| --- |
| license: apache-2.0 |
| library_name: diffusers |
| pipeline_tag: text-to-image |
| tags: |
| - diffusers |
| - remote-sensing |
| - earth-observation |
| - satellite-imagery |
| - text-to-image |
| - flow-matching |
| - diffusion-transformer |
| - geospatial |
| datasets: |
| - lcybuaa/Git-10M |
| inference: true |
| language: |
| - en |
| widget: |
| - text: A parking lot full of cars is located next to some trees. |
| output: |
| url: demo/parking.png |
| - text: Many buildings are located in a commercial area. |
| output: |
| url: demo/commercial.png |
| - text: A roundabout is surrounded by grass. |
| output: |
| url: demo/roundabout.png |
| - text: Two tennis courts are semi-surrounded by several green trees. |
| output: |
| url: demo/tennis.png |
| --- |
| |
| # BiliSakura/GeoCore-9B-diffusers |
|
|
| Self-contained [Diffusers](https://github.com/huggingface/diffusers) checkpoint for **GeoCore-9B**: 256×256 optical satellite generation from text, with optional GSD, latitude, and longitude. |
|
|
| Converted from [`JeonghyeokDo/GeoCore-9B`](https://huggingface.co/JeonghyeokDo/GeoCore-9B). `DiffusionPipeline.from_pretrained` loads the DiT, Flux.2 VAE, CLIP-L, T5-XXL, and `FlowMatchEulerDiscreteScheduler` from this repo (`trust_remote_code=True`). |
|
|
| [Project](https://kaist-viclab.github.io/GeoCore-9B_site/) · |
| [Code](https://github.com/KAIST-VICLab/GeoCore-9B) · |
| [Original weights](https://huggingface.co/JeonghyeokDo/GeoCore-9B) |
|
|
| <Gallery /> |
|
|
| ## Demo |
|
|
| 256×256, 50 Euler steps, `guidance_scale=4.0`, seed `42`, `lon=126.97`, `lat=37.56`, `res=0.0` (Seoul). |
|
|
| | Parking lot | Commercial | Roundabout | Tennis courts | |
| | --- | --- | --- | --- | |
| |  |  |  |  | |
|
|
| ## Model details |
|
|
| | Field | Value | |
| | --- | --- | |
| | Pipeline | `GeoCorePipeline` (`pipeline.py`) | |
| | Transformer | Flow-matching DiT, 8 double-stream + 24 single-stream blocks, hidden 4096, 32 heads, **9.24B** | |
| | Text encoders | CLIP ViT-L/14 + T5-XXL (bundled) | |
| | VAE | Flux.2 `AutoencoderKLFlux2` (Apache-2.0, bundled) | |
| | Scheduler | `FlowMatchEulerDiscreteScheduler`, `shift=1.0` | |
| | Resolution | 256×256 | |
| | Training data | [Git-10M](https://huggingface.co/datasets/lcybuaa/Git-10M) | |
| | License | Apache-2.0 | |
|
|
| ## Load from Hugging Face |
|
|
| ```python |
| import torch |
| from diffusers import DiffusionPipeline |
| |
| pipe = DiffusionPipeline.from_pretrained( |
| "BiliSakura/GeoCore-9B-diffusers", |
| trust_remote_code=True, |
| torch_dtype=torch.bfloat16, |
| ).to("cuda") |
| |
| image = pipe( |
| prompt="A parking lot full of cars is located next to some trees.", |
| lon=126.97, |
| lat=37.56, |
| res=0.0, |
| height=256, |
| width=256, |
| num_inference_steps=50, |
| guidance_scale=4.0, |
| generator=torch.Generator(device="cuda").manual_seed(42), |
| ).images[0] |
| image.save("demo.png") |
| ``` |
|
|
| The DiT is ~18.5 GB in bf16. A 24 GB GPU is tight; on two GPUs, split only the transformer with Accelerate and keep CLIP, T5, and the VAE on one device. |
|
|
| ## Conditioning |
|
|
| Captions follow the Git-10M style (short scene descriptions). Geospatial fields are optional. |
|
|
| | Argument | Meaning | |
| | --- | --- | |
| | `prompt` | Natural-language description | |
| | `res` | Resolution index `17 - z` for Google XYZ zoom `z`. `0` ≈ 1.2 m/px at the equator; each `+1` doubles GSD | |
| | `lon`, `lat` | Degrees | |
| | `-999.0` | Learned null embedding — any of `res` / `lon` / `lat` may be omitted | |
|
|
| ## License |
|
|
| DiT weights © 2026 Jeonghyeok Do and Munchurl Kim, Apache-2.0. VAE is an unmodified Apache-2.0 Flux.2 autoencoder from [`black-forest-labs/FLUX.2-klein-base-4B`](https://huggingface.co/black-forest-labs/FLUX.2-klein-base-4B); see [`LICENSE-FLUX2-VAE.md`](LICENSE-FLUX2-VAE.md). [Git-10M](https://huggingface.co/datasets/lcybuaa/Git-10M) is CC BY-NC-ND 4.0 (non-commercial retraining). |
|
|
| ## Citation |
|
|
| ```bibtex |
| @article{do2026geocore, |
| title = {GeoCore-9B: Towards Geo-Aware Generative Foundation Models in Earth Observation}, |
| author = {Do, Jeonghyeok and Kim, Munchurl}, |
| year = {2026} |
| } |
| ``` |
|
|
