| | --- |
| | 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). |
| |
|
| |
|
