Blue2Giant/FreeStyle_Dataset

FreeStyle Dataset: cref/sref LoRA Triplets

This repository contains a file-based image triplet dataset for content-reference (cref) and style-reference (sref) training/evaluation. The exported payload is under cref_sref/.

Each row in a triplets.csv file represents one training sequence with three images:

• content: image used as the content reference (cref_0)
• style: image used as the style reference (sref_0)
• target: image for the combined content+style result

The images are deduplicated within each source/role. A single image file can be reused by multiple triplet rows.

Quick Start

Install lightweight dependencies:

pip install huggingface_hub pandas pillow

Download and inspect the metadata for one source without downloading all images:

from huggingface_hub import hf_hub_download
import pandas as pd

repo_id = "Blue2Giant/FreeStyle_Dataset"
source = "qwen"  # one of: qwen, flux, illustrious

triplets_csv = hf_hub_download(
    repo_id=repo_id,
    repo_type="dataset",
    filename=f"cref_sref/{source}/triplets.csv",
)
triplets = pd.read_csv(triplets_csv)

print(len(triplets))
print(triplets[[
    "sequence_id",
    "content_image_path",
    "style_image_path",
    "target_image_path",
    "content_generation_prompt",
    "style_generation_prompt",
    "target_generation_prompt",
]].head())

Download the three images for one triplet:

from huggingface_hub import hf_hub_download
from PIL import Image
import pandas as pd

repo_id = "Blue2Giant/FreeStyle_Dataset"
source = "qwen"

triplets_csv = hf_hub_download(repo_id=repo_id, repo_type="dataset", filename=f"cref_sref/{source}/triplets.csv")
triplets = pd.read_csv(triplets_csv)
row = triplets.iloc[0]

paths = {}
for role in ["content", "style", "target"]:
    rel = row[f"{role}_image_path"]  # relative to cref_sref/<source>/
    paths[role] = hf_hub_download(
        repo_id=repo_id,
        repo_type="dataset",
        filename=f"cref_sref/{source}/{rel}",
    )

images = {role: Image.open(path).convert("RGB") for role, path in paths.items()}
print(row["sequence_id"], images["content"].size, images["style"].size, images["target"].size)

Recommended Download Patterns

This is a large file-tree dataset. Prefer selective downloads rather than cloning/downloading the entire repository at once.

Download all CSV/JSON/README metadata, but no images:

from huggingface_hub import snapshot_download

snapshot_dir = snapshot_download(
    repo_id="Blue2Giant/FreeStyle_Dataset",
    repo_type="dataset",
    allow_patterns=[
        "README.md",
        "cref_sref/README.md",
        "cref_sref/*/README.md",
        "cref_sref/*/summary.json",
        "cref_sref/*/*.csv",
    ],
)
print(snapshot_dir)

Download one complete source, including images:

from huggingface_hub import snapshot_download

source = "qwen"  # qwen is the smallest; flux and illustrious are much larger
snapshot_dir = snapshot_download(
    repo_id="Blue2Giant/FreeStyle_Dataset",
    repo_type="dataset",
    allow_patterns=["README.md", "cref_sref/README.md", f"cref_sref/{source}/**"],
)
print(snapshot_dir)

If you need the full dataset, use snapshot_download(repo_type="dataset"), but expect many files.

Repository Layout

<repo-root>/
  README.md                         # this guide
  cref_sref/
    README.md                       # payload-level guide
    HF_UPLOAD_CHECKLIST.md
    qwen/
      README.md
      summary.json
      triplets.csv
      content_images.csv
      style_images.csv
      target_images.csv
      images/
        content/...
        style/...
        target/...
    flux/
      ... same layout ...
    illustrious/
      ... same layout ...

Public upload notes:

• _state/ export-resume folders are omitted.
• logs/ are omitted.
• Paths stored in triplets.csv are relative to the source directory, e.g. relative to cref_sref/qwen/.

Sources and Counts

Source	Triplet rows	Unique content images	Unique style images	Unique target images
qwen	33,582	90	424	5,162
flux	273,682	482	13,037	129,237
illustrious	172,589	8,045	1,581	65,952
Total	479,853	8,617	15,042	200,351

Total exported image files across source/role directories: 224,010.

File Semantics

triplets.csv

Use triplets.csv as the main training/evaluation index. Important columns include:

• sequence_id: unique sequence id
• base_model: one of qwen, flux, illustrious
• pair_key: pair identifier
• content_model_id, style_model_id
• content_image_path, style_image_path, target_image_path
• content_original_path, style_original_path, target_original_path
• content_match_status, style_match_status, target_match_status
• content_prompt_status, style_prompt_status, target_prompt_status
• content_generation_prompt, style_generation_prompt, target_generation_prompt
• vault_texts_json

For model training, the most common fields are the three *_image_path columns and, when available, the three *_generation_prompt columns.

content_images.csv, style_images.csv, target_images.csv

These files are deduplicated image-level metadata tables. Important columns include:

• exported_image_path: image path relative to the source directory
• original_path: best-effort recovered original generation image path
• match_status: original-path matching status
• prompt_status: prompt recovery status
• generation_prompt
• base_prompt
• sequence_count: number of triplet rows that reuse this image
• sequence_ids_json: triplet ids that reuse this image

Join keys:

• triplets.csv.content_image_path -> content_images.csv.exported_image_path
• triplets.csv.style_image_path -> style_images.csv.exported_image_path
• triplets.csv.target_image_path -> target_images.csv.exported_image_path

Example join:

from huggingface_hub import hf_hub_download
import pandas as pd

repo_id = "Blue2Giant/FreeStyle_Dataset"
source = "qwen"

triplets = pd.read_csv(hf_hub_download(repo_id=repo_id, repo_type="dataset", filename=f"cref_sref/{source}/triplets.csv"))
content_meta = pd.read_csv(hf_hub_download(repo_id=repo_id, repo_type="dataset", filename=f"cref_sref/{source}/content_images.csv"))
content_meta = content_meta.set_index("exported_image_path")

row = triplets.iloc[0]
print(content_meta.loc[row["content_image_path"]])

Match and Prompt Status Values

match_status describes whether the exporter could map an exported vault image back to an original candidate image:

• matched: exact visual-key match found in the candidate pool
• unmatched: candidate pool existed, but no exact unique match was found
• ambiguous: more than one candidate matched the same visual key
• no_candidates: no candidate pool was available for that lookup

prompt_status describes whether generation prompt metadata was recovered:

• resolved: prompt metadata was recovered
• unmatched_original: original image path was not matched
• missing_prompt_payload: prompt sidecar JSON was missing
• missing_prompt_entry: prompt file existed, but the specific image entry was missing
• missing_prompt_index: image filename could not be mapped to a prompt index

Important Notes

• Exported images are vault training images, not guaranteed to be raw copies of the original one-LoRA or dual-LoRA generation files.
• original_path and prompt recovery fields are best-effort provenance fields. Do not assume every row has a resolved prompt.
• Rows with unresolved provenance are intentionally kept rather than forcing incorrect prompt assignments.
• The repository is organized as ordinary files. Use huggingface_hub, pandas, and Pillow for flexible access.

Where to Start

1. Choose a source: start with qwen for a smaller first pass.
2. Read cref_sref/<source>/README.md and summary.json.
3. Load cref_sref/<source>/triplets.csv.
4. For each row, resolve image paths relative to cref_sref/<source>/.
5. Optionally join to the deduplicated *_images.csv metadata tables for provenance and reuse statistics.