# 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.