Duplicate from WithAnyone/MultiID-2M

Co-authored-by: WithAnyone <WithAnyone@users.noreply.huggingface.co>

.gitattributes

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+*.7z filter=lfs diff=lfs merge=lfs -text
+*.arrow filter=lfs diff=lfs merge=lfs -text
+*.bin filter=lfs diff=lfs merge=lfs -text
+*.bz2 filter=lfs diff=lfs merge=lfs -text
+*.ckpt filter=lfs diff=lfs merge=lfs -text
+*.ftz filter=lfs diff=lfs merge=lfs -text
+*.gz filter=lfs diff=lfs merge=lfs -text
+*.h5 filter=lfs diff=lfs merge=lfs -text
+*.joblib filter=lfs diff=lfs merge=lfs -text
+*.lfs.* filter=lfs diff=lfs merge=lfs -text
+*.lz4 filter=lfs diff=lfs merge=lfs -text
+*.mds filter=lfs diff=lfs merge=lfs -text
+*.mlmodel filter=lfs diff=lfs merge=lfs -text
+*.model filter=lfs diff=lfs merge=lfs -text
+*.msgpack filter=lfs diff=lfs merge=lfs -text
+*.npy filter=lfs diff=lfs merge=lfs -text
+*.npz filter=lfs diff=lfs merge=lfs -text
+*.onnx filter=lfs diff=lfs merge=lfs -text
+*.ot filter=lfs diff=lfs merge=lfs -text
+*.parquet filter=lfs diff=lfs merge=lfs -text
+*.pb filter=lfs diff=lfs merge=lfs -text
+*.pickle filter=lfs diff=lfs merge=lfs -text
+*.pkl filter=lfs diff=lfs merge=lfs -text
+*.pt filter=lfs diff=lfs merge=lfs -text
+*.pth filter=lfs diff=lfs merge=lfs -text
+*.rar filter=lfs diff=lfs merge=lfs -text
+*.safetensors filter=lfs diff=lfs merge=lfs -text
+saved_model/**/* filter=lfs diff=lfs merge=lfs -text
+*.tar.* filter=lfs diff=lfs merge=lfs -text
+*.tar filter=lfs diff=lfs merge=lfs -text
+*.tflite filter=lfs diff=lfs merge=lfs -text
+*.tgz filter=lfs diff=lfs merge=lfs -text
+*.wasm filter=lfs diff=lfs merge=lfs -text
+*.xz filter=lfs diff=lfs merge=lfs -text
+*.zip filter=lfs diff=lfs merge=lfs -text
+*.zst filter=lfs diff=lfs merge=lfs -text
+*tfevents* filter=lfs diff=lfs merge=lfs -text
+# Audio files - uncompressed
+*.pcm filter=lfs diff=lfs merge=lfs -text
+*.sam filter=lfs diff=lfs merge=lfs -text
+*.raw filter=lfs diff=lfs merge=lfs -text
+# Audio files - compressed
+*.aac filter=lfs diff=lfs merge=lfs -text
+*.flac filter=lfs diff=lfs merge=lfs -text
+*.mp3 filter=lfs diff=lfs merge=lfs -text
+*.ogg filter=lfs diff=lfs merge=lfs -text
+*.wav filter=lfs diff=lfs merge=lfs -text
+# Image files - uncompressed
+*.bmp filter=lfs diff=lfs merge=lfs -text
+*.gif filter=lfs diff=lfs merge=lfs -text
+*.png filter=lfs diff=lfs merge=lfs -text
+*.tiff filter=lfs diff=lfs merge=lfs -text
+# Image files - compressed
+*.jpg filter=lfs diff=lfs merge=lfs -text
+*.jpeg filter=lfs diff=lfs merge=lfs -text
+*.webp filter=lfs diff=lfs merge=lfs -text
+# Video files - compressed
+*.mp4 filter=lfs diff=lfs merge=lfs -text
+*.webm filter=lfs diff=lfs merge=lfs -text

LICENSE.md

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+# MultiID-2M License (Non-Commercial, Restrictive Use)
+**v1.0 — 2025.10.16**  
+
+## Preamble
+This Dataset (the “Dataset”) contains audiovisual content assembled and curated by the Dataset author(s).  
+The Dataset includes images collected from publicly accessible sources (search engines, social media, etc.).  
+The Dataset may include photographs depicting public figures and other identifiable individuals.  
+The Dataset is provided subject to the terms below.
+
+## Definitions
+- **Dataset Provider**: the individual or entity that publishes the Dataset (Copyright holder).  
+- **You / Licensee**: any person or organization accessing, downloading, using, or deriving work from the Dataset.  
+- **Derivative Materials**: any model, dataset, software, media, or other artifact that is trained on, derived from, or includes portions of the Dataset.  
+- **Recognizable Individual**: any person whose face, voice, likeness, or other identifying attributes are discernible from Dataset content.
+
+## License Grant (Limited)
+Subject to the restrictions and conditions below, the Dataset Provider grants You a non-exclusive, non-transferable, revocable license to access and use the Dataset **solely for non-commercial, academic, or research purposes** (including internal research and manuscript preparation).
+
+## Ownership and Source Content Disclaimer
+The Dataset Provider **does not claim ownership** of any underlying images, videos, or audiovisual materials contained within the Dataset that were obtained from publicly accessible sources.  
+All copyrights, trademarks, and other intellectual property rights in the original content remain with their respective rights-holders.  
+
+The Dataset is provided **solely for research and analysis purposes** and may include third-party content reproduced under fair use, fair dealing, or similar legal exceptions.  
+If You are a rights-holder and believe your content has been included improperly, please contact the Dataset Provider as described in the “Rights-Holder Requests; Removal Procedure” section.
+
+
+## Prohibited Uses (Fundamental Restrictions)
+You shall **NOT**, under any circumstances:
+
+1. Use the Dataset, or any Derivative Materials, for **commercial purposes** (including but not limited to productizing, offering services, monetized models, or any activity for direct or indirect commercial advantage).  
+2. Redistribute, publish, host, or otherwise make the Dataset (or any substantially unmodified subset of the Dataset) available to third parties, including by reposting raw image/video files, without the express written permission of the Dataset Provider.  
+3. Use the Dataset to develop, train, fine-tune, or benchmark systems intended for **automated facial recognition, biometric identification, or verification** of individuals.  
+4. Use the Dataset to create, generate, or distribute **synthetic media** (including but not limited to deepfakes, voice cloning, or photorealistic imagery) that depicts a Recognizable Individual in a manner that is likely to mislead, defame, deceive, impersonate, or materially harm that individual.  
+5. Publicly present or publish Derivative Materials that display an identifiable individual's face or voice in a manner inconsistent with the individual's reasonable expectation of privacy, or in violation of applicable law.
+
+## Obligations and Best Efforts
+1. **Attribution:** When You publish research results that rely materially on the Dataset, you **MUST** include a citation to the associated paper or the Dataset Provider's citation statement:  
+   “Multi-human Talking Video Dataset” (or the canonical citation provided in the Dataset README).  
+2. **Recordkeeping:** You should keep a record of how the Dataset is used, including experiments, model checkpoints, and publications that rely on it, and provide such records to the Dataset Provider upon reasonable request.  
+3. **Compliance:** You must comply with all applicable laws, regulations, contractual terms, and platform policies (including copyright and data protection laws) when using the Dataset.  
+4. **Security:** You must take reasonable technical and organizational measures to protect the Dataset from unauthorized access, leaks, or misuse.
+
+## Rights-Holder Requests; Removal Procedure
+If a rights-holder, data subject, or other person with a legitimate interest contacts the Dataset Provider or You and requests removal or anonymization of Dataset items that depict them, You shall:
+
+1. Promptly cease distribution of the identified items and remove them from any resources under Your control that are accessible by others.  
+2. Notify the Dataset Provider and assist in confirming and documenting the removal.  
+3. If You have shared Derivative Materials that include the removed items, use reasonable efforts to remove or sanitize those Derivative Materials and document such actions.
+
+## Warranty Disclaimer and Limitation of Liability
+- THE DATASET IS PROVIDED “AS IS” AND THE DATASET PROVIDER DISCLAIMS ALL WARRANTIES, EXPRESS OR IMPLIED, INCLUDING WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE, AND NON-INFRINGEMENT.  
+- TO THE MAXIMUM EXTENT PERMITTED BY APPLICABLE LAW, THE DATASET PROVIDER SHALL NOT BE LIABLE FOR ANY INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES ARISING OUT OF THE USE OR INABILITY TO USE THE DATASET.
+
+## Indemnity
+You agree to indemnify, defend, and hold harmless the Dataset Provider from and against any and all claims, liabilities, losses, damages, costs, and expenses (including reasonable attorneys' fees) arising out of or relating to Your breach of this License or Your use of the Dataset (including any claims by third parties, rights-holders, or data subjects).
+
+## Termination
+This License and the rights granted herein terminate automatically if You fail to comply with any term herein.  
+Upon termination You must cease all use of the Dataset, delete all copies in Your possession (including Derivative Materials that are disallowed under this License), and certify destruction to the Dataset Provider upon request.
+
+## Jurisdiction; Governing Law
+This License shall be governed by and construed in accordance with the laws of the jurisdiction specified by the Dataset Provider (see Dataset README).  
+To the extent that mandatory local laws apply and impose different obligations, those laws control.
+
+## Acknowledgement of Residual Risk
+You acknowledge that:
+
+1. The Dataset may contain copyrighted works, personal data, or other protected content that may give rise to third-party claims.  
+2. This License does not and cannot exempt You or the Dataset Provider from compliance with applicable privacy, publicity, or copyright laws.
+
+## Contact / Notices
+For rights requests, takedown notices, licensing inquiries, or other legal communications, contact:  
+**<your-contact-email@example.org>**
+
+
+## Final Note (Important)
+This is a **restrictive academic / research license** intended to reduce certain legal risks.  
+It is not a substitute for legal advice.  
+Before public release, you should:
+1. Perform a dataset-level legal audit.  
+2. Consider excluding or blurring high-risk content (e.g., EU residents or easily-identifiable faces without consent).  
+3. Obtain review and sign-off from counsel experienced in intellectual property and data protection law.

README.md

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+---
+license: other
+task_categories:
+- text-to-image
+license_name: multiid-2m
+license_link: LICENSE.md
+language:
+- en
+size_categories:
+- 1M<n<10M
+tags:
+- face-generation
+- identity-preserving
+- diffusion
+- controllable-generation
+- multi-person
+---
+
+# MultiID-2M
+[![arXiv](https://img.shields.io/badge/arXiv-2510.14975-b31b1b.svg)](https://arxiv.org/abs/2510.14975)
+[![Project Page](https://img.shields.io/badge/Project-Page-blue.svg)](https://doby-xu.github.io/WithAnyone/)
+[![HuggingFace Model](https://img.shields.io/badge/HuggingFace-Model-yellow.svg)](https://huggingface.co/WithAnyone/WithAnyone)
+[![HuggingFace Dataset](https://img.shields.io/badge/HuggingFace-Dataset-Green.svg)](https://huggingface.co/datasets/WithAnyone/MultiID-2M)
+[![MultiID-Bench](https://img.shields.io/badge/MultiID-Bench-Green.svg)](https://huggingface.co/datasets/WithAnyone/MultiID-Bench)
+[![GitHub Code](https://img.shields.io/badge/GitHub-Code-orange.svg)](https://github.com/Doby-Xu/WithAnyone)
+
+<p align="center">
+  <img src="https://github.com/Doby-Xu/WithAnyone/blob/main/assets/withanyone.gif?raw=true" alt="WithAnyone in action" width="800"/>
+</p>
+
+This repository contains the **MultiID-2M** dataset, a large-scale paired dataset specifically constructed for multi-person scenarios in identity-consistent image generation. It provides diverse references for each identity, enabling the development of advanced diffusion-based models like WithAnyone, which aim to mitigate "copy-paste" artifacts and improve controllability over pose and expression in generated images.
+
+-   **Paper:** [WithAnyone: Towards Controllable and ID Consistent Image Generation](https://huggingface.co/papers/2510.14975)
+-   **Code:** [https://github.com/Doby-Xu/WithAnyone](https://github.com/Doby-Xu/WithAnyone)
+-   **Project Page:** [https://doby-xu.github.io/WithAnyone/](https://doby-xu.github.io/WithAnyone/)
+
+## Paper Abstract
+
+The abstract of the paper is the following:
+
+Identity-consistent generation has become an important focus in text-to-image research, with recent models achieving notable success in producing images aligned with a reference identity. Yet, the scarcity of large-scale paired datasets containing multiple images of the same individual forces most approaches to adopt reconstruction-based training. This reliance often leads to a failure mode we term copy-paste, where the model directly replicates the reference face rather than preserving identity across natural variations in pose, expression, or lighting. Such over-similarity undermines controllability and limits the expressive power of generation. To address these limitations, we (1) construct a large-scale paired dataset MultiID-2M, tailored for multi-person scenarios, providing diverse references for each identity; (2) introduce a benchmark that quantifies both copy-paste artifacts and the trade-off between identity fidelity and variation; and (3) propose a novel training paradigm with a contrastive identity loss that leverages paired data to balance fidelity with diversity. These contributions culminate in WithAnyone, a diffusion-based model that effectively mitigates copy-paste while preserving high identity similarity. Extensive qualitative and quantitative experiments demonstrate that WithAnyone significantly reduces copy-paste artifacts, improves controllability over pose and expression, and maintains strong perceptual quality. User studies further validate that our method achieves high identity fidelity while enabling expressive controllable generation.
+
+| <img src="assets/stat1.jpg" width="100%"> | <img src="assets/stat2.jpg" width="83%"> |
+|:--:|:--:|
+
+## Download
+
+Currently, 1M images and their metadata are available for download.
+
+[HuggingFace Dataset](https://huggingface.co/datasets/WithAnyone/MultiID-2M)
+
+## File Structure
+```
+MultiID-2M/
+├── ref/
+│   ├── cluster_centers.tar
+│   └── tars/                  # reference tars
+│       ├── ...
+│
+├── train_rec/                 # reconstruction training data
+│   ├── re_000000.tar
+│   ├── re_000001.tar
+│   └── ...
+│
+└── train_cp/                  # identifiable paired data
+    ├── re_000000.tar
+    ├── re_000001.tar
+    └── ...
+```
+
+- `ref/cluster_centers.tar`: Contains the cluster centers of all the identifiable identities in the dataset.
+- `ref/tars`: Contains the reference images for each identifiable identity.
+- `train_cp`: Contains the training images only of the identifiable identities.
+- `train_rec`: Contains the training images of both identifiable and unidentifiable identities.
+
+## Labels
+The dataset contains dense labels for each image, including:
+- `url`: The original URL of the original image.
+- `ram_score`: Scores from recognize anything model.
+- `bboxes`: Bounding boxes of detected faces.
+- `aesthetics_score`: Aesthetic score of the image.
+- `caption_en`: English caption generated by VLMs.
+- `name`: ID number of the identifiable identity (if identifiable, otherwise `none`).
+- `embeddings` (or `embedding`): Face embeddings extracted using ArcFace antelopev2 model. This corresponds to the bboxes.
+
+## Sample Usage
+This section provides instructions for quickly getting started with the `WithAnyone` model, which can be trained using this dataset.
+
+### Requirements
+
+Use `pip install -r requirements.txt` to install the necessary packages.
+
+### Gradio Demo
+
+The Gradio GUI demo is a good starting point to experiment with WithAnyone. Run it with:
+
+```bash
+python gradio_app.py --flux_path <path to flux1-dev directory> --ipa_path <path to withanyone directory> \
+    --clip_path <path to clip-vit-large-patch14> \
+    --t5_path <path to xflux_text_encoders> \
+    --siglip_path <path to siglip-base-patch16-256-i18n> \
+    --model_type "flux-dev" # or "flux-kontext" for WithAnyone.K
+```
+
+❗ WithAnyone requires face bounding boxes (bboxes). You should provide them to indicate where faces are. You can provide face bboxes in two ways:
+1. Upload an example image with desired face locations in `Mask Configuration (Option 1: Automatic)`. The face bboxes will be extracted automatically, and faces will be generated in the same locations. Do not worry if the given image has a different resolution or aspect ratio; the face bboxes will be resized accordingly.
+2. Input face bboxes directly in `Mask Configuration (Option 2: Manual)`. The format is `x1,y1,x2,y2` for each face, one per line.
+3. <span style="color: #999;">(NOT recommended) leave both options empty, and the face bboxes will be randomly chosen from a pre-defined set. </span>
+
+⭕ WithAnyone works well with LoRA. If you have any stylized LoRA checkpoints, use `--additional_lora_ckpt <path to lora checkpoint>` when launching the demo. The LoRA will be merged into the diffusion model.
+```bash
+python gradio_app.py --flux_path <path to flux1-dev directory> --ipa_path <path to withanyone directory> \
+    --additional_lora_ckpt <path to lora checkpoint> \
+    --lora_scale 0.8 # adjust the weight as needed
+```
+
+### Batch Inference
+
+You can use `infer_withanyone.py` for batch inference. The script supports generating multiple images with MultiID-Bench.
+
+First, download MultiID-Bench:
+
+```bash
+huggingface-cli download WithAnyone/MultiID-Bench --repo-type dataset --local-dir <path to MultiID-Bench directory>
+```
+
+And convert the parquet file to a folder of images and a json file using `MultiID_Bench/parquet2bench.py`:
+
+```bash
+python MultiID_Bench/parquet2bench.py --parquet <path to parquet file> --output_dir <path to output directory>
+```
+
+You will get a folder with the following structure:
+
+```
+<output_dir>/
+  ├── p1/untar
+  ├── p2/untar
+  ├── p3/
+  ├── p1.json
+  ├── p2.json
+  └── p3.json
+```
+
+Then run batch inference with:
+
+```bash
+python infer_withanyone.py \
+  --eval_json_path <path to MultiID-Bench subset json> \
+  --data_root <path to MultiID-Bench subset images> \
+  --save_path <path to save results>  \
+  --use_matting True \ # set to True when siglip_weight > 0.0
+  --siglip_weight 0.0 \ # Resemblance in Spirit vs Resemblance in Form, higher means more similar to reference
+  --id_weight 1.0 \ # usually, set it to 1 - id_weight, higher means more controllable
+  --t5_path <path to xflux_text_encoders> \
+  --clip_path <path to clip-vit-large-patch14> \
+  --ipa_path <path to withanyone> \
+  --flux_path <path to flux1-dev>
+```
+Where the `data_root` should be `p1/untar`, `p2/untar`, or `p3/` depending on which subset you want to evaluate. The `eval_json_path` should be the corresponding json file converted from the parquet file.
+
+### Face Edit with FLUX.1 Kontext
+
+You can use `gradio_edit.py` for face editing with FLUX.1 Kontext and WithAnyone.Ke.
+```bash
+python gradio_edit.py --flux_path <path to flux1-dev directory> --ipa_path <path to withanyone directory> \
+    --clip_path <path to clip-vit-large-patch14> \
+    --t5_path <path to xflux_text_encoders> \
+    --siglip_path <path to siglip-base-patch16-256-i18n> \
+    --model_type "flux-kontext"
+```
+
+## License and Disclaimer
+
+This dataset is provided for non-commercial academic research purposes only. By accessing or using this dataset you agree to the terms in the [LICENSE](./LICENSE.md).
+
+- **No ownership claim**: The project does not claim ownership of the original images, metadata, or other content included in this dataset. Copyright and other rights remain with the original rights holders.  
+- **User responsibility**: Users are responsible for ensuring their use of the dataset complies with all applicable laws, regulations, and third‑party terms (including platform policies).  
+- **Takedown / correction requests**: If a rights holder believes content in this dataset infringes their rights, please submit a removal or correction request via the [HuggingFace dataset page](https://huggingface.co/datasets/WithAnyone/MultiID-2M) or the [project page](https://doby-xu.github.io/WithAnyone/), including sufficient proof of ownership and specific identifiers/URLs. After verification of a valid claim, we will remove or correct the affected items as soon as reasonably practicable.  
+- **No warranty; limitation of liability**: The dataset is provided "as is" without warranties of any kind. The project and maintainers disclaim liability for any direct, indirect, incidental, or consequential damages arising from use of the dataset.  
+- **Prohibited commercial use**: Commercial use is prohibited unless you obtain separate permission from the dataset maintainers; unauthorized commercial use may result in legal liability.  
+- **Contact**: Use the HuggingFace dataset page or the project website to submit requests or questions.

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