File size: 1,501 Bytes
5440cac 6686e7d 5440cac | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 | ---
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.
|