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cref_sref/README.md

@@ -1,205 +1,25 @@
-# 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`.
+# cref_sref
+
+This directory is the Hugging Face payload for the 0426 lora-triplet release.
+
+Contents:
+- `qwen/`
+- `flux/`
+- `illustrious/`
+
+Each source directory includes:
+- `README.md`
+- `summary.json`
+- `triplets.csv`
+- `content_images.csv`
+- `style_images.csv`
+- `target_images.csv`
+- `images/`
+
+Notes:
+- `triplets.csv` is sequence-level.
+- `*_images.csv` files are deduplicated image-level metadata.
+- `_state/` is omitted from the public upload.
+- `logs/` are omitted from the public upload.
+
+Start with the source-level `README.md` and `triplets.csv`.