cref_sref/README.md

cref_sref Payload Guide

This folder contains the content-reference/style-reference LoRA triplet payload for the FreeStyle dataset.

Start from the root README.md for full usage examples. This file summarizes the payload layout.

Sources

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

Layout

Each source directory has the same structure:

<source>/
  README.md
  summary.json
  triplets.csv
  content_images.csv
  style_images.csv
  target_images.csv
  images/
    content/...
    style/...
    target/...

_state/ and logs/ are intentionally not included in the public upload.

How to Use

For a source such as qwen:

1. Read qwen/triplets.csv; it is the sequence-level index.
2. For each row, use:
   • content_image_path
   • style_image_path
   • target_image_path
3. Resolve those paths relative to cref_sref/qwen/.
4. Use content_images.csv, style_images.csv, and target_images.csv only when you need image-level metadata, reuse counts, original-path provenance, or prompt recovery details.

Minimal Python example:

from huggingface_hub import hf_hub_download
from PIL import Image
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"))
row = triplets.iloc[0]

content_path = hf_hub_download(repo_id=repo_id, repo_type="dataset", filename=f"cref_sref/{source}/{row['content_image_path']}")
style_path = hf_hub_download(repo_id=repo_id, repo_type="dataset", filename=f"cref_sref/{source}/{row['style_image_path']}")
target_path = hf_hub_download(repo_id=repo_id, repo_type="dataset", filename=f"cref_sref/{source}/{row['target_image_path']}")

content = Image.open(content_path).convert("RGB")
style = Image.open(style_path).convert("RGB")
target = Image.open(target_path).convert("RGB")

Notes

• triplets.csv is sequence-level.
• *_images.csv files are deduplicated image-level metadata.
• The same image can be reused by many triplets; check sequence_count in the image metadata tables.
• Prompt/provenance fields are best effort. Rows with unresolved provenance are intentionally preserved.