Update dataset README usage guide

README.md

@@ -1,258 +1,241 @@
----
-pretty_name: "FreeStyle Dataset: cref/sref LoRA Triplets"
-task_categories:
-- image-to-image
-tags:
-- image-to-image
-- style-reference
-- content-reference
-- lora
-- triplet
-- prompt-provenance
-size_categories:
-- 100K<n<1M
----
+# 0426 CRef/SRef LoRA Triplet Dataset
 
-# FreeStyle Dataset: cref/sref LoRA Triplets
+This dataset is a normalized export of the LoRA-triplet portion of the 0426 CRef/SRef diffusion training configuration. It contains paired **content reference**, **style reference**, and **target** training images, plus CSV metadata that links each training sample to its images and recovered prompt/provenance information.
 
-This repository contains a file-based image triplet dataset for content-reference (`cref`) and style-reference (`sref`) training/evaluation. The exported payload is under [`cref_sref/`](./cref_sref/).
+The export covers three base-model sources:
 
-Each row in a `triplets.csv` file represents one training sequence with three images:
+| Source directory | Original source name | Triplets |
+| --- | --- | ---: |
+| `qwen/` | `cref_sref_qwen_lora_part1` | 33,582 |
+| `flux/` | `cref_sref_flux_lora_part1` | 273,682 |
+| `illustrious/` | `cref_sref_illustrious_lora_part1` | 172,589 |
 
-- **content**: image used as the content reference (`cref_0`)
-- **style**: image used as the style reference (`sref_0`)
-- **target**: image for the combined content+style result
+## Start Here: How To Use The Dataset
 
-The images are deduplicated within each source/role. A single image file can be reused by multiple triplet rows.
+Pick one source directory under `cref_sref/` according to the base model you want to train or evaluate:
 
-## Quick Start
-
-Install lightweight dependencies:
-
-```bash
-pip install huggingface_hub pandas pillow
+```text
+cref_sref/qwen/
+cref_sref/flux/
+cref_sref/illustrious/
 ```
 
-Download and inspect the metadata for one source without downloading all images:
+For ordinary use, start from that source's `triplets.csv`. Each row is one training example:
 
-```python
-from huggingface_hub import hf_hub_download
-import pandas as pd
-
-repo_id = "Blue2Giant/FreeStyle_Dataset"
-source = "qwen"  # one of: qwen, flux, illustrious
-
-triplets_csv = hf_hub_download(
-    repo_id=repo_id,
-    repo_type="dataset",
-    filename=f"cref_sref/{source}/triplets.csv",
-)
-triplets = pd.read_csv(triplets_csv)
-
-print(len(triplets))
-print(triplets[[
-    "sequence_id",
-    "content_image_path",
-    "style_image_path",
-    "target_image_path",
-    "content_generation_prompt",
-    "style_generation_prompt",
-    "target_generation_prompt",
-]].head())
-```
+- `content_image_path`: content/reference image, corresponding to `cref_0`
+- `style_image_path`: style/reference image, corresponding to `sref_0`
+- `target_image_path`: target image combining the content and style
+- `*_generation_prompt`: recovered generation prompt when available
+- `vault_texts_json`: original text fields from the vault sequence
 
-Download the three images for one triplet:
+All image paths in the CSV are **relative to the source directory**. For example, if a row in `cref_sref/qwen/triplets.csv` contains:
 
-```python
-from huggingface_hub import hf_hub_download
-from PIL import Image
-import pandas as pd
-
-repo_id = "Blue2Giant/FreeStyle_Dataset"
-source = "qwen"
-
-triplets_csv = hf_hub_download(repo_id=repo_id, repo_type="dataset", filename=f"cref_sref/{source}/triplets.csv")
-triplets = pd.read_csv(triplets_csv)
-row = triplets.iloc[0]
-
-paths = {}
-for role in ["content", "style", "target"]:
-    rel = row[f"{role}_image_path"]  # relative to cref_sref/<source>/
-    paths[role] = hf_hub_download(
-        repo_id=repo_id,
-        repo_type="dataset",
-        filename=f"cref_sref/{source}/{rel}",
-    )
-
-images = {role: Image.open(path).convert("RGB") for role, path in paths.items()}
-print(row["sequence_id"], images["content"].size, images["style"].size, images["target"].size)
+```text
+content_image_path = images/content/xxx.png
+style_image_path   = images/style/yyy.png
+target_image_path  = images/target/zzz.png
 ```
 
-## Recommended Download Patterns
+then the corresponding files are:
 
-This is a large file-tree dataset. Prefer selective downloads rather than cloning/downloading the entire repository at once.
+```text
+cref_sref/qwen/images/content/xxx.png
+cref_sref/qwen/images/style/yyy.png
+cref_sref/qwen/images/target/zzz.png
+```
 
-Download all CSV/JSON/README metadata, but no images:
+### Minimal Python Example
 
 ```python
-from huggingface_hub import snapshot_download
-
-snapshot_dir = snapshot_download(
-    repo_id="Blue2Giant/FreeStyle_Dataset",
-    repo_type="dataset",
-    allow_patterns=[
-        "README.md",
-        "cref_sref/README.md",
-        "cref_sref/*/README.md",
-        "cref_sref/*/summary.json",
-        "cref_sref/*/*.csv",
-    ],
-)
-print(snapshot_dir)
+import csv
+from pathlib import Path
+from PIL import Image
+
+source_dir = Path("/path/to/dataset/cref_sref/qwen")  # or 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")
+
+sample = {
+    "sequence_id": row["sequence_id"],
+    "base_model": row["base_model"],
+    "content": content,
+    "style": style,
+    "target": target,
+    "content_prompt": row.get("content_generation_prompt", ""),
+    "style_prompt": row.get("style_generation_prompt", ""),
+    "target_prompt": row.get("target_generation_prompt", ""),
+}
 ```
 
-Download one complete source, including images:
+### Minimal PyTorch Dataset Wrapper
 
 ```python
-from huggingface_hub import snapshot_download
-
-source = "qwen"  # qwen is the smallest; flux and illustrious are much larger
-snapshot_dir = snapshot_download(
-    repo_id="Blue2Giant/FreeStyle_Dataset",
-    repo_type="dataset",
-    allow_patterns=["README.md", "cref_sref/README.md", f"cref_sref/{source}/**"],
-)
-print(snapshot_dir)
+import csv
+from pathlib import Path
+from PIL import Image
+from torch.utils.data import Dataset
+
+class CrefSrefTriplets(Dataset):
+    def __init__(self, dataset_root, source="qwen", transform=None):
+        root = Path(dataset_root)
+        # Accept the repo root, the cref_sref directory, or a source directory.
+        if (root / "triplets.csv").exists():
+            self.source_dir = root
+        elif (root / source / "triplets.csv").exists():
+            self.source_dir = root / source
+        else:
+            self.source_dir = root / "cref_sref" / source
+
+        if not (self.source_dir / "triplets.csv").exists():
+            raise FileNotFoundError(f"Could not find triplets.csv under {self.source_dir}")
+
+        self.transform = transform
+
+        with open(self.source_dir / "triplets.csv", newline="", encoding="utf-8") as f:
+            self.rows = list(csv.DictReader(f))
+
+    def __len__(self):
+        return len(self.rows)
+
+    def _load_image(self, rel_path):
+        image = Image.open(self.source_dir / rel_path).convert("RGB")
+        return self.transform(image) if self.transform else image
+
+    def __getitem__(self, idx):
+        row = self.rows[idx]
+        return {
+            "sequence_id": row["sequence_id"],
+            "base_model": row["base_model"],
+            "content_image": self._load_image(row["content_image_path"]),
+            "style_image": self._load_image(row["style_image_path"]),
+            "target_image": self._load_image(row["target_image_path"]),
+            "content_prompt": row.get("content_generation_prompt", ""),
+            "style_prompt": row.get("style_generation_prompt", ""),
+            "target_prompt": row.get("target_generation_prompt", ""),
+            "metadata": row,
+        }
+
+# Example:
+# ds = CrefSrefTriplets("/path/to/dataset", source="flux")
+# item = ds[0]
 ```
 
-If you need the full dataset, use `snapshot_download(repo_type="dataset")`, but expect many files.
-
 ## Repository Layout
 
+The Hugging Face repository is organized as:
+
 ```text
 <repo-root>/
-  README.md                         # this guide
+  README.md
   cref_sref/
-    README.md                       # payload-level guide
+    README.md
     HF_UPLOAD_CHECKLIST.md
     qwen/
-      README.md
-      summary.json
-      triplets.csv
-      content_images.csv
-      style_images.csv
-      target_images.csv
-      images/
-        content/...
-        style/...
-        target/...
     flux/
-      ... same layout ...
     illustrious/
-      ... same layout ...
 ```
 
-Public upload notes:
-
-- `_state/` export-resume folders are omitted.
-- `logs/` are omitted.
-- Paths stored in `triplets.csv` are relative to the source directory, e.g. relative to `cref_sref/qwen/`.
-
-## Sources and Counts
+Each source directory has the same structure:
 
-| 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 |
-| **Total** | **479,853** | **8,617** | **15,042** | **200,351** |
-
-Total exported image files across source/role directories: **224,010**.
-
-## File Semantics
-
-### `triplets.csv`
-
-Use `triplets.csv` as the main training/evaluation index. Important columns include:
-
-- `sequence_id`: unique sequence id
-- `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`
-
-For model training, the most common fields are the three `*_image_path` columns and, when available, the three `*_generation_prompt` columns.
-
-### `content_images.csv`, `style_images.csv`, `target_images.csv`
-
-These files are deduplicated image-level metadata tables. Important columns include:
-
-- `exported_image_path`: image path relative to the source directory
-- `original_path`: best-effort recovered original generation image path
-- `match_status`: original-path matching status
-- `prompt_status`: prompt recovery status
-- `generation_prompt`
-- `base_prompt`
-- `sequence_count`: number of triplet rows that reuse this image
-- `sequence_ids_json`: triplet ids that reuse this image
-
-Join keys:
+```text
+cref_sref/<source>/
+  README.md
+  summary.json
+  triplets.csv
+  content_images.csv
+  style_images.csv
+  target_images.csv
+  images/
+    content/...
+    style/...
+    target/...
+  _state/                 # optional internal export/resume state
+```
 
-- `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`
+For most users, the important files are `triplets.csv` and the `images/` directory. The `*_images.csv` files are useful when you need deduplicated image-level metadata.
 
-Example join:
+## Files And Relationships
 
-```python
-from huggingface_hub import hf_hub_download
-import pandas as pd
+| File | Level | Use it for |
+| --- | --- | --- |
+| `triplets.csv` | sequence/triplet | Main training/evaluation table: one row per content-style-target example. |
+| `content_images.csv` | deduplicated image | Metadata for unique content images. |
+| `style_images.csv` | deduplicated image | Metadata for unique style images. |
+| `target_images.csv` | deduplicated image | Metadata for unique target images. |
+| `summary.json` | source summary | Counts and match/prompt recovery statistics. |
+| `images/content/` | files | Exported content reference images. |
+| `images/style/` | files | Exported style reference images. |
+| `images/target/` | files | Exported target images. |
 
-repo_id = "Blue2Giant/FreeStyle_Dataset"
-source = "qwen"
+The images are deduplicated. The same image file can be reused by multiple triplet rows.
 
-triplets = pd.read_csv(hf_hub_download(repo_id=repo_id, repo_type="dataset", filename=f"cref_sref/{source}/triplets.csv"))
-content_meta = pd.read_csv(hf_hub_download(repo_id=repo_id, repo_type="dataset", filename=f"cref_sref/{source}/content_images.csv"))
-content_meta = content_meta.set_index("exported_image_path")
+To join triplet-level rows to image-level metadata:
 
-row = triplets.iloc[0]
-print(content_meta.loc[row["content_image_path"]])
+```text
+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
 ```
 
-## Match and Prompt Status Values
-
-`match_status` describes whether the exporter could map an exported vault image back to an original candidate image:
-
-- `matched`: exact visual-key match found in the candidate pool
-- `unmatched`: candidate pool existed, but no exact unique match was found
+## Key `triplets.csv` Columns
+
+| Column | Meaning |
+| --- | --- |
+| `sequence_id` | Unique id of the vault training sequence. |
+| `base_model` | One of `qwen`, `flux`, or `illustrious`. |
+| `pair_key` | Pair/group identifier from the export. |
+| `content_model_id`, `style_model_id` | LoRA/model identifiers associated with the content and style sides. |
+| `content_image_path`, `style_image_path`, `target_image_path` | Relative paths to the three exported training images. |
+| `content_original_path`, `style_original_path`, `target_original_path` | Best-effort matched original generation image paths. |
+| `content_match_status`, `style_match_status`, `target_match_status` | Whether original-image matching succeeded. |
+| `content_prompt_status`, `style_prompt_status`, `target_prompt_status` | Whether prompt metadata was recovered. |
+| `content_generation_prompt`, `style_generation_prompt`, `target_generation_prompt` | Recovered prompts when available. |
+| `vault_texts_json` | Original text metadata from the vault sequence, encoded as JSON. |
+
+## Key `*_images.csv` Columns
+
+| Column | Meaning |
+| --- | --- |
+| `exported_image_path` | Relative image path under the source directory. |
+| `original_path` | Best-effort recovered original generation image path. |
+| `match_status` | Original-path match status. |
+| `prompt_status` | Prompt recovery status. |
+| `generation_prompt` | Recovered generation prompt when available. |
+| `base_prompt` | Recovered base prompt when available. |
+| `sequence_count` | Number of triplets that reuse this image. |
+| `sequence_ids_json` | JSON list of triplet sequence ids using this image. |
+
+## Provenance Status Fields
+
+`original_path` and prompt fields are best-effort provenance metadata. Some rows intentionally remain unresolved rather than assigning an incorrect original path or prompt.
+
+`match_status` values:
+
+- `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` describes whether generation prompt metadata was recovered:
+`prompt_status` values:
 
-- `resolved`: prompt metadata was recovered
+- `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 Notes
-
-- Exported images are vault training images, not guaranteed to be raw copies of the original one-LoRA or dual-LoRA generation files.
-- `original_path` and prompt recovery fields are best-effort provenance fields. Do not assume every row has a resolved prompt.
-- Rows with unresolved provenance are intentionally kept rather than forcing incorrect prompt assignments.
-- The repository is organized as ordinary files. Use `huggingface_hub`, `pandas`, and `Pillow` for flexible access.
+## Notes
 
-## Where to Start
+- Exported images are vault training images, not raw copies of the original one-LoRA or dual-LoRA generation PNG files.
+- Use `triplets.csv` for training/evaluation samples; use `*_images.csv` only when you need image-level deduplication or provenance analysis.
+- `_state/` is internal export/resume state and is not required for normal dataset consumption.
+- `logs/`, if present in a working export, is internal and should not be treated as dataset payload.
 
-1. Choose a source: start with `qwen` for a smaller first pass.
-2. Read `cref_sref/<source>/README.md` and `summary.json`.
-3. Load `cref_sref/<source>/triplets.csv`.
-4. For each row, resolve image paths relative to `cref_sref/<source>/`.
-5. Optionally join to the deduplicated `*_images.csv` metadata tables for provenance and reuse statistics.
+## Download
 
+Download or clone the Hugging Face dataset repository using your usual workflow. The examples above assume the downloaded repository root is `/path/to/dataset` and the data lives under `/path/to/dataset/cref_sref/`.