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third-eye-models

PyTorch
third-eye
image-restoration
video-restoration
super-resolution
denoise
deblur
colorization
frame-interpolation
Model card Files
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third-eye-models
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---
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.