| --- |
| license: cc-by-nc-sa-4.0 |
| tags: |
| - third-eye |
| - image-restoration |
| - video-restoration |
| - super-resolution |
| - denoise |
| - deblur |
| - colorization |
| - frame-interpolation |
| library_name: pytorch |
| --- |
| |
| # Third Eye - Model Weights Bundle |
|
|
| Reliable mirror of AI model weights used by [Third Eye](https://github.com/Jacid23/Third_Eye), a media organizer with hidden editing dimensions. |
|
|
| These weights are downloaded automatically by `scripts/fetch_models.py` during installation. Self-hosting them here removes dependency on upstream Google Drive links and unreliable community mirrors. |
|
|
| ## License |
|
|
| The bundle is tagged **CC BY-NC-SA 4.0** — the most restrictive license among the included models. By using these weights you agree to: |
|
|
| - **Non-commercial use only** |
| - Provide **attribution** to the original authors (listed below) |
| - Distribute any derivatives under the **same license** |
|
|
| ## Files and Attribution |
|
|
| Every weight in this repo is a verbatim copy of the file released by its original author. Original sources and licenses below. |
|
|
| ### Denoise / Deblur (NAFNet) |
|
|
| - `NAFNet-SIDD-width64.pth` — denoise model |
| - `NAFNet-REDS-width64.pth` — deblur model |
|
|
| **Authors:** Liangyu Chen, Xiaojie Chu, Xiangyu Zhang, Jian Sun (Megvii Research) |
| **Upstream:** https://github.com/megvii-research/NAFNet |
| **License:** MIT |
| **Paper:** "Simple Baselines for Image Restoration" (ECCV 2022) |
|
|
| ### Frame Interpolation (RIFE) |
|
|
| - `flownet.pkl` — RIFE 4.6 weights |
|
|
| **Authors:** Zhewei Huang et al. (Practical-RIFE team) |
| **Upstream:** https://github.com/hzwer/Practical-RIFE |
| **License:** MIT (code) / non-commercial (weights, per author note) |
| **Paper:** "Real-Time Intermediate Flow Estimation for Video Frame Interpolation" |
|
|
| ### Community Upscale Models |
|
|
| - `4x-UltraSharp.pth` — community upscale model by Kim2091 |
| - `foolhardy_Remacri.pth` — community model by foolhardy |
| - `RealisticRescaler_100000_G.pth` — community upscale model |
| - `4x-UniScale-Balanced [72000g].pth` — UniScale community variant |
| - `4x-UniScale-Strong [42400g].pth` — UniScale community variant |
|
|
| **Upstream catalog:** https://openmodeldb.info/ |
| **License:** CC BY-NC-SA 4.0 (community convention for ESRGAN-derived models) |
|
|
| Architecture is RRDBNet from Real-ESRGAN. Original Real-ESRGAN architecture: |
| - **Authors:** Xintao Wang et al. (Tencent ARC Lab) |
| - **Upstream:** https://github.com/xinntao/Real-ESRGAN |
| - **License:** BSD-3-Clause |
|
|
| ## Usage |
|
|
| Download programmatically via the Third Eye installer: |
|
|
| ```bat |
| install.bat |
| ``` |
|
|
| Or directly: |
|
|
| ```bash |
| wget https://huggingface.co/Jacid23/third-eye-models/resolve/main/NAFNet-SIDD-width64.pth |
| ``` |
|
|
| ## Source Code |
|
|
| Third Eye source: https://github.com/Jacid23/Third_Eye |
| |
| Model download script: `scripts/fetch_models.py` |
|
|
| ## Acknowledgements |
|
|
| All credit for the models goes to their original authors and research teams. This repository exists only to provide reliable download mirrors for an open-source application that integrates these models. No modifications have been made to any weight file. |
|
|
