Blue2Giant/FreeStyle_Dataset

0426 CRef/SRef LoRA Triplet Dataset

This dataset contains CRef/SRef LoRA triplets exported from the 0426 diffusion training data. Each training example has three images:

• content: content reference image, used as cref_0
• style: style reference image, used as sref_0
• target: image generated from the combined content + style condition

Use triplets.csv as the main entry point. Image-level CSV files are provided only for deduplicated metadata and provenance lookup.

Sources

Directory	Base model	Original source	Triplets
cref_sref/qwen/	qwen	cref_sref_qwen_lora_part1	33,582
cref_sref/flux/	flux	cref_sref_flux_lora_part1	273,682
cref_sref/illustrious/	illustrious	cref_sref_illustrious_lora_part1	172,589

Layout

<repo-root>/
  README.md
  cref_sref/
    README.md
    qwen/
      triplets.csv
      content_images.csv
      style_images.csv
      target_images.csv
      images/content/...
      images/style/...
      images/target/...
    flux/
      ... same structure ...
    illustrious/
      ... same structure ...

How To Use

Pick one source directory and read its triplets.csv:

import csv
from pathlib import Path
from PIL import Image

source_dir = Path("/path/to/FreeStyle_Dataset/cref_sref/qwen")  # qwen / flux / illustrious

with open(source_dir / "triplets.csv", newline="", encoding="utf-8") as f:
    row = next(csv.DictReader(f))

content = Image.open(source_dir / row["content_image_path"]).convert("RGB")
style = Image.open(source_dir / row["style_image_path"]).convert("RGB")
target = Image.open(source_dir / row["target_image_path"]).convert("RGB")

print(row["sequence_id"])

# One training-compatible text pair. The original training samples one of several
# instruction/caption choices; see the next section.
instruction = row["vault_primary_instruction_en_123"]
target_caption = row["vault_captions_scene_3_en"]
print(instruction)
print(target_caption)

Which Prompt Fields Are Used For Training?

The 0426 training config uses the three lora-triplet sources:

cref_sref_qwen_lora_part1
cref_sref_flux_lora_part1
cref_sref_illustrious_lora_part1

In the training loader, a sample is not represented by a single prompt string. Each training choice is:

<cref_0 image> <sref_0 image> <instruction text> <target caption text> <target image>

Only the final target image has require_loss=True; the two text fields are conditioning text.

For these lora-triplet sources, the training DB provides 8 text choices per sequence. Each choice uses exactly one instruction field plus one target-caption field:

Instruction field in this CSV	Vault text index
vault_primary_instruction_en_123	primary_instruction_en_123
vault_primary_instruction_cn_123	primary_instruction_cn_123
vault_sample_instruction_en_123	sample_instruction_en_123
vault_sample_instruction_cn_123	sample_instruction_cn_123

paired with one of:

Target-caption field in this CSV	Vault text index
vault_captions_scene_3_en	captions/scene_3_en
vault_captions_scene_3	captions/scene_3

So, to reproduce the training text conditioning, use one of these pairs, for example:

instruction = row["vault_primary_instruction_en_123"]
target_caption = row["vault_captions_scene_3_en"]
texts = [instruction, target_caption]

or sample uniformly from the 8 combinations:

import random

instruction_key = random.choice([
    "vault_primary_instruction_en_123",
    "vault_primary_instruction_cn_123",
    "vault_sample_instruction_en_123",
    "vault_sample_instruction_cn_123",
])
caption_key = random.choice([
    "vault_captions_scene_3_en",
    "vault_captions_scene_3",
])

texts = [row[instruction_key], row[caption_key]]

The columns content_generation_prompt, style_generation_prompt, and target_generation_prompt are provenance fields recovered from the original image-generation pipeline. They are useful for analysis, but they are not the primary text fields used by the 0426 VGO training loader.

All image paths in triplets.csv are relative to the source directory. For example, in cref_sref/qwen/triplets.csv:

images/content/xxx.png -> cref_sref/qwen/images/content/xxx.png
images/style/yyy.png   -> cref_sref/qwen/images/style/yyy.png
images/target/zzz.png  -> cref_sref/qwen/images/target/zzz.png

Main Files

File	Meaning
triplets.csv	One row per training example. This is the file most users should start from.
content_images.csv	Deduplicated metadata for unique content images.
style_images.csv	Deduplicated metadata for unique style images.
target_images.csv	Deduplicated metadata for unique target images.
summary.json	Per-source counts and match/prompt recovery statistics.

Important triplets.csv columns:

• sequence_id
• base_model
• content_image_path, style_image_path, target_image_path
• vault_primary_instruction_en_123, vault_primary_instruction_cn_123
• vault_sample_instruction_en_123, vault_sample_instruction_cn_123
• vault_captions_scene_3_en, vault_captions_scene_3
• vault_texts_json
• content_generation_prompt, style_generation_prompt, target_generation_prompt provenance fields
• content_original_path, style_original_path, target_original_path provenance fields
• content_match_status, style_match_status, target_match_status
• content_prompt_status, style_prompt_status, target_prompt_status

Notes

• Images are deduplicated; the same image file may appear in multiple triplet rows.
• original_path and prompt fields are best-effort provenance metadata and may be unresolved for some rows.
• _state/, if present, is internal export/resume state and is not needed for normal dataset use.
• For detailed column definitions and provenance status values, see cref_sref/README.md.