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sttkw/TAID-Models

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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

bash

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.