| --- |
| license: agpl-3.0 |
| library_name: pytorch |
| tags: |
| - static2dynamic |
| - diffusion |
| - generative-modeling |
| - biology |
| - microscopy |
| - image-to-video |
| - trajectory-inference |
| pipeline_tag: image-to-video |
| --- |
| |
| # Static2Dynamic |
|
|
| Pretrained checkpoints for **Static2Dynamic**, a method for reconstructing videos of unobservable cellular, developmental, and disease processes from static, unpaired snapshots. |
|
|
| - [Code repository](https://github.com/biocompibens/static2dynamic) |
| - [Preprint](https://www.biorxiv.org/content/10.64898/2026.05.18.725860) |
|
|
| ## Checkpoints |
|
|
| Each checkpoint subfolder contains: |
|
|
| - `net/`: diffusion model config and weights |
| - `video_time_encoder/`: time conditioning encoder config and weights |
| - `dynamic/`: scheduler configuration |
| - `my_conf/`: run configuration |
| - `train_samples.parquet`: training split samples |
| - `test_samples.parquet`: test split samples |
| - `pseudotime_predictions/`: pseudotime predictions |
|
|
|
|
| ## Download example |
|
|
| Download one checkpoint folder: |
|
|
| ```sh |
| hf download thethomasboyer/Static2Dynamic --include 'NASH_steato/*' --local-dir ./Static2Dynamic_models |
| ``` |
|
|
