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+# 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/`:
+
+```text
+<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:
+
+```text
+<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`
+
+```bash
+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
+
+```bash
+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`.