| # FreeStyle Dataset — sref split |
|
|
| This subfolder is the cref+sref+target triplet export of the |
| `cref_sref_oneig_filter_part1..part5` vault sources used by the VGO project. |
| The layout is intentionally simple so that downstream readers can use the |
| dataset **with nothing more than Python's standard library + Pillow + pandas** |
| — DuckDB and the internal `vault` package are **not** required. |
|
|
| ## Contents |
|
|
| ``` |
| sref/ |
| ├── README.md # this file |
| ├── pairs.csv # one row per (sequence_id, choice_id) — fully self-contained |
| ├── metadata.jsonl # one row per sequence_id, preserves the original vault structure |
| ├── export_summary.json # counts and export-time metadata |
| └── images/<uu>/<image_uuid>.<ext> |
| # deduplicated image pool. <uu> is the first two hex chars of the uuid. |
| ``` |
|
|
| - Exported sources: 5 |
| - `cref_sref_oneig_filter_part1` |
| - `cref_sref_oneig_filter_part2` |
| - `cref_sref_oneig_filter_part3` |
| - `cref_sref_oneig_filter_part4` |
| - `cref_sref_oneig_filter_part5` |
| - Unique vault sequences exported: **417,761** |
| - Rows in `pairs.csv` (= sequences × choices): **3,342,076** |
| - Unique images in `images/`: **1,238,604** |
| - Export wall-clock: started 2026-06-11T08:11:57.855884Z, finished 2026-06-11T08:33:59.192685Z |
|
|
| ## Schema — `pairs.csv` |
|
|
| | Column | Description | |
| | --- | --- | |
| | `sequence_id` | Vault sequence UUID. Eight rows share the same id, one per choice. | |
| | `choice_id` | 0..7. Index of the instruction × caption × language combination. | |
| | `source` | One of `cref_sref_oneig_filter_part{1..5}`. | |
| | `task_type` | Always `edit` for this export. | |
| | `cref_image` / `sref_image` / `target_image` | Relative path inside this folder, e.g. `images/ab/ab12….webp`. Open with PIL. | |
| | `cref_width` / `cref_height` / `sref_*` / `target_*` | Pixel dimensions reported by the vault. | |
| | `instruction` / `instruction_index` | Instruction text + its vault-internal key (`sample_instruction_en_123` etc). | |
| | `target_caption` / `target_caption_index` | Target image caption + its vault key (`captions/scene_3` etc). | |
| | `content_trigger_words` / `style_trigger_words` | Trigger phrases attached to the content/style references. | |
| | `message_formatter` | The original modality template, always `<image_0><image_1><text_0><text_1><image_2>`. | |
| | `require_loss_json` | JSON list aligned with the formatter modalities — `[false, false, false, false, true]` for edit. | |
| | `uri` | Vault-side original uri string. | |
| | `create_time` | ISO-8601 timestamp recorded in the vault. | |
|
|
| ## Loading with pandas (no DuckDB needed) |
|
|
| ```python |
| import pandas as pd |
| from PIL import Image |
| from pathlib import Path |
| |
| root = Path("/path/to/sref") # local clone of this folder |
| df = pd.read_csv(root / "pairs.csv") |
| |
| row = df.iloc[0] |
| cref = Image.open(root / row["cref_image"]) |
| sref = Image.open(root / row["sref_image"]) |
| target = Image.open(root / row["target_image"]) |
| prompt = row["instruction"] |
| caption = row["target_caption"] |
| ``` |
|
|
| To keep only English instructions: |
|
|
| ```python |
| en_df = df[df["instruction_index"].str.endswith("_en_123")] |
| ``` |
|
|
| To deduplicate to one row per sequence (e.g. for visualizing pairs only): |
|
|
| ```python |
| unique = df.drop_duplicates(subset="sequence_id") |
| ``` |
|
|
| ## Loading with `datasets` |
|
|
| ```python |
| from datasets import load_dataset |
| |
| ds = load_dataset( |
| "Blue2Giant/FreeStyle_Dataset", |
| data_files="sref/pairs.csv", |
| split="train", |
| ) |
| ``` |
|
|
| Image columns are stored as relative paths inside this folder. After cloning |
| the repo (or using `huggingface_hub.snapshot_download`), resolve them against |
| the local snapshot root. |
|
|
| ## Schema — `metadata.jsonl` (optional, for power users) |
|
|
| Each line is a JSON object preserving the original vault structure for a |
| single `sequence_id`. Useful if you need to: |
|
|
| - Reconstruct the eight `sequence_choices` exactly as the trainer saw them. |
| - Access the auxiliary texts that are not surfaced in `pairs.csv`, such as |
| `captions/scene_1`, `captions/scene_2`, `target_caption`, and the |
| per-language variants. |
|
|
| ```json |
| { |
| "sequence_id": "...", |
| "source": "cref_sref_oneig_filter_part1", |
| "task_type": "edit", |
| "uri": "...", |
| "create_time": "2026-04-26T...", |
| "images": [{"id": "...", "index": "cref_0", "width": 1024, "height": 1024, "file_name": "images/.../....webp"}, ...], |
| "texts": [{"id": "...", "index": "...", "language": null, "content": "..."}, ...], |
| "choices": [{"choice_id": 0, "message_formatter": "<image_0><image_1><text_0><text_1><image_2>", "image_indices": ["cref_0", "sref_0", "target"], "text_indices": ["sample_instruction_cn_123", "captions/scene_3_en"], "require_loss": [false, false, false, false, true]}, ...], |
| "choices_weights": [] |
| } |
| ``` |
|
|
| ## Image dedup |
|
|
| Each image is stored once under its vault UUID, regardless of how many |
| sequences reference it. `pairs.csv` rows for different sequences therefore |
| reuse the same `cref_image` / `sref_image` paths whenever a cref/sref appears |
| in multiple pairs — this is the intended dedup behaviour and **not** a bug. |
|
|
| ## License & provenance |
|
|
| These triplets were assembled by sampling cref/sref references from internal |
| collections; please refer to the parent repository's top-level dataset card |
| for licensing. |
|
|
