TAID Models
Pretrained weights for Atmosphere-Aware Intrinsic Decomposition from a Single Terrain Image with Latent Diffusion Models.
- Code: https://github.com/sttkw/terrain-atmospheric-intrinsic-decomposition
- Project page: https://sttkw.github.io/terrain-atmospheric-intrinsic-decomposition/
| Path | Description |
|---|---|
decomposition/unet/ |
Intrinsic decomposition U-Net (InstructPix2Pix fine-tune, step 18,000). Predicts Albedo, Diffuse Shading, Specular Shading and Volume. |
decomposition/terrain_decomposition_config.json |
Inference metadata (resolution, target encodings). |
atmosphere/terrain_difference.pt |
Atmospheric editor (12ch → 9ch U-Net) that changes D, S and V for new air / aerosol / ozone densities. |
The decomposition U-Net is loaded on top of the other components of
timbrooks/instruct-pix2pix (VAE, text encoder, tokenizer, scheduler).
Usage
git clone https://github.com/sttkw/terrain-atmospheric-intrinsic-decomposition
cd terrain-atmospheric-intrinsic-decomposition
pip install -r requirements.txt
python demo.py --input_image demo_image/test1.jpg --water_mask demo_image/test1.png \
--p_control 0 -3 0 --output_dir outputs/demo/test1
demo.py downloads these weights automatically.
License
MIT, same as the code.
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Base model
timbrooks/instruct-pix2pix