| --- |
| 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} |
| } |
| ``` |
|
|
