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library_name: pytorch
pipeline_tag: image-to-image
tags:
- super-resolution
- remote-sensing
- flow-matching
- gaussian-splatting
- continuous-scale
---
# FlowGS pretrained models
Pretrained models for **Flow-based Gaussian Splatting for Continuous-Scale
Remote Sensing Image Super-Resolution**.
- Paper: https://doi.org/10.1109/LGRS.2026.3729993
- arXiv: https://arxiv.org/abs/2605.22147
- Code: https://github.com/hanlinwu/FlowGS
## Files
| File | Purpose | Size | SHA-256 |
| --- | --- | ---: | --- |
| `first_stage_gs_res_v1.ckpt` | Continuous-scale Gaussian renderer required by the final config | 189 MiB | `68990a8884f2f27f6912664333698409b4047a1352e31317c49b5deaa1620f1b` |
| `flowgs.ckpt` | Final one-step FlowGS checkpoint | 1.3 GiB | `5874d635d1a86a6b33c44dc43c518008f094760b9e1067a54b9d610dff81f174` |
Both checkpoints are required for evaluation.
## Download
```bash
hf download hlwu/FlowGS --include "*.ckpt" --local-dir checkpoints
```
## Evaluation
Place the files under `checkpoints/` in the code repository, then run:
```bash
python test.py \
--config configs/second_stage_fm_shortcut_1.yaml \
--checkpoint checkpoints/flowgs.ckpt \
--datasets AID,DOTA_512 \
--scales 2.6,3.4,6.0,10.0 \
--gpu 0
```
See the code repository README for environment setup, dataset preparation,
training stages, and DIOR evaluation.
## Citation
```bibtex
@article{mo2026flow,
title = {Flow-Based Gaussian Splatting for Continuous-Scale Remote Sensing Image Superresolution},
author = {Mo, Jiangwei and Lu, Xi and Wu, Hanlin},
journal = {IEEE Geoscience and Remote Sensing Letters},
volume = {23},
pages = {6019205--6019205},
year = {2026},
doi = {10.1109/LGRS.2026.3729993}
}
```
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