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

hf download hlwu/FlowGS --include "*.ckpt" --local-dir checkpoints

Evaluation

Place the files under checkpoints/ in the code repository, then run:

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

@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}
}