes to the v7 training / validation tables (incl. repeats) and its split |

Load with `datasets.load_dataset("SumomoLee/personal-source", split="train")` (the `image` column is decoded as an image).

## Statistics

source: archive: 17123, hub: 1026, cdn: 632, r2: 511, x: 273, gelbooru: 13

mime type: image/jpeg: 12784, image/png: 6747, image/webp: 42, image/avif: 5

quality label: normal: 8189, masterpiece: 5470, good: 5105, bad: 496, worst: 318

complexity tag: None: 10780, high visual complexity: 4815, low visual complexity: 3983

year source: danbooru_real_source_rule: 7024, ep13_file_mtime: 3347, crystal_annotation_year: 3281, existing_year_tag: 2500, ep13_tag: 1573, filename_source_date: 1567, tweet_snowflake: 273, gelbooru_real_source_rule: 13

split (v7): train: 19223, validation: 355

OCR status: complete: 17502, None: 2073, error: 3

tags source: composed_from_final_tags: 13470, training_new_meta: 6108
caption source: personal_heretic_v7_20261009: 9427, personal_heretic_v7_20261012: 7204, personal_heretic_v7_20261013_curated: 1961, personal_heretic_v7_20261012_special: 975, personal_heretic_20261008: 11
Years (annotation year): 2026: 6282, 2025: 5792, 2024: 3235, 2023: 1927, 2022: 1081, 2021: 577, 2020: 275, 2019: 214, 2013: 172, 2018: 12, 2011: 6, 2015: 2, 2016: 2, 2010: 1
Image size: width 300-10000 px (mean 1923), height 300-9698 px (mean 2269); file size mean 2.49 MB.
### Column fill rates (share of rows with a non-null value)
| column | fill |
| --- | --- |
| `folder` | 100.0% |
| `source` | 100.0% |
| `caption_en` | 100.0% |
| `caption_zh` | 100.0% |
| `caption_ja` | 99.9% |
| `tags` | 100.0% |
| `tags_v7_train` | 97.9% |
| `pixai_tags_json` | 99.9% |
| `quality_score` | 100.0% |
| `complexity_tag` | 44.9% |
| `complexity_score` | 100.0% |
| `ocr_text` | 89.4% |
| `danbooru_id` | 53.5% |
| `post_url` | 55.0% |
| `source_url` | 27.9% |
| `source_date` | 62.1% |
| `booru_score` | 52.6% |
| `repeats_d01` | 96.9% |
| `repeats_d02` | 98.1% |
| `repeats_d03` | 90.5% |
| `repeats_d04` | 89.8% |

Notes: `ocr_text` is non-null for 89.4% of the rows (OCR not run for 4 folders; of the rows where it ran, 7,563 contain text).
`complexity_tag` is null for images in the middle complexity band (not tagged in training).
`caption_*` is missing for 1 image (tag-only row).

## Provenance

Built by `personal-source-release` (2026-10-12) from: the personal-update-20261009 work directory (selection, captions DB, quality / complexity / OCR / PixAI
annotation runs, real source dates), the original personal-source archives, the Index-Translate translations of the captions, and the `data_t2i_v7rc` training table
(tags, caption text and repeat information as actually used by the training runs).

	---
	license: other
	license_name: personal-use-only
	pretty_name: personal-source
	task_categories:
	- text-to-image
	- image-to-text
	size_categories:
	- 10K<n<100K
	configs:
	- config_name: default
	data_files:
	- split: train
	path: data/train-*.parquet
	extra_gated_prompt: >-
	This is a private personal image collection with annotations. Access is granted manually, for the owner's collaborators only.
	You must not redistribute the images or annotations.
	extra_gated_fields:
	Name: text
	Affiliation: text
	Reason for access: text
	I will not redistribute any part of this dataset: checkbox
	---

	# personal-source (v7)

	Personal training set (images and all annotations) used by the Krea2 / Anima personal LoRA and full fine-tuning runs, version v7
	(2026-10-09 update). Gated, manual approval. This card contains aggregate statistics only.

	- 19,578 images in 90 subset folders, 99 parquet shards (~500 MB, zstd for text columns; image bytes stored as-is), total 48.9 GB.
	- Existing images: 17,123 (original pixels from the personal-source archives). Added in v7: 2,455.
	- Removed from the set: two artist folders, one teaser collection, one low-quality character image, and 53 images: 31 early-year images of the newly added artist folders (real source year before 2020 or before the per-artist cutoff), 21 betanonbeet images dated before 2025-01-01, and one todo image.
	- Update 2026-10-12: artist tags are `@`-prefixed in `tags` / `tag_groups_json` (AI folders excluded) and folder artists were added where missing; affected captions were regenerated and re-translated.
	- Verified: row count = 19578, sha256 and md5 of every stored image match, every image decodes (PIL `verify()` + full `load()`, width/height match): 19578 / 19578.

	## Schema (one row per image)

	\| column \| type \| description \|
	\| --- \| --- \| --- \|
	\| `image` \| struct{bytes,path} \| original image file bytes, exactly as stored in the source archive / download (not re-encoded); `path` is the file name \|
	\| `folder` \| string \| subset folder of the personal dataset (artist / concept / character / collection folder, 90 values) \|
	\| `file_name` \| string \| original file name (stem + extension); `folder` + file stem is unique \|
	\| `mime_type` \| string \| MIME type detected from the image bytes (PIL) \|
	\| `width / height` \| int \| pixel size of the stored image \|
	\| `file_size` \| int \| byte size of the stored image \|
	\| `md5 / sha256` \| string \| hash of the stored image bytes (sha256 is also the image id used in the training tables) \|
	\| `source` \| string \| where the pixels come from: `archive` (original personal-source archives, existing images), `hub`, `r2`, `cdn`, `x`, `gelbooru` (images added in v7) \|
	\| `new_group` \| string \| selection group of an image added in v7 (null for existing images) \|
	\| `is_new_v7` \| bool \| true = added in the 2026-10-09 update (v7), false = existing before the update \|
	\| `danbooru_id / gelbooru_id / tweet_id` \| int / int / string \| post ids where known \|
	\| `post_url` \| string \| canonical post URL built from the ids above (Danbooru / Gelbooru / X) \|
	\| `source_url` \| string \| original source URL recorded by the booru post (pixiv / twitter / ...), where known \|
	\| `source_date / source_date_method` \| string \| real source date (YYYY-MM-DD) and how it was derived (booru API, tweet snowflake, pixiv CDN path, file name, ...); null when only an imputed year exists \|
	\| `year / year_source` \| int / string \| annotation year used as the `year NNNN` prompt token in training, and the rule that produced it \|
	\| `rating` \| string \| g / s / q / e where known (booru rating or PixAI-derived) \|
	\| `tags` \| string \| tag string as used in training, format `prefix \|\|\| tail` where prefix = subject counts, artist, `year NNNN`, quality tag, complexity tag and tail = character, copyright, general/meta tags, rating \|
	\| `tags_source` \| string \| `training_new_meta` = the string stored in the v7 training table (new-meta format); `composed_from_final_tags` = old-format training strings are recomposed into the new format from the final merged tag groups (booru/user tags + PixAI) and the final year / quality / complexity labels \|
	\| `tag_groups_json` \| string (JSON) \| tag groups behind `tags` (artist, character, copyright, general, rating) \|
	\| `tags_v7_train` \| string \| the raw alternate caption stored in the v7 training table (may still be the old 3-segment format); null when no tag row exists \|
	\| `pixai_status / pixai_tags_json` \| string / string (JSON) \| PixAI tagger output: lists of [tag, score] for general / character / copyright / style / meta / rating \|
	\| `pixai_film_grain / pixai_chromatic_aberration` \| float \| PixAI detector scores \|
	\| `caption_en` \| string \| natural-language caption (English), heretic-Qwen3.8 v7 (OCR text and final labels in the prompt) \|
	\| `caption_zh / caption_ja` \| string \| translations of `caption_en` (Index-Translate-35B-A3B-preview) \|
	\| `caption_source` \| string \| caption generation run tag \|
	\| `quality_label / quality_score / quality_score_source` \| string / float / string \| final quality label used in training (masterpiece / good / normal / bad / worst) and its score (0-1); source = `crystal` (production metadata) or `personal_run` (noobreward + aes25 + aim + popularity pipeline) \|
	\| `complexity_tag / complexity_score` \| string / float \| `high visual complexity` / `low visual complexity` (null = middle, not tagged), and the overall complexity score (0-100) \|
	\| `aes25_score / aim_score / noob_p_good` \| float \| individual quality model outputs \|
	\| `booru_score / booru_fav_count` \| int \| booru popularity where known \|
	\| `crystal_matched` \| bool \| image matched a row of the production_0925_crystal Danbooru metadata \|
	\| `ocr_allowed / ocr_status / ocr_has_text / ocr_text` \| bool / string / bool / string \| OCR annotation (VLM). Not run (null) for the `add`, `add-n`, `todo`, `todo-n` folders; `ocr_text` null unless status is `complete`, empty string = no text \|
	\| `ocr_used_in_caption` \| bool \| OCR text was part of the caption prompt \|
	\| `repeats_d01 .. repeats_d04` \| int \| num_repeats of the caption branch in each resolution tier (768 / 1024 / 1280 / 1536 views); the tag branch uses 2x; null where the image has no such tier \|
	\| `train_rows_v7 / val_rows_v7 / split_v7` \| int / int / string \| rows the image contributes to the v7 training / validation tables (incl. repeats) and its split \|

	Load with `datasets.load_dataset("SumomoLee/personal-source", split="train")` (the `image` column is decoded as an image).

	## Statistics

	source: archive: 17123, hub: 1026, cdn: 632, r2: 511, x: 273, gelbooru: 13

	mime type: image/jpeg: 12784, image/png: 6747, image/webp: 42, image/avif: 5

	quality label: normal: 8189, masterpiece: 5470, good: 5105, bad: 496, worst: 318

	complexity tag: None: 10780, high visual complexity: 4815, low visual complexity: 3983

	year source: danbooru_real_source_rule: 7024, ep13_file_mtime: 3347, crystal_annotation_year: 3281, existing_year_tag: 2500, ep13_tag: 1573, filename_source_date: 1567, tweet_snowflake: 273, gelbooru_real_source_rule: 13

	split (v7): train: 19223, validation: 355

	OCR status: complete: 17502, None: 2073, error: 3

	tags source: composed_from_final_tags: 13470, training_new_meta: 6108

	caption source: personal_heretic_v7_20261009: 9427, personal_heretic_v7_20261012: 7204, personal_heretic_v7_20261013_curated: 1961, personal_heretic_v7_20261012_special: 975, personal_heretic_20261008: 11

	Years (annotation year): 2026: 6282, 2025: 5792, 2024: 3235, 2023: 1927, 2022: 1081, 2021: 577, 2020: 275, 2019: 214, 2013: 172, 2018: 12, 2011: 6, 2015: 2, 2016: 2, 2010: 1

	Image size: width 300-10000 px (mean 1923), height 300-9698 px (mean 2269); file size mean 2.49 MB.

	### Column fill rates (share of rows with a non-null value)

	\| column \| fill \|
	\| --- \| --- \|
	\| `folder` \| 100.0% \|
	\| `source` \| 100.0% \|
	\| `caption_en` \| 100.0% \|
	\| `caption_zh` \| 100.0% \|
	\| `caption_ja` \| 99.9% \|
	\| `tags` \| 100.0% \|
	\| `tags_v7_train` \| 97.9% \|
	\| `pixai_tags_json` \| 99.9% \|
	\| `quality_score` \| 100.0% \|
	\| `complexity_tag` \| 44.9% \|
	\| `complexity_score` \| 100.0% \|
	\| `ocr_text` \| 89.4% \|
	\| `danbooru_id` \| 53.5% \|
	\| `post_url` \| 55.0% \|
	\| `source_url` \| 27.9% \|
	\| `source_date` \| 62.1% \|
	\| `booru_score` \| 52.6% \|
	\| `repeats_d01` \| 96.9% \|
	\| `repeats_d02` \| 98.1% \|
	\| `repeats_d03` \| 90.5% \|
	\| `repeats_d04` \| 89.8% \|

	Notes: `ocr_text` is non-null for 89.4% of the rows (OCR not run for 4 folders; of the rows where it ran, 7,563 contain text).
	`complexity_tag` is null for images in the middle complexity band (not tagged in training).
	`caption_*` is missing for 1 image (tag-only row).

	## Provenance

	Built by `personal-source-release` (2026-10-12) from: the personal-update-20261009 work directory (selection, captions DB, quality / complexity / OCR / PixAI
	annotation runs, real source dates), the original personal-source archives, the Index-Translate translations of the captions, and the `data_t2i_v7rc` training table
	(tags, caption text and repeat information as actually used by the training runs).