---
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}
}
```