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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
```
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