Link paper, project page and improve metadata

README.md

@@ -1,16 +1,25 @@
 ---
-pretty_name: 0426 CRef/SRef LoRA Triplet Dataset
 language:
 - en
 - zh
 task_categories:
 - image-to-image
+pretty_name: FreeStyle CRef/SRef LoRA Triplet Dataset
+license: cc-by-nc-4.0
+tags:
+- style-transfer
+- diffusion
+- lora
+- dual-reference-generation
 ---
 
-# 0426 CRef/SRef LoRA Triplet Dataset
+# FreeStyle: Free Control of Style-Content Dual-Reference Generation from Community LoRA Mining
 
-This dataset contains CRef/SRef LoRA triplets exported from the 0426 diffusion training data. Each training example has three images:
+[**Project Page**](https://blue2giant.github.io/FreeStyle/) | [**Paper**](https://huggingface.co/papers/2606.20506) | [**GitHub**](https://github.com/Blue2Giant/FreeStyle)
 
+This dataset contains CRef/SRef LoRA triplets exported from the 0426 diffusion training data. It was introduced as part of **FreeStyle**, a scalable dual-reference generation framework that treats community LoRAs as compositional anchors for style and content.
+
+Each training example in this dataset consists of 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
@@ -77,24 +86,16 @@ 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`, and `cref_sref_illustrious_lora_part1`.
 
-```text
-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:
-
+In the training loader, a sample is represented by:
 ```text
 <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 |
+| Instruction field | Vault text index |
 | --- | --- |
 | `vault_primary_instruction_en_123` | `primary_instruction_en_123` |
 | `vault_primary_instruction_cn_123` | `primary_instruction_cn_123` |
@@ -103,75 +104,24 @@ For these lora-triplet sources, the training DB provides 8 text choices per sequ
 
 paired with one of:
 
-| Target-caption field in this CSV | Vault text index |
+| Target-caption field | 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:
-
-```python
-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:
-
-```python
-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`:
-
-```text
-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`.
+
+## Citation
+
+```bibtex
+@article{lan2026freestyle,
+  title         = {FreeStyle: Free Control of Style-Content Dual-Reference Generation from Community LoRA Mining},
+  author        = {Lan, Jinghong and Cheng, Wei and Chen, Yunuo and Ye, Ziqi and Xing, Peng and Fang, Yixiao and Wang, Rui and Yang, Yufeng and Zhang, Xuanyang and Zou, Difan and Zeng, Xianfang and Yu, Gang and Zhang, Chi},
+  journal       = {arXiv preprint arXiv:2606.20506},
+  year          = {2026}
+}
+```