---
license: apache-2.0
tags:
  - image-generation
  - diffusion
  - imagenet
  - flow-matching
  - self-supervised
datasets:
  - imagenet-1k
pipeline_tag: image-to-image
library_name: pytorch
---

# Self-Flow ImageNet 256×256

**Self-Flow** is a self-supervised training method for diffusion transformers that combines flow matching with a self-supervised feature reconstruction objective. This checkpoint is trained on ImageNet 256×256.

### Key Features

- **Architecture**: SiT-XL/2 with per-token timestep conditioning
- **Training**: Flow matching + self-supervised feature reconstruction
- **Resolution**: 256×256 pixels
- **Parameters**: ~675M

## Evaluation Results

| Metric | Value |
|--------|-------|
| FID ↓ | 5.7 |
| IS ↑ | 151.40  |
| sFID ↓ | 4.97 |
| Precision | 0.72 |
| Recall | 0.67 |

*Results computed on 50,000 generated samples vs ImageNet validation set.*

## Usage


### Download Checkpoint

```
python -c "
from huggingface_hub import hf_hub_download
hf_hub_download(
    repo_id='Hila/Self-Flow',
    filename='selfflow_imagenet256.pt',
    local_dir='./checkpoints'
)
print('Downloaded!')
"
```
and follow the instructions in our repository: https://github.com/black-forest-labs/Self-Flow

## License

This model is released under the [Apache 2.0 License](https://www.apache.org/licenses/LICENSE-2.0).