---
license: apache-2.0
library_name: pytorch
tags:
- flow-matching
- image-generation
- celeba
- unet
---

# Flow Matching for CelebA

This model learns a velocity field that transports Gaussian noise into 64x64 CelebA face images using flow matching.

## Architecture

- Custom U-Net with sinusoidal time embeddings
- Input image size: 64x64
- Input channels: 3
- Base channels: 64

## Loading

```python
import torch
from modeling import FlowMatchingModel

model = FlowMatchingModel.from_pretrained(".")
model.eval()
```

## Training setup

This project uses a minimal Euler ODE sampler with flow matching and a velocity target:

```python
x_t = (1 - t) * x0 + t * x1
target_v = x1 - x0
```

The checkpoint in this repo is a raw PyTorch state dict generated by the training script.