| --- |
| license: mit |
| tags: |
| - face-generation |
| - flow-matching |
| - arc2face |
| - synthetic-data |
| - fairness |
| --- |
| |
| # ID-Flow |
|
|
| Pretrained **ID-Flow** weights from [**SynthFaces: Balanced Large Scale Human Dataset**](https://arxiv.org/abs/2610.40119v1) |
| (accepted to BMVC 2026). |
|
|
| ID-Flow is a label-conditional geodesic flow-matching model on the unit hypersphere of ArcFace identity embeddings |
| (~11.8M parameters). Given an (age, race, gender) combination it samples a new identity embedding, which |
| [Arc2Face](https://github.com/foivospar/Arc2Face) turns into face images. Sampling all 126 label combinations uniformly |
| gives a demographically balanced set of synthetic identities. |
|
|
| - Code: [github.com/ControlGenAI/SynthFaces](https://github.com/ControlGenAI/SynthFaces) (to be released) |
| - Paper: [arXiv:2610.40119](https://arxiv.org/abs/2610.40119v1) |
|
|
| ## Files |
|
|
| | file | description | |
| |---|---| |
| | `idflow.pt` | weights-only, self-describing checkpoint (model weights + hyper-parameters), 47 MB | |
|
|
| Label order is `[age, race, gender]`: age `0-2, 3-9, 10-19, 20-29, 30-39, 40-49, 50-59, 60-69, 70+`; race |
| `White, Black, Latino_Hispanic, East Asian, Southeast Asian, Indian, Middle Eastern`; gender `Male, Female`. |
|
|
| ## Usage: ID-Flow → Arc2Face |
|
|
| Setup (from the code repository): |
|
|
| ```bash |
| git clone https://github.com/ControlGenAI/SynthFaces && cd SynthFaces |
| pip install -r requirements.txt |
| huggingface-cli download shredder67/id-flow idflow.pt --local-dir . |
| python scripts/download_arc2face.py --root Arc2Face # Arc2Face + InsightFace models |
| ``` |
|
|
| Sample a balanced set of identities (all 126 label groups), then generate images with Arc2Face: |
|
|
| ```bash |
| python scripts/sample_identities.py --ckpt idflow.pt --n_total 1000 --output_dir outputs/identities |
| |
| python scripts/sample_images.py sample \ |
| --embeddings_path outputs/identities/id_embeddings.pt \ |
| --output_dir outputs/images --images_per_identity 4 |
| python scripts/sample_images.py merge --output_dir outputs/images --n_parts 1 |
| ``` |
|
|
| Or sample identity embeddings for chosen labels directly in Python: |
|
|
| ```python |
| import torch |
| from idflow import load_idflow |
| |
| model = load_idflow("idflow.pt", device="cuda:0") |
| y = torch.tensor([[3, 0, 1]] * 16, device="cuda:0") # age 20-29, White, Female |
| ids = model.sample(bs=16, y=y, guidance_scale=2.0, device="cuda:0") # (16, 512) unit-norm ArcFace embeddings |
| ``` |
|
|
| The embeddings can be passed to Arc2Face as `id_embeddings.pt` (see `scripts/sample_images.py`). A guidance scale of |
| 2.0 is used in the paper; higher values follow the labels more closely at the cost of diversity. |
|
|
| ## Notes |
|
|
| The Arc2Face, InsightFace and Stable Diffusion 1.5 weights used for image generation are distributed under their own |
| licenses (the Arc2Face and InsightFace weights are non-commercial); please check them before use. |
|
|
| ## Citation |
|
|
| ```bibtex |
| @article{karpukhin2026synthfaces, |
| title = {SynthFaces: Balanced Large Scale Human Dataset}, |
| author = {Karpukhin, Sergey and Bobkov, Denis and Morozov, Kirill and Alanov, Aibek}, |
| journal = {arXiv preprint arXiv:2610.40119}, |
| year = {2026}, |
| note = {Accepted to BMVC 2026} |
| } |
| ``` |
|
|
