| --- |
| license: apache-2.0 |
| library_name: onnx |
| tags: |
| - onnx |
| - depth-estimation |
| - image-matting |
| - inpainting |
| - android |
| --- |
| |
| # Parallax Wallpaper Studio models |
|
|
| ONNX exports of three open models, made to run on the phone's CPU (ONNX Runtime) inside the Android app |
| Parallax Wallpaper Studio. The app downloads them on demand: the depth model for depth-based parallax, and all |
| three for "Split into layers", which turns one photo into a layered wallpaper. |
|
|
| | File | Model | Used for | Licence | |
| |---|---|---|---| |
| | `depth-anything-v2-small-int8.onnx` | [Depth Anything V2 Small](https://huggingface.co/depth-anything/Depth-Anything-V2-Small-hf), int8 dynamic quantisation | Depth map | Apache-2.0 | |
| | `vitmatte-small-512-fp16w.onnx` | [ViTMatte Small (Composition-1k)](https://huggingface.co/hustvl/vitmatte-small-composition-1k), fixed 512×512 input, fp16 weights | Refining layer edges | Apache-2.0 | |
| | `big-lama-512-fp16w.onnx` | [LaMa](https://github.com/advimman/lama) (big-lama), fixed 512×512 input, fp16 weights | Filling the holes behind cut-out layers | Apache-2.0 | |
|
|
| Notes on the exports: |
|
|
| - The depth model matches the full-precision model to 0.9998 correlation. |
| - LaMa's FFTs have no ONNX export, so they are rewritten as constant DFT matrix multiplications. That fixes the |
| input size at 512×512. |
| - "fp16 weights" means the weights are stored as float16 and the maths runs in float32. |
|
|
| ## Credits |
|
|
| - **Depth Anything V2**: Lihe Yang, Bingyi Kang, Zilong Huang, Zhen Zhao, Xiaogang Xu, Jiashi Feng, Hengshuang Zhao. |
| *Depth Anything V2*, 2024. Only the Small model is Apache-2.0; Base and Large are not, and are not used here. |
| - **ViTMatte**: Jingfeng Yao, Xinggang Wang, Shusheng Yang, Baoyuan Wang. *ViTMatte: Boosting Image Matting with |
| Pretrained Plain Vision Transformers*, 2023 (hustvl). |
| - **LaMa**: Roman Suvorov, Elizaveta Logacheva, Anton Mashikhin, Anastasia Remizova, Arsenii Ashukha, Aleksei |
| Silvestrov, Naejin Kong, Harshith Goka, Kiwoong Park, Victor Lempitsky. *Resolution-robust Large Mask Inpainting |
| with Fourier Convolutions*, WACV 2022 (Samsung AI Center). |
|
|
| These are format conversions of the original weights. The licences and copyright of the original works apply. |
|
|
