Blue2Giant/FreeStyle_Dataset

0426 Lora Triplet Dataset

This directory contains the normalized export of the lora-triplet portion of:

• /data/vgo/xingpeng/new_vgo/Sref_Cref_MiniVGO/configs/data/0426_cref_sref_full_diffusion.yaml

It covers three nonzero-weight lora-triplet sources:

• cref_sref_qwen_lora_part1
• cref_sref_flux_lora_part1
• cref_sref_illustrious_lora_part1

Directory Layout

For the Hugging Face release, the dataset card stays at repository root and the exported data is placed under cref_sref/:

<repo-root>/
  README.md
  cref_sref/
    HF_UPLOAD_CHECKLIST.md
    README.md
    qwen/
    flux/
    illustrious/

The working export root may also contain logs/. That directory is internal and can be excluded from the Hugging Face upload.

Each source subdirectory under cref_sref/ has the same structure:

<source-name>/
  README.md
  summary.json
  triplets.csv
  content_images.csv
  style_images.csv
  target_images.csv
  images/
    content/...
    style/...
    target/...
  _state/
    manifest.json
    triplets.jsonl
    content_images.jsonl
    style_images.jsonl
    target_images.jsonl

What A Triplet Means

Each triplet row corresponds to one vault training sequence and three training images:

• content: the image used for cref_0
• style: the image used for sref_0
• target: the image used for the combined content+style target

So the key relationship is:

• triplets.csv = one row per training sequence
• content_images.csv = one row per unique content image
• style_images.csv = one row per unique style image
• target_images.csv = one row per unique target image

The images are deduplicated. The same exported image path can appear in many triplet rows.

How To Read The Files

1. triplets.csv

Use this file when you want to understand the training example itself.

Important columns:

• sequence_id: unique id of the vault sequence
• 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

2. content_images.csv / style_images.csv / target_images.csv

Use these files when you want image-level metadata.

Important columns:

• exported_image_path: relative path under the source directory
• original_path: recovered original generation image path when matched
• match_status: whether original-path matching succeeded
• prompt_status: whether the original generation prompt was recovered
• generation_prompt
• base_prompt
• sequence_count: how many triplets reuse this exported image
• sequence_ids_json: which triplets reuse this image

How To View One Triplet

Method 1: inspect one row from triplets.csv

python3 - <<'PY'
import csv
path = '/path/to/repo/cref_sref/qwen/triplets.csv'
with open(path, 'r', encoding='utf-8', newline='') as fh:
    row = next(csv.DictReader(fh))
for key in [
    'sequence_id',
    'pair_key',
    'content_image_path',
    'style_image_path',
    'target_image_path',
    'content_match_status',
    'style_match_status',
    'target_match_status',
    'content_generation_prompt',
    'style_generation_prompt',
    'target_generation_prompt',
]:
    print(f'{key}: {row[key]}')
PY

Method 2: load the three images for a given sequence

python3 - <<'PY'
import csv
from pathlib import Path

base = Path('/path/to/repo/cref_sref/qwen')
with open(base / 'triplets.csv', 'r', encoding='utf-8', newline='') as fh:
    row = next(csv.DictReader(fh))

print('sequence_id:', row['sequence_id'])
print('content image:', base / row['content_image_path'])
print('style image:', base / row['style_image_path'])
print('target image:', base / row['target_image_path'])
PY

Method 3: join a triplet row to image-level metadata

Join:

• 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

This lets you answer:

• Which triplets reuse the same image?
• What is the recovered original path?
• Was the original prompt recovered?

How To Interpret Match And Prompt Status

match_status

• matched: exact visual-key match found in the original candidate pool
• unmatched: candidate pool exists, 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

• resolved: generation 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 Semantics

• Exported images are vault training images, not raw copies of original one-lora or dual-lora PNG files.
• original_path and prompt recovery fields are best-effort provenance fields.
• Some rows intentionally remain unmatched rather than risk incorrect prompt assignment.
• _state/ is internal resume state used during export; it is not required for ordinary dataset consumption.

Source-Level Summary

Final exported sequence counts:

• qwen: 33,582
• flux: 273,682
• illustrious: 172,589

For detailed per-source match counts, see each source's summary.json.