Improve dataset card for MultiID-Bench: Add task categories, tags, intro, usage, and full license

README.md

@@ -2,6 +2,15 @@
 license: other
 license_name: multiid-2m
 license_link: LICENSE.md
+language:
+- en
+task_categories:
+- text-to-image
+tags:
+- face-generation
+- identity-consistent
+- multi-person
+- benchmark
 dataset_info:
   features:
   - name: ID
@@ -33,7 +42,6 @@ configs:
     path: data/train-*
 ---
 
-
 # MultiID-Bench in WithAnyone
 
 [![arXiv](https://img.shields.io/badge/arXiv-2510.14975-b31b1b.svg)](https://arxiv.org/abs/2510.14975)
@@ -44,61 +52,43 @@ configs:
 [![MultiID-2M](https://img.shields.io/badge/MultiID_2M-Dataset-Green.svg)](https://huggingface.co/datasets/WithAnyone/MultiID-2M)
 [![Demo](https://img.shields.io/badge/HuggingFace-Demo-Yellow.svg)](https://huggingface.co/spaces/WithAnyone/WithAnyone_demo)
 
-**Please refer to [GitHub repo](https://github.com/Doby-Xu/WithAnyone) for the usage of this benchmark.**
+The MultiID-Bench dataset is a benchmark introduced in the paper [WithAnyone: Towards Controllable and ID Consistent Image Generation](https://huggingface.co/papers/2510.14975). It is specifically tailored for multi-person scenarios in text-to-image research, providing diverse references for each identity. This dataset aims to quantify "copy-paste" artifacts and evaluate the trade-off between identity fidelity and variation, enabling models like WithAnyone to achieve controllable and identity-consistent image generation.
 
-## Download
+## Links
 
-[HuggingFace Dataset](https://huggingface.co/datasets/WithAnyone/MultiID-Bench)
+*   **Paper:** [WithAnyone: Towards Controllable and ID Consistent Image Generation](https://huggingface.co/papers/2510.14975)
+*   **Project Page:** https://doby-xu.github.io/WithAnyone/
+*   **GitHub Repository:** https://github.com/Doby-Xu/WithAnyone
+*   **WithAnyone Model:** https://huggingface.co/WithAnyone/WithAnyone
+*   **WithAnyone Demo:** https://huggingface.co/spaces/WithAnyone/WithAnyone_demo
 
-```
-huggingface-cli download WithAnyone/MultiID-Bench --repo-type dataset --local-dir <path to MultiID-Bench directory>
-```
+## Sample Usage
 
-## Evaluation
+This section provides instructions for downloading the MultiID-Bench dataset and preparing it for evaluation.
 
-**Please refer to [GitHub repo](https://github.com/Doby-Xu/WithAnyone) for the usage of this benchmark.**
+### Download the Dataset
 
-### Environment Setup
-
-Besides the `requirements.txt` in [GitHub repo](https://github.com/Doby-Xu/WithAnyone), you need to install the following packages:
+You can download the MultiID-Bench dataset using the Hugging Face CLI:
 
 ```bash
-pip install aesthetic-predictor-v2-5 
-pip install facexlib
-pip install colorama
-pip install pytorch_lightning
-git clone https://github.com/timesler/facenet-pytorch.git facenet_pytorch
-
-# in MultiID_Bench/
-mkdir pretrained
+huggingface-cli download WithAnyone/MultiID-Bench --repo-type dataset --local-dir <path to MultiID-Bench directory>
 ```
 
+### Prepare Data for Evaluation
 
-You need the following models to run the evaluation:
+After downloading, if your dataset is in a `parquet` file format, you can convert it into a structured directory of images and JSON metadata using the `parquet2bench.py` script provided in the GitHub repository.
 
-- CLIP
-- arcface
-- aesthetic-v2.5
-- adaface
-- facenet
-
-For the first three models, they will be automatically downloaded when you run the evaluation script for the first time. Most of the models will be cached in the `HF_HOME` directory, which is usually `~/.cache/huggingface`. About 5GB of disk space is needed.
-
-For adaface, you need to download the model weights from [adaface_ir50_ms1mv2.ckpt](https://drive.google.com/file/d/1eUaSHG4pGlIZK7hBkqjyp2fc2epKoBvI/view?usp=sharing) (This is the original link provided by the authors of AdaFace) and put it in the `pretrained` directory.
-
-This repository includes code from [AdaFace](https://github.com/mk-minchul/AdaFace?tab=readme-ov-file). AdaFace is included in this codebase for merely easier import. You can also clone it separately from its original repository, and modify the import paths accordingly.
-
-
-### Data to Evaluate
-
-By running:
+First, ensure you have cloned the GitHub repository:
+```bash
+git clone https://github.com/Doby-Xu/WithAnyone
+cd WithAnyone
 ```
-python hf2bench.py \
-    --dataset WithAnyone/MultiID-Bench \
-    --output <root directory to save the data> \
-    --from_hub
+
+Then, convert the downloaded parquet file:
+```bash
+python MultiID_Bench/parquet2bench.py --parquet <path to downloaded parquet file> --output_dir <root directory to save the processed data>
 ```
-you can arrange the generated images and the corresponding text prompts in the following structure:
+The output directory will contain a structure like this, with subfolders for each ID and `meta.json` files containing prompts:
 ```
 root/
 ├── id1/
@@ -120,38 +110,42 @@ root/
 │   └── meta.json
 │
 └── ...
-``` 
-
-Or you can manually download the data by
-```
-huggingface-cli download WithAnyone/MultiID-Bench --repo-type dataset --local-dir <root directory to save the data>
 ```
-and arrange the files:
-```
-python hf2bench.py --dataset <root directory to save the data> --output <root directory to save the data>
-```
-
-If you run the `infer_withanyone.py` script in this repository, the output directory will be in the correct format.
-
 The `meta.json` file should contain the prompt used to generate the image, in the following format:
-
 ```json
 {
     "prompt": "a photo of a person with blue hair and glasses"
 }
 ```
 
+### Environment Setup for Evaluation
+
+To run the evaluation scripts, you need to install several packages. Besides the `requirements.txt` from the [GitHub repo](https://github.com/Doby-Xu/WithAnyone), install the following:
+
+```bash
+pip install aesthetic-predictor-v2-5 
+pip install facexlib
+pip install colorama
+pip install pytorch_lightning
+git clone https://github.com/timesler/facenet-pytorch.git facenet_pytorch
+
+# in MultiID_Bench/
+mkdir pretrained
+```
+
+You will also need the following models to run the evaluation: CLIP, arcface, aesthetic-v2.5, adaface, and facenet. The first three will be automatically downloaded. For `adaface`, download `adaface_ir50_ms1mv2.ckpt` from [this link](https://drive.google.com/file/d/1eUaSHG4pGlIZK7hBkqjyp2fc2epKoBvI/view?usp=sharing) and place it in the `pretrained` directory.
+
 ### Run Evaluation
 
-You can run the evaluation script as follows:
+You can run the evaluation script as follows, using the prepared data:
 
 ```python
 from eval import BenchEval_Geo
 
 def run():
     evaler = BenchEval_Geo(
-        target_dir=<root directory mentioned above>,
-        output_dir=<output directory to save the evaluation results>,
+        target_dir="<root directory mentioned above>",
+        output_dir="<output directory to save the evaluation results>",
         ori_file_name="ori.jpg", # the name of the ground truth image file
         output_file_name="out.jpg", # the name of the generated image file
         ref_1_file_name="ref_1.jpg", # the name of the first reference image file
@@ -167,3 +161,43 @@ if __name__ == "__main__":
     run()
 ```
 
+## License and Disclaimer
+
+The **code** of WithAnyone is released under the [**Apache License 2.0**](https://www.apache.org/licenses/LICENSE-2.0), while the WithAnyone **model and associated datasets** are made available **solely for non-commercial academic research purposes**.
+
+-   **License Terms:**  
+    The WithAnyone model is distributed under the [**FLUX.1 [dev] Non-Commercial License v1.1.1**](https://huggingface.co/black-forest-labs/FLUX.1-dev/blob/main/LICENSE.md). All underlying base models remain governed by their respective original licenses and terms, which shall continue to apply in full. Users must comply with all such applicable licenses when using this project.
+
+-   **Permitted Use:**  
+    This project may be used for lawful academic research, analysis, and non-commercial experimentation only. Any form of commercial use, redistribution for profit, or application that violates applicable laws, regulations, or ethical standards is strictly prohibited.
+
+-   **User Obligations:**  
+    Users are solely responsible for ensuring that their use of the model and dataset complies with all relevant laws, regulations, institutional review policies, and third-party license terms.
+
+-   **Disclaimer of Liability:**  
+    The authors, developers, and contributors make no warranties, express or implied, regarding the accuracy, reliability, or fitness of this project for any particular purpose. They shall not be held liable for any damages, losses, or legal claims arising from the use or misuse of this project, including but not limited to violations of law or ethical standards by end users.
+
+-   **Acceptance of Terms:**  
+    By downloading, accessing, or using this project, you acknowledge and agree to be bound by the applicable license terms and legal requirements, and you assume full responsibility for all consequences resulting from your use.
+
+## Acknowledgement
+We thank the following prior art for their excellent open source work: 
+- [PuLID](https://github.com/ToTheBeginning/PuLID)
+- [UNO](https://github.com/bytedance/UNO)
+- [UniPortrait](https://github.com/junjiehe96/UniPortrait)
+- [InfiniteYou](https://github.com/bytedance/InfiniteYou)
+- [DreamO](https://github.com/bytedance/DreamO)
+- [UMO](https://github.com/bytedance/UMO)
+
+## Citation
+
+If you find this project useful in your research, please consider citing:
+
+```bibtex
+@article{xu2025withanyone,
+  title={WithAnyone: Towards Controllable and ID-Consistent Image Generation}, 
+  author={Hengyuan Xu and Wei Cheng and Peng Xing and Yixiao Fang and Shuhan Wu and Rui Wang and Xianfang Zeng and Gang Yu and Xinjun Ma and Yu-Gang Jiang},
+  journal={arXiv preprint arxiv:2510.14975},
+  year={2025}
+}
+```