| --- |
| license: mit |
| library_name: diffusers |
| base_model: timbrooks/instruct-pix2pix |
| datasets: |
| - sttkw/TAID-Dataset |
| - sttkw/TAID-AtmosEdit |
| tags: |
| - intrinsic-decomposition |
| - terrain |
| - atmosphere |
| --- |
| |
| # 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. |
|
|
